Shun Zhang 2260 Hayward St Ann Arbor, MI 48109 Homepage: ​https://sites.google.com/a/umich.edu/shunzh/

Phone: Email: [email protected]

EDUCATION University of Michigan Ph.D. candidate, Artificial Intelligence Lab Sep. 2015 - Present ● Advisors: Satinder Singh, Edmund Durfee ● GPA: 3.9 / 4.0 University of Texas at Austin M.S. in Computer Science Aug. 2015 ● Master Thesis: Parameterized Modular Inverse Reinforcement Learning Committee members: Dana Ballard, Peter Stone ● GPA: 3.8 / 4.0 B.S. in Computer Science May 2014 ● GPA: (major) 3.8 / 4.0, (overall) 3.6 / 4.0 WORK EXPERIENCE Amazon​ (Seattle, WA) Jun. 2014 - Aug. 2014 SDE Intern ● Created a prototype of a WebRTC-related internal tool. Semantic Designs ​(Austin, TX) May. 2013 - Jul. 2013 SDE Intern ● Created a GUI interface for a program language analysis tool. PUBLICATIONS 1. Shun Zhang​, Edmund Durfee, and Satinder Singh. Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes. ​International Joint Conference on Artificial Intelligence (IJCAI),​ 2018. 2. Shun Zhang​, Edmund Durfee, and Satinder Singh. Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes. ​International Conference on Automated Planning and Scheduling (ICAPS)​, 2017. 3. Ruohan Zhang, ​Shun Zhang​, Matthew Tong, Mary Hayhoe, and Dana Ballard. Modeling Sensorimotor Behavior through Modular Inverse Reinforcement Learning with Discount Factors. Journal of Vision​, 2017. 4. Katie Genter, ​Shun Zhang​, and Peter Stone. Determining Placements of Influencing Agents in a Flock. ​Autonomous Agents and Multiagent Systems (AAMAS)​, 2015. 5. Tsz-Chiu Au, ​Shun Zhang​, and Peter Stone. Semi-Autonomous Intersection Management (extended abstract). ​Autonomous Agents and Multiagent Systems (AAMAS)​, 2014. RELEVANT COURSES

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University of Michigan: machine learning, theoretical foundations of machine learning, advanced artificial intelligence. University of Texas at Austin: ○ (graduate-level) machine learning, reinforcement learning, autonomous robots, large scale optimization, Markov chain and mixing time, automated logic reasoning. ○ (undergraduate-level) artificial intelligence, information retrieval and web search.

SKILLS ● Familiar with general machine learning algorithms, with expertise in reinforcement learning algorithms. ● Programming languages: ○ Proficient in Python (used for research on daily basis). ○ Comfortable with Java, C++, Matlab (used for research projects/courses before). ○ Familiar with Lisp, SQL, JavaScript. ● Natural languages: Mandarin Chinese (native), English (fluent), Japanese (conversational).

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Shun Zhang

Master Thesis: Parameterized Modular Inverse Reinforcement Learning. Committee members: Dana Ballard, Peter Stone. ○ GPA: 3.8 / 4.0. B.S. in Computer ...

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