Richard Y. Zhang

Postdoctoral Scholar in Industrial Engineering & Operations Research
Co-PI on NSF Award 1808859. Mentor: Javad Lavaei.

Ph.D., MIT, EECS, 2017. Advisor: Jacob K. White.
S.M., MIT, EECS, 2012. Advisor: John G. Kassakian.
B.E. (hons), University of Canterbury, EE, 2009.

My research is on computational methods—using advanced computational capabilities to learn from large datasets and solve societal problems in energy and transportation.

Many computational problems remain unsolved at the scale, speed, and quality necessary for real-world engineering. This is the case even with cloud computing, GPUs, and supercomputers! The fundamental challenge lies in the dual issues of complexity and nonconvexity. My research uses domain expertise to identify favorable mathematical structure, such as the graph theoretic notion of treewidth, or the existence of a hierarchy of interactions, or the low-rank factorization of a dense matrix. Then, I develop domain-specific algorithms to exploit such structure.

What's the deal with complexity? (A great wiki article)

Why should nonconvexity be a problem in optimization? (My answer on stack exchange)

We are applying domain-specific algorithms to real-world systems with hundreds of millions of parameters and constraints! See the US Department of Energy’s first ever Grid Optimization Competition.

Recent News

Publications (Select / All)

Preprints 2019 2018 2017 2016 2015 2014 2011-2013
© 2018 Richard Y. Zhang.