imec.AI-labs Research Team

Core Research Leadership

Summary: Expert in sequential learning, adaptive policies, and “agentic AI.”
Past Experience: Previously a Full Professor at Sorbonne University (LIP6) (on sabbatical). He brings significant industry experience from Meta (FAIR), Ubisoft, and the French AI startup “H Company”.

Summary: Bridges the gap between GenAI and physical hardware/sensing.
Past Experience: A long-term imec leader, he manages the intersection of chip-level constraints and deep learning. His work focuses on end-to-end AI solutions for autonomy (automotive and robotics). He founded imec Germany and raised 65+M EUR in competitive funds and grants.

Summary: Leading funding models, strategic partnerships and venture strategy for the lab.
Past Experience: Holds PhD in Biomedical Sciences (KU Leuven). Venture builder & tech start-up leadership in international setting (imec.istart, imec.venturing & Yesse Technologies). Experienced in shaping & driving strategic initiatives (imec, imec.health).

Summary: A heavyweight in Multi-Agent Reinforcement Learning (MARL) and Game Theory.
Past Experience: Formerly a research lead at Google DeepMind (Game Theory and Multi-Agent teams) and a Professor at the University of Liverpool. He is a pioneer in using evolutionary game theory to model AI learning dynamics.

Summary: Everything you need, Franciska can do it !
Past Experience: Francisca Vanheusden is a highly dedicated professional with over 28 years of experience at imec, where she has progressed into her current role as Executive Assistant.

Summary: Researching how agents “invent” goals (autotelic RL) and use language as a tool for cognition.
Past Experience: PhD from Inria, followed by a postdoc at EPFL and a Research Scientist role at InstaDeep.
Notable Publication: Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration

Summary: Specialist in Continual Learning and preventing “catastrophic forgetting.”
Past Experience: PhD from the Institut Polytechnique de Paris and postdoctoral research at Mila under Irina Rish.
Notable Publication: Continual Pre-Training of Large Language Models: How to (re)warm your model?

Summary: María Santos is a Research Scientist specializing in control theory and machine learning to design scalable, decentralized algorithms for multi-agent systems.
Past Experience: She received her Ph.D. from the Georgia Institute of Technology and was a postdoctoral researcher at Princeton University. Prior to joining imec, she was a researcher and a member of the founding team at H Company.
Notable Publication: Coverage control for multirobot teams with heterogeneous sensing capabilities

Summary: Expert in reinforcement learning and multi-agent systems.
Past Experience: Formerly a professor at the VUB AI Lab and a research lead at Secondmind.io.
Notable Publication: Soft Q-Learning with Mutual-Information Regularization

Summary: Specialist in neuroevolution and evolutionary computation.
Past Experience: PhD from the University of Liverpool (2023). He was the first dedicated Research Scientist for imec’s Frontier AI team in Paris.
Notable Publication: Manifolds & Memory: Improving the Search Speed of Evolutionary Algorithms

Summary: Working at the nexus of multi-agent reinforcement learning (MARL), social psychology, and behavioral economics.
Past Experience: Research background at IIIA-CSIC (Spanish National Research Council), applying physics and math modeling to human teamwork challenges.
Notable Publication: Grounded Predictions of Teamwork as a One-Shot Game

Summary: Expert in ML systems, High Performance Computing and AI for Science.
Past Experience: PhD in Statistical Physics with work experience at Berkeley Lab and Duke university. Formerly, lead of AI Models group at imec, a professor at the IIT Hyderabad. His work led to Imec.kelis, one of imec’s commercial tools: https://www.imec-int.com/en/expertise/compute-system-architecture/imeckelis.
Notable Publication: Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference

Summary: An expert in budget-aware and memory-efficient machine learning.
Past Experience: PhD from Sorbonne University and Co-Founder/CTO of Reveality.
Notable Publication: Learning Time/Memory-Efficient Deep Architectures With Budgeted Super Networks

Summary: A computer Vision expert focused on representation learning and image generation.
Past Experience: PhD from KU Leuven (ESAT-PSI); his work was recognized as an ICIP Best Paper Award finalist.
Notable Publication: Unpaired Image-To-Image Shape Translation Across Fashion Data

Summary: An expert in applying insights from human (visual) cognition to deep learning.
Past Experience: PhD from KU Leuven, with research training at MIT CSAIL and Brown University.
Notable Publication: Computing a human-like reaction time metric from stable recurrent vision models
Personal Page: https://loregoetschalckx.github.io/

Summary: Medical-AI expert focusing on radiotherapy and clinical deep learning applications.
Past Experience: Supported by an FWO PhD fellowship, she developed automated systems for cancer treatment workflows.
Notable Publication: Benefits of deep learning for delineation of organs at risk in head and neck cancer

Summary: PMTS (Principal Member of the Technical Staff) Data & AI at imec; co-founded and led the ExaScience Life Lab, a partnership for HPC in life sciences. Focus on reducing cost and latency in pre- and post-sequencing pipelines and on industrializing health-data analysis (including federated and distributed learning in sensitive settings).
Past Experience: Computer science at Brussels Free University; Researcher (Parallel Programming Languages) there; Partner & CTO at MediaGenix; Group Director “Digital Components” at imec (from ~2004); co-founder and director of the ExaScience Life Lab (from ~2011).
Notable Publication: Comparing ease of programming in C++, Go, and Java for implementing a next-generation sequencing tool (Evolutionary Bioinformatics, 2019)

Summary: A leading expert in Multi-Objective Decision Making and Reinforcement Learning. He is renowned for his work on how AI can balance conflicting goals—such as efficiency versus safety—rather than optimizing for a single scalar reward.
Past Experience: Currently a Senior Researcher at Vrije Universiteit Brussel. He previously conducted influential research at the University of Oxford and the University of Amsterdam, authoring foundational surveys that defined the field of multi-objective agents.

Summary: Leads Infrastructure & Engineering for the lab.
Past Experience: An “ex-Googler” specialized in MLOps and the scalable compute architectures required for large-scale frontier AI experimentation.
Notable Role: Lead architect of the imec.AI-labs engineering pipeline.