Han Shao
Assistant Professor
Studies learning theory, including robustness to strategic and adversarial behavior and incentives in collaborative learning.
Han Shao's lab at UMD works on trustworthy & safe ML, learning theory & optimization and human-AI collaboration & trust. 16 papers since 2023, 14 at top venues, mostly NeurIPS and ICML.
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 88%
- PhD students
- 0
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 6 · collecting
- 3 papers2025 · T1 3
- 4 papers2024 · T1 3
- 2 papers2023 · T1 2
T1 venuesOtherVenues: NeurIPS, ICML, COLT, EC, ICLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization6 papers
- Human-AI collaboration & trust1 paper
- Trustworthy & safe ML0 papers
- collaborative learning0 papers
- studies learning0 papers
Paper list
From the lab homepage.
20267 papers
- FORCA Machine Learning Theory Perspective on Strategic Litigation (opens in a new tab)
- ICML (T1)A Theoretical Framework for Statistical Evaluability of Generative Models (opens in a new tab)
- ICLR (T1)Incentives in Federated Learning with Heterogeneous Agents (opens in a new tab)
- COLT (T1)On Randomized Algorithms in Online Strategic Classification (opens in a new tab)
- NeurIPS (T1)Online Set Learning from Precision and Recall Feedback (opens in a new tab)
- ICML (T1)Pluralistic Leaderboards
- NeurIPS (T1)Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale (opens in a new tab)
20253 papers
- NeurIPS (T1)How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension (opens in a new tab)
- NeurIPS (T1)Probably Approximately Precision and Recall Learning (opens in a new tab)
- ICML (T1)Should Decision-Makers Reveal Classifiers in Online Strategic Classification? (opens in a new tab)
20244 papers
- EC (T1)Efficient Prior-Free Mechanisms for No-Regret Agents (opens in a new tab)
- FORCIncentivized Collaboration in Active Learning (opens in a new tab)
- COLT (T1)Learnability Gaps of Strategic Classification (opens in a new tab)
- NeurIPS (T1)Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UMDFall 2027
Not announced yet
UMD Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
Some numbers could not be verified:
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.