Max Simchowitz
Assistant Professor
Studies reinforcement learning and control theory, with applications to robot learning.
Max Simchowitz's lab at CMU works on reinforcement learning, learning theory & optimization and manipulation & robot learning. 35 papers since 2023, 31 at top venues, mostly ICLR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 89%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 7 · collecting
- 12 papers2025 · T1 11
- 5 papers2024 · T1 4
- 11 papers2023 · T1 9
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, COLT, CoRL
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Manipulation & robot learning11 papers
- Reinforcement learning10 papers
- control theory0 papers
- studies reinforcement0 papers
Paper list
From the lab homepage.
20267 papers
- ICML (T1)Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps (opens in a new tab)
- CoRL (T1)FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control (opens in a new tab)
- NeurIPS (T1)Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps (opens in a new tab)
- NeurIPS (T1)Implicit Goal Conditioning via Value Disaggregation (opens in a new tab)
- NeurIPS (T1)MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI (opens in a new tab)
- ICML (T1)OGPO: Sample Efficient Full-Finetuning of Generative Control Policies (opens in a new tab)
- CoRL (T1)Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision (opens in a new tab)
202512 papers
- CDCA Test Function Approach to Incremental Stability (opens in a new tab)
- ICLR (T1)Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control (opens in a new tab)
- ICLR (T1)Diffusion Policy Policy Optimization (opens in a new tab)
- ICLR (T1)e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs (opens in a new tab)
- ICML (T1)History-Guided Video Diffusion (opens in a new tab)
- ICRA (T1)Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans? (opens in a new tab)
- NeurIPS (T1)Is Your Diffusion Model Actually Denoising? (opens in a new tab)
- ICLR (T1)Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models (opens in a new tab)
- ICLR (T1)Much Ado About Noising: Dispelling the Myths of Generative Robotic Control (opens in a new tab)
- IJRR (T1)Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets
- ICLR (T1)Self-Improvement in Language Models: The Sharpening Mechanism (opens in a new tab)
- COLT (T1)The Pitfalls of Imitation Learning when Actions are Continuous (opens in a new tab)
20245 papers
- ICLR (T1)Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression (opens in a new tab)
- ICRA (T1)Constrained Bimanual Planning with Analytic Inverse Kinematics (opens in a new tab)
- NeurIPS (T1)Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion (opens in a new tab)
- ISRRFaster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space (opens in a new tab)
- ICLR (T1)Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use (opens in a new tab)
202311 papers
- Mathematics of Operations ResearchCorruption Robust Exploration in Episodic Reinforcement Learning (opens in a new tab)
- Operations Research (T1)Exploration and Incentives in Reinforcement Learning (opens in a new tab)
- Frontiers4LCD Workshop, ICMLImitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior
- ICLR (T1)Learning to Extrapolate: A Transductive Approach (opens in a new tab)
- COLT (T1)Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making (opens in a new tab)
- NeurIPS (T1)Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior (opens in a new tab)
- NeurIPS (T1)RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability (opens in a new tab)
- NeurIPS (T1)Smoothed Online Learning for Prediction in Piecewise Affine Systems (opens in a new tab)
- ICML (T1)Statistical Learning under Heterogenous Distribution Shift (opens in a new tab)
- COLT (T1)Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective (opens in a new tab)
- ICML (T1)The Power of Learned Locally Linear Models for Nonlinear Policy Optimization (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
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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