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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