Research Overview

• Probabilistic Programming and Machine Learning. We develop algorithms, semantics, and compilers for probabilistic programming languages in general. Recent results include work on delayed sampling [AISTATS 2018], proving correctness of Sequential Monte Carlo (SMC) inference within PPLs [ESOP 2021], and efficient compilation of universal probabilistic programming of SMC to GPUs [ESOP 2022]. We have earlier worked on non-PPL-based supervised and unsupervised learning methods in the context of software engineering and automated bug assignment [Empirical Software Engineering 2016] and as generalized methods for parallelization of Latent Dirichlet allocation models [Journal of Computational and Graphical Statistics 2017]. Recently, we showed for the first time how universal probabilistic programming offers a new, unique, and a powerful approach to statistical phylogenetics, enabling rapid modeling that was not possible before [Communication Biology 2021].
• Differentiable and Equation-Based Languages. We have for a long time been developing static and dynamic semantics for equation-based object-oriented (EOO) modeling languages, which are based on differentiable-algebraic equations. I have been part of the Modelica language design group since 2005. Some results and contributions are on types [Modelica 2006], [GPCE 2006], higher-order models [SNE 2009], connection semantics [PADL 2012], and some involvement in the open-source tool OpenModelica [SNE 2005]. We are currently working on incorporating automatic differentiation as first-class citizens in languages, both in an efficient and semantically correct way.

Key areas where we are actively doing research in: (i) defining a new domain-specific modeling language within phylogenetics, (ii) develop more domain-specific compilation techniques for both automatic differentiation and probabilistic inference, and (iii) develop reinforcement techniques that incorporate differentiable probabilistic programming concepts, including equation-based modeling and simulation.

• The Miking System. Central for our current group's activities is the open-source platform Miking that we use as a research platform (see the Vision paper in [SLE 2019] or the Github repositories). We develop new techniques for composability and a technique called Resolvable Ambiguity [CC 2021], as well as a model for interactive programmatic modeling [TECS 2021]. A central part of this line of work is the ability to compose language fragments so that new domain-specific languages can be rapidly defined without starting from scratch. For instance, the work on probabilistic programming in Area 1 (published in ESOP 2021 and 2022) is developed on top of the Miking framework. We base many aspects of the Miking framework on experiences and ideas from David's previous system called Modelyze (see [PEPM 2018] and www.modelyze.org).
• Co-simulation. Another aspect of modular systems is co-simulation. We have done work in this area for many years, which has resulted in several visible publications, including determinate composition for co-simulation [EMSOFT 2013], techniques for hybrid co-simulation [SoSym 2019], and requirements for hybrid co-simulation [HSCC 2015], and the leading survey on the topic [ACM Computing Survey in 2019].

Key areas where we currently do research are: (i) automatic tuning techniques to improve compilation performance, (ii) acceleration and domain-specific compilation to GPUs, (iii) theories for language fragment composition, both at syntax and semantic level.

• Timed Programming Languages and compilers. A line of work that our group has recently been developing is language primitives for efficient and simple real-time programming in standard programming languages. In particular, we have developed a language extension called Timed C [RTAS 2018 paper], where the standard C language is extended with a small set of timing primitives. We have also developed end-to-end tools for Timed C [RTSS 2019]. We have also investigated WCET-aware code mapping techniques [TECS 2017] and models for approximate synchrony [CAV 2015].
• Predictable Processors and Mixed-Critical Systems. Another line of research has focused on what is called Precision Timed (PRET) machines, mainly developed at UC Berkeley. An essential step in this development is the FlexPRET architecture [RTAS 2014], a predictable RISC-V-based softcore processor for mixed-critical systems. We have also developed predictable DRAM controllers [RTAS 2015] and models for relaxing the synchronous approach for mixed-critical systems [RTAS 2014].

During the past years, the group's research has focused mostly on compiler and language perspectives in general, and especially on research related to Timed C. Out of the three research areas, there are fewer planned activities within Area 3. However, one important aspect is connecting predictable and timed programming with co-simulation aspects and differentiable programming. There are both interesting semantic problems and compiler challenges.