Machine Learning for Education

I am an assistant professor in the Manning College of Information and Computer Sciences, University of Massachusetts Amherst, since Jan. 2019.

From Feb. 2017 to Dec. 2018, I was a postdoctoral research associate in the EDGE Lab at the Department of Electrical Engineering, Princeton University, advised by Prof. Mung Chiang. I received my M.S. and Ph.D. degrees in Electrical and Computer Engineering in May 2014 and May 2016, respectively, from the Digital Signal Processing (DSP) group at Rice University, advised by Prof. Richard Baraniuk. I also worked for OpenStax. Prior to that, I received my BS degree in Physics and Mathematics from the Hong Kong University of Science and Technology in 2010. During that, I also studied at the Georgia Institute of Technology for a semester.

My research focuses on the development of artificial intelligence (AI) and especially natural language processing (NLP) methods to enable scalable and effective personalized learning in education. My research spans areas such as learner modeling, personalization, content generation, and human-in-the-loop AI. My vision is to build an machine learning system that enables a world in which every learner has access to highquality, affordable, and personalized learning. I have co-organized a series of workshops on machine learning for education; see ml4ed for details.

At the same time, I am generally interested in machine learning topics, including convex and nonconvex optimization, Bayesian inference, deep learning, and the future of work.