Yorie Nakahira
Associate Professor
Develops control and learning techniques for safe autonomous systems, including risk quantification and robust control.
Yorie Nakahira's lab at CMU works on planning, control & multi-robot systems, autonomous vehicles & drones and embodied & active vision. 31 papers since 2023, 8 at top venues, mostly TAC and AAAI. 5 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 26%
- PhD students
- 5
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 3
- 6 papers2024 · T1 3
- 9 papers2023 · T1 2
T1 venuesOtherVenues: TAC, AAAI, ACC, L4DC, AISTATS
Research focuspaper titles that use each topic's words
- Planning, control & multi-robot systems19 papers
- Autonomous vehicles & drones3 papers
- Embodied & active vision0 papers
- develops control0 papers
- robust control0 papers
Paper list
From the lab homepage.
20264 papers
- American Control Conference (ACC)Context-Aware LLM-Based Safe Control Against Latent Risks (opens in a new tab)
- Learning for Dynamics and Control Conference (L4DC)Online Adaptive Probabilistic Safety Certificate with Language Guidance (opens in a new tab)
- International Conference on Learning Representations (ICLR)Polynomial Convergence of Riemannian Diffusion Models (opens in a new tab)
- International Conference on Machine Learning (ICML)Training-Free Guided Diffusion for Planning: A Unified Framework via Doob's h-Transform with Safety Guarantees
202512 papers
- Neural ComputationDiversity Deconstrains Component Limitations in Sensorimotor Control (opens in a new tab)
- ACCFast Bandit-Based Policy Adaptation in Diverse Environments (opens in a new tab)
- TAC (T1)Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems (opens in a new tab)
- IEEE Conference on Decision and Control (CDC)Kalman Bayesian Transformer (opens in a new tab)
- TAC (T1)Myopically Verifiable Probabilistic Certificates for Safe Control and Learning (opens in a new tab)
- IEEE Conference on Decision and Control (CDC)Neural Spline Operators for Risk Quantification in Stochastic Systems (opens in a new tab)
- Journal of Machine Learning Research (JMLR), acceptedPhysics-Informed Deep B-Spline Networks for Dynamical Systems (opens in a new tab)
- IEEE Transactions on Control of Network SystemsProbabilistic Safety Certificate for Multi-Agent Safe Control and Learning
- TAC (T1)Rate-Cost Tradeoffs in Continuous-Time Control with a Biomolecular Application (opens in a new tab)
- International Conference on Machine Learning (ICML)Safety Certificate Against Latent Variables with Partially Unidentifiable Dynamics (opens in a new tab)
- Conference on Uncertainty in Artificial Intelligence (UAI)Sample Complexity of Stabilizing LTI Systems on a Single Trajectory under Stochastic Noise (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)Stabilizing Linear Systems under Partial Observability: Sample Complexity and Fundamental Limits (opens in a new tab)
20246 papers
- AISTATS (T1)An Analytic Solution to Covariance Propagation in Neural Networks (opens in a new tab)
- ITSCAutonomous Drifting Based on Maximal Safety Probability Learning (opens in a new tab)
- IEEE Control Systems LettersOrthogonal Modal Representation in Long-Term Risk Quantification for Dynamic Multi-Agent Systems
- AAAI (T1)Physics-Informed Representation and Learning: Control and Risk Quantification (opens in a new tab)
- ACCPhysics-Informed RL for Maximal Safety Probability Estimation (opens in a new tab)
- ICRA (T1)Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction (opens in a new tab)
20239 papers
- L4DCA Generalizable Physics-Informed Learning Framework for Risk Probability Estimation (opens in a new tab)
- L4DCA Learning and Control Perspective for Microfinance (opens in a new tab)
- IFACAn Occlusion- and Interaction-Aware Safe Control Strategy for Autonomous Vehicles (opens in a new tab)
- Building and EnvironmentCalibrating Subjective Data Biases and Model Predictive Uncertainties in Machine Learning-Based Thermal Perception Predictions
- Operations Research (T1)Generalized Exact Scheduling: A Minimal-Variance Distributed Deadline Scheduler (opens in a new tab)
- ASCE International Conference on Computing in Civil Engineering (i3CE)Optimizing HVAC Systems for Energy Efficiency and Control: A Scalable and Robust Multi-Zone Control Approach with Uncertainty Considerations
- ComputerResearch Challenges for Energy-Efficient Computing in Automated Vehicles
- AAAI (T1)Rethinking Safe Control in the Presence of Self-Seeking Humans (opens in a new tab)
- IEEE Control Systems LettersScalable Long-Term Safety Certificate for Large-Scale Systems (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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- USUC Berkeley
Koushil Sreenath
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Data
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