Andrew Lan

Andrew Lan's research focuses on the development of human-in-the-loop machine learning methods to enable scalable, effective, and fail-safe personalized learning in education, by collecting and analyzing massive and multimodal learner and content data. This massive and multimodal learner and content data can be collected in both traditional classrooms and through online learning platforms, e.g., during massive open online courses (MOOCs). His vision is to develop a system that delivers high-quality, affordable, and personalized learning experiences to every learner in the world. He is also broadly interested in areas including convex optimization, probabilistic models, machine learning, and signal processing. 

Prior to joining UMass, Lan was a postdoctoral research associate in the EDGE Lab, Princeton University, from 2017 to 2018. He was a postdoctoral research associate in the Digital Signal Processing (DSP) group at Rice University in 2016; he received his MS and PhD degrees from Rice University in 2014 and 2016, respectively.  He also attended the Georgia Institute of Technology as a visiting student in 2009.

Lan also serves regularly on program committees of several conferences on educational data mining, machine learning, and signal processing. He has also co-organized a series of workshops on machine learning for education; see http://ml4ed.cc/ for details. Some of his works have been integrated into OpenStax Tutor, a commercial-grade personalized learning platform.