Ann Lee
Professor
Develops statistical machine learning for the physical sciences, including likelihood-free inference and uncertainty quantification.
Ann Lee's lab at CMU works on learning theory & optimization, probabilistic & causal inference and efficient training & inference. 1 paper since 2023. 3 PhD students. 11 alumni. Applications close .
At a glance
- papers, 2023–2026
- 0/ year
- at T1 venues
- 0%
- PhD students
- 3
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 1 papers2024 · T1 0
- 0 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference1 paper
- Efficient training & inference1 paper
- Learning theory & optimization0 papers
- uncertainty quantification0 papers
- quantification0 papers
Paper list
From the lab homepage.
20241 paper
- Proceedings of the Forty-First International Conference on Machine Learning (ICMLClassification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference (opens in a new tab)
Where alumni went11 listed
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
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- The lab homepage
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