Stephen Tu
Assistant Professor of Electrical and Computer Engineering and Computer Science
Studies learning for control, imitation learning and statistical learning with dynamical systems.
Stephen Tu's lab at USC works on learning theory & optimization and manipulation & robot learning. 23 papers since 2023, 14 at top venues, mostly CDC and ICML. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 61%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 4 · collecting
- 6 papers2025 · T1 2
- 4 papers2024 · T1 3
- 7 papers2023 · T1 5
T1 venuesOtherVenues: CDC, ICML, L4DC, NeurIPS, COLT
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Manipulation & robot learning10 papers
- dynamical2 papers
- imitation learning1 paper
- imitation1 paper
Paper list
From the lab homepage.
20266 papers
- IROS (T1)CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations (opens in a new tab)
- CDCCLT-Optimal Parameter Error Bounds for Linear System Identification (opens in a new tab)
- ICML (T1)CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing (opens in a new tab)
- CDCForward Trajectory Steering for Hamilton-Jacobi Reachability Analysis (opens in a new tab)
- COLT (T1)On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry (opens in a new tab)
- RSS (T1)Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons (opens in a new tab)
20256 papers
- CDCData-Driven Reachability with Scenario Optimization and the Holdout Method (opens in a new tab)
- CDCIncremental Composition of Learned Control Barrier Functions in Unknown Environments (opens in a new tab)
- NeurIPS (T1)Integration Matters for Learning PDEs with Backwards SDEs (opens in a new tab)
- CDCLatent Representations for Control Design with Provable Stability and Safety Guarantees (opens in a new tab)
- Foundations of Computational MathematicsSafely learning dynamical systems (opens in a new tab)
- ICLR (T1)Single hidden layer diffusion models provably learn simple low-dimensional structure (opens in a new tab)
20244 papers
- JMLR (T1)Learning from many trajectories (opens in a new tab)
- IEEE Open Journal of Control SystemsLearning Robust Output Control Barrier Functions from Safe Expert Demonstrations (opens in a new tab)
- TMLR (T1)Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models (opens in a new tab)
- ICML (T1)Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss (opens in a new tab)
20237 papers
- L4DCAgile Catching with Whole-Body MPC and Blackbox Policy Learning (opens in a new tab)
- ICML (T1)Bootstrapped Representations in Reinforcement Learning (opens in a new tab)
- L4DCMulti-Task Imitation Learning for Linear Dynamical Systems (opens in a new tab)
- CoRL (T1)Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners (opens in a new tab)
- NeurIPS (T1)The noise level in linear regression with dependent data (opens in a new tab)
- ICML (T1)The Power of Learned Locally Linear Models for Nonlinear Policy Optimization (opens in a new tab)
- ICRA (T1)Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to USCFall 2027
Computer Science PhD (Viterbi)
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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Data
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- The alumni list could not be found on the lab site.
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