Maggie Makar

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst.

Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems.

Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications.

• Education:
  • PhD in Computer Science, MIT (CSAIL), 2018
  • Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013
• Awards:
  • CAREER Award (NSF, 2024)
  • CRII: SCH Grant (NSF, 2022)
• Research Themes:
  • Causal inference in machine learning
  • Data-efficient methods
  • Healthcare applications
  • Algorithmic fairness