Site being updated, see my Research Lab page for current information.
My research seeks dependable and transparent ways to augment human decision making in complex domains in the presence of spatial structure, large scale streaming data, or uncertainty. My focus is on developing new algorithms within the fields of Reinforcement Learning, Deep Learning and Ensemble Methods, and Manifold Learning (Dimensionality Reduction) as well as other Research Topics.
I often work in collaboration with researchers in applied fields such as Computational Sustainability (including Sustainable Forest Management), Autonomous Driving, AI for Physics (including Combustion Modelling and Digital Chemistry) and Medical Imaging.
Note, that I do not take on many incoming graduate students, so if you are hoping to join my lab as a graduate student for research please read this note.
Besides my publications, you can follow my Computationally Thinking blog or @compthink on twitter for links and thoughts on Artificial Intellgience, Machine Learning and how technology and science are advancing. I’m always happy to talk to people about my research, or the impact of AI/ML/RL on our world and its role in our society in the future.
|Mar 7, 2022||
Lab News for March 2022
more: Some great publications accepted in past four months.
- QQEQuantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distributionMachine Learning with Applications (MLWA) 2021
- Upcoming BookElements of Dimensionality Reduction and Manifold Learning2022
- Mean Field MARLDecentralized Mean Field GamesIn Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) 2022
- MARLEmpircalInvestigation of Independent Reinforcement Learning Algorithms in Multi-Agent EnvironmentsFrontiers in Artificial Intelligence 2022
- MARLEmpircalInvestigation of Independent Reinforcement Learning Algorithms in Multi-Agent EnvironmentsIn NeurIPS 2021 Deep Reinforcement Learning Workshop 2021