people

Nils Jansen

I am interested in various aspects of dependability and safety in AI, intelligent decision-making under uncertainty, and safe reinforcement learning. A key aspect of my research is a thorough understanding of the (epistemic or aleatoric) uncertainty that may occur when AI systems operate in the real world.

• • artificial intelligence
• • planning under uncertainty
• • POMDPs
• • safe reinforcement learning
• • formal verification

Thom Badings

My primary research interests are on the intersection between Engineering, Systems & Control, and Formal Verification. Currently, I am working on sequential decision making under uncertainty with correctness guarantees, with applications to autonomous and robotics systems, predictive maintenance, and electrical power systems.

• • systems and control
• • decision making under uncertainty
• • artificial intelligence
• • abstraction-based controller synthesis
• • predictive maintenance

Eline Bovy

My research interests are formal verification and decision-making problems. My current work focuses on uncertain partially observable Markov decision processes.

• • formal verification
• • decision making under uncertainty
• • POMDPs

Maris Galesloot

My primary research interests lie in sequential decision making in uncertain environments with partial observability. My work focuses on the intersection of a formal model-based approach and neural function approximation methods in reinforcement learning and aims to improve the scalability, applicability, and robustness of these algorithms in the real world.

• • model-based reinforcement learning
• • deep learning
• • online planning

Dennis Gross

I am a PhD student at the department of software science at Radboud University, working on the formal verification of neural network classifiers and policies under supervision of dr. Nils Jansen, dr. Guillermo Alberto Perez, and prof. dr. Frits Vaandrager.

• • formal methods
• • machine learning

Merlijn Krale

I am interested in model-based algorithms with strong theoretical foundations for complex decision-making processes. My current work focusses on partially-observable environments where agents have explicity control over their observations.

• • POMDPs
• • robustness
• • model abstractions

Christoph Schmidl

My research interests are safe reinforcement learning and formal verification applied to real-world scheduling problems. My current work focuses on robust scheduler learning with constrained interactions using reinforcement learning techniques applied to different shop types such as job shops and flow shops.

• • safe reinforcement learning
• • scheduling
• • robustness
• • AI
• • DevOps
• • formal verification
• • security

Marnix Suilen

My research interests are formal verification and learning in probabilistic systems such as (partially observable) Markov decision processes and variations that extend these models with uncertainty. Other interests include coalgebra (especially for probabilistic systems), (robust) convex optimization, and algorithms in general.

• • decision making under uncertainty
• • POMDPs
• • formal verification
• • artificial intelligence
• • offline reinforcement learning

Wietze Koops

I am interested in decision making problems from various perspectives. Currently, I am focussing on developing exact and approximate algorithms for solving Decentralized POMDPs. In general, I have broad interests including mathematics (in particular combinatorics, probability theory and optimization), operations research, machine learning and algorithms.

• • decision making
• • design of algorithms
• • probability theory
• • optimization
• • operations research
• • microeconomics
• • machine learning
• • decentralized POMDPs

Markel Zubia

I am interested in safe reinforcement learning, my current research focuses on the safe transfer of agents between environments with differing dynamics. Other research interests include computational dynamics, complexity theory, and automata theory.

• • safe reinforcement learning
• • robustness
• • adversarial machine learning
• • AI

Yannick Hogewind

ELLIS Excellence Fellow (2022 - 2023)

Next: Data Scientist at ABN AMRO

Thiago D. Simão

Postdoc (2022 - 2023)

Next: Assistant Professor at Eindhoven University of Technology

The motivation for my research revolves around making AI techniques more reliable, to enable their deployment in real-world applications. I focus on developing AI algorithms for scenarios with constrained interactions with an unknown environment.

• • decision making under uncertainty
• • safe reinforcement learning
• • offline reinforcement learning
• • probabilistic planning
• • AI

Zaharah A. Bukhsh

Postdoc (2020 - 2021)

Next: Assistant Professor at Eindhoven University of Technology

My research focus on developing tools and techniques to improve operational processes. The core research areas include business intelligence, business engineering and process engineering.

• • data-driven decision-making
• • predictive maintenance
• • deep reinforcement learning
• • business process monitoring and optimisation
• • self-supervised and transfer learning