Yixuan Wang

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Hi, welcome to my personal page!

I'm a fifth-year Ph.D. student in the Computer Engineering at Northwestern University, supervised by Prof. Qi Zhu. I also work closely with Prof. Chao Huang from Unversity of Liverpool and Prof. Zhaoran Wang in IEMS at Northwestern. My research interest falls in the learning-enabled cyber-physical systems(LE-CPSs) where the learning components act as the functionalities for perception, planning, decision-making and control. I am exploring the intersection of formal methods and machine learning with the consideration of safety, stability, robustness and correctness.

Prior to Northwestern, I received my B.E. from Tsinghua University.

Department of Electrical and Computer Engineering,
Northwestern University
2145 Sheridan Road
Tech Room L476
Evanston, IL 60208, United States
E-mail: yixuanwang2024 [AT] u [DOT] northwestern [DOT] edu

Recent News

  • 2023.10 Our paper POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems is accepted to TCAD.

  • 2023.10 I presented the paper in IROS.

  • 2023.08 The co-authored book chapter Safety-Assured Design and Adaptation of Connected and Autonomous Vehicles is online.

  • 2023.07 I presented the paper in ICML.

  • 2023.06 Our paper Safety-Assured Speculative Planning with Adaptive Prediction is accepted to IROS 2023, see u in Detroit this October!

  • 2023.05 I will give a talk to the 6th IEEE International Workshop on Design Automation for Cyber-Physical Systems (DACPS), in July. The title of this talk is “Explainable safe reinforcement learning for safety-critical CPSs” which will mainly cover our recent ICML and ICCPS paper, where we developed a differentiable joint-learning bi-level optimization framework for safe RL with both deterministic and stochastic environment.

  • 2023.04 Our paper Enforcing Hard Constraints with Soft Barriers: Safety-driven Reinforcement Learning in Unknown Stochastic Environments has been accepted to ICML 2023, see u in Hawaii this Summer!

  • 2023.03 I passed the Ph.D. Prospectus Exam!

  • 2023.01 Our paper Joint Differentiable Optimization and Verification for Certified Reinforcement Learning is accepted by ICCPS 2023.

  • 2022.11 Our paper REGLO: Provable Neural Network Repair for Global Robustness Properties has been accepted by Trustworthy and Socially Responsible Machine Learning (TSRML) Workshop at NeurIPS 2022.

  • 2022.10 I will join Bosch Sunnyvale as an intern with the topic of 'Hybrid Behavior Planner via Learning Based Approaches’ in March 2023, under the supervision of Dr. Jarrett Holtz.

  • 2022.10 Paper Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments is available on arXiv.

  • 2022.09 Paper Accelerate Online Reinforcement Learning for Building HVAC Control with Heterogeneous Expert Guidances is accepted by Buildsys 2022.

  • 2022.08 Paper A Tool for Neural Network Global Robustness Certification and Training is on arXiv.

  • 2022.07 I am in San Francisco to present our verification-in-the-loop paper at Design Automation Conference 2022.

  • 2022.02 Our paper Design-while-Verify: Correct-by-Construction Control Learning with Verification in the Loop is accepted by DAC 2022, see u at SF this summer.

  • 2022.01 Our paper Joint Differentiable Optimization and Verification for Certified Reinforcement Learning is available on arXiv.

  • 2022.01 Our paper Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner by ICCPS 2022.

  • 2021.12 I presented the Cocktail paper at Design Automation Conference at San Francisco.

  • 2021.09 I finished the internship at Ford.

  • 2021.07 Our paper Weak Adaptation Learning – Addressing Cross-domain Data Insufficiency with Weak Annotator is accepted by ICCV 2021 and now available at arXiv.

  • 2021.06 I join Ford as a summer intern, focusing on safe driving under uncertainties. I am working with Dr. Devesh Upadhyay.

  • 2021.02 Our paper Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation is accepted by DAC 2021, see u at San Francisco this December!

  • 2020.12 Our paper Bounding Perception Neural Network Uncertaintyfor Safe Control of Autonomous Systems is accepted by DATE 2021.

  • 2020.12. Our paper Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems is accepted by ASP-DAC 2021.

  • 2020.11. I presented the paper in ICCAD 2020.

  • 2020.09. Our paper One for Many: Transfer Learning for Building HVAC Control is accepted by Buildsys 2020.

  • 2020.08. Our paper Know the Unknowns: Addressing Disturbances and Uncertainties in Autonomous Systems is accepted by ICCAD special session.

  • 2020.08. Our paper Accurate kinematics calibration method for a large-scale machine tool is accepted by IEEE Transaction on Industrial Electronics(IF=7.5+, 2019.) This work was done when I was an undergraduate student in Tsinghua Univerisity…

  • 2020.07. Our paper Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems is accpted by ICCAD 2020 as Best Paper Candidate!

  • 2020.07. I am honored to participate in the Design Automation Conference 2020(DAC 20) Young Fellow program in July.


  • Tsinghua University: B.S., Mesurement and Control, 2014 - 2018(GPA: 91.6, ranking 2/71)

  • Northwestern Univerisity: M.S., Computer Engineering, 2019 - 2022.

  • Northwestern University: Ph.D., Computer Engineering, 2019 - 2024 (expected)

Working Experience

  • Aurora Innovation, Mountain View, CA, USA, 2023.07 - 2023.09, Machine Learning Engineering Intern

  • Bosch USA, Sunnyvale, CA, USA, 2023.03 - 2023.06, Machine Learning Engineering Intern

  • Ford Motor Company, Dearborn, MI, USA, 2021.06 - 2021.09, Research Intern

  • Sensetime, Shenzhen, China 2019.04 - 2019.08, Machine Learning Engineering Intern

  • Mech-Mind Robotics, Beijing, China, 2017.12 - 2018.02, Software Development Engineering Intern


  • DAC 2020 Young Fellow.


  • Reviewer for AAAI, NeurIPS, ICML, ICLR, RAL, CDC, IV, etc


  • Teaching Assistant for ELEC_ENG 373, 473 Deep Reinforcement Learning from Scratch, Spring 2022.


  • Terminal Year Fellowship, Nothwestern University, 2023.

  • Best Paper Candidate, ICCAD 2020.

  • Northwestern Univerisity Ph.D. Fellowship, 2019-2020.

  • Academic Excellence Award(top 10%), Tsinghua University, in all the undergraduate years(2014-2018).

  • National Scholarship(top 2%), 2016.

  • Outstanding Undergraduate Students(top 2%), Tsinghua Univerisity, 2018.

  • Outstanding Undergraduate Students of Beijing(top 5%), 2018.

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