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Megan Shearer defends thesis proposal

On June 1 2021, Megan Shearer presented and successfully defended her dissertation proposal titled "Modeling Trading Strategies in Financial Markets with Data, Simulation, and Deep Reinforcement Learning". The dissertation committee comprises: …
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Max Smith defends thesis proposal

On 27 April 2021, Max Smith presented and successfully defended his dissertation proposal titled "On Efficient Deep Multiagent Reinforcement Learning Through Transfer Learning". The dissertation committee comprises: Michael Wellman [chair] …
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Katherine Mayo passes prelim

Katherine Mayo passed the CSE prelim exam, based on her directed study project: A Strategic Analysis of Portfolio Compression. Congratulations, Katherine!  
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Xintong Wang defends dissertation

On Dec 21, 2020, Xintong Wang successfully defended her PhD dissertation titled, "Computational Modeling and Design of Financial Markets: Towards Manipulation-Resistant and Expressive Markets." Congratulations, Dr. Wang! We wish you a…
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Megan receives DEI service award

Megan Shearer is one of the graduate student recipients of the first annual CSE Service Award for Excellence in Climate, Diversity, Equity and Inclusion. The awardees for the 2019-20 cycle were officially recognized by Prof. Westley Weimer at…
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Xintong finishes as finalist at CSE Graduate Honors Competition

Xintong Wang was one of the five finalists at the 17th Annual CSE Graduate Honors Competition held virtually on Nov 11, 2020, where she delivered a 15 min presentation (with QnA) on her research topic "Combining Agent-Based Simulation and…
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Yongzhao Wang passes prelim

Yongzhao Wang passed the CSE prelim exam, based on his directed study project: Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis. Congratulations, Yongzhao.
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Zun Li Passes Prelim

Zun Li passed the CSE prelim exam, based on his directed study project: Structure learning for approximate solution of many-player games. Congratulations, Zun.
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Max Smith passes prelim

Max Smith passed the CSE prelim exam, based on his directed study project: Learning to Play against Any Mixture of Opponents. Congratulations, Max.