Publications and Presentations

Year 5

Publications:

Ai, M., Wei, T., Chen, Y., Zeng, Z., Zhao, R., Varatkar, G., Rouhani, B. D., Tang, X., Tong, H., & He, J. (2025). ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration. 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25), 1–12. doi.org/10.1145/3690624.3709196
Albarenque, S., Basso, B., & Melchiori, R. (2024). Emergence delay effect on maize (Zea mays L.) nitrogen uptake. Agronomy Journal, 116(6), 2872–2884. doi.org/10.1002/agj2.21678
Allabadi, G., Lucic, A., Wang, Y.-X., & Adve, V. (2025). Learning to Detect Novel Species with SAM in the Wild. International Journal of Computer Vision, 133(5), 2247–2258. doi.org/10.1007/s11263-024-02234-0
Bhatnagar, S., & Ahuja, N. (2025). Potential Field Based Deep Metric Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 25549–25559. doi.org/10.48550/arXiv.2405.18560
Chakraborty, N., Fang, Y., Schreiber, A., Ji, T., Huang, Z., Mihigo, A., Wall, C., Almana, A., & Driggs-Campbell, K. (2025). Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation (No. arXiv:2409.09921). arXiv. doi.org/10.48550/arXiv.2409.09921
Choudhuri, A., Chowdhary, G., & Schwing, A. G. (2024). OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning (No. arXiv:2404.03657). arXiv. doi.org/10.48550/arXiv.2404.03657
Debruin, J., Aref, T., Tolosa, S. T., Hensley, R., Underwood, H., Mcguire, M., Soman, C., Nystrom, G., Parkinson, E., Li, C., Moose, S. P., & Chowdhary, G. (2025). Breaking the field phenotyping bottleneck in maize with autonomous robots. Communications Biology, 8(1). doi.org/10.1038/s42003-025-07890-7
Dongre, V., Gui, C., Garg, S., Nayyeri, H., Tur, G., Hakkani-Tür, D., & Adve, V. S. (2025). MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations. arXiv Preprint arXiv:2506.20100.
Gao, L., Guan, K., Jiang, C., Lu, X., Wang, S., Ainsworth, E. A., Wu, X., & Chen, M. (2025). Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands. Remote Sensing Of Environment, 316. doi.org/10.1016/j.rse.2024.114516
Gummadi, S., Gasparino, M. V., Vasisht, D., & Chowdhary, G. (2024). Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning for Autonomous Visual Robot Navigation. IEEE Robotics And Automation Letters, 9(12), 11841–11848. doi.org/10.1109/LRA.2024.3498778
He, X., Kang, J., Qiu, R., Wang, F., Sepulveda, J., & Tong, H. (2024). On the Sensitivity of Individual Fairness: Measures and Robust Algorithms. Proceedings Of The 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, 829–838. doi.org/10.1145/3627673.3679721
Jin, B., Liu, G., Han, C., Jiang, M., Ji, H., & Han, J. (2024). Large Language Models on Graphs: A Comprehensive Survey. IEEE Transactions on Knowledge and Data Engineering, 36(12), 8622–8642. doi.org/10.1109/TKDE.2024.3469578
Khanna, M., Atallah, S. S., Heckelei, T., Wu, L., & Storm, H. (2024). Economics of the Adoption of Artificial Intelligence-Based Digital Technologies in Agriculture. Annual Review Of Resource Economics, 16, 41–61. doi.org/10.1146/annurev-resource-101623-092515
Koe, K., Marri, S., Walt, B., Kamtikar, S., Uppalapati, N. K., Krishnan, G., & Chowdhary, G. (2025). Learning-Based Position and Orientation Control of a Hybrid Rigid-Soft Arm Manipulator. Journal of Mechanisms and Robotics, 17(071010). doi.org/10.1115/1.4067872
Koe, K., Shah, P. K., Walt, B., Westphal, J., Marri, S., Kamtikar, S., Nam, J. S., Uppalapati, N. K., Krishnan, G., & Chowdhary, G. (2025). Precision Harvesting in Cluttered Environments: Integrating End Effector Design with Dual Camera Perception (No. arXiv:2501.19395). arXiv. doi.org/10.48550/arXiv.2501.19395
Lawson, T., & Leakey, A. D. B. (2024). Stomata: Custodians of leaf gaseous exchange. Journal of Experimental Botany, 75(21), 6677–6682. doi.org/10.1093/jxb/erae425
Lee, S. W., Swinton, S. M., & Basso, B. (2025). Comparing profitability of variable rate nitrogen prescriptions. Precision Agriculture, 26(4). doi.org/10.1007/s11119-025-10256-y
Lu, X., Guan, K., Jiang, C., Gao, L., Wang, S., & Zhang, J. (2024). Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands. Journal Of Hydrology, 645(B). doi.org/10.1016/j.jhydrol.2024.132201
Marques, J. M. C., Dengler, N., Zaenker, T., Mucke, J., Wang, S., Bennewitz, M., & Hauser, K. (2025). Map Space Belief Prediction for Manipulation-Enhanced Mapping (No. arXiv:2502.20606). arXiv. doi.org/10.48550/arXiv.2502.20606
Peng, S., Chen, H., & Driggs-Campbell, K. (2025). Towards Uncertainty Unification: A Case Study for Preference Learning (No. arXiv:2503.19317). arXiv. doi.org/10.48550/arXiv.2503.19317
Qiu, R.-Z., Wang, Y.-X., & Hauser, K. (2025). AlignDiff: Aligning Diffusion Models for General Few-Shot Segmentation. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024, PT XLI (Vol. 15099, pp. 384–400). doi.org/10.1007/978-3-031-72940-9_22
Rawlekar, S., Bhatnagar, S., & Ahuja, N. (2025). PositiveCoOp: Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 5863–5872. doi.org/10.1109/WACV61041.2025.00572
Rawlekar, S., Bhatnagar, S., Srinivasulu, V. P., & Ahuja, N. (2025). Improving Multi-label Recognition using Class Co-Occurrence Probabilities. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, C.-L. Liu, S. Bhattacharya, & U. Pal (Eds.), Pattern Recognition (pp. 424–439). Springer Nature Switzerland.
Ritte, I. P., Egnin, M., Bernard, G. C., Mortley, D., Idehen, O., Okoma, M. P., & Bonsi, C. (2025). Morpho-Physiological and Molecular Responses to Seedling-Stage Drought Stress in Different Cowpea Cultivars. International Journal of Plant Biology, 16(1). doi.org/10.3390/ijpb16010025
Sivakumar, A. N., Wang, N., Tommaselli, F., Gasparino, M. V., Becker, M., & Chowdhary, G. (2025). CropFollowRL: Learning under-canopy navigation policy with keypoints abstraction. Nature-Bots Workshop. Nature-Bots Workshop.
Sommer, K. M., Sutkus, L., Senthil, P., & Dilger, R. N. (2025). Feeding style alters the growth and behavior of artificially-reared pigs. Journal of Animal Science, 103, skaf098. doi.org/10.1093/jas/skaf098
Sutkus, L., Sommer, K., Li, Z., Sutton, B., Donovan, S., & Dilger, R. (2025). Experimentally induced colitis impacts myelin development and home-cage behavior in young pigs regardless of supplementation with oral gamma-cyclodextrin-encapsulated tributyrin. Frontiers in Neuroscience, 19, 1484497. doi.org/doi.org/10.3389/fnins.2025.1484497
Tan, G. D., Chaudhuri, U., Varela, S., Ahuja, N., & Leakey, A. D. B. (2024). Machine learning-enabled computer vision for plant phenotyping: A primer on AI/ML and a case study on stomatal patterning. Journal of Experimental Botany, 75(21), 6683–6703. doi.org/10.1093/jxb/erae395
Varela, S., Zheng, X., Njuguna, J., Sacks, E., Allen, D., Ruhter, J., & Leakey, A. D. B. (2025). Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery. Plant Physiology, 197(4), kiaf132. doi.org/10.1093/plphys/kiaf132
Wei, T., Chen, Y., He, X., Bao, W., & He, J. (2025). Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 1563–1574. doi.org/10.1145/3690624.3709280
Wu, J., & He, J. (2024). Trustworthy Transfer Learning: A Survey (No. arXiv:2412.14116). arXiv. doi.org/10.48550/arXiv.2412.14116
Yao, S., Pan, S., Bennewitz, M., & Hauser, K. (2025). Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits (No. arXiv:2409.17389). arXiv. doi.org/10.48550/arXiv.2409.17389
Yao, S., Zhu, Y., & Hauser, K. (2025). Structured Bayesian Meta-Learning for Data-Efficient Visual-Tactile Model Estimation. In P. Agrawal, O. Kroemer, & W. Burgard (Eds.), Proceedings of The 8th Conference on Robot Learning (Vol. 270, pp. 3072–3093). PMLR. https://proceedings.mlr.press/v270/yao25a.html
Yu, C., Khanna, M., Atallah, S. S., Kar, S., Bagavathiannan, M., & Chowdhary, G. (2024). Herbicide-resistant weed management with robots: A weed ecological–economic model. Agricultural Economics, 55(6), 943–962. doi.org/10.1111/agec.12856
Zhang, H., Chang, D., Li, F., Soleymani, M., & Ahuja, N. (2025). MagicPose4D: Crafting Articulated Models with Appearance and Motion Control (No. arXiv:2405.14017). arXiv. doi.org/10.48550/arXiv.2405.14017
Zhang, H., Xu, H., Feng, C., Jampani, V., & Ahuja, N. (2025). PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling (No. arXiv:2506.20936). arXiv. doi.org/10.48550/arXiv.2506.20936
Zhou, Q., Guan, K., Wang, S., Hipple, J., & Chen, Z. (2024). From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest. ISPRS Journal of Photogrammetry and Remote Sensing, 216, 259–273. doi.org/10.1016/j.isprsjprs.2024.07.031

Presentations:

Chowdhary, G. (2024, December 6). Where are the field robots? [Seminar presentation]. Stanford Robotics Seminar, Stanford University.
Chowdhary, G. (2025, June 12). Farms of the future: Lessons from the US Midwest [Invited talk]. Vision for Ag Workshop, in conjunction with Agriculture‑Vision: Challenges & Opportunities for Computer Vision in Agriculture, IEEE/CVF CVPR 2025.
Dengler, N., Marques, J. M. C., Zaenker, T., Kalagaturu, V., Wang, S., Bennewitz, M., & Hauser, K. (2024, October). Uncertainty-aware map-space dynamics models for manipulation-enhanced mapping [Workshop presentation]. IROS 2024 3rd Workshop on Mobile Manipulation and Embodied Intelligence.
Dengler, N., Marques, J. M. C., Mücke, J., Zaenker, T., Wang, S., Hauser, K., & Bennewitz, M. (2025, March). Manipulation-enhanced spatial mapping via belief prediction [Conference presentation]. German Robotics Conference.
Driggs-Campbell, K. (2025, May 23). On the roles of humans in human-Agbot teams: Refining, monitoring, and supervising [Invited talk]. Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots, IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA.
Han, J., Yao, S., & Hauser, K. (2025, May). Estimating high-resolution neural stiffness fields using visuotactile sensors [Conference presentation]. IEEE International Conference on Robotics and Automation (ICRA).
Hauser, K. (2024, October 14). Representation learning to interact with an uncertain world [Invited talk]. IROS 2024 Workshop on Mobile Manipulation and Embodied Intelligence.
Hauser, K. (2024, October 25). Understanding and optimizing physical interactions in the wild [Invited talk]. Amazon Robotics Research Symposium.
Gupta, S. (2024, November). Improving imitation learning [Invited talk]. Workshop on Robotics, Cancer Center at Illinois.
Gupta, S. (2025, July). Towards generalizable mobile manipulation [Invited talk]. Fifth International Workshop on Generative AI and Human-Robot Interaction, Indian Institute of Technology, Allahabad.
Koe, K., Shah, P. K., Walt, B., Westphal, J., Marri, S., Kamtikar, S., Nam, J. S., Uppalapati, N. K., Krishnan, G., & Chowdhary, G. (2025, May). Precision harvesting in cluttered environments: Integrating end effector design with dual camera perception [Conference presentation]. IEEE International Conference on Robotics and Automation.
Krishnan, G. (2024, October). Navigation and manipulation challenges in urban high tunnels [Invited talk]. Machine Learning for Cyber-Agricultural Systems (MLCAS) 2024, Lincoln, NE.
Marques, J. M. C., Dengler, N., Zaenker, T., Wang, S., Bennewitz, M., & Hauser, K. (2025, June). Map space belief prediction for manipulation-enhanced mapping [Conference presentation]. Robotics: Science and Systems.
Schwing, A. (2025, June 12). Foundations of foundation models [Tutorial]. Multi-Modal Computer Vision and Foundation Models in Agriculture, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN.
Yao, S., & Hauser, K. (2024, November). Structured Bayesian meta-learning for data-efficient visual-tactile model estimation [Conference presentation]. Conference on Robot Learning (CoRL).
Yao, S., Pan, S., Bennewitz, M., & Hauser, K. (2025, May). Safe leaf manipulation for accurate shape and pose estimation of occluded fruits [Conference presentation]. IEEE International Conference on Robotics and Automation (ICRA).
Abdalla, F.; Benicio, L.; Rahman, M.; de Souza Silva, M. C.; Souza, V. H. S.; & Condotta, I. C. F. S. (2025). Individual identification of Holstein cows using computer vision. Accepted for 3rd US-PLF.
Benicio, L. M., Condotta, I. C. F. S., Abdala, F., & Xavier, D. B. (2024, October). Individual identification of cattle using computer vision models [Poster session]. AIFARMS Year 4 Annual Conference and Review, St. Louis, MO.
Benicio, L. M., Abdala, F., Xavier, D. B., Cardoso, F., & Condotta, I. C. F. S. (2025, March 5). Individual identification of cattle using computer vision models [Poster session]. CDA Annual Conference – Center for Digital Agriculture, Urbana, IL.
Condotta, I. C. F. S. (2024). Precision management of animals: Role in the future of animal production [Webinar presentation]. Curricular Unit of Emerging Technologies in Animal Production, Master’s in Animal Science, University of Trás‑os‑Montes and Alto Douro (virtual).
Condotta, I. C. F. S. (2025). Animal welfare in production systems: Precision livestock farming role [Invited talk]. 1st International Congress on Veterinary and Animal Science: Under One Health Concept (virtual), University of Trás‑os‑Montes and Alto Douro, Portugal.
de Souza Silva, M. C., Rahman, M., Souza, V. H. S., Cantarelli, V. de S., & Condotta, I. C. F. S. (2024, October). Deep learning-based detection of piglet feeding and drinking behaviors in clean and dirty pen environments [Poster session]. AIFARMS Year 4 Annual Conference and Review, St. Louis, MO.
de Souza Silva, M. C., Rahman, M., Souza, V. H. S., Yarmohammadi, S., Cantarelli, V. de S., Campos, A. T., & Condotta, I. C. F. S. (2025, June). Integration of human monitoring and computer vision for evaluating nursery piglet health and performance [Conference presentation]. U.S. Precision Livestock Farming Conference (USPLF 2025), Lincoln, NE .
Felton, M., Gjata, F., Williams-Stroud, T., Agarwal, A., Green-Miller, A., (2025, July). Application of Two Computer Vision Models on Commercial Finishing Pig Novel Data [Conference Presentation Lightning Talk]. 2025 ASABE Annual International Meeting, Toronto, ON, Canada.
Felton, M. (2025). The sorting hat: An automated activity index based on finishing pig behavior [Master’s thesis, University of Illinois at Urbana-Champaign]. University of Illinois at Urbana-Champaign, Urbana, IL.
Freeman, B., Williams‑Stroud, T., Rinehart, H., Fonseca, I., Fuentes, E., Felton, M., & Green‑Miller, A. (2025, April 24). Comparing behavior of pigs prior to simulated respiratory illness [Poster presentation]. 2025 Undergraduate Research Symposium, University of Illinois at Urbana–Champaign, Urbana, IL.
Green‑Miller, A. (2025, April 26). Farming smarter: AI tools and insights for small‑scale agriculture [Invited plenary talk]. Langston Conference on Food & Agricultural Systems: Including Goats, Hair Sheep, and Emerging Technology, Langston University, Langston, OK.
Rahman, M., Souza, V. H. S., de Souza Silva, M. C., & Condotta, I. C. F. S. (2024, October). Automatic monitoring of pre- and post-farrowing activities of sows through computer vision technologies [Poster session]. AIFARMS Year 4 Annual Conference and Review, St. Louis, MO.
Rahman, M., Souza, V. H. S., Brown‑Brandl, T., Rohrer, G. A., Shi, Y., & Condotta, I. C. F. S. (2025, June). Automated monitoring of sow nursing behaviors in farrowing crates through computer vision techniques [Conference presentation]. U.S. Precision Livestock Farming Conference (USPLF 2025), Lincoln, NE .
Rahman, M., Souza, V. H. S., de Souza Silva, M. C., & Condotta, I. C. F. S. (2025, June). Automatic detection of nursery piglets’ feeding and drinking behavior using YOLO11n with slicing‑aided hyper inference (SAHI) [Conference presentation]. CDA Annual Conference – Center for Digital Agriculture 2025, Urbana, IL.
Rinehart, H., Williams-Stroud, T., Freeman, B., Fonseca, I., Fuentes, E., Felton, M., & Green-Miller, A. (2025, March). Identification of important behaviors and postures to indicate respiratory illness in pigs [Conference poster]. Center for Digital Agriculture Conference, Urbana, IL.
Rinehart, H., Williams-Stroud, T., Freeman, B., Fonseca, I., Fuentes, E., Felton, M., & Green-Miller, A. (2025, May). Identification of important behaviors and postures to indicate respiratory illness in pigs [Conference poster]. Annual National Center for Supercomputing Applications Student Research Conference, Urbana, IL.
Rinehart, H., Williams‑Stroud, T., Freeman, B., Fonseca, I., Fuentes, E., Felton, M., & Green‑Miller, A. (2025, July 6–10). Identification of important behaviors and postures to indicate respiratory distress in pigs [Poster presentation]. 2025 ASAS‑CSAS Annual Meeting, Diplomat Beach Resort, Hollywood, FL.
Souza, V. H. S., Rahman, M., de Souza Silva, M. C., Cantarelli, V. de S., & Condotta, I. C. F. S. (2024, October). Assessment of growth and feeding behavior of piglets during the first week post-weaning at varying densities using computer vision [Poster session]. AIFARMS Year 4 Annual Conference and Review, St. Louis, MO.
Souza, V. H. S., Rahman, M., de Souza Silva, M. C., Benicio, L., Cantarelli, V., & Condotta, I. C. F. S. (2025, June). Impact of density and sex on growth and feeding behavior of piglets post‑weaning using a computer vision approach [Conference presentation]. U.S. Precision Livestock Farming Conference (USPLF 2025), Lincoln, NE.
Souza, V. H. S., Rahman, M., de Souza Silva, M. C., Cantarelli, V. de S., & Condotta, I. C. F. S. (2025, September). Assessment of growth and feeding behavior of piglets during the first week post‑weaning at varying densities using computer vision [Conference presentation]. Allen D. Leman Swine Conference, St. Paul, MN.
Yarmohammadi, S., Rahman, M., Souza, V. H. S., de Souza Silva, M. C., Benicio, L., Cantarelli, V., & Condotta, I. C. F. S. (2025, June). Automatic detection of nursery piglets’ behaviors and activities using computer vision technologies [Conference presentation]. U.S. Precision Livestock Farming Conference (USPLF 2025), Lincoln, NE.
Guan, K. (2025, May). AI-enabled phenomics and engineering of stomata and water use efficiency for resilient agriculture [Conference presentation]. Joint Meeting of French Society of Photosynthesis and the GdR Integrative Biology of CO2 Capture, Sorbonne University, Paris, France.
Jiang, Z., Guan, K., Wang, S., Nafziger, E. D., Ciampitti, I. A., Schaefer, D., & Pike, J. (2024, December). Monitoring early‑season crop nitrogen status via airborne‑satellite cross‑scale sensing and nitrogen nutrition index [Abstract]. AGU Fall Meeting Abstracts, Vol. 2024, GC21G‑03.
He, J. (2024, October). Multifaceted robustness in transfer learning [Invited talk]. University of North Dakota Distinguished Webinar.
He, J. (2024, October). Federated learning with data heterogeneity [Invited talk]. INFORMS.
He, J. (2024, November). Multifaceted robustness in transfer learning [Invited talk]. Vanderbilt Machine Learning Seminar.
He, J. (2025). Exploitation vs. exploration in sequential decision-making [Keynote talk]. ICLR 2025 Workshop on Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation.
Hudson, M. (2024, October). AI in agriculture: Special side event on artificial intelligence, digital agriculture, and global food security. World Food Prize Symposium, Des Moines, IA.
Hudson, M. E. (2025, January). Structural variation and genotype–phenotype analysis in sorghum [Conference presentation]. Plant and Animal Genome Conference (PAG 32), San Diego, CA.
Hudson, M. (2025, July). Structural variation and genome size in soybean [Conference presentation]. Soy 2025 Conference, University of Wisconsin–Madison, Madison, WI.
Bergschneider, L., & Margenot, A. J. (2024, December 2). Summary of the past five years of on-farm BMPs for reducing tile N losses [Presentation]. Princeton, IL.
Bergschneider, L., & Margenot, A. J. (2025, February 13). Takeaways from phase 1 of on-farm tile water quality monitoring in Bureau County, Illinois [Presentation]. NREC Investment Insights Live!
Bergschneider, L., & Margenot, A. J. (2025, July 29). Using on-farm research to guide nutrient reduction and soil health strategies [Presentation]. Practical Farmers of Iowa (PFI), Von Holten Farms, Walnut, IL.
Margenot, A. J. (2025, March 13). Quantifying P fluxes and pools across the terrestrial–aquatic continuum [Conference presentation]. STEPS, Raleigh, NC.
Bowman, D. (2024, September 27–28). Autonomous Technology and AI solving Farming Challenges [Presentation]. Farm Foundation Event, Chicago, IL.
Bowman, D. (2024, October 9). Autonomous Technology and AI solving Farming Challenges [Presentation]. Heartland Community College AgTech Demo.
Bowman, D. (2024, October 11). Autonomous Technology and AI solving Farming Challenges [Presentation]. Chicago Farmers Campus Demos.
Bowman, D. (2024, December 3-5). Autonomous Technology and AI solving Farming Challenges [Presentation]. Peoria Farm Show.
Bowman, D. (2025, January 21). Autonomous Technology and AI solving Farming Challenges[Presentation]. Normal Chidren’s Discovery Center Exhibit Planning.
Bowman, D. (2025, January 23). Autonomous Technology and AI solving Farming Challenges [Presentation]. Crocker Elementary School STEAM Fair.
Bowman, D. (2025, January 28). Autonomous Technology and AI solving Farming Challenges [Presentation]. Edwards County Agronomy Summit.
Bowman, D. (2025, February 10-11). Autonomous Technology and AI solving Farming Challenges [Presentation]. ISA Soybean Summit Trade Show.
Bowman, D. (2025, February 12). Autonomous Technology and AI solving Farming Challenges[Presentation]. CMC Champaign.
Bowman, D. (2025, February 19). Autonomous Technology and AI solving Farming Challenges[Presentation]. CMC Sycamore.
Bowman, D. (2025, March 13). Autonomous Technology and AI solving Farming Challenges
[Presentation]. Parkland Comm College AgTech Career Fair.
Bowman, D. (2025, May 20). Autonomous Technology and AI solving Farming Challenges [Presentation]. Danville High School Student visit to iFARM.
Bowman, D. (2025, June 16). Autonomous Technology and AI solving Farming Challenges [Presentation]. 2025 Sustainable Research and Innovation Congress.
Essakkat, K. (2025, July). Dynamic adoption of AI weeding robots: Empirical insights from a choice experiment based ABM [Oral presentation]. AAEA/WAEA Joint Meeting, Denver, CO.
Essakkat, K. (2025, July). From data to decisions: Modeling farmer adoption of AI weed technology [Online presentation]. The International Conference on Digital Technologies for Sustainable Crop Production, Bonn, Germany.
Pagadala, A., Wahle, E., Bowman, D., Sunoj, S., Reid, J. F., & Adve, V. (2024, September 23). Robotic vision-based weed management in horseradish production [Oral presentation]. Fall 2024 Engineering Research Fair, Illini Union, University of Illinois, Urbana-Champaign.
Pagadala, A., Sunoj, S., & Reid, J. F. (2025, July). Precision mechanical robotic weeding for specialty crops [Virtual conference presentation]. DIGICROP 2025 Virtual Conference.
Sunoj, S., Pagadala, A., Wahle, E., Bowman, D., & Reid, J. F. (2024, November 18–21). From labor to automation: AI innovations in horseradish weed management [Oral presentation]. USDA AI User Forum, Texas A&M University.
Sunoj, S., Poudel, S., & Reid, J. F. (2025, January 22). Is AI smart and fast enough to manage weeds in horseradish fields? [Conference presentation]. Horseradish Growers Conference, Madison County Bureau.
Bertolini, E. (2024, October). Genomic prediction of maize tassel traits through LiDAR point cloud segmentation and machine learning phenotyping [Lightning talk]. AIFARMS Conference, St. Louis, MO.
Chaudhari, V. (2024, October). Predicting end-of-season sorghum biomass from seedling-stage traits [Poster presentation]. AIFARMS Conference, St. Louis, MO.
Chaudhari, V. (2025, March). Predicting end-of-season sorghum biomass from seedling-stage traits [Lightning talk and poster presentation]. Annual Maize Genetics Conference, St. Louis, MO.
Eveland, A. (2025, May). Integrating molecular genetics and precision phenotyping to uncover regulatory variation underlying drought resilience in sorghum [Plenary presentation]. IX Simpósio Brasileiro de Genética Molecular de Plantas: Exploring Innovations in Plant Genetics for Sustainable Agriculture, Búzios, Rio de Janeiro, Brazil.
Eveland, A. (2025, June). Foundational AI and real-world plant phenotyping outcomes [Panelist and presentation]. Sustainable Research and Innovation Conference, Chicago, IL.
Bernard, G. (2024, September). Tuskegee University project updates highlighting AIFARMS research [Invited presentation]. U.S. House Committee on Agriculture, Washington, DC.
Bernard, G. (2024, September). The use of autonomous robots in urban agriculture [Guest lecture]. USDA Cochran Fellowship Program, Tuskegee University.
Bernard, G. (2024, October 1). The use of autonomous crop phenotyping technologies in small-scale production [Conference presentation]. AIFARMS Annual Conference, Urbana, IL.
Bernard, G. (2024). Enhancing agricultural empowerment: Integrating precision solutions for plant disease management, advancing small-scale farming, educating the next generation of agricultural leaders, and fostering community engagement [Conference presentation abstract]. 1890 Research Directors Symposium, Atlanta, GA.
Bernard, G. (2024, November). Empowering small-scale and minority farmers: Expanding access to autonomous crop phenotyping technologies through targeted outreach [Presentation and moderation]. PAWC Conference.
Bernard, G. (2024, December). Bridging the gap: Technology adoption in smallholder farms [Invited lecture]. I-FARM Lecture Series, University of Illinois Urbana-Champaign.
Bernard, G. (2025, June). Bridging the gap: Technology adoption in smallholder farms [Panelist and presentation]. SRI Congress, Chicago, IL.
Kooper, R., Lučić, A., Bodony, N., & Wedow, J. (2024, December 8–13). AIFARMS dataset publication and best practices [Poster presentation]. AGU24 Conference, Washington, USA. https://www.agu.org/annual-meeting

PUBLICATIONS AND PRESENTATIONS

Year 4

Publications:

Schreiber, A. N. Sivakumar, P. Du, M. V. Gasparino, G. Chowdhary, & K. Driggs-Campbell. (2024). W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics. IEEE Robotics and Automation Letters, 9(6), 5623–5630. doi.org/10.1109/LRA.2024.3396095
Adve, V. S., Wedow, J. M., Ainsworth, E. A., Chowdhary, G., Green-Miller, A., & Tucker, C. (2024). AIFARMS: Artificial intelligence for future agricultural resilience, management, and sustainability. AI Magazine, 45(1, SI), 83–88. doi.org/10.1002/aaai.12152
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., & Melchiori, R. (2023). Plant emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops Research, 302. doi.org/10.1016/j.fcr.2023.109090
Ban, Y., Qi, Y., Wei, T., Liu, L., & He, J. (2024). Meta Clustering of Neural Bandits. Proceedings Of The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, 95–106. doi.org/10.1145/3637528.3671691
Bao, W., Wei, T., Wang, H., & He, J. (2023). Adaptive test-time personalization for federated learning. Advances in Neural Information Processing Systems, 36, 77882–77914.
Bao, Z., Li, Y., Singh, K. K., Wang, Y.-X., & Hebert, M. (2024). Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models. Proceedings Of SIGGRAPH 2024 Conference Papers. doi.org/10.1145/3641519.3657527
Bhatnagar, S., & Ahuja, N. (2024). Piecewise-Linear Manifolds for Deep Metric Learning. In Y. Chi, G. Dziugaite, Q. Qu, A. Wang, & Z. Zhu (Eds.), CONFERENCE ON PARSIMONY AND LEARNING, VOL 234 (Vol. 234, pp. 269–281).
Bhatnagar, S., Gopal, S., Ahuja, N., & Ren, L. (2023). Long-Distance Gesture Recognition using Dynamic Neural Networks. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 1307–1312. doi.org/10.1109/IROS55552.2023.10342147
Cao, S., Gu, J., Kuen, J., Tan, H., Zhang, H., Nenkova, A., Gui, L. Y., Sun, T., & Wang, Y. X. (2024, May). SOHES: Self-supervised Open-world Hierarchical Entity Segmentation. 12th International Conference on Learning Representations, ICLR 2024. doi.org/doi.org/10.48550/arXiv.2404.12386
Cao, S., Joshi, D., Gui, L.-Y., & Wang, Y.-X. (2023). HASSOD: Hierarchical Adaptive Self-Supervised Object Detection. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Chang, P., Liu, S., Ji, T., Chakraborty, N., Hong, K., & Driggs-Campbell, K. (2023). A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots. In J. Tan, M. Toussaint, & K. Darvish (Eds.), Conference on Robot Learning, Vol 229.
Chen, J.-K., Buio, S. R., Muller, N., Porzi, L., Kontschieder, P., & Wang, Y.-X. (2024). ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21071–21080. doi.org/10.1109/CVPR52733.2024.01991
Cheng, H. K., Oh, S. W., Price, B., Lee, J.-Y., & Schwing, A. (2024). Putting the Object Back into Video Object Segmentation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, 3151–3161. doi.org/10.1109/CVPR52733.2024.00304
Cheng, H. K., Oh, S. W., Price, B., Schwing, A., & Lee, J.-Y. (2023). Tracking Anything with Decoupled Video Segmentation. 2023 IEEE/CVF International Conference on Computer Vision, ICCV, 1316–1326. doi.org/10.1109/ICCV51070.2023.00127
Dong, J., & Wang, Y.-X. (2023). ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Du, B., Yuan, C., Wang, F., & Tong, H. (2023). Geometric Matrix Completion via Sylvester Multi-Graph Neural Network. Proceedings Of The 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, 3860–3864. doi.org/10.1145/3583780.3615170
Feng, S., Jing, B., Zhu, Y., & Tong, H. (2024). ARIEL: Adversarial Graph Contrastive Learning. ACM Transactions on Knowledge Discovery from Data, 18(4). doi.org/10.1145/3638054
Gao, L., Guan, K., He, L., Jiang, C., Wu, X., Lu, X., & Ainsworth, E. A. (2024). Tropospheric ozone pollution increases the sensitivity of plant production to vapor pressure deficit across diverse ecosystems in the Northern Hemisphere. Science of The Total Environment, 951, 175748. doi.org/10.1016/j.scitotenv.2024.175748
Gasparino, M. V., Sivakumar, A. N., & Chowdhary, G. (2024). WayFASTER: a Self-Supervised Traversability Prediction for Increased Navigation Awareness. 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, 8486–8492. doi.org/10.1109/ICRA57147.2024.10610436
He, X., Wei, T., & He, J. (2023). Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning. PROCEEDINGS OF THE 32ND ACM International Conference on Information and Knowledge Management, CIKM 2023, 709–719. doi.org/10.1145/3583780.3615039
Hill, B., Liu, L., & Tong, H. (2024). Ginkgo-P: General Illustrations of Knowledge Graphs for Openness as a Platform. Proceedings Of The 17th ACM International Conference on Web Search And Data Mining, WSDM 2024, 1066–1069. doi.org/10.1145/3616855.3635701
Hu, Y.-T., Schwing, A. G., & Yeh, R. A. (2023). Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), International Conference on Machine Learning, (Vol 202).
Isik, B., Pase, F., Gunduz, D., Koyejo, S., Weissman, T., & Zorzi, M. (2024). Adaptive Compression in Federated Learning via Side Information. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), International Conference on Artificial Intelligence and Statistics, (Vol. 238).
Jing, B., Yan, Y., Ding, K., Park, C., Zhu, Y., Liu, H., & Tong, H. (2024a). STERLING: Synergistic Representation Learning on Bipartite Graphs. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), 38th AAAI Conference on Artificial Intelligence, 38(12) (pp. 12976–12984). Assoc Advancement Artificial Intelligence.
Jing, B., Yan, Y., Ding, K., Park, C., Zhu, Y., Liu, H., & Tong, H. (2024b). Sterling: Synergistic Representation Learning on Bipartite Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 12976–12984. doi.org/10.1609/aaai.v38i12.29195
Li, J., Hu, X., Lucic, A., Wu, Y., Condotta, I. C. F. S., Dilger, R. N., Ahuja, N., & Green-Miller, A. R. (2024). Promote computer vision applications in pig farming scenarios: High-quality dataset, fundamental models, and comparable performance1. Journal of Integrative Agriculture. doi.org/10.1016/j.jia.2024.08.014
Lin, X., Kang, J., Cong, W., & Tong, H. (2023). BeMap: Balanced Message Passing for Fair Graph Neural Network. In S. Villar & B. Chamberlain (Eds.), Learning on Graphs Conference (Vol. 231).
Lin, X., Liu, Z., Fu, D., Qiu, R., & Tong, H. (2024). BackTime: Backdoor Attacks on Multivariate Time Series Forecasting. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), Advances in Neural Information Processing Systems (Vol. 37, pp. 131344–131368). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2024/file/ed3cd2520148b577039adfade82a5566-Paper-Conference.pdf
Liu, L., Hill, B., Dut, B., Wang, F., & Tong, H. (2024). Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph. In A. Martins, V. Srikumar, & L. Ku (Eds.), Findings of The Association for Computational Linguistics: ACL 2024 (pp. 839–850).
Lorberbom, G., Gat, I., Adi, Y., Schwing, A., & Hazan, T. (2024). Layer Collaboration in the Forward-Forward Algorithm. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), 38^th AAAI Conference on Artificial Intelligence, 38(13) (pp. 14141–14148). Assoc Advancement Artificial Intelligence.
Man, Y., Gui, L., & Wang, Y.-X. (2023). DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception. 2023 IEEE/RSJ International Conference on Intelligent Robots And Systems (IROS), 10910–10917. doi.org/10.1109/IROS55552.2023.10341473
Man, Y., Gui, L.-Y., & Wang, Y.-X. (2024). Situational Awareness Matters in 3D Vision Language Reasoning. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13678–13688. doi.org/10.1109/CVPR52733.2024.01298
Mou, L., Chen, J.-K., & Wang, Y.-X. (2024). Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20176–20185. doi.org/10.1109/CVPR52733.2024.01907
Pang, Z., Ramanan, D., Li, M., & Wang, Y.-X. (2023). Streaming Motion Forecasting for Autonomous Driving. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7407–7414. doi.org/10.1109/IROS55552.2023.10341894
Pang, Z., Xie, Z., Man, Y., & Wang, Y.-X. (2023). Frozen transformers in language models are effective visual encoder layers. arXiv Preprint arXiv:2310.12973.
Patel, A. K., Bertolini, E., Sagan, V., Alifu, H., Braud, M., Shrestha, N., Gul, C., & Eveland, A. L. (2024). Genomic prediction of maize tassel traits through LiDAR point cloud segmentation and machine learning phenotyping. CABI. doi.org/10.31220/agriRxiv.2024.00269
Peng, J.-C., Yao, S., & Hauser, K. (2024). 3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor Using FEM. 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, 5666–5672. doi.org/10.1109/ICRA57147.2024.10610233
Potash, E., Guan, K., Margenot, A. J., Lee, D., Boe, A., Douglass, M., Heaton, E., Jang, C., Jin, V., Li, N., Mitchell, R., Namoi, N., Schmer, M., Wang, S., & Zumpf, C. (2023). Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields. GEODERMA, 438. doi.org/10.1016/j.geoderma.2023.116587
Reddy, R. G., Attia, O., Li, Y., Ji, H., & Potdar, S. (2024). AGRAME: Any-Granularity Ranking with Multi-Vector Embeddings. In Y. Al-Onaizan, M. Bansal, & Y. Chen (Eds.), 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 (pp. 8630–8641).
Reddy, R. G., Doo, J., Xu, Y., Sultan, M. A., Swain, D., Sil, A., & Ji, H. (2024). FIRST: Faster Improved Listwise Reranking with Single Token Decoding. In Y. Al-Onaizan, M. Bansal, & Y. Chen (Eds.), 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 (pp. 8642–8652).
Safanelli, J. L., Sanderman, J., Bloom, D., Todd-Brown, K., Parente, L. L., Hengl, T., Adam, S., Albinet, F., Ben-Dor, E., Boot, C. M., Bridson, J. H., Chabrillat, S., Deiss, L., Dematte, J. A. M., Demyan, M. S., Dercon, G., Doetterl, S., van Egmond, F., Ferguson, R., … Zelazny, W. R. (2023). An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter. GEODERMA, 440. doi.org/10.1016/j.geoderma.2023.116724
Salaudeen, O., & Koyejo, S. (2024). Causally Inspired Regularization Enables Domain General Representations. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), International Conference On Artificial ℡Intelligence and Statistics, Vol. 238.
Schaeffer, R., Khona, M., Ma, T., Eyzaguirre, C., Koyejo, S., & Fiete, I. R. (2023). Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Shuai, G., Fowler, A., & Basso, B. (2024). Within-season vegetation indices and yield stability as a predictor of spatial patterns of Maize (Zea mays L) yields. Precision Agriculture, 25(2), 963–982. doi.org/10.1007/s11119-023-10101-0
Sivakumar, A. N., Gasparino, M. V., McGuire, M., Higuti, V. A., Akcal, M. U., & Chowdhary, G. (2024). Lessons from deploying CropFollow++: Under-canopy agricultural navigation with keypoints. Proceedings in Robotics: Science and Systems 2024. Robotics: Science and Systems, Deift, Netherlands. doi.org/10.48550/arXiv.2404.17718
Sivakumar, A. N., Thangeda, P., Fang, Y., Gasparino, M. V., Cuaran, J., Ornik, M., & Chowdhary, G. (2024). Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots. arXiv Preprint arXiv:2408.03059.
Sun, X., Ponce, J., & Wang, Y.-X. (2023). Revisiting Deformable Convolution for Depth Completion. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 1300–1306. doi.org/10.1109/IROS55552.2023.10342026
Tan, G. D., Chaudhuri, Ushasi, Varela, Sebastian, Ahuja, Narendra, & Leakey, Andrew D.B. (2024). Machine learning-enabled computer vision for plant phenotyping: A primer on AI/ML and a case study on stomatal patterning. 75(21), 6683–6703. doi.org/10.1093/jxb/erae395
Tang, Z., Ren, Z., Zhao, X., Wen, B., Tremblay, J., Birchfield, S., & Schwing, A. (2024). NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10293–10303. doi.org/10.1109/CVPR52733.2024.00980
Tsai, K., Pfohl, S. R., Salaudeen, O., Chiou, N., Kusner, M. J., DAmour, A., Koyejo, S., & Gretton, A. (2024). Proxy Methods for Domain Adaptation. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), International Conference on Artificial Intelligence and Statistics, VOL 238 (Vol. 238.
Tsai, K., Zhao, B., Koyejo, S., & Kolar, M. (2024). Latent Multimodal Functional Graphical Model Estimation. Journal of the American Statistical Association, 119(547), 2217–2229. doi.org/10.1080/01621459.2023.2252142
Walt, B., & Krishnan, G. (2023). Grasp State Classification in Agricultural Manipulation. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 4250–4255. doi.org/10.1109/IROS55552.2023.10341881
Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., Truong, S. T., Arora, S., Mazeika, M., Hendrycks, D., Lin, Z., Cheng, Y., Koyejo, S., Song, D., & Li, B. (2023). DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Wang, Q., Downey, D., Ji, H., & Hope, T. (2024). SCIMON : Scientific Inspiration Machines Optimized for Novelty. In L. Ku, A. Martins, & V. Srikumar (Eds.), Proceedings Of The 62nd Annual Meeting of the Association for Computational Linguistics, Vol 1: Long Papers (pp. 279–299).
Wang, X., Chen, Y., Yuan, L., Zhang, Y., Li, Y., Peng, H., & Ji, H. (2024). Executable Code Actions Elicit Better LLM Agents. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, & F. Berkenkamp (Eds.), Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 50208–50232). PMLR. https://proceedings.mlr.press/v235/wang24h.html
Wang, X., Dimitriadis, D., Koyejo, S., & Tople, S. (2024). Invariant Aggregator for Defending against Federated Backdoor Attacks. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), INTERNATIONAL CONFERENCE ON ARTIFICIAL IN℡LIGENCE AND STATISTICS, VOL 238 (Vol. 238).
Wei, T., Jin, B., Li, R., Zeng, H., Wang, Z., Sun, J., Yin, Q., Lu, H., Wang, S., He, J., & Tang, X. (2024). Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond (No. arXiv:2403.10667). arXiv. https://doi.org/10.48550/arXiv.2403.10667
Wen, J., Zhao, X., Ren, Z., Schwing, A. G., & Wang, S. (2024). GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh. 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2024, 2059–2069. https://doi.org/10.1109/CVPR52733.2024.00201
Williams-Stroud, T. N., Dilger, R. N., & Green-Miller, A. R. (2024). 210 Sleeping beauties: A daily time budget for individually housed research pigs at 6-d of age. Journal of Animal Science, 102(Supplement_2), 6–6. https://doi.org/10.1093/jas/skae102.006
Wu, J., Ainsworth, E., Leakey, A., Wang, H., & He, J. (2023). Graph-Structured Gaussian Processes for Transferable Graph Learning. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023).
Wu, J., & He, J. (2023). A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 35(11), 11039–11050. https://doi.org/10.1109/TKDE.2022.3230825
Wu, J., He, J., & Tong, H. (2024). Distributional Network of Networks for Modeling Data Heterogeneity. PROCEEDINGS OF THE 30TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2024, 3379–3390. https://doi.org/10.1145/3637528.3671994
Xu, S., Li, Z., Wang, Y.-X., & Gui, L.-Y. (2023). InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion. 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023), 14882–14894. doi.org/10.1109/*\751070.2023.01371
Yan, Y., Jing, B., Liu, L., Wang, R., Li, J., Abdelzaher, T., & Tong, H. (2023). Reconciling Competing Sampling Strategies of Network Embedding. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Yoo, H., Zeng, Z., Kang, J., Qiu, R., Zhou, D., Liu, Z., Wang, F., Xu, C., Chan, E., & Tong, H. (2024). Ensuring User-side Fairness in Dynamic Recommender Systems. Proceedings of the ACM Web Conference 2024, 3667–3678. doi.org/10.1145/3589334.3645536
Yu, J., Li, X., Zhao, X., Zhang, H., & Wang, Y.-X. (2023). Video State-Changing Object Segmentation. 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023), 20382–20391. doi.org/10.1109/ICCV51070.2023.01869
Zeng, Z., Du, B., Zhang, S., Xia, Y., Liu, Z., & Tong, H. (2024). Hierarchical Multi-Marginal Optimal Transport for Network Alignment. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), 38th AAAI Conference on Artificial Intelligence, 38(15) (pp. 16660–16668).
Zhang, H., Li, F., & Ahuja, N. (2024). Open-NeRF: Towards Open Vocabulary NeRF Decomposition. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, 3444–3453. doi.org/10.1109/WACV57701.2024.00342
Zhang, H., Li, F., Qi, L., Yang, M.-H., & Ahuja, N. (2024). CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), 38th AAAI Conference on Artificial Intelligence, 38(7) (pp. 7078–7086). Assoc Advancement Artificial Intelligence.
Zhang, X., Chang, M., Kumar, P., & Gupta, S. (2024, July 19). Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning. Proceedings of Robotics: Science and Systems (RSS). Robotics: Science and Systems (RSS), Delft, Netherlands. doi.org/doi.org/10.15607/RSS.2024.XX.048
Zhang, X., & Gupta, S. (2023). Push Past Green: Learning to Look Behind Plant Foliage by Moving It. In J. Tan, M. Toussaint, & K. Darvish (Eds.), Conference on Robot Learning, Vol 229.
Zhang, Z., Cao, S., & Wang, Y.-X. (2024). TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21413–21423. doi.org/10.1109/CVPR52733.2024.02023
Zheng, S., Bao, Z., Hebert, M., & Wang, Y.-X. (2023). Multi-task View Synthesis with Neural Radiance Fields. 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023), 21481–21492. doi.org/10.1109/ICCV51070.2023.01969
Zhong, Y., Bhattad, A., Wang, Y.-X., & Forsyth, D. (2023). Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors. 2023 IEEE/CVF International Conference on Computer VISION (ICCV 2023), 21718–21728. doi.org/10.1109/ICCV51070.2023.01990
Zhong, Y., Tang, H., Chen, J.-K., & Wang, Y.-X. (2023). Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study. 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023), 16281–16290. doi.org/10.1109/ICCV51070.2023.01496
Zhou, A., Li, S., Sriram, P., Li, X., Dong, J., Sharma, A., Zhong, Y., Luo, S., Jaromin, M., Kindratenko, V., Heintz, G., Zallek, C., & Wang, Y.-X. (2023). YouTubePD: A Multimodal Benchmark for Parkinson’s Disease Analysis. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Zhou, A., Wang, J., Wang, Y.-X., & Wang, H. (2023). Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (NEURIPS 2023).
Zhou, Q., Chen, Y., Xu, Z., Wu, Y., Pan, M., Das, M., Yang, H., & Tong, H. (2024). Graph Anomaly Detection with Adaptive Node Mixup. Proceedings Of The 33rd ACM International Conference On Information and Knowledge Management, CIKM 2024, 3494–3504. doi.org/10.1145/3627673.3679577
Zhou, Q., Ding, K., Liu, H., & Tong, H. (2023). Learning Node Abnormality with Weak Supervision. Proceedings Of The 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, 3584–3594. doi.org/10.1145/3583780.3614950

Presentations:

Bushman, J., Condotta, I, Knox, R., Caesar, M., Green-Miller, A. (May, 2023). I-SEEDS: Illinois System for Electronic Estrus Detection and Stimulation. [Poster] Proceedings of 2nd US-PLF Conference.
Condotta, I, Benicio, L., Dunning, N., Dilger, R. (May, 2023). I-PICS: Illinois Pig Identification through Computer vision System. In: [Presentation]. Proceedings of 2nd US-PLF Conference.
Khanna. M. (February 2023). Owning robots or hiring services for weed management? Bioeconomic and behavioral drivers. [Conference presentation]. The Eastern Economic Association 49th Annual Conference.
Khanna. M. (April 2023). Owning robots or hiring services for weed management? Bioeconomic and behavioral drivers. [Conference presentation]. The Midwest Economics Association 87th Annual Meetings.

Year 3

Publications:

Allabadi, G., Lucic, A., Aananth, S., Yang, T., Wang, Y.-X., & Adve, V. (2023). Generalized Open-World Semi-Supervised Object Detection. arXiv Preprint arXiv:2307.15710.
Bao, W., Wang, H., Wu, J., & He, J. (2023). Optimizing the Collaboration Structure in Cross-Silo Federated Learning. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), International Conference on Machine, (202).
Bao, Z., Tokmakov, P., Wang, Y.-X., Gaidon, A., & Hebert, M. (2023). Object Discovery from Motion-Guided Tokens. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22972–22981. doi.org/10.1109/CVPR52729.2023.02200
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Casebeer, J., Bryan, N. J., & Smaragdis, P. (2023). Meta-AF: Meta-Learning for Adaptive Filters. IEEE-ACM Transactions on Audio Speech and Language Processing, 31, 355–370. doi.org/10.1109/TASLP.2022.3224288
Chang, P., Liu, S., McPherson, D. L., & Driggs-Campbell, K. (2023). Learning Visual-Audio Representations for Voice-Controlled Robots. 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), 9508–9514. doi.org/10.1109/ICRA48891.2023.10161461
Chatterjee, M., Ahuja, N., & Cherian, A. (2022). Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NEURIPS 2022).
Chen, J.-K., Lyu, J., & Wang, Y.-X. (2023). NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12439–12448. doi.org/10.1109/CVPR52729.2023.01197
Cheng, Y.-C., Lee, H.-Y., Tulyakov, S., Schwing, A., & Gui, L. (2023). SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4456–4465. doi.org/10.1109/CVPR52729.2023.00433
Choudhuri, A., Chowdhary, G., & Schwing, A. G. (2023). Context-Aware Relative Object Queries to Unify Video Instance and Panoptic Segmentation. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6377–6386. doi.org/10.1109/CVPR52729.2023.00617
Cisneros-Velarde, P., & Koyejo, S. (2023). Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation. In R. Evans & I. Shpitser (Eds.), Uncertainty in Artificial Intelligence (Vol. 216, pp. 424–432).
Cisneros-Velarde, P., Lyu, B., Koyejo, S., & Kolar, M. (2023). One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. In F. Ruiz, J. Dy, & J. VanDeMeent (Eds.), International Conference On Artificial Intelligence and Statistics, (Vol. 206).
Fang, T., Sun, R., & Schwing, A. (2022). DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data. Advances In Neural Information Processing Systems 35 (NEURIPS 2022), 31782–31795.
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Fowler, A. F., Basso, B., Millar, N., & Brinton, W. F. (2023). A simple soil mass correction for a more accurate determination of soil carbon stock changes. Scientific Reports, 13(1). doi.org/10.1038/s41598-023-29289-2
Gasparino, M. V., Higuti, V. A. H., Sivakumar, A. N., Velasquez, A. E. B., Becker, M., & Chowdhary, G. (2023). CropNav: A Framework for Autonomous Navigation in Real Farms. 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), 11824–11830. doi.org/10.1109/ICRA48891.2023.10160990
Hu, C., Zhou, Q., & Tong, H. (2024). Genius: Subteam Replacement with Clustering-based Graph Neural Networks. In V. Papalexakis, S. Shekhar, J. Gao, Z. Jiang, & M. Riondato (Eds.), Proceedings of the 2024 Siam International Conference on Data Mining, SDM (pp. 10–18). SIAM.
Jain, S., & Tong, H. (2022). YACC: A Framework Generalizing TURANSHADOW for Counting Large Cliques. In A. Banerjee, Z. Zhou, E. Papalexakis, & M. Riondato (Eds.), Proceedings of the 2024 Siam International Conference on Data Mining, SDM (pp. 684–692).
Jing, B., Feng, S., Xiang, Y., Chen, X., Chen, Y., & Tong, H. (2022). X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning. Proceedings Of The 31st ACM International Conference On Information And Knowledge Management, CIKM 2022, 894–904. doi.org/10.1145/3511808.3557490
Kamboj, A., Ji, T., & Driggs-Campbell, K. (2022). Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots. 2022 31ST IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2022), 293–300. doi.org/10.1109/RO-MAN53752.2022.9900859
Kamtikar, S., Samhita Marri, Walt, B. T., Uppalapati, N. K., Krishnan, G., & Chowdhary, G. (2023, March). Towards Autonomous Berry Harvesting using Visual Servoing of Soft Continuum. Proceedings of AI for Agriculture and Food Systems.
Kang, J., Zhou, Q., & Tong, H. (2022). JURYGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. Proceedings Of The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, 742–752. doi.org/10.1145/3534678.3539286
Lai, T. M., Zhai, C., & Ji, H. (2023). KEBLM: Knowledge-Enhanced Biomedical Language Models. Journal Of Biomedical Informatics, 143. doi.org/10.1016/j.jbi.2023.104392
Lee, K.-Y., Zhong, Y., & Wang, Y.-X. (2023). Do Pre-trained Models Benefit Equally in Continual Learning? 2023 IEEE/CVF Winter Conference On Applications of Computer Vision (WACV), 6474–6482. doi.org/10.1109/WACV56688.2023.00642
Li, J., Green-Miller, A. R., Hu, X., Lucic, A., Mohan, M. R. M., Dilger, R. N., Condotta, I. C. F. S., Aldridge, B., Hart, J. M., & Ahuja, N. (2022). Barriers to computer vision applications in pig production facilities. Computers And Electronics in Agriculture, 200. doi.org/10.1016/j.compag.2022.107227
Li, N., Zhou, S., & Margenot, A. J. (2023). From prairie to crop: Spatiotemporal dynamics of surface soil organic carbon stocks over 167 years in Illinois, USA. Science Of The Total Environment, 857(1). doi.org/10.1016/j.scitotenv.2022.159038
Liu, J., Xie, C., Koyejo, O. O., & Li, B. (2022). CoPur: Certifiably Robust Collaborative Inference via Feature Purification. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances In Neural Information Processing Systems 35 (NEURIPS 2022).
Liu, L., & Tong, H. (2023). Knowledge Graph Reasoning and Its Applications. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 5813–5814. doi.org/10.1145/3580305.3599564
Liu, S., Gupta, S., & Wang, S. (2023). Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21138–21147. doi.org/10.1109/CVPR52729.2023.02025
Man, Y., Gui, L.-Y., & Wang, Y.-X. (2023). BEV-Guided Multi-Modality Fusion for Driving Perception. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21960–21969. doi.org/10.1109/CVPR52729.2023.02103
Qi, Y., Ban, Y., & He, J. (2023). Graph Neural Bandits. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 1920–1931. doi.org/10.1145/3580305.3599371
Qiu, R., Wang, D., Ying, L., Poor, H. V., Zhang, Y., & Tong, H. (2023). Reconstructing Graph Diffusion History from a Single Snapshot. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 1978–1988. doi.org/10.1145/3580305.3599488
Robertson, Z., Zhang, H., & Koyejo, O. (2023). Cooperative Inverse Decision Theory for Uncertain Preferences. In F. Ruiz, J. Dy, & J. VanDeMeent (Eds.), International Conference on Artificial Intelligence and Statistics, (Vol. 206).
Rojas-Gomez, R. A., Lim, T.-Y., Schwing, A. G., Do, M. N., & Yeh, R. A. (2022). Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NEURIPS 2022).
Sun, Z., Shen, S., Cao, S., Liu, H., Li, C., Shen, Y., Gan, C., Gui, L.-Y., Wang, Y.-X., Yang, Y., Keutzer, K., & Darrell, T. (2024). Aligning Large Multimodal Models with Factually Augmented RLHF. In A. Martins, V. Srikumar, & L. Ku (Eds.), Findings of The Association For Computational Linguistics: ACL 2024 (pp. 13088–13110). Assoc Computat Linguist; Apple; LG AI Res; Newsbreak; MetaAI; Google DeepMind; Megagon Labs; Baidu; SCB IOX; SONY; Alibaba Cloud Tongyi; Amazon Sci; ByteDance; IBM; Meituan; Oracle; Ahrefs; Cohere; MI; Tianqiao & Chrissy, Chen Inst; Ant Grp; Adobe; Babelscape; Translated; DataoceanAI; Thailand Convent & Exhibit Bur; KBTG; ETDA; Artificial Intelligence Assoc Thailand; NSTDA, NECTEC.
Wang, D., Yan, Y., Qiu, R., Zhu, Y., Guan, K., Margenot, A., & Tong, H. (2023). Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 2256–2268. doi.org/10.1145/3580305.3599444
Wang, H., Huang, W., Wu, Z., Margenot, A., Tong, H., & He, J. (2022). Deep Active Learning by Leveraging Training Dynamics. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances In Neural Information Processing Systems 35 (NEURIPS 2022).
Wang, Q., Li, M., Chan, H. P., Huang, L., Hockenmaier, J., Chowdhary, G., & Ji, H. (2023). Multimedia Generative Script Learning for Task Planning. In J. Boyd-Graber, N. Okazaki, & A. Rogers (Eds.), Findings of the Association for Computational Linguistics, ACL 2023.
Wang, S., Guan, K., Zhang, C., Zhou, Q., Wang, S., Wu, X., Jiang, C., Peng, B., Mei, W., Li, K., Li, Z., Yang, Y., Zhou, W., Huang, Y., & Ma, Z. (2023). Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data. Remote Sensing of Environment, 285. doi.org/10.1016/j.rse.2022.113366
Wang, Z., Subakan, C., Jiang, X., Wu, J., Tzinis, E., Ravanelli, M., & Smaragdis, P. (2022). Learning Representations for New Sound Classes With Continual Self-Supervised Learning. IEEE Signal Processing Letters, 29, 2607–2611. doi.org/10.1109/LSP.2022.3229643
Wei, T., Guo, Z., Chen, Y., & He, J. (2023). NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), International Conference on Machine Learning.
Wei, T., You, Y., Chen, T., Shen, Y., He, J., & Wang, Z. (2022). Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NEURIPS 2022).
Wu, J., Bao, W., Ainsworth, E., & He, J. (2023). Personalized Federated Learning with Parameter Propagation. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 2594–2605. doi.org/10.1145/3580305.3599464
Wu, J., Casebeer, J., Bryan, N. J., & Smaragdis, P. (2022). Meta-Learning for Adaptive Filters with Higher-Order Frequency Dependencies. 2022 International Workshop on Acoustic Signal Enhancement (IWAENC 2022). doi.org/10.1109/IWAENC53105.2022.9914695
Wu, J., & He, J. (2022). Dynamic transfer learning with progressive meta-task scheduler. Frontiers In Big Data, 5. doi.org/10.3389/fdata.2022.1052972
Wu, J., & He, J. (2023). Trustworthy Transfer Learning: Transferability and Trustworthiness. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 5829–5830. doi.org/10.1145/3580305.3599576
Wu, J., He, J., & Ainsworth, E. (2023). Non-IID Transfer Learning on Graphs. In B. Williams, Y. Chen, & J. Neville (Eds.), 37th AAAI Conference on Artificial Intelligence, VOL 37 NO 9 (pp. 10342–10350). Assoc Advancement Artificial Intelligence.
Wu, J., He, J., Wang, S., Guan, K., & Ainsworth, E. (2022). Distribution-Informed Neural Networks for Domain Adaptation Regression. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NEURIPS 2022).
Xu, Z., Chen, Y., Pan, M., Chen, H., Das, M., Yang, H., & Tong, H. (2023). Kernel Ridge Regression-Based Graph Dataset Distillation. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 2850–2861. doi.org/10.1145/3580305.3599398
Xu, Z., Chen, Y., Zhou, Q., Wu, Y., Pan, M., Yang, H., & Tong, H. (2023). Node Classification Beyond Homophily: Towards a General Solution. Proceedings Of The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 2862–2873. doi.org/10.1145/3580305.3599446
Yan, K., Schwing, A. G., & Wang, Y.-X. (2022). CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NEURIPS 2022).
Yan, Y., Zhou, Q., Li, J., Abdelzaher, T., & Tong, H. (2022). Dissecting Cross-Layer Dependency Inference on Multi-Layered Inter-Dependent Networks. Proceedings Of The 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, 2341–2351. doi.org/10.1145/3511808.3557291
Yao, S., & Hauser, K. (2023). Estimating Tactile Models of Heterogeneous Deformable Objects in Real Time. 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), 12583–12589. doi.org/10.1109/ICRA48891.2023.10160731
Zeng, Z., Zhu, R., Xia, Y., Zeng, H., & Tong, H. (2023). Generative Graph Dictionary Learning. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), International Conference on Machine Learning, Vol 202.
Zhang, M., Zheng, S., Bao, Z., Hebert, M., & Wang, Y.-X. (2023). Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 795–805. doi.org/10.1109/WACV56688.2023.00086
Zhang, S., Xia, Y., Zhu, Y., & Tong, H. (2023). Representation Learning on Dynamic Network of Networks. In S. Shekhar, Z. Zhou, Y. Chiang, & G. Stiglic (Eds.), Proceedings Of The 2023 Siam International Conference on Data Mining, SDM (pp. 298–306). SIAM.
Zhang, Y., Xia, Y., Zhu, Y., Chi, Y., Ying, L., & Tong, H. (2022). Active Heterogeneous Graph Neural Networks with Per-step Meta-Q-Learning. In X. Zhu, S. Ranka, M. Thai, T. Washio, & X. Wu (Eds.), 2022 IEEE International Conference on Data Mining (ICDM) (pp. 1329–1334). IEEE; IEEE Comp Soc; US Natl Sci Fdn. doi.org/10.1109/ICDM54844.2022.00176
Zhao, X., Hu, Y.-T., Ren, Z., & Schwing, A. G. (2023). Occupancy Planes for Single-View RGB-D Human Reconstruction. In B. Williams, Y. Chen, & J. Neville (Eds.), 37th AAAI Conference on Artificial Intelligence, Vol. 37, No. 3 (pp. 3633–3641). Assoc Advancement Artificial Intelligence.
Zhao, Y., Sharif, H., Pao-Huang, P., Shah, V., Sivakumar, A. N., Valverde Gasparino, M., Mahmoud, A., Zhao, N., Adve, S., Chowdhary, G., Misailovic, S., & Adve, V. (2023). ApproxCaliper: A Programmable Framework for Application-aware Neural Network Optimization. In D. Song, M. Carbin, & T. Chen (Eds.), Proceedings of Machine Learning and Systems (Vol. 5, pp. 400–413). Curan. https://proceedings.mlsys.org/paper_files/paper/2023/file/89efa87dc8f0a5d18e4ae0a479658f60-Paper-mlsys2023.pdf
Zhou, Q., Li, L., Cao, N., Ying, L., & Tong, H. (2023). Adversarial Attacks on Multi-Network Mining: Problem Definition and Fast Solutions. IEEE Transactions on Knowledge and Data Engineering, 35(1), 96–107. doi.org/10.1109/TKDE.2021.3078634
Zhou, Y., Wu, J., Wang, H., & He, J. (2022). Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. Proceedings Of The 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, 2753–2762. doi.org/10.1145/3511808.3557232
Zhuang, P., Ma, L., Koyejo, S., & Schwing, A. (2022). Controllable Radiance Fields for Dynamic Face Synthesis. 2022 International Conference on 3D Vision, 3DV, 646–656. doi.org/10.1109/3DV57658.2022.00075

Presentations:

Adve, V. (2022, February). National AI Institutes program panel – Overview of AIFARMS [Panel presentation]. AAAI Conference on Artificial Intelligence. Online.
Adve, V. (2022, February). An overview of AIFARMS [Presentation]. Agricultural Genome to Phenome Initiative Field Day, Online. https://www.ag2pi.org/workshops-and-activities/field-day-2022-02-16/
Adve, V. (2022). A perspective on AI for Science [Panel discussion]. Communications of the ACM, Online.
Becker, M (October 2022). Presented to the Ag teaching staff at the Chicago High School for Agricultural Sciences.
Becker, M (November 2022). Presented to the Board of Directors for the Elevate 217 non-profit in Mattoon, IL.
Bernard, G.C. (2022). Current research and new agricultural technologies [Presentation]. Center for Research Excellence Symposium Research, Alcorn State University, Lorman, MS, United States.
Bernard, G.C. (2022). Current research developments, modern farming technologies, including autonomous farming and career readiness [Presentation]. North Carolina A&T State University, Greensboro, NC, United States.
Bolden-Tiller, O. (2022, February). Tuskegee University Project Updates/ Opportunities [Conference presentation]. 130^th Annual Small Farmers Conference, Tuskegee, AL, United States.
Bolden-Tiller, O., Bernard, G. C., Adve, V. S. (July, 2022). Can data science and Artificial Intelligence (AI) enable new collaborative platforms between diverse land-grant institutions and create more impactful outcomes? [Meeting, with presentations]. National Academies Sciences, Engineering, and Medicine. Online. https://www.nationalacademies.org/event/07-27-2022/enhancing-collaboration-and-deepening-impact-can-data-science-and-artificial-intelligence-ai-enable-new-collaborative-platforms-between-diverse-land-grant-institutions-and-create-more-impactful-outcomes
Guha, S. (2022, April). IoT Sensor networks for soil and water quality [Presentation]. Environmental Engineering Department, University of Wisconsin-Madison, Madison, WI, United States.
He, J. (2022, February). Towards Understanding Rare Categories on Graphs [Conference presentation]. The International Workshop on Machine Learning on Graphs.
Khanna, M. (2022, March). Economic Incentives for Robotic Weed Control in Row Crop Agriculture [Conference presentation]. DigiCrop 200 PhenoRob, Online. https://www.youtube.com/watch?v=PiTwrLT_JcE
Khanna. M. (August 2022). Bioeconomic and behavioral incentives for robotic weed control in row crop agriculture [Conference presentation]. Agricultural and Applied Economics Association (AAEA) Annual Meeting.
Khanna. M. (November 2022). Bioeconomic and behavioral incentives for robotic weed control in row crop agriculture [Conference presentation]. The Southern Economic Association 92nd Annual Meeting.
Kamtikar, S. (2022, October). Realistic Simulation Environments to Achieve Visual Servoing on Soft Continuum Arms in Constrained Environments.[Presentation]. Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2022).
Kuhl, A.S., Guha, S., Matamala, R., He, J., Tong, H., Wei, T., Douglass, M., Kemner, J., and A.J. Margenot., (December 2022). An AI Approach to Understanding Soil Health and Nutrient Cycling. American Geophysical Union Fall Meeting.
Leakey, A. (2022, March). Bioenergy Research and Development for the Fuels and Chemicals of Tomorrow. Energy Subcommittee [Congressional House Committee]. Energy subcommittee hearing. Online. https://republicans-science.house.gov/hearings?ID=2E8CE7A5-616B-4794-A3EA-C88BE42D35BEHearing – Bioenergy Research and Development for the Fuels and Chemicals of Tomorrow – Hearings – House Committee on Science Space & Tech – Republicans
Leakey, A. (May 2022). The Phenomics of Stomata and Water Use Efficiency in C4 crops [Keynote]. UIUC Institute for Genomics Biology, Fellow’s Symposium.
Leakey, A. (May 2022). Phenomics of Stomata and Water Use Efficiency in C4 crops. [Presentation]. Center for Sorghum Improvement Seminar, Kansas State University.
Leakey, A. (June 2022). Phenomics of Stomata and Water Use Efficiency in C4 crops. [Presentation]. Gordon Research Conference, Vascular Plant Biology.
Leakey, A. (December 2022). Interdisciplinary research in the Center for Advanced Bioenergy and Bioproducts Innovation. [Presentation]. Oklahoma State University, Biobased Products and Energy Center.
Li, N., Margenot, A.J. (November 2022). Distinct Soil Health Indicators Are Associated with on-Farm Variation in Maize Yield and Tile Drain Nitrate Losses across Contrasting Nitrogen Application in Central Illinois. [Conference presentation]. ASA-CSSA-SSSA Annual Meeting. Baltimore, MD.
Li, N., Margenot, A.J. (November 2022). Quantifying Field-Scale SOC Stock: Fusion Remote and Proximal Sensing Data [Conference presentation]. ASA-CSSA-SSSA Annual Meeting. Baltimore, MD. Nov 6-8, 2022.
Tong. H. (2022, February). NetFair: Toward the Why Question of Network Mining’ at Artificial Intelligence [Conference presentation]. Machine Learning and Data Science World Forum, Online.
Tong, H. (2022). User Response Prediction and Beyond: A Graph-based Approach. [Keynote]. Meta.
Tucker, C. (November 2022). CDA/AIFARMS Overview and Educational Activities. UIUC Ag Tech Breakfast.
Tucker, C. (2022, February). History of Agriculture and Innovations [Education outreach presentation].National 4H Agriculture Innovators Challenge Training, Urbana, IL, United States.
Wang, S., Guan, K., Ainsworth, E., Zhang, C., Leakey, A., Zhou, Q., Margenot, A., Townsend, P., Jiang, C., (December 2022). AI-empowered hyperspectral sensing advances agricultural ground truthing across scales. [Conference presentation]. American Geophysical Union Annual Conference.
Wang, S., Guan, K., Zhang, C., Zhou, Q., Ainsworth, E., Margenot, A., Jiang, C., Peng, B., Wu, X. (December 2022). Cross-scale sensing of field-level crop residue cover and cover crops: integrating field observations, airborne hyperspectral imaging, and satellite data. [Conference presentation]. American Geophysical Union Annual Conference.
Wedow, J.M. (2022, July). AIFARMS Overview [Presentation]. The Internet of Things for Agriculture Summer Series, online. https://iot4ag.us/summerseries/
Wu. J. (October 2022).Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.[Conference presentation]. 31^st ACM International Conference on Information & Knowledge Management.
Wu, J., He, J. (2022, August). Domain Adaptation with Dynamic Open-Set Targets [Conference presentation]. Knowledge discovery and data mining (KDD), Washington DC, United States.
Yu, C. (2022, August). Economic Incentives for Robotic Weed Control in Row Crop Agriculture [Conference presentation]. Agricultural and Applied Economics Association Meetings, Anaheim, CA, United States.
Zhou, Q., Guan, K., Wang, S., Jiang, C., Peng, B., Huang, Y. (December 2022). Quantifying field-level cover cropping in the U.S. Midwest using multi-source satellite data. [Conference presentation]. American Geophysical Union Annual Conference

Year 2

Publications:

Aneja, J., Schwing, A. G., Kautz, J., & Vahdat, A. (2021). A Contrastive Learning Approach for Training Variational Autoencoder Priors. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. Vaughan (Eds.), Advances in Neural Information Processing Systems 34 (NEURIPS 2021) (Vol. 34).
Balivada, S., Grant, G., Zhang, X., Ghosh, M., Guha, S., & Matamala, R. (2022). A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation. SENSORS, 22(10). doi.org/10.3390/s22103913
Bernard, G., Bolden-Tiller, G., Egnin, M., Bonsi, C., McKinstry, A., Landon, Z., Inocent, R., Archie, T., Chowdhury, S., Charleston, C., Turner, A., Brown, A., Idehen, O., Mitchell, I., Boone, J., Peterson, C., Lockett, A., Mortley, D., & Yiyang, C. (2022). The Use of Autonomous Robots to Address Labor Demands and Improve Efficacy in Agriculture. COJ Robotics & Artificial Intelligence, 1(5), 1–3.
Chatterjee, M., Ahuja, N., & Cherian, A. (2021). A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction. 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 9731–9741. doi.org/10.1109/ICCV48922.2021.00961
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention Mask Transformer for Universal Image Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), 1280–1289. doi.org/10.1109/CVPR52688.2022.00135
Cheng, B., Schwing, A. G., & Kirillov, A. (2021). Per-Pixel Classification is Not All You Need for Semantic Segmentation. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. Vaughan (Eds.), Advances in Neural Information Processing Systems 34 (NEURIPS 2021) (Vol. 34).
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Kang, J., & Tong, H. (2021). Fair Graph Mining. Proceedings of the 30^th ACM International Conference on Information & Knowledge Management, CIKM 2021, 4849–4852. doi.org/10.1145/3459637.3482030
Khanna, M., Atallah, S. S., Kar, S., Sharma, B., Wu, L., Yu, C., Chowdhary, G., Soman, C., & Guan, K. (2022). Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges. Agricultural Economics, 3(6), 924–937. doi.org/10.1111/agec.12733
Khanna, M., & Miao, R. (2021). Inducing the adoption of emerging technologies for sustainable intensification of food and renewable energy production: Insights from applied economics*. Australian Journal of Agriculture and Resource Economics, 66(1), 1–23. doi.org/10.1111/1467-8489.12461
Lai, T., Ji, H., & Zhai, C. (2021). BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks. In M. Moens, X. Huang, L. Specia, & S. Yih (Eds.), Findings of the Association for Computational Linguistics, EMNLP 2021 (pp. 1631–1639).
Lai, T. M., Ji, H., & Zhai, C. (2022). Improving candidate retrieval with entity profile generation for wikidata entity linking. arXiv Preprint arXiv:2202.13404.
Li, B., Jing, B., & Tong, H. (2022). Graph Communal Contrastive Learning. Proceedings of the ACM Web Conference 2022 (WWW’22), 1203–1213. doi.org/10.1145/3485447.3512208
Liu, I.-J., Yuan, X., Cote, M.-A., Oudeyer, P.-Y., & Schwing, A. G. (2022). Asking for Knowledge (sic): Training RL Agents to Query External Knowledge Using Language. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), International Conference on Machine Learning, VOL 162.
Maestrini, B., & Basso, B. (2021). Subfield crop yields and temporal stability in thousands of US Midwest fields. Precision Agriculture, 22(6), 1749–1767. doi.org/10.1007/s11119-021-09810-1
Ren, Z., Agarwala, A., Russell, B., Schwing, A. G., & Wang, O. (2022). Neural Volumetric Object Selection. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), 6123–6132. doi.org/10.1109/CVPR52688.2022.00604
Ren, Z., Zhao, X., & Schwing, A. G. (2021). Class-agnostic Reconstruction of Dynamic Objects from Videos. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. Vaughan (Eds.), Advances in Neural Information Processing Systems 34 (NEURIPS 2021) (Vol. 34).
Shirke, A., Saifuddin, A., Luthra, A., Li, J., Williams, T., Hu, X., Kotnana, A., Kocabalkanli, O., Ahuja, N., & Green-Miller, A. (2021). Tracking grow-finish pigs across large pens using multiple cameras. arXiv Preprint arXiv:2111.10971.
Shuai, G., & Basso, B. (2022). Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models. Remote Sensing of Environment, , 272. doi.org/10.1016/j.rse.2022.112938
Sivakumar, A. N., Modi, S., Gasparino, M. V., Ellis, C., Velasquez, A. E. B., Chowdhary, G., & Gupta, S. (2021). Learned Visual Navigation for Under-Canopy Agricultural Robots. In D. Shell, M. Toussaint, & M. Hsieh (Eds.), Robotics: Science and Systems XVII.
Tzinis, E., Adi, Y., Ithapu, V. K., Xu, B., Smaragdis, P., & Kumar, A. (2022). RemixIT: Continual Self-Training of Speech Enhancement Models via Bootstrapped Remixing. IEEE Journal of Selected Topics in Signaal Processing, 16(6), 1329–1341. doi.org/10.1109/JSTSP.2022.3200911
Velasquez, A. E. B., Higuti, V. A. H., Gasparino, M. V., Sivakumar, A. N. V., Becker, M., & Chowdhary, G. (2022). Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots. Field Robotics, 2, 1291–1319. https://doi.org/10.55417/fr.2022043
Wang, H., Huang, W., Tong, H., Margenot, A. J., & He, J. (2021). Deep Active Learning by Leveraging Training Dynamics. https://openreview.net/forum?id=8XM-AXMnAk_
Wang, S., Guan, K., Wang, Z., Ainsworth, E. A., Zheng, T., Townsend, P. A., Liu, N., Nafziger, E., Masters, M. D., Li, K., Wu, G., & Jiang, C. (2021). Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling. International Journal of Applied Earth Observations and Geoinformation, 105. doi.org/10.1016/j.jag.2021.102617
Wang, S., Guan, K., Zhang, C., Lee, D., Margenot, A. J., Ge, Y., Peng, J., Zhou, W., Zhou, Q., & Huang, Y. (2022). Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing. Remote Sensing of Environment, 271, 112914. https://doi.org/10.1016/j.rse.2022.112914
Wang, X., Nahrstedt, K., & Koyejo, S. (2022). Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision. In B. Scholkopf, C. Uhler, & K. Zhang (Eds.), CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 177 (Vol. 177).
Wei, T., & He, J. (2022). Comprehensive Fair Meta-learned Recommender System. PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 1989–1999. https://doi.org/10.1145/3534678.3539269
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Wu, J., & He, J. (2022a). A Unified Meta-Learning Framework for Dynamic Transfer Learning. In L. DeRaedt (Ed.), PROCEEDINGS OF THE THIRTY-FIRST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL IN℡LIGENCE, IJCAI 2022 (pp. 3573–3579). Int Joint Conf Artifical Intelligence.
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Xie, J., Fernandes, S. B., Mayfield-Jones, D., Erice, G., Choi, M., Lipka, A. E., & Leakey, A. D. B. (2021). Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping. PLANT PHYSIOLOGY, 187(3), 1462–1480. https://doi.org/10.1093/plphys/kiab299
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Presentations:

Adve, V. (2021, October). Beyond ML in Agricultural Intelligence [Presentation]. Online.
Bernard, G.C. (2021, December). Agricultural robots and Autonomous farming: Ag Modernization to improve efficacy in agricultural production [Conference presentation]. Professional Agricultural Worker’s Conference, Online.
Chowdhary, G. (2021, November). The robots are coming to you farm [Seminar]. Michigan State University, Robotics and Control seminar, Online.
Khanna, M. (2021, August). Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges [Conference presentation]. International Conference of Agricultural Economists, Online.
Khanna, M. (2021, September). Economic Incentives for Robotic Weed Control [Presentation]. PhenoRob Cluster of Excellence – University of Bonn, Online.
Kuhl, A.S., Guha, S., He, J., Tong, H., Margenot, A.J., Douglass, M., Kemner, J., Matamala, R. (2021, December). An AI Approach to Soil Health and Nutrient Management [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, USA.
Leakey, A. (2021, September). Phenotyping stomatal anatomy and function [Virtual workshop]. Society for Experimental Biology Environmental Physiology Group, Virtual Workshop on Field and Laboratory Techniques, Online.
Margenot, A. (2021, December). AIFARMS – Soil Health and Monitory Thrust Summary [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, United States.
Wang, H. (2021, November). Deep Active Learning for Agricultural Tasks [Flash talk]. Third International Workshop on Machine Learning for Cyber-Agricultural Systems, Online.
Wang, S., Guan, K., Zhou, Q., Zhang, C., Jiang, C., Li, K., Qin, Z., Ainsworth, E.A., Margenot, A.J., Schaefer, D. Gentry, L. (2021, December). Airborne hyperspectral imaging of cover crop outcomes and tillage intensity in croplands by machine learning and radiative transfer modeling [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, USA.
Wu, J. (2021, November). Adaptive Transfer Learning for Plant Phenotyping [Conference presentation]. Third International Workshop on Machine Learning for Cyber-Agricultural Systems, Online.

Year 1

Publications:

Basso, B. (2021). Precision conservation for a changing climate. Nature Food, 2(5), 322–323. doi.org/10.1038/s43016-021-00283-z
Basso, B., Martinez-Feria, R. A., Rill, L., & Ritchie, J. T. (2021). Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications, 12(1). doi.org/10.1038/s41467-021-21763-7
Gat, I., Schwartz, I., Schwing, A., & Hazan, T. (2020). Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, NEURIPS 2020 (Vol. 33).
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. (2021). Panoptic Segmentation Forecasting. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, 12512–12521. doi.org/10.1109/CVPR46437.2021.01233
Havens, A., & Chowdhary, G. (2021). Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems. In A. Jadbabaie, J. Lygeros, G. Pappas, P. Parrilo, B. Recht, C. Tomlin, & M. Zeilinger (Eds.), Learning for Dynamics and Control (Vol. 144). Bosch; IBM Res Europe; MathWorks; Mitsubishi Elect Res Labs; Toyota Res Inst.
Jing, B., Tong, H., & Zhu, Y. (2021). Network of Tensor Time Series. Proceedings of the World Wide Web Conference (WWW 2021), 2425–2437. doi.org/10.1145/3442381.3449969
Khanna, M. (2020). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications. Applied Economic Perspectives and Policy, 43(4), 1221–1242. doi.org/10.1002/aepp.13103
Liu, I.-J., Yeh, R. A., & Schwing, A. G. (2020). High-Throughput Synchronous Deep RL. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Ren, Z., Misra, I., Schwing, A. G., & Girdhar, R. (2021). 3D Spatial Recognition without Spatially Labeled 3D. 2021 IEEE/CVF Conference on Computer Vision and Pattern, CVPR 2021, 13199–13208. doi.org/10.1109/CVPR46437.2021.01300
Ren, Z., Yeh, R. A., & Schwing, A. G. (2020). Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Sun, R., Fang, T., & Schwing, A. (2020). Towards a Better Global Loss Landscape of GANs. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Wu, J., & He, J. (2021). Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1852–1862. doi.org/10.1145/3447548.3467214
Zhao, X., Agrawal, H., Batra, D., & Schwing, A. (2021). The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 16107–16116. doi.org/10.1109/ICCV48922.2021.01582
Zhou, Y., Xu, J., Wu, J., Taghavi, Z., Korpeoglu, E., Achan, K., & He, J. (2021). PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2409–2419. doi.org/10.1145/3447548.3467234
Zhuang, P., Koyejo, O., & Schwing, A. G. (2021). Enjoy your editing: Controllable GANs for image editing via latent space navigation. arXiv Preprint arXiv:2102.01187.

Presentations:

Adve, V. (October 7, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Adve, V. (November 19, 2020). Why Digital Agriculture is Fertile Ground for Software Systems Research. SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity.
Adve, V. (November 10, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Leakey, A. (October 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America.
Leakey, A. (December 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Virtual.

Year 1

Publications:

Basso, B. (2021). Precision conservation for a changing climate. Nature Food, 2(5), 322–323. doi.org/10.1038/s43016-021-00283-z
Basso, B., Martinez-Feria, R. A., Rill, L., & Ritchie, J. T. (2021). Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications, 12(1). doi.org/10.1038/s41467-021-21763-7
Gat, I., Schwartz, I., Schwing, A., & Hazan, T. (2020). Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, NEURIPS 2020 (Vol. 33).
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. (2021). Panoptic Segmentation Forecasting. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, 12512–12521. doi.org/10.1109/CVPR46437.2021.01233
Havens, A., & Chowdhary, G. (2021). Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems. In A. Jadbabaie, J. Lygeros, G. Pappas, P. Parrilo, B. Recht, C. Tomlin, & M. Zeilinger (Eds.), Learning for Dynamics and Control (Vol. 144). Bosch; IBM Res Europe; MathWorks; Mitsubishi Elect Res Labs; Toyota Res Inst.
Jing, B., Tong, H., & Zhu, Y. (2021). Network of Tensor Time Series. Proceedings of the World Wide Web Conference (WWW 2021), 2425–2437. doi.org/10.1145/3442381.3449969
Khanna, M. (2020). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications. Applied Economic Perspectives and Policy, 43(4), 1221–1242. doi.org/10.1002/aepp.13103
Liu, I.-J., Yeh, R. A., & Schwing, A. G. (2020). High-Throughput Synchronous Deep RL. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Ren, Z., Misra, I., Schwing, A. G., & Girdhar, R. (2021). 3D Spatial Recognition without Spatially Labeled 3D. 2021 IEEE/CVF Conference on Computer Vision and Pattern, CVPR 2021, 13199–13208. doi.org/10.1109/CVPR46437.2021.01300
Ren, Z., Yeh, R. A., & Schwing, A. G. (2020). Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Sun, R., Fang, T., & Schwing, A. (2020). Towards a Better Global Loss Landscape of GANs. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Wu, J., & He, J. (2021). Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1852–1862. doi.org/10.1145/3447548.3467214
Zhao, X., Agrawal, H., Batra, D., & Schwing, A. (2021). The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 16107–16116. doi.org/10.1109/ICCV48922.2021.01582
Zhou, Y., Xu, J., Wu, J., Taghavi, Z., Korpeoglu, E., Achan, K., & He, J. (2021). PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2409–2419. doi.org/10.1145/3447548.3467234
Zhuang, P., Koyejo, O., & Schwing, A. G. (2021). Enjoy your editing: Controllable GANs for image editing via latent space navigation. arXiv Preprint arXiv:2102.01187.

Presentations:

Adve, V. (October 7, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Adve, V. (November 19, 2020). Why Digital Agriculture is Fertile Ground for Software Systems Research. SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity.
Adve, V. (November 10, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Leakey, A. (October 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America.
Leakey, A. (December 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Virtual.

Year 1

Publications:

Basso, B. (2021). Precision conservation for a changing climate. Nature Food, 2(5), 322–323. doi.org/10.1038/s43016-021-00283-z
Basso, B., Martinez-Feria, R. A., Rill, L., & Ritchie, J. T. (2021). Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications, 12(1). doi.org/10.1038/s41467-021-21763-7
Gat, I., Schwartz, I., Schwing, A., & Hazan, T. (2020). Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, NEURIPS 2020 (Vol. 33).
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. (2021). Panoptic Segmentation Forecasting. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, 12512–12521. doi.org/10.1109/CVPR46437.2021.01233
Havens, A., & Chowdhary, G. (2021). Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems. In A. Jadbabaie, J. Lygeros, G. Pappas, P. Parrilo, B. Recht, C. Tomlin, & M. Zeilinger (Eds.), Learning for Dynamics and Control (Vol. 144). Bosch; IBM Res Europe; MathWorks; Mitsubishi Elect Res Labs; Toyota Res Inst.
Jing, B., Tong, H., & Zhu, Y. (2021). Network of Tensor Time Series. Proceedings of the World Wide Web Conference (WWW 2021), 2425–2437. doi.org/10.1145/3442381.3449969
Khanna, M. (2020). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications. Applied Economic Perspectives and Policy, 43(4), 1221–1242. doi.org/10.1002/aepp.13103
Liu, I.-J., Yeh, R. A., & Schwing, A. G. (2020). High-Throughput Synchronous Deep RL. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Ren, Z., Misra, I., Schwing, A. G., & Girdhar, R. (2021). 3D Spatial Recognition without Spatially Labeled 3D. 2021 IEEE/CVF Conference on Computer Vision and Pattern, CVPR 2021, 13199–13208. doi.org/10.1109/CVPR46437.2021.01300
Ren, Z., Yeh, R. A., & Schwing, A. G. (2020). Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Sun, R., Fang, T., & Schwing, A. (2020). Towards a Better Global Loss Landscape of GANs. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Wu, J., & He, J. (2021). Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1852–1862. doi.org/10.1145/3447548.3467214
Zhao, X., Agrawal, H., Batra, D., & Schwing, A. (2021). The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 16107–16116. doi.org/10.1109/ICCV48922.2021.01582
Zhou, Y., Xu, J., Wu, J., Taghavi, Z., Korpeoglu, E., Achan, K., & He, J. (2021). PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2409–2419. doi.org/10.1145/3447548.3467234
Zhuang, P., Koyejo, O., & Schwing, A. G. (2021). Enjoy your editing: Controllable GANs for image editing via latent space navigation. arXiv Preprint arXiv:2102.01187.

Presentations:

Adve, V. (October 7, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Adve, V. (November 19, 2020). Why Digital Agriculture is Fertile Ground for Software Systems Research. SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity.
Adve, V. (November 10, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Leakey, A. (October 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America.
Leakey, A. (December 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Virtual.

Year 1

Publications:

Basso, B. (2021). Precision conservation for a changing climate. Nature Food, 2(5), 322–323. doi.org/10.1038/s43016-021-00283-z
Basso, B., Martinez-Feria, R. A., Rill, L., & Ritchie, J. T. (2021). Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications, 12(1). doi.org/10.1038/s41467-021-21763-7
Gat, I., Schwartz, I., Schwing, A., & Hazan, T. (2020). Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, NEURIPS 2020 (Vol. 33).
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. (2021). Panoptic Segmentation Forecasting. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, 12512–12521. doi.org/10.1109/CVPR46437.2021.01233
Havens, A., & Chowdhary, G. (2021). Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems. In A. Jadbabaie, J. Lygeros, G. Pappas, P. Parrilo, B. Recht, C. Tomlin, & M. Zeilinger (Eds.), Learning for Dynamics and Control (Vol. 144). Bosch; IBM Res Europe; MathWorks; Mitsubishi Elect Res Labs; Toyota Res Inst.
Jing, B., Tong, H., & Zhu, Y. (2021). Network of Tensor Time Series. Proceedings of the World Wide Web Conference (WWW 2021), 2425–2437. doi.org/10.1145/3442381.3449969
Khanna, M. (2020). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications. Applied Economic Perspectives and Policy, 43(4), 1221–1242. doi.org/10.1002/aepp.13103
Liu, I.-J., Yeh, R. A., & Schwing, A. G. (2020). High-Throughput Synchronous Deep RL. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Ren, Z., Misra, I., Schwing, A. G., & Girdhar, R. (2021). 3D Spatial Recognition without Spatially Labeled 3D. 2021 IEEE/CVF Conference on Computer Vision and Pattern, CVPR 2021, 13199–13208. doi.org/10.1109/CVPR46437.2021.01300
Ren, Z., Yeh, R. A., & Schwing, A. G. (2020). Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Sun, R., Fang, T., & Schwing, A. (2020). Towards a Better Global Loss Landscape of GANs. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33, (NEURIPS 2020 (Vol. 33).
Wu, J., & He, J. (2021). Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1852–1862. doi.org/10.1145/3447548.3467214
Zhao, X., Agrawal, H., Batra, D., & Schwing, A. (2021). The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 16107–16116. doi.org/10.1109/ICCV48922.2021.01582
Zhou, Y., Xu, J., Wu, J., Taghavi, Z., Korpeoglu, E., Achan, K., & He, J. (2021). PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD `21: Proceedings of the 27^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2409–2419. doi.org/10.1145/3447548.3467234
Zhuang, P., Koyejo, O., & Schwing, A. G. (2021). Enjoy your editing: Controllable GANs for image editing via latent space navigation. arXiv Preprint arXiv:2102.01187.

Presentations:

Adve, V. (October 7, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Adve, V. (November 19, 2020). Why Digital Agriculture is Fertile Ground for Software Systems Research. SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity.
Adve, V. (November 10, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
Leakey, A. (October 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America.
Leakey, A. (December 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Virtual.

The Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability (AIFARMS) Institute is supported by the USDA National Institute of Food and Agriculture and the National Science Foundation.

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