Research projects

The following projects have received funding from Chalmers AI Research Centre.


Projects started 2020

Deep Learning and likelihood-free Bayesian inference for intractable stochastic models

We construct new deep neuronal networks (DNNs) to learn the parameters of complex stochastic dynamical models that do not have tractable likelihood functions. Specifically, we leverage the expressive approximation power of our DNNs to extract essential information from time-series data, both Markovian and not-Markovian, and then learn model parameters using likelihood-free methodology, such as approximate Bayesian computation. Special (though not exclusive) focus is directed to stochastic differential equation models and state space models (SSMs), where SSMs represent noisy observations of a latent Markovian process. The result will be a flexible plug-and-play machine learning methodology, allowing inference for complex stochastic models.
Applicant: Umberto Picchini, Department of Mathematical Sciences, Chalmers


Energy-based models for supervised deep neural networks and their applications

Despite deep learning-based methods being the state-of-the-art in many AI-related applications, there is a lack of consensus of how to understand and interpret deep neural networks in order to reason about their strengths and weaknesses. Energy-based models in machine learning have a long tradition as a framework to learn from unlabeled data, i.e. unsupervised learning. Recently, it has been shown that supervised learning of deep neural networks using the back propagation method is a limiting case of a suitably defined approach for learning energy-based models using a so-called contrastive loss. This connection is the basis for our interest in a tighter connection between deep learning and energy-based models.
Applicants: Christopher Zach, Department of Electrical Engineering, Chalmers, and Morteza Haghir Chehreghani, Department of Computer Science and Engineering, Chalmers 



Mechanisms for secure and private machine learning

We envision secure and privacy-preserving machine learning algorithms for artificial intelligence applications in everyday life, that can provide confidentiality and integrity guarantees. In particular, we aim to:
  1. Safeguard the privacy of individuals that participate by either (a) providing their data to build the system, or (b) being end-users of the system.
  2.  Safeguard the integrity of the system by (a) ensuring its robustness to adversarial inputs (b) cryptographically limiting the possible points of adversarial manipulation.
Applicants: Aikaterini Mitrokotsa, Department of Computer Science and Engineering, Chalmers, and Christos Dimitrakakis, Department of Computer Science and Engineering, Chalmers


Stochastic continuous-depth neural networks

We will advance the understanding of deep neural networks through the investigation of stochastic continuous-depth neural networks. These can be thought of as deep neural networks (DNN) composed of infinitely many stochastic layers, where each single layer only brings about a gradual change to the output of the preceding layers. We will analyse such stochastic continuous-depth neural networks using tools from stochastic calculus and Bayesian statistics. From that, we will derive practically relevant and novel training algorithms for stochastic DNNs with the aim to capture the uncertainty associated with the predictions of the network.
Applicant: Moritz Schauer, Department of Mathematical Sciences


VisLocLearn - Understanding and Overcoming the Limitations of Convolutional Neural Networks for Visual Localization

Visual localization is the problem of estimating the position and orientation from which an image was taken with respect to the scene. In other words, visual localization allows an AI system to determine its position in the world through a camera. Understanding why current approaches fail and proposing novel approaches that are able to accurately localize a camera are problems of high practical relevance. This is the purpose for the proposed project, VisLocLearn.
Applicant: Torsten Sattler, Department of Electrical Engineering


Projects started 2019

3D Perception and Prediction Based on Deep Learning

The overall purpose of the project is to basic framework for combining perception, prediction, and control to optimize system level performance. Deep learning algorithms in the vision system will be developed in connection to dynamic models of the pedestrians, where the uncertainty of the predictions will be crucial for the control performance.
PI: Fredrik Kahl, Electrical Engineering, Chalmers


AI-Integromics: Interpretable deep modeling for Systems Biology

We aim to provide AI solutions for Integromics that are effective and interpretable. To this end, an AI-Integromics approach should predict phenotypes for in-house data, attribute predictive strength to the multi-omics source data, yet also provide a mapping to extensive public data resources to facilitate drug re-purposing.
PI: Rebecka Jörnsten, Mathematical Sciences, Chalmers


Deep Reinforcement Learning: Principles and Applications in Cognitive Science and Networks

The overall project goals are to carry out basic research in Deep Reinforcemnet Learning (DRL) which is one of the most powerful and exible frameworks available today for general purpose learning, and to apply it in two areas: (a) fundamental problems in cognitive science in collaboration with the world's leading group in this area at UC Berkeley and (b) network policies for applications at Ericsson.
PI: Devdatt Dubhashi, Computer Science and Engineering, Chalmers


INNER: information theory of deep neural networks

Over the last decade, deep-learning algorithms have dramatically improved the state of the art in many machine-learning problems, including computer vision, speech recognition, natural language processing, and audio recognition. Despite their success, however, there is no satisfactory mathematical theory that explains the functioning of such algorithms. Indeed, a common critique is that deep-learning algorithms are often used as black box, which is unsatisfactory in all applications for which performance guarantees are critical (e.g., traffic-safety applications). The purpose of this project is to increase our theoretical understanding of deep neural networks. This will be done by relying on tools of information theory and focusing on specific tasks that are relevant to computer vision.
PI: Giuseppe Durisi, Electrical Engineering, Chalmers


Software Engineering for AI/ML/DL

The development and evolution of software is quite fundamentally changing. Whereas earlier software was exclusively built based on requirements, in more recent years we see that there are three distinct approaches to the creation of software, i.e. requirements-driven, outcome-driven and AI-driven.
PI: Jan Bosch, Computer Science and Engineering, Chalmers


Verification of Machine Learning Algorithms (Vermillion)

Investigate if new methods of testing of cyber-physical systems being developed in our VR Researchv Environment [SyTec,CSE18] can be adapted to work on neural networks. Study applications in ML based Natural language translation (where errors are everywhere, and grouping them is what is needed). Study testing and formal verification of ML based autonomous driving in collaboration with RiSE Viktoria. Study testing in the context of machine learning based software development (see Erik Meijer’s recent talk at Chalmers; Meijer has agreed to be an advisor on the project). The postdoc will likely concentrate on one of these application domains, depending on their background.The long term goal is to develop methods to design neural networks and machine learning based software with guaranteed properties.
PI: Mary Sheeran, Computer Science and Engineering, Chalmers


Vision and machine learning for collaborative robotics (ViMCoR)

In this project we will develop the vision capabilities of collaborative robots by using machine learning methods. In manufacturing, of cars and trucks, industrial robots have been mainly used during the body in white stages (i.e. to do welding of the main body components).
PI: Bengt Lennartson, Electrical Engineering, Chalmers


Published: Thu 16 Jan 2020.