Ian Quah
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About
Ian Quah is a Ph.D. student at the University of Washington, researching the intersection of Neuroscience and Machine Learning. His research focuses on the robustness of large-scale neural circuits and translating biological insights into advanced deep learning algorithms. He aims to develop sample-efficient, power-efficient, and interpretable algorithms. His research interests include reservoir computing, neural simulation models, graph-neural-networks, and deep reinforcement learning, where he explores ideas from psychology and neuroscience. He also advises junior ML Engineers and Data Scientists.
Platform
Task
Features
• graph-neural-networks
• neural simulation models
• reservoir computing
• deep reinforcement learning research with focus on meta-cognition and episodic learning principles from psychology and neuroscience to train more sample efficient and robust deep rl agents
• development of interpretable algorithms
• development of sample-efficient and power-efficient algorithms
• design of advanced deep learning algorithms
• research on robustness of large-scale neural circuits
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