Research

Bair, A., Yin, H., Shen, M., Molchanov, P., Alvarez, J. (2024). Adaptive sharpness-aware pruning for robust sparse networks. ICLR 2024.

[Paper]

Sun, M., Liu, Z. Bair, A., Kolter, Z. (2024). A simple and effective pruning approach for large language models. ICLR 2024.

[Paper]

Feng, Z., Bair, A. , Kolter, Z. (2024). Text Descriptions are Compressive and Invariant Representations for Visual Learning. TMLR 2024.

[Paper]

Rice, L., Bair, A., Zhang, H., & Kolter, Z. (2021). Robustness between the worst and average case. NeurIPS 2021.

[Paper]

Gombolay, M., Bair, A., Huang, C., & Shah, J. (2017). Computational design of mixed-initiative human-robot teaming that considers human factors: situational awareness, workload, and workflow preferences. The International Journal of Robotics Research, 36(57), 597-617.

[Paper]

Bair, A., McDermott, M., Wang, J., Zhao, W., Sheridan, S., Szolovits, P., Kohane, I., Haggarty, S., Perlis, R. (2018, December 3). Improved Modeling and Analysis of Gene Expression. Poster presented at Women in Machine Learning (WiML) Workshop, Montr‌éal, Canada.

[Poster]

Bair, A., McDermott, M., Wang, J., Zhao, W., Sheridan, S., Szolovits, P., Kohane, I., Haggarty, S., Perlis, R. (2019, March 4). Using Machine Learning to Improve Drug Development. Poster presented at Women in Data Science (WiDS) Cambridge Conference, Cambridge, MA.

[Poster]

Imputation and Supervised Learning on Sparse Datasets

Final group project for Machine Learning (6.867)

Improved Modeling and Analysis of Gene Expression

Research through MIT's SuperUROP Program