Atul Prakash
Richard H. Orenstein Division Chair of Computer Science and Engineering; Professor, EECS – Computer Science and Engineering
Studies computer systems security and the security and privacy of machine learning models, including adversarial robustness and trustworthy AI.
Atul Prakash's lab at Michigan works on trustworthy & safe ML and interpretability, evaluation & safety of LMs. 5 papers since 2023, 2 at top venues, mostly ACL and ICLR. 2 PhD students. 31 alumni. Applications close .
At a glance
- papers, 2023–2026
- 1/ year
- at T1 venues
- 40%
- PhD students
- 2
- MS students
- 0
- alumni listed
- 31
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 3 papers2024 · T1 1
- 2 papers2023 · T1 1
T1 venuesOtherVenues: ACL, ICLR
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML0 papers
- Interpretability, evaluation & safety of LMs0 papers
- security0 papers
- systems security0 papers
- computer systems0 papers
Paper list
From the lab homepage.
20243 papers
- Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles
- Proc. NeurIPS (Spotlight)Learn To be Efficient: Build Structured Sparsity in Large Language Models (opens in a new tab)
- ACL (T1)PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails (opens in a new tab)
20232 papers
- International Conference on Machine Learning (ICML)Concept-based Explanations for Out-Of-Distribution Detectors (opens in a new tab)
- ICLR (T1)Coverage-centric Coreset Selection for High Pruning Rates (opens in a new tab)
Where alumni went31 listed
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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Your notes
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Data
Checked against the lab homepage.
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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