Meet the team

Consortium as a whole

Epistemic AI’s consortium uniquely combines 3 academic partners from 3 different countries, ensuring a large network for dissemination and significant geographical outreach. Whereas each Partner excels in one or more areas (e.g. KUL in robust optimisation), the consortium assembles all the crucial ingredients (technical expertise, business and market experience, expertise in end-user domains) to make the introduction of an Epistemic AI finally possible. Several sub-areas are suitably covered by multiple units, such as uncertainty theory (KUL, OBU), AI and reinforcement learning (OBU, TUD), autonomous driving (TUD, OBU). The partners will also prove science-to-technology transfer capability in the intended technology cases, and a living lab for innovation in the field of AI for autonomous driving, thanks to their proven expertise and past track record on this. The project leader (OBU) has prior working relations with all the partners, and demonstrable past experience to qualify to coordinate the project. 

Oxford Brookes University is consistently recognised as one of the top modern universities in the United Kingdom, and was ranked the best new UK university for both teaching and research, and the only UK university in the Top 50 under 50 World Ranking. The School of Engineering, Computing and Mathematics (ECM) is the result of the fusion of the late Departments of Computing and Communication Technologies, and of Mechanical Engineering. The School comprises some 60-65 faculty members and around 15 research groups, active in a variety of fields, including in Artificial Intelligence, Cognitive Robotics, Autonomous Driving and Augmented Reality. The School is part of the Faculty of Technology, Design and Environment. 

KU Leuven is a research-intensive, internationally oriented university that carries out both fundamental and applied scientific research. It is strongly inter- and multidisciplinary in focus and strives for international excellence. The Department of Mechanical Engineering of the KU Leuven consists of 25 faculty members, 50 postdoc researchers and 250 PhD researchers. Research is organized around six major clusters: advanced manufacturing, mechatronics, operational research, travel and logistics, biomedical science, robotics and control. 

The Delft University of Technology (TU Delft) is the oldest, largest and most comprehensive university of technology in the Netherlands. With over 24,000 students and around 3,000 scientists (including 300 full professors), it is an establishment of national importance and of significant international standing. The University collaborates on a structural basis with other international education and research institutes and has partnerships with governments, branch organizations, numerous consultancies, industry partners and companies from the small and medium business sectors. TUD ranks 54th on the 2018 QS World University Rankings and 51-60 on the 2017 The World Reputation Rankings list. 

The Epistemic AI team

The key people associated with the project are shown below.

Professor of Artificial Intelligence

Oxford Brookes University

Fabio Cuzzolin received a laurea degree magna cum laude in Computer Engineering from the University of Padua, Italy, in 1997. He was awarded a PhD by the same institution in 2001 for the thesis Visions of a generalized probability theory. After conducting research at Politecnico di Milano, the Washington University in St Louis, UCLA and INRIA Rhone-Alpes, he joined Oxford Brookes University (UK) where he is currently a Professor of Artificial Intelligence and the Director of the Visual Artificial Intelligence Laboratory. He is a world expert in the theory of belief functions, and the author of about 110 peer-reviewed publications.

Andrew Bradley

Senior Lecturer

Oxford Brookes University

Andrew Bradley is the head of the Autonomous Driving and Human-Vehicle Interaction Research Group at Oxford Brookes University, and lectures in Automotive and Motorsport Engineering. His research interests span across vehicle dynamics and control systems, vehicle modelling and simulation, driver behavioural analysis, Advanced Driver Assistance Systems (ADAS), and fully autonomous vehicles. Andrew is a Visiting Professor in Electric and Autonomous Vehicles at the Universidad de los Andes, Bogota, Colombia, and a member of the steering committee for the Institute of Mechanical Engineers’ Formula Student: Artificial Intelligence competition.

Associate Professor

KU Leuven

Hans Hallez is active in the field of computer sciences  and sensors  at the Department of Mechanical Engineering Campus Bruges-KU Leuven. Since 2014, he has been Assistant Professor at Faculty of Engineering Technology at the KU Leuven where he leads the Networked Embedded Systems team at the M-group research group at the KU Leuven Campus Bruges. He is strongly affiliated with the Distrinet research group of the Department of Computer Science.  In 2015, he was a visiting researcher at the Embedded Systems research group at the University of Freiburg under the supervision of prof. Frederik Van Laerhoven.

Professor in the Faculty of Engineering Technology

KU Leuven

David Moens received his PhD in 2002 and is currently an Associate Professor at KU Leuven, specialised in interval methods for uncertainty quantification. He coordinates the Reliable and Robust Design research group at campus De Nayer. He is the author of more than 200 different publications, and has an h-index of 27. He has/is supervised/supervising 11 PhD students and 2 post-docs. He is a member of the editorial board of Mechanical Systems and Signal Processing and, since 2018, a member of the executive committee of the Belgian National Committee on Theoretical and Applied Mechanics (BNCTAM). 

Senior Researcher

KU Leuven

Keivan Shariatmadar is active in the field of imprecise probability theory, optimisation under uncertainty, imprecise decision theory, system engineering, Human/Machine modelling and interactions. He has both an academic and industrial track record in research and innovation. He owns several patents and is the author of books and papers in the fields of applied mathematics, automotive, mechatronics, healthcare and industry 4.0, which are now being deployed as products/research paths. 

Associate Professor of Socio-Technical Algorithmics

TU Delft 

Neil Yorke-Smith focuses on intelligent decision making in complex socio-technical situations, with a particular interest in agent-based methodologies and behavioural factors in automated planning and scheduling. He directs the STAR Lab at TU Delft. Among the spin-out  companies from his research was Siri, subsequently acquired by Apple. Yorke-Smith was Programme co-chair of AAMAS 2020 and of IAAI 2021. He is a Senior Member of AAAI and of ACM. PhD degree in Artificial Intelligence (2004) from Imperial College London on uncertainty in constrained optimisation.


Associate Professor in Algorithmics

TU Delft

Matthijs Spaan focuses on one of the core questions of Artificial Intelligence: ​ how can an agent autonomously take the right decisions? He has studied several variations of this question, all revolving around the notion that in order to be successful, an intelligent agent needs to be equipped with smart algorithms that properly handle uncertainty. He is motivated by the potential that AI has to address some of the major challenges of our times, such as realizing the energy transition and revolutionizing transportation through self-driving vehicles.

Assistant Professor in Intelligent Vehicles 

TU Delft

Julian Kooji is Assistant Professor at TU Delft since 2016, working on deep learning and probabilistic models to infer and predict traffic situations from multi-modal sensor data. He obtained his PhD degree in 2015 at the University of Amsterdam. In 2013 he worked at Daimler AG, Germany, on online path prediction of vulnerable road users in highly automated vehicles. He publishes in leading conferences and journals (e.g. ECCV,ICRA,IV,CVIU,IJCV,T-PAMI,T-ITS), acts as Associate Editor and organizes the Unsupervised Learning workshop at IEEE IV, and reviews for NeurIPS, CVPR, ICRA, PR, IV and T-ITS among others.

Research Fellows

Maryam Sultana

Oxford Brookes University

Maryam Sultana is a Research Fellow at Visual Artificial Intelligence Laboratory (VAIL), Oxford Brooks University, UK. Previously she worked as a Research Associate at Mohamed Bin Zayed University of Artificial Intelligence (the world’s first specialized research-based AI university), United Arab Emirates. She received her MSc and MPhil degrees in electronics from Quaid-i-Azam University Pakistan in 2013 and 2016, respectively. She completed her doctoral degree with the Virtual Reality Laboratory at the School of Computer Science and Engineering, Kyungpook National University, South Korea in 2021. She has an active collaboration with the MIA Laboratory, University of La Rochelle, La Rochelle, France, and Information Technology University, Pakistan. Her research interests include deep learning, generative adversarial networks, background modeling, foreground detection, and domain generalization.

Salman Khan

Oxford Brookes University 

Salman Khan is a Research Fellow at the Visual Artificial Intelligence Laboratory at Oxford Brookes University. He earned his PhD in 2023 under the supervision of Prof. Fabio Cuzzolin at the same institution. Before that, he completed his Master’s in 2020 at Sejong University in South Korea. His research interests include action and activity recognition/detection, epistemic uncertainty, continual learning, surgical robotics, and fire and smoke detection/segmentation.

Guopeng Li

TU Delft 

Guopeng Li is a Postdoctoral Researcher at the TU Delft Mechanical Engineering faculty, the Netherland. He received the B.S. degree in Physics from Nanjing University (China) in 2015 and the Engineering Diploma and Master’s degree in Applied Mathematics from ENSTA-Paris (France) in 2018. He finished his PhD at TU Delft at the Civil Engineering faculty, department of Transport and Planning (the Netherlands) in 2023. In his research, he works on the application of AI and machine learning for autonomous driving and intelligent transportation systems, especially with a critical look at modelling uncertainty and explainable AI.

PhD Researchers

Shireen Kudukkil Manchingal

Oxford Brookes University

Shireen Kudukkil Manchingal works on modeling epistemic uncertainty in machine learning models and their application in safety-critical domains such as autonomous driving and healthcare. This involves optimization under epistemic uncertainty and developing an epistemic learning theory for random sets using the theory of belief functions.

Muhammad Mubashar

Oxford Brookes University

Muhammad Mubashar works on modeling epistemic uncertainty under unsupervised learning. This involves quantifying epistemic uncertainty in traditional clustering as well as working on generative approaches which are truly robust to real-world uncertainty, ultimately laying the foundations for a general theory of robust unsupervised learning.

Kaizheng Wang

KU Leuven

Kaizheng Wang's research focuses on epistemic uncertainty modeling and optimization in supervised learning under advanced uncertainty theory to achieve robust prediction and decision-making under uncertainty in safety-critical applications. 

Ghifari Adam Faza

KU Leuven

Ghifari Adam Faza (Adam) works on epistemic uncertainty in both supervised and unsupervised learning with a focus on their application in physics-informed machine learning. His research aims to improve the safety, robustness, and reliability aspects of ML-based physical systems modeling.

Moritz Zanger

TU Delft

Moritz Zanger's research focuses on epistemic uncertainty in deep reinforcement learning. In particular, He is interested in epistemic uncertainty quantification, propagation, and exploitation in model-free reinforcement learning algorithms. Moreover,  he is always happy to find creative ways of using uncertainty to improve algorithms in reinforcement learning and machine learning in general.

Pascal van der Vaart

TU Delft

Pascal van der Vaart works on quantifying epistemic uncertainty using ensembles of Neural networks, with a focus on their applications in deep Reinforcement learning. His current interests are approximate Bayesian inference with ensembles for Thompson sampling to achieve better exploration in difficult tasks.

Noah Schutte

TU Delft

Noah Schutte's research is focused on optimization under epistemic uncertainty. How to optimize more efficiently and accurately when uncertainty is present? When does appropriately modeling epistemic uncertainty affect optimal solutions? What is the impact of this on decision making?

Administrative Staff

Clarie Ferone

Oxford Brookes University


Naeemullah Khan

King Abdullah University of Science and Technology

Naeemullah Khan is a Computer Vision researcher, working as an Instructional Assistant Professor at King Abdullah University of Science and Technology. He was previously a Junior Research Fellow at Lady Margaret Hall, Univeristy of Oxford. He was part of Torr Vision Group at Univeristy of Oxford and Visual Artificial Intelligence Laboratory at Oxford Brookes University.

Calvin R. Hubbard

CapSen Robotics

Calvin R. Hubbard is a robotics engineer, working on enabling elegant movement for robots at CapSen Robotics. He was a PhD researcher at KU Leuven.