To join this seminar virtually: Please request Zoom connection details from ea [at] stat.ubc.ca.
Abstract: Probabilistic programming languages (PPLs) enable a clean separation between probabilistic models and Bayesian inference algorithms. Ideally, such separation makes it possible for the modeler to focus on the probabilistic modeling task without knowing the details of how to implement the inference method. However, making such general inference machinery efficient and automatic is challenging. In this talk, I will discuss our ongoing work on developing a modular framework for efficient compilation of domain-specific languages (DSLs) targeting PPLs. Specifically, I will discuss techniques that enable modular design and compilation algorithms for efficient inference. Moreover, I will also briefly discuss two application areas, including probabilistic programming of real-time systems and statistical phylogenetics.
Bio: David Broman is a Professor at the Department of Computer Science, KTH Royal Institute of Technology, a Visiting Professor at the Computer Science Department, Stanford University, and an Associate Director Faculty for Digital Futures. He received his Ph.D. in Computer Science in 2010 from Linköping University, Sweden. Between 2012 and 2014, he was a visiting scholar at the University of California, Berkeley, where he also was employed as a part-time researcher until 2016. His research focuses on the intersection of (i) programming languages and compilers, (ii) real-time and cyber-physical systems, and (iii) probabilistic machine learning. David has received the Best ETAPS paper award on on programming languages and systems (the EAPLS Award, co-authored 2023), a Distinguished Artifact Award at ESOP (co-authored 2022), an outstanding paper award at RTAS (co-authored 2018), a best paper award in the journal Software & Systems Modeling (SoSyM award 2018), the award as teacher of the year, selected by the student union at KTH (2017), the best paper award at IoTDI (co-authored 2017), and awarded the Swedish Foundation for Strategic Research's individual grant for future research leaders (2016). He has worked several years within the software industry, co-founded companies, co-founded the EOOLT workshop series, and is a member of IFIP WG 2.4, Modelica Association, a senior member of IEEE, and a former board member of Forskning och Framsteg.