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,[ref]We previously interviewed Emina on this blog.[/ref] 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.
I recently had the pleasure of co-organizing a Dagstuhl Seminar on the synergy between ideas, methods, and research in programming languages and cryptography.
Dagstuhl Seminar on the Synergy between Programming Languages and Cryptography
This post and the next will summarize some interesting discussions from the seminar. In this post, I will look at how programming languages often interface with cryptography, surveying the research of the seminar participants. In my next post, I’ll dig a little deeper into one topic in particular, which is how formal reasoning in PL and Crypto compare and contrast, and how ideas from one area might be relevant to the other.
Ultimately, I came away convinced that the combination of PL and Crypto has much to offer to the problem of building secure systems.
In this post, I’ll continue our ongoing discussion of applications of PL research in computer-assisted education. Specifically, I’ll summarize a talk that Loris D’Antoni of Penn gave at this year’s Workshop on Programming Language Technology for Massive Open Online Courses (PLOOC). I was intrigued by this work, and I think a lot of you may be too.
Filed under Education, MOOCs
At the recent PLDI conference, Armando Solar-Lezama and I organized a workshop called PLOOC: “Programming Language Tools for Massive Open Online Courses.” The high-level goal of the workshop was to discuss ways in which tools coming out of PL research can be used in K-16 education. Over the years, PL researchers have developed many techniques for automating and simplifying the design and analysis of programs. For the most part, these techniques have targeted the professional programmer. However, techniques developed for industrial code can also be applied to student-written programs in computer science courses.
Filed under Education, MOOCs
Automated analysis of programs is one of the major success stories in PL. The goal here is to algorithmically infer properties of a program’s runtime behavior without executing the program on concrete inputs. This may be done for many reasons, including optimization and reasoning about correctness. If you are trying to optimize a program, it helps to know that a statement executed within a loop always performs the same update, and can therefore be moved out of the loop. If you want to be certain that your C program doesn’t have buffer overflows, you want to infer bounds on the indices used for array accesses in the program.
Over the years, systems for program analysis have increased in sophistication and entered the mainstream of software development. But how do you know that what your analysis tells you is correct? To be certain that it is, we must relate the program’s semantics – what the program does at runtime – to what the analysis algorithm computes. The framework of abstract interpretation is the gold standard for doing so.
Radhia Cousot, co-inventor of abstract interpretation, passed away earlier this summer after a long struggle with cancer. While her death was tragic, I am consoled that she lived to see her work impact the world in a way that most researchers can only dream of.