'Probabilistic Programming: Algorithms, Applications and Synthesis,' Aditya Nori

Recent years have seen a huge shift in the kind of programs that most programmers write. Programs are increasingly data driven instead of being algorithm driven. They use various forms of machine learning techniques to build models from data, for the purpose of decision making. Indeed, search engines, social networks, speech recognition, computer vision, and applications that use data from clinical trials, biological experiments, and sensors, are all examples of data driven programs. We use the term "probabilistic programs" to refer to data driven programs that are written using higher-level abstractions. Though they span various application domains, all data driven programs have to deal with uncertainty in the data, and face similar issues in design, debugging, optimization and deployment. In this talk, we describe connections this research area called "Probabilistic Programming" has with programming languages and software engineering — this includes language design, static and dynamic analysis of programs, and program synthesis. We survey current state of the art and speculate on promising directions for future research.

Aditya Nori, Senior Researcher, Microsoft Research India
Aditya Nori is a member of the Programming Languages and Tools group at Microsoft Research India. His research interests are: the design and analysis of reliable intelligent systems, probabilistic programming and synthesis. Over the past few years, he has worked on exploring various synergies between programming languages and machine learning – these include the use of machine learning techniques for proving programs correct, specification inference via Bayesian analysis, probabilistic programming via program analysis, and productivity tools for machine learning tasks. He is a co-winner of the ACM SIGSOFT Distinguished Paper award at FSE 2006 and FSE 2015. He received his PhD in computer science from the Indian Institute of Science, Bangalore.

Robert Dyer, Bowling Green State University
Robert Dyer is an Assistant Professor in the Department of Computer Science at Bowling Green State University. He received his Ph.D. from Iowa State University in 2013. His research areas are in Software Engineering, Big Data applications, and Programming Languages. Currently his research focuses on the Boa project, that provides a domain-specific language and infrastructure to allow researchers to easily mine a very large number of software repositories. Robert has served on the organizing committee for ICSE, program committee for Modularity and OOPSLA Artifacts, and reviewed for journals such as Empirical Software Engineering. He is a member of ACM SIGSOFT and SIGPLAN and is the ACM SIGSOFT Webinar Coordinator.