Chandler Squires is a PhD student in Electrical Engineering and Computer Science at MIT. His research focuses on how machines can learn to reason about the effects of their actions, including basic questions about how machines can form causal models of the world by interacting with it. He is interested in the application of causal reasoning to healthcare and biology applications, in particular drug discovery. He is a National Science Foundation Graduate Research Fellow and a Lemelson Engineering Presidential Fellow, and received the second place David Adler Electrical Engineering MEng Thesis Award. Chandler received a Bachelor of Science and a Master of Engineering, both in Electrical Engineering and Computer Science, from MIT. He enjoys teaching and mentoring and hopes to expand the mentorship opportunities available to students of all backgrounds.
Squires, C., Wang, Y., Uhler, C. (2020). Permutation-Based Causal Structure Learning with Unknown Intervention Targets., UAI 2020
Bernstein, D., Saeed,B., Squires, C., Uhler, C. (2020). Ordering-based causal structure learning in the presence of latent variables., AISTATS 2020
Katz, D., Shanmugan, K., Squires, C., Uhler, C. (2019). Size of Interventional Markov Equivalence Classes in random DAG models, AISTATS 2019
Agarwal, R., Squires, C., Yang, K., Uhler, C. (2019). ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery, AISTATS 2019
Wang, Y., Squires, C., Belyaeva, A., Uhler, C. (2019). Direct Estimation of Differences in Causal Graphs, NeurIPS 2018
HONORS & AWARDS:
2nd place David Adler Electrical Engineering MEng Thesis Award