Activities & Results

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Activities, awards, and other outputs

Tutorial at the IEEE International Conference on Signal Processing and Communications (SPCOM) 2022. Tutorial title: Generative models and role of deep neural networks. Lecturer: Saikat Chatterjee.
Two-day Tutorial at Digital Futures on the Fundamentals of Bayesian Inference using Probabilistic Programming Probabilistic programming. Lecturer: David Broman.
Distinguished Artifact Award at European Symposium on Programming (ESOP 2022), for the paper Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. David Broman and co-authors.
Keynote at the Workshop on Nonlinear System Identification Benchmarks. Speaker: Håkan Hjalmarsson
Organization of the Data-limited learning workshop (DLL) in fall 2021. Organized by David Broman together with Co-PIs.
Organization of the MATH, AI, and Neuroscience (MAIN) workshop at Digital Futures in 2021. Organizer: Saikat Chatterjee
Invited talk at the Stochastic Networks conference (Cornell in 2022). Speaker: Alexandre Proutiere
Keynote at YEQTIV Eindhoven and in the SNAPP seminar series. Speaker: Alexandre Proutiere
The organizer of the International Conference on Embedded Software (EMSOFT), the premier venue for embedded software, 2022. PC Chair: David Broman

Results

This project has so far resulted in several fundamental research results. The results include but are not limited to: a new biological modelling approach that includes transcriptional information, new system identification techniques for differential-algebraic equations subject to disturbances, fundamental limits for sample complexity and lower bounds for the regret of deterministic discrete dynamical systems.

The results are published in top-tier journals and conferences such as ICML, NeurIPS, and CDC.