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Larkin Liu
Larkin Liu - 刘炜
Bio
My specific research objectives involve bridging the gap between operations research and machine learning, and finding novel applications in the various areas of economics and engineering. I am also interested in the theoretical foundations of learning algorithms.
From a methodology perspective, I conduct research in stochastic optimization, in areas such as,- Online learning (reinforcement learning, multi-armed bandits etc.)
- Sequential decision making (Monte Carlo tree search, simulation etc.)
- Multi-agent competitive games (equilibrium computation, mechanism design etc.)
Academic Background
- Technical University of Munich (2021-Present)
- PhD Candidate in Computer Science
- Advisor: Prof. Jalal Etesami
- Working Thesis: Robust Online Learning and Optimization in Applied Multi-Agent Systems
- University of Toronto (2015-2017)
- MASc in Operations Research
- Advisor: Prof. Viliam Makis
- Thesis: Comparative Study between Statistical Fraud Detection Methods on eCommerce Networks
- University of Toronto (2010–2015)
- BASc in Mechanical Engineering, with Honours
- Minor in Robotics & Mechatronics
Awards
- Ergodic AI Research Fellowship (2025)
- Mitacs Accelerate Industry Government Joint Research Grant (2015)
- Wallace G Chalmers Engineering Design Award (2013)
- Faculty of Applied Science Engineering Research Fellowship (2012)
- Magna Family Scholarship (2010)