Publications in reverse chronological order:

S. Johnson-Yu*, S. H. Singh*, Z. Lu, A. Walsman, F. Pedraja, D. Turcu, P. Sharma, N. Saphra, N. B. Sawtell, and K. Rajan. "Active electrosensing and communication in MARL-trained weakly electric fish foraging agents." In COSYNE 2026.

S. H. Singh*, S. Johnson-Yu*, Z. Lu, A. Walsman, F. Pedraja, D. Turcu, P. Sharma, N. Saphra, N. B. Sawtell, and K. Rajan. "Understanding Electro-communication and Electro-sensing in Weakly Electric Fish using Multi-Agent Deep Reinforcement Learning." In NeurIPS 2025 AI for Non-Human Animal Communication Workshop.

S. H. Singh, S. Johnson-Yu, Z. Lu, A. Walsman, F. Pedraja, D. Turcu, P. Sharma, N. Saphra, N. B. Sawtell, and K. Rajan. "Proposal: Deciphering Electrocommunication with MARL and Unsupervised Machine Translation." In NeurIPS 2025 AI for Non-Human Animal Communication Workshop.

K. Zheng, S. Johnson-Yu, S. H. Singh, D. Turcu, F. Pedraja, P. Sharma, N. Saphra, N. B. Sawtell, and K. Rajan. "Keypoint Annotation for Electrocommunication Source Separation with PIKAChU and RAIChU." In NeurIPS 2025 AI for Non-Human Animal Communication Workshop.

R. Malik*, S. H. Singh*, S. Johnson-Yu*, R. Harpaz, and K. Rajan. "Dissecting Zebrafish Hunting Behavior using Deep Reinforcement Learning trained RNNs." In NeurIPS 2025 AI for Science Workshop. Selected for a Spotlight.

S. Johnson-Yu*, S. H. Singh*, H. Lu, A. Walsman, F. Pedraja, D. Turcu, P. Sharma, N. B. Sawtell, and K. Rajan. "Investigating active electrosensing and communication in deep-reinforcement learning trained artificial fish collectives." Extended Abstract. In Reinforcement Learning and Decision Making 2025. Selected for a Contributed Talk.

Z. H. Lu, S. H Singh, S. Johnson-Yu, A. Walsman, K. Rajan. “Emergent small-group foraging under variable group size, food scarcity, and sensory capabilities.” Extended Abstract. In COSYNE 2025.

S. Johnson-Yu, S. H. Singh, F. Pedraja, D. Turcu, P. Sharma, N. Saphra, N. Sawtell, and K. Rajan. “Understanding biological active sensing behaviors by interpreting learned artificial agent policies.” In InterpPol Workshop @ RLC-2024.

S. Johnson-Yu, S. H. Singh, F. Pedraja, D. Turcu, P. Sharma, N. Saphra, N. B. Sawtell, and K. Rajan. “Emergent active sensing behaviors in artificial electric fish agents.” Extended Abstract. In Cognitive Computational Neuroscience 2024.

N. Ramachandran, J. Irvin, H. Sheng, S. Johnson-Yu, K. Story, R. Rustowicz, A. Y. Ng, and K. Austin. “Automatic deforestation driver attribution using deep learning on satellite imagery.” In Global Environmental Change, vol. 86, Pergamon, 2024, pp. 102843.

S. Johnson-Yu, J. Finocchiaro, A. Sinha, K. Wang, Y. Vorobeychik, A. Taneja, and M. Tambe. “Characterizing and Improving the Robustness of Predict-Then-Optimize Frameworks.” In Conference on Decision and Game Theory for Security (GameSec) 2023.

S. Johnson-Yu, K. Wang, J. Finocchiaro, A. Taneja, and M. Tambe. “Modeling Robustness in Decision-Focused Learning as a Stackelberg Game.” Extended Abstract. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2023.

J. Killian*, L. Xu*, A. Biswas*, S. Verma*, V. Nair, A. Taneja, A. Hegde, N. Madhiwalla, P. Rodriguez Diaz, S. Johnson-Yu, M. Tambe. 2/9/2023. “Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program.” In AAAI Conference on Artificial Intelligence 2023.

S. Johnson-Yu, N. Bowman, M. Sahami, C. Piech. “SimGrade: Using Code Similarity Measures for More Accurate Human Grading.” Educational Data Mining 2021.

J. Irvin, H. Sheng, N. Ramachandran, S. Johnson-Yu, et al. “ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery.” Tackling Climate Change with Machine Learning at NeurIPS 2020.