imec.AI-labs Research Team

Core Research Leadership

Ludovic Denoyer (Lead of imec.AI-labs)

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”.

Ben Stoffelen (Head of Applied AI)

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.

Elke Giets (Lead Strategy & Development)

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).

Karl Tuyls (Research Advisor)

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.

Foundational & Agentic AI Researchers

Tristan Karch

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

Timothée Lesort

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?

Maria Santos

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

Peter Vrancx

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

James Butterworth

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

Alejandra López de Aberasturi Gómez

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

Joyjit Kundu

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

Tom Veniat

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

Kaili Wang

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

Lore Goetschalckx

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/

Siri Willems

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

Wilfried Verachtert

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)

Diederik M. Roijers

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.

Anthony Kobanda

Summary: Deep Reinforcement Learning researcher focused on Continual RL, Goal-Conditioned RL, and Offline RL for adaptive agents.
Past Experience: PhD in Deep RL through a collaboration between Inria (Scool team) and Ubisoft, supervised by Odalric-Ambrym Maillard and Rémy Portelas. Graduate of École Polytechnique.
Notable Publication: Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning

Federico Baldo

Summary: Researcher specializing in causal inference and causal structure learning, hybridizing optimization and machine learning techniques.
Past Experience: Postdoctoral researcher in Causal Inference at INSERM (Paris). Previously a PhD student at the University of Bologna, working on methods combining optimization and machine learning.
Notable Publication: Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases

Infrastructure & Systems

Philippe Modard (Mod)

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.