Emma Brunskill
Associate Professor of Computer Science
Develops reinforcement learning and offline policy evaluation methods, with applications to education and healthcare.
Emma Brunskill's lab at Stanford works on reinforcement learning, language & society and interpretability, evaluation & safety of LMs. 35 papers since 2023, 16 at top venues, mostly NeurIPS and AAAI. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 46%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 7 papers2026 · T1 4 · collecting
- 6 papers2025 · T1 2
- 14 papers2024 · T1 5
- 8 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, AAAI, CHI, SIGCSE, EC
Research focuspaper titles that use each topic's words
- Reinforcement learning11 papers
- Interpretability, evaluation & safety of LMs3 papers
- Language & society2 papers
- policy evaluation2 papers
- develops reinforcement0 papers
Paper list
From the lab homepage and DBLP.
20267 papers
- AAAI (T1)Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study (opens in a new tab)
- CHI (T1)Bloom: Designing for LLM-Augmented Behavior Change Interactions (opens in a new tab)
- CHI (T1)Can LLM-Simulated Practice and Feedback Upskill Human Counselors? A Randomized Study with 90+ Novice Counselors (opens in a new tab)
- EC-TELDosage and Dialogue: Understanding Human Support in Hybrid Human-AI Math Tutoring (opens in a new tab)
- Manag. Sci.Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making (opens in a new tab)
- Trans. Mach. Learn. Res.PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data (opens in a new tab)
- KDD (T1)SYNAPSE: SImulation Benchmark of Neuro-Adaptive Patient-Specific Evaluation for Episodic Decision-Making (opens in a new tab)
20256 papers
- AAAI (T1)Cost-Aware Near-Optimal Policy Learning (opens in a new tab)
- LAKExploring the Benefit of Customizing Feedback Interventions For Educators and Students With Offline Contextual Multi-Armed Bandits (opens in a new tab)
- CHI (T1)GPTCoach: Towards LLM-Based Physical Activity Coaching (opens in a new tab)
- AIEDHuman Tutoring Improves the Impact of AI Tutor Use on Learning Outcomes (opens in a new tab)
- LAKPredicting Long-Term Student Outcomes from Short-Term EdTech Log Data (opens in a new tab)
- L@SThe GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement But May Increase Adopters' Exam Performances (opens in a new tab)
202414 papers
- SIGCSEA Fast and Accurate Machine Learning Autograder for the Breakout Assignment (opens in a new tab)
- ICLR (T1)Adaptive Instrument Design for Indirect Experiments (opens in a new tab)
- CHILAdaptive Interventions with User-Defined Goals for Health Behavior Change (opens in a new tab)
- SIGCSEBrief, Just-in-Time Teaching Tips to Support Computer Science Tutors (opens in a new tab)
- Trans. Mach. Learn. Res.Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity (opens in a new tab)
- LAKEstimating the Causal Treatment Effect of Unproductive Persistence (opens in a new tab)
- EDMEvaluating and Optimizing Educational Content with Large Language Model Judgments (opens in a new tab)
- L@SExamining the Use of an AI-Powered Teacher Orchestration Tool at Scale (opens in a new tab)
- LAKImproving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation (opens in a new tab)
- AAAI (T1)MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records (opens in a new tab)
- EC (T1)Minimax-Regret Sample Selection in Randomized Experiments (opens in a new tab)
- NeurIPS (T1)OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators (opens in a new tab)
20238 papers
- NeurIPS (T1)Experiment Planning with Function Approximation (opens in a new tab)
- AAAI (T1)Model-Based Offline Reinforcement Learning with Local Misspecification (opens in a new tab)
- NeurIPS (T1)Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization (opens in a new tab)
- NeurIPS (T1)Supervised Pretraining Can Learn In-Context Reinforcement Learning (opens in a new tab)
- JOURNAL OF EDUCATIONAL RESEARCHTexting and tutoring: Short-term K-3 reading interventions during the pandemic (opens in a new tab)
- AIEDTowards the Future of AI-Augmented Human Tutoring in Math Learning (opens in a new tab)
- AIEDUnderstanding the Impact of Reinforcement Learning Personalization on Subgroups of Students in Math Tutoring (opens in a new tab)
- NeurIPS (T1)Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
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- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
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Confirmed on the program page.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- Members
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