Maggie Makar
Assistant Professor, EECS – Computer Science and Engineering
Studies causal inference and machine learning that stays reliable under distribution shift, with applications to healthcare.
Maggie Makar's lab at Michigan works on trustworthy & safe ML, probabilistic & causal inference and efficient training & inference. 7 papers since 2023, 3 at top venues, mostly AISTATS and TMLR. 4 PhD students and 1 master's student. 6 alumni, most often to Apple and Fintica AI. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 43%
- PhD students
- 4
- MS students
- 1
- alumni listed
- 6
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 5 papers2024 · T1 2
- 2 papers2023 · T1 1
T1 venuesOtherVenues: AISTATS, TMLR, UAI
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML1 paper
- Probabilistic & causal inference1 paper
- Efficient training & inference0 papers
- studies causal0 papers
- shift0 papers
Paper list
From the lab homepage.
20245 papers
- —AIStats 2024 [paper]
- —forthcoming (opens in a new tab)
- AISTATS (T1)Learning to Rank for Optimal Treatment Allocation Under Resource Constraints
- UAI (T1)Partial identification of the maximum mean discrepancy with mismeasured data
- —workshop version (opens in a new tab)
20232 papers
- TMLR (T1)Fairness and robustness in anti-causal prediction settings
- MLHCUncovering the Varied Impact of Behavioral Change Messages on Population Groups
Where alumni went6 listed, 67% to industry
Industry 4Academia 2
Most often to
- Apple
- Fintica AI
Also to
- MIT
- UC San Diego
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
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- Single round
- Opens
- —
- GRE
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- English
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- Fee
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Not yet confirmed for this intake.
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- Papers
- The lab homepage
- Members
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Tiers are venue classifications, not lab rankings.
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