× Dashboard Register Log in
ICT Open 2022



Nele Mentens

Wednesday 6 april | 17:45 - 18:30

Nele Mentens is a professor at Leiden University and KU Leuven. She was a visiting researcher at Ruhr University Bochum in 2013 and at EPFL in 2017. Her research interests are in the field of configurable computing and hardware security. She was/is the PI in around 20 finished and ongoing research projects with national and international funding. She serves as a program committee member of renowned international conferences on security and hardware design, such as NDSS, USENIX Security Symposium, ACM CCS, Asiacrypt, CHES, ESORICS, DAC, DATE, FPL and ESSCIRC. She was the general co-chair of FPL'17 and the program chair of EWME'18, PROOFS'18, FPL'20, CARDIS'20, RAW'21 and VLSID'22. She is (co-)author in over 100 publications in international journals, conferences and books. She received best paper awards and nominations at CHES'19, Asian HOST'17 and DATE'16. Nele serves as an associate editor for IEEE Transactions on Information Forensics and Security, IEEE Circuits and Systems Magazine, and IEEE Security and Privacy.


Security challenges and opportunities in emerging device technologies: a case study on flexible electronics

While traditional chips in bulk silicon technology are widely used for reliable and highly ef´Čücient systems, there are applications that call for devices in other technologies. On the one hand, novel device technologies need to be re-evaluated with respect to potential threats and attacks, and how these can be faced with existing and novel security solutions and methods. On the other hand, emerging device technologies bring opportunities for building the secure systems of the future. This talk gives an overview of the minimal hardware resources that are needed to build secure systems and discusses a case study on flexible electronics on plastics.


Karl Tuyls

Thursday 7 April | 09:30 - 10:30

Karl Tuyls (FBCS) is a team lead at DeepMind, an honorary professor of Computer Science at the University of Liverpool, UK, and a Guest Professor at the University of Leuven, Belgium. Previously, he held academic positions at the Vrije Universiteit Brussel, Hasselt University, Eindhoven University of Technology, and Maastricht University. Prof. Tuyls has received several awards with his research, amongst which: the Information Technology prize 2000 in Belgium, best demo award at AAMAS’12, winner of various Robocup@Work competitions ('13, '14), and he was a co-author of the runner-up best paper award at ICML’18. Furthermore, his research has received substantial attention from national and international press and media, most recently his work on Sports Analytics featured in Wired UK. He is a fellow of the British Computer Society (BCS), is on the editorial board of the Journal of Autonomous Agents and Multi-Agent Systems, and is editor-in-chief of the Springer briefs series on Intelligent Systems. Prof. Tuyls is also an emeritus member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems.


Multi-agent learning: from games to sports.

Recent progress in Artificial Intelligence has led to some great successes varying from games like Go and Chess to robotic footballing agents, and have made the practical utility of reinforcement learning and game theory in real-world multi-agent systems like for example sports a genuine reality. These new techniques, combined with a huge increase in data collection, have also opened unprecedented possibilities for AI to be applied in various team and individual sports, including football. In this talk I will review some of the challenges associated with predictive and prescriptive football analytics at the intersection of machine learning, game theory, and computer vision, and illustrate some of the latest results applying game-theoretic and multi-agent learning models in the sports domain.




Hinda Haned

Thursday 7 April | 13:30 - 14:30

Hinda Haned is an endowed professor of data science at the university of Amsterdam, where she is scientific co-director of the Civic AI lab. Her research focuses on developing solutions for best practices for safe and responsible data science. Hinda obtained her PhD in applied statistics from the university of Lyon (France) in 2010. Some of her most recent work revolves around explaining why a model makes errors in forecasting tasks and investigating whether explaining these errors increases user-trust


Defining and mitigating algorithmic bias: a practitioner's perspective

Modern day decision-making systems based on machine learning algorithms have an increasing impact on our lives in diverse domains such as health, mobility and education. While these systems can be useful, they can also produce erroneous or biased outcomes that can be harmful to individuals and communities, often without the possibility for meaningful recourse or feedback. To mitigate these issues, responsible data science has become an important area of focus for many data science practitioners and researchers in the past few years. As one of the focus areas, algorithmic solutions are being regularly developed to mitigate or fix biases. But how can we detect and measure bias with the help of these technical solutions? And what does it mean to fix bias? In this talk, I will discuss different definitions of bias, and bias mitigation through so-called ‘fairness algorithms’. Drawing from practical examples, I will argue that the most fundamental question we are facing as researchers and practitioners, is not how to fix bias with new technical solutions, but whether we should be designing and deploying potentially harmful automated systems in the first place.

naar boven