We work on four major research areas:
Machine Learning Tools for Behavior Analysis We develop computer vision and machine learning tools for the analysis and quantification of animal and human behavior.
AI4Science with Language Models We work at the interface of Natural Language Processing (NLP) and behavioral analysis to create visual-language models for behavioral understanding.
Modeling of Sensorimotor Learning and Control We develop normative theories of neural systems to elucidate their underlying principles. In particular we are interested in reverse-engineering the mammalian sensorimotor pathway.
Brain-Inspired Motor Skill Learning Watching any expert athlete reveals how remarkably brains have mastered elegant body control—an astonishing feat given the inherent challenges of slow biological hardware and the sensory and motor latencies that impede control. Understanding how the brain achieves skilled behavior is one of neuroscience’s core questions, which we tackle through modeling & experiments using Reinforcement Learning and Control Theory.
We have many projects for each research direction that can be made suitable for bachelor projects, lab immersions, and master’s projects.
If any of those topics excite you, check out our papers to get more background. You can find code and details for most of our projects on GitHub: https://github.com/amathislab Feel free to propose projects in the scope of the topics above. We also have a list of specific MA theses projects that you can only accessible with your EPFL email. Check them out!
Please fill out this Google form, if you are interested!
Furthermore, together with Prof. Mackenzie Mathis’ lab, we are actively developing DeepLabCut and other tools. We have multiple projects available ranging from developing new features (e.g. improved active learning, novel GUI features), to software engineering (profiling and unit tests).