Songyang Han

Songyang Han

PhD student | Computer Science and Engineering | Artificial Intelligence

University of Connecticut

Biography

I am currently a final-year Ph.D. student in computer science and engineering working on artificial intelligence supervised by Prof. Fei Miao at the University of Connecticut. I was working on game theoretic energy management approaches in Dynamic Systems Control Lab at the University of Michigan-Shanghai Jiao Tong University Joint Institute supervised by Prof. Chengbin Ma.

My current research interests include robust and scalable multi-agent reinforcement learning, artificial intelligence, deep learning, autonomous driving, computer vision, and game theory.

News

  • [2022/9] Our paper “Uncertainty Quantification of Collaborative Detection for Self-Driving” is available on arxiv, website.
  • [2022/9] Our paper “A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles” is available on arxiv, website.
  • [2022/8] I get the General Electric (GE) fellowship of excellence. The GE Fellowship for Excellence program is established to recognize excellence of current graduate students and to facilitate their completion of the Ph.D. program.
  • [2022/7] Our paper “Towards Safe Autonomy in Hybrid Traffic: The Power of Information Sharing in Detecting Abnormal Human Drivers Behaviors” is presented in the AI4TS workshop at the 31st International Joint Conference On Artificial Intelligence (IJCAI 2022).
  • [2022/7] Our paper “DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services” is accepted by ACM Transactions on Cyber-Physical Systems. (available online)
  • [2022/5] Our paper “Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles” is presented on the 2022 IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, USA, May 2022.
  • [2022/3] I get the second place award in the 8th annual graduate poster competition hosted by the School of Engineering of University of Connecticut. I also get the first place award in the Computer Science and Engineering Department.
Interests
  • Artificial Intelligence
  • Reinforcement learning
  • Deep learning
  • Autonomous driving
  • Computer vision
  • Game theory
Education
  • PhD in Computer Science and Engineering, 2023 (expected)

    University of Connecticut

  • MS in Electrical and Computer Engineering, 2018

    Shanghai Jiao Tong University

  • BEng in Automation, 2015

    Nanjing University

Experience

 
 
 
 
 
Research internship
Baidu USA Apollo team
May 2020 – Aug 2020 Sunnyvale, CA, USA
  • Summarized exiting reinforcement learning methods and the state-of-art deep learning methods used in autonomous driving.
  • Built a prototype platform to train and test RL algorithms for autonomous vehicles in the Apollo platform.
 
 
 
 
 
Research assistant
Aug 2018 – Present Storrs, CT, USA
  • Design a safe and scalable multi-agent reinforcement learning framework for the behavior planning and control of connected autonomous vehicles to improve traffic efficiency and safety.
  • Propose a stable and efficient reward reallocation algorithm to motivate cooperation for multi-agent reinforcement learning assuming all agents are self-interested.
  • Study the fundamental properties of the robust multi-agent reinforcement learning under adversarial state perturbations and propose a new objective and an algorithm to increase the mean episode reward.
 
 
 
 
 
Research assistant
Sep 2015 – Mar 2018 Shanghai, China
  • Proposed a flexible energy management approach to handle the uncertainties of weather and sizing in an isolated microgrid, which would not be influenced dramatically by different weather conditions.
  • Designed and fabricated high efficient bidirectional DC/DC converters to conduct and validate energy management approaches in a downsized system.

Publications

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(2022). Uncertainty Quantification of Collaborative Detection for Self-Driving.

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(2022). A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles. In arXiv.

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(2021). DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services. In ICCPS (Best paper award).

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(2020). A Game Theoretic Security Framework for Quantum Cryptography: Performance Analysis and Application. In Quantum Information Processing.

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