Matt at the White House, Jan 2015
This post is the second part of my March 10th interview of Matt Might, a PL researcher and Associate Professor in the Department of Computer Science at the University of Utah.
In Part I, we talked about Matt’s academic background, his PL research (including his favorite among the papers he’s written), and his work on understanding and treating rare disease, which began with the quest to diagnose his son Bertrand, and has led to a role in the President’s Initiative on Precision Medicine.
In this post, our conversation continues, covering the topics of blogging, privacy, managing a crazy schedule, and looking ahead to promising PL research directions. Continue reading
If you are in the world of programming languages research, the announcement that UW had hired Ras Bodik away from Berkeley was big news. Quoting UW’s announcement:
Ras’s arrival creates a truly world-class programming languages group in UW CSE that crosses into systems, databases, security, architecture, and other areas. Ras joins recent hires Emina Torlak, Alvin Cheung, Xi Wang, and Zach Tatlock, and senior faculty members Dan Grossman and Mike Ernst.
And there’s also Luis Ceze, a regular publisher at PLDI, who ought to be considered as part of this group. With him, UW CSE has 8 out of 54 faculty with strong ties to PL. Hiring five PL-oriented faculty in three years, thus making PL a significant fraction of the faculty’s expertise, is (highly) atypical. What motivated UW CSE in its decision-making? I don’t know for sure, but I suspect they see that PL-oriented researchers are making huge inroads on important problems, bringing a useful perspective to unlock new results.
In this post, I argue why studying PL (for your PhD, Masters, or just for fun) can be interesting and rewarding, both because of what you will learn, and because of the increasing opportunities that are available, e.g., in terms of impactful research topics and funding for them.
In this post, I introduce the emerging area of probabilistic programming, showing how probabilistic programs will hopefully make it easier to perform Bayesian-style machine learning, among other applications. Probabilistic programming is an exciting, and growing, area of research, with fantastic people in both AI/ML and PL working together and making big strides. PL methods — including formal semantics, optimization techniques, and forms of static analysis — have proven very useful in advancing this area forward.