Chuchu Fan

About

Chuchu Fan is an Associate Professor (pre-tenure) in the Department of Aeronautics and Astronautics (AeroAstro) and the Laboratory for Information and Decision Systems (LIDS) at MIT. Her research group, Realm at MIT, focuses on using rigorous mathematics, including formal methods, machine learning, and control theory, for the design, analysis, and verification of safe autonomous systems. She is dedicated to providing safety-critical autonomous systems with rigorous proof of their safety, efficiency, and performance. Before joining MIT, she was a postdoctoral researcher at Caltech and earned her Ph.D. at the University of Illinois at Urbana-Champaign. She completed her bachelor’s degree at Tsinghua University. Her specialization and research interests include formal methods, control, and machine learning for the design and analysis of safe autonomous systems, cyber-physical systems, and robotic systems. Her work has been recognized with several awards, including an NSF CAREER Award, an AFOSR Young Investigator Program (YIP) Award, an ONR Young Investigator Program (YIP) Award, and the 2020 ACM Doctoral Dissertation Award.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Information Retrieval
• Mathematics
• Engineering
• Computer Security
• Data Mining
• Natural Language Processing
• Theoretical computer science

Selected publications

• Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control

  IEEE Transactions on Robotics · 2023 · 208 citations

  Senior authorCorresponding

  Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced transparency and lack of guarantees on the safety or stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies—these certificates provide concise data-driven proofs that guarantee the safety and stability of the lear…

  DOI

• Multi-Agent Motion Planning From Signal Temporal Logic Specifications

  IEEE Robotics and Automation Letters · 2022 · 93 citations

  Senior authorCorresponding

  We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on <italic xm…

  DOI

• Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime

  IEEE Robotics and Automation Letters · 2025-08-11 · 3 citations

  articleSenior author

  Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies…

  PublisherDOI

• Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

  Nature Communications · 2025-10-06 · 2 citations

  articleOpen access

  The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a rea…

  PublisherOA PDFDOI

• Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning

  Communications Physics · 2025-06-03 · 2 citations

  articleOpen accessSenior author

  The tokamak offers a promising path to fusion energy, but disruptions pose a major economic risk, motivating solutions to manage their consequence. This work develops a reinforcement learning approach to this problem by training a policy to ramp-down the plasma current while avoiding limits on a number of quantities correlated with disruptions. The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp…

  PublisherOA PDFDOI

Recent grants

• CAREER: DeepCertify: Data-driven Formal Approach to Safe Autonomy

  NSF · $583k · 2023–2028

Frequent coauthors

• Charles Dawson

  Massachusetts Institute of Technology

  50 shared

• Sayan Mitra

  University of Illinois Urbana-Champaign

  36 shared

• Oswin So

  15 shared

• Zengyi Qin

  Tsinghua University

  14 shared

• Yue Meng

  American Institute of Aeronautics and Astronautics

  13 shared

• Songyuan Zhang

  13 shared

• Songyuan Zhang

  10 shared

• Kunal Garg

  Massachusetts Institute of Technology

  10 shared

Awards & honors

• NSF CAREER Award (2023)
• AFOSR Young Investigator Program (YIP) Award (2023)
• ONR Young Investigator Program (YIP) Award (2025)
• Innovators under 35 by MIT Technology Review (2021)
• ACM Doctoral Dissertation Award (2020)

Similar researchers at Massachusetts Institute of Technology

• Luca Carloneh-60
• Olivier L. de Weckh-52
• Edward F. Crawleyh-44