Ongoing Projects

HMC - Heterogeneous Model Compilers for Uncertain Environments

HMC is a 5-year research project awarded to David Broman, as part of the Swedish Foundation for Strategic Research (SSF)'s individual grant for future leaders (FFL 6). The project started in January 2017 and has a total funding of 12 million SEK. The overall goal is to develop new techniques and tools that make it easier to develop complex systems, by combining heterogeneous models and to automatically generate efficient runtime systems on heterogeneous platforms. The concrete expected output is an open-source model-based compiler that will be directly usable to the industry.

For more information about the HMC project, see the project website. This project is financially supported by the Swedish Foundation for Strategic Research (SSF).

 

ASSEMBLE - Automating System SpEcific Model-Based Learning

ASSEMBLE is a 5-year research project that is financially supported by the Swedish Foundation for Strategic Research (SSF). The project started in July 2016 and has a total funding of 29 million SEK. The objective of the project is to develop a new probabilistic modeling language together with new machine learning inference algorithms to enable fast and complex development of smart systems. The project team consists of four researchers: From KTH: David Broman and Joakim Jaldén. From Uppsala University: Thomas Schön (main PI) and David Black-Schaffer.

For more information about the ASSEMBLE project, see the project website. This project is financially supported by the Swedish Foundation for Strategic Research (SSF).

 

DPPL - Differentiable Probabilistic Programming Languages

The DPPL project is a personal grant awarded to David Broman (4.2 million SEK) and is sponsored by the Swedish research council (VR). The project started January 2019 and continues for 4 years, with the possibility of one year extension. The overall goal of this project is to develop a new theoretical foundation for differentiable probabilistic programming languages that enables efficient and robust learning-based real-time systems. A novelty of our approach is to combine state-of-the-art techniques from three separate research communities: (i) machine learning (stochastic gradient descent and Bayesian inference methods), (ii) programming and modeling languages (operation semantics, type theory, meta-programming, and partial evaluation), and (iii) real-time systems (scheduling theory and timing analysis).

This project is financially supported by the Swedish Research Council (VR).

 

High-Confidence Formal Verification of Real Cyber-Physical Systems: from Models to Machine Code

This project is an expedition project funded by the Wallenberg AI, Autonomous Systems and Software Program (area WASP-AS). The project started in 2019, and is a 2-year project with possible extension. It is a collaboration between two co-PI:s, David Broman (KTH) and Magnus Myreen (Chalmers). The project funds three postdocs (one in Chalmers and two at KTH). The overall research goal of this project is to develop a new theoretical foundation of formally verified cyber-physical domain-specific model compilation, from high-level real system models down to machine code, satisfying both functional and temporal constraints. The project is implemented using Coq (KTH) and HOL4 (Chalmers).

This project is financially supported by the Wallenberg AI, Autonomous Systems and Software Program (area WASP-AS).

 

Deep Probabilistic Neural Networks for Survival Analysis

This project is a collaboration project between David Broman (KTH) and Thomas Schön (Uppsala University). It is funded by the Wallenberg AI, Autonomous Systems and Software Program (area WASP-AI) and funds two PhD students (one at KTH and one in Uppsala). The project started in the fall 2019, and is a 5-year project. The aim of the project is to introduce a novel way to combine deep neural networks with probabilistic programming to address the fundamental problem of learning representations for probabilistic deep neural networks.

This project is financially supported by the Wallenberg AI, Autonomous Systems and Software Program (area WASP-AI).

 

DLL: Data-Limited Learning of Complex Dynamical Systems

The DLL project is an internal KTH project, sponsored by KTH and by the SRA ICT TNG. The project is a collaboration between five co-PIs: David Broman (main PI), Saikat Chatterjee, Veronique Chotteau, Håkan Hjalmarsson, and Alexandre Proutiere. The project is initially funded as a start-up project during 2019, with potential extensions. The overall research objective is development of new techniques, methods, and tools to learn and control complex dynamical systems using limited number of data samples and structural information in a reliable manner.

This project is financially supported by KTH and SRA ICT TNG.

 


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