Yixuan Wang

Recent News

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.

Education

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

Bosch USA, Sunnyvale, CA, 2023.03 - 2023.06, Machine Learning Engineering Intern
Ford Motor Company, 2021.06 - 2021.09, Research Intern
Sensetime, Shenzhen, 2019.04 - 2019.08, Machine Learning Engineering Intern
Mech-Mind Robotics, Beijing, 2017.12 - 2018.02, Software Development Engineering Intern

Activities

DAC 2020 Young Fellow.

Services

Reviewer for AAAI, NeurIPS, ICML, RAL, CDC, etc

Teaching

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

Awards

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.

Follow me

Google Scholar, Github, twitter

What I love

Sports(basketball, tennis, badminton, billiard, ect.)
Cooking
League of Legends
Movies(Oh captain, my captain! Dead Poets Society, The Silence of the Lambs, Farewell My Concubine , The Professional, A Chinese Odyssey, The Lord of Rings, Harry Potter, Devils on the Doorstep, The Godfather, Memories of Murder, Pirates of the Caribbean, etc)
TV series(My Own Swordsman, Game of Thrones(seasons 1 - 5), Friends, etc)
Music(Jay Chou, JJ Lin)
Letters on Beauty is my favorite book talking about a systematic view of aesthetics. After you finish reading this book, I believe you will find some aesthetic pleasure in the ordinary life which is often ignored. Walk slowly, and appreciate the life.