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Ming Lin

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Ming Lin’s research focuses on  high dimensional high order statistics and the related applications in real world machine learning problems. His recent research topics includes symmetric matrix sensing, Positive Unlabeled learning, One-bit Active learning and nonconvex tensor machine.

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Michael Wellman

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In a strategic environment, agents face decisions where the outcomes depend on the behavior of other autonomous agents. The strategic reasoning group develops techniques for understanding and engineering complex multiagent environments, using concepts and methods from economics as well as computer science. Specifically, we apply game-theoretic principles to data from large-scale agent-based simulation, in an approach called empirical game-theoretic analysis (EGTA). EGTA combines simulation, machine learning, and other empirical methods to reason about the strategic issues in complex multiagent settings. We are particularly interested in domains characterized by dynamism, networks, and uncertainty, including applications in financial markets, information security, and sustainable transportation.

The iterative empirical game-theoretic analysis process.

The iterative empirical game-theoretic analysis process.

Chestek, Cynthia - cchestek

Cynthia Chestek

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Cynthia Chestek is an Assistant Professor of Biomedical Engineering, Electrical Engineering – Electrical and Computer Engineering Division, and the Neurosciences Graduate Program.

The Chestek lab focuses on brain machine interface (BMI) systems using 100 channel arrays implanted in motor and pre-motor cortex. The goal of this research is to eventually develop clinically viable systems to enable paralyzed individuals to control prosthetic limbs, as well as their own limbs using functional electrical stimulation and assistive exoskeletons. The lab apply a variety of machine learning algorithms to large-scale neural datasets obtained from spiking activity or field potentials in order to decode the motor commands. This is done both offline, and in real-time during experiments. Other computational challenges include mitigating non-stationarities in neural recordings over time. Over the next few decades, the size of these datasets is most likely to increase with the development of larger electrode arrays, and novel surgical techniques for implanting them.
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