Andrew Lan

Machine Learning for Education

Andrew (Shiting) Lan, Assistant Professor

College of Information and Computer Sciences
University of Massachusetts Amherst

Address: 140 Governors Dr., Amherst, MA 01003
E-mail: andrewlan at cs dot umass dot edu

I am recruiting PhD students interested in developing and applying machine learning methods in educational applications.

I am an assistant professor in the 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 and affiliated with Prof. H Vincent Poor. 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) 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.

Nov 2021: Congratulations to Aritra on receiving a Duolingo English Test's Doctoral Dissertation Award!

July 2021: We will be starting new projects sponsored by the NSF, the IES, and Schmidt Futures. Thank you for your support!

June 2021: Our Workshop on Intelligent Textbooks at AIED 2021 was a great success! We have started a Google Group on Intelligent Textbooks.

June 2021: Congratulations to Aritra on passing his portfolio with distinction!

Apr 2021: Congratulations to Shamya on being nominated for the best paper award at ACM LAK 2021!

Dec 2020: Congratulations to Aritra on receiving the best student paper award at IEEE Big Data 2020!

Oct 2020: We participated in 3 tasks in the NeurIPS 2020 Education Challenge, placing 1st in one task and 3rd in two others. Check it out here and here!

May 2020: See you at our Workshop on Fairness, Accountability, and Transparency in Educational Data at EDM 2020!

May 2020: See you at our Second Workshop on Intelligent Textbooks at AIED 2020!

Sep. 2019: We will start a new project "DIRECT: A Framework for Diagnosis, Recommendation, and Training in Continuous Workforce Development" in Sep. 2019. Thanks, NSF!

Aug. 2019: Our Workshop on Deep Learning for Education at KDD 2019 was a great success. Thanks to all organizers and participants!

July 2019: We will start a new project "Student Affect Detection and Intervention with Teachers in the Loop" in Sep. 2019. Thanks, NSF! See our project website here.