Flanders AI Research Day

Pieter Delobelle is currently an AI engineer at Aleph Alpha focussing on inference, alignment and fairness of large language models. Previously, he was a postdoctoral researcher at KU Leuven with a specialization in bias and fairness in large language models and he also developed the state-of-the-art Dutch language model RobBERT. He obtained a Masters in Engineering Technology from KU Leuven in 2018 at the Ghent Technology Campus, Belgium. Subsequently, he obtained an Advanced Masters in Artificial Intelligence from KU Leuven, and he stayed on for a Ph.D. in Computer Science under Professor Bettina Berendt and Professor Luc De Raedt, which he started in 2019 and defended in 2023, titled 'Towards fairer foundation models'. His current research on bias and fairness in large language models led to research visits at Weizenbaum Institute and Bocconi University, as well as an internship at Apple Inc.

Currently I am an assistant professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. I'm co-leading the Text-to-Knowledge research cluster with prof. Chris Develder, where we work on natural language processing (NLP) in general, for applications in several domains (the media, biomedical applications, and economics and law). Our areas of focus in NLP are information extraction, conversational agents, and representation learning in general.

Given our expertise in neural sequence modeling, we recently started looking into the use of protein language models for drug design. Give it another year before we have something decent to show here ;). My other interests include energy-based models, combining knowledge and neural networks, and synthetic data generation.

José Oramas is an Assistant Professor at the Internet Data Lab (IDLab) a joint research lab between the University of Antwerp and IMEC. He received his Ph.D. at the Center for Processing Speech and Images (ESAT-PSI)of KU Leuven in April 2015. Earlier he received his engineering degree from Escuela Superior Politecnica del Litoral in Ecuador. During his Ph.D. he conducted research on understanding how groups of elements from the image (objects, object-parts, image regions, trajectories, etc.) interact and how the relationships between them can be exploited to improve artificial visual perception problems. This fueled his interest towards investigating exploratory/explanatory models that can identify informative intermediate representations and use them as means to justify the predictions that they make.

Research Interests: Representation Learning, Interpretability and Explainability, Multiple Instance Learning, Machine Learning, Deep Learning and Computer Vision

PhD Student at UGent

Machine Learning, Artificial Intelligence, AI safety.

Assisted in linked open data research at IDLab between March and December 2017. Helped develop an intrusion detection system using deep learning on system log data, between July and September 2018 and Robovision.

Jefrey Lijffijt is a professor of Data Science at Ghent University - IDLab and, together with Tijl De Bie , leads the AI & Data Analytics research group. The AI & Data Analytics research group designs, implements, and analyzes algorithms and systems to extract knowledge and insights from data. Nearly all of our tools and articles are open source and open access. Jefrey Lijffijt chairs the AI working group at the Faculty of Engineering and Architecture and is a member of the AI core group at Ghent University. He actively contributes to (Gen)AI education at Ghent University and previously at VAIA.

His expertise includes artificial intelligence, machine learning, knowledge discovery, data visualization, data mining, data exploration, visual analytics, computational complexity analysis, algorithm design, information theory, statistical hypothesis testing, interactivity, and tools and applications. For an overview of recent research, see https://aida.ugent.be/

Dr. Peter Karsmakers received the M.Sc. degree in artificial intelligence in 2004 and the PhD degree from the Department of Electrical Engineering, KU Leuven, in 2010. From 2010 to 2013, he was a post-doctoral researcher in the MOBILAB research team from Thomas More. From 2013 to 2018, he worked as a post-doctoral researcher at KU Leuven where he co-founded the ADVISE research team. Currently, he is an Associate Professor within the Computer Science Department in the DTAI section at KU Leuven and is a member of the Leuven.AI institute. Since 2022, he is a PI of Flanders Make@KU Leuven. His research interests include designing machine learning algorithms that consider application-specific constraints. These can, for example, relate to the computing platform on which the machine learning algorithm will be deployed on, to the need of physical consistency between model variables, to the lack of data annotation or to other application related specifications. He worked on diverse projects, mostly in collaboration with industrial partners, that involve monitoring of both humans as machines using sensors such as microphones, accelerometers and radars.

Assistant Professor at KU Leuven, Declaratieve Talen en Artificiële Intelligentie (DTAI)

Luc De Raedt is full professor at the Department of Computer Science, KU Leuven, and director of Leuven.AI, the newly founded KU Leuven Institute for AI. He is a guestprofessor at Örebro University in the Wallenberg AI, Autonomous Systems and Software Program. He received his PhD in Computer Science from KU Leuven (1991), and was full professor (C4) and Chair of Machine Learning at the Albert-Ludwigs-University Freiburg, Germany (1999-2006). His research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his work on probabilistic and inductive programming.

Toon Calders is professor at the computer science department of the University of Antwerp in Belgium. He is an active researcher in the area of data mining and machine learning.

He is editor of the data mining journal, and has been program chair of a number of data mining and machine learning conferences, including ECML/PKDD 2014 and Discovery Science 2016.

Toon Calders was one of the first researchers to study how to measure and avoid algorithmic bias in machine learning and is one of the editors of the book “Discrimination and Privacy in the Information Society – Data Mining and Profiling in Large Databases”, published by Springer in 2013.

He is currently leading a group of 6 researchers studying theoretical aspects of fairness in machine learning, as well as looking into practical use cases in collaboration with Flemish tax authorities, public welfare organizations, and an insurance company.

Tias Guns is an AI researcher working at the intersection of data science and constraint optimisation. Constraint solving is a key technology for solving scheduling, planning and configuration problems across all industries.

Tias' expertise is in the hybridisation of machine learning systems with constraint solving systems, more specifically building constraint solving systems that reason both on explicit knowledge as well as knowledge learned from data. For example learning the preferences of planners in vehicle routing, and solving new routing problems taking both operational constraints and learned human preferences into account; or building energy price predictors specifically for energy-aware scheduling, and planning maintenance crews based on expected failures.

This has the potential to capture both the problem structure and more subjective aspects such as human preferences and changing environments. The ultimate goal is to make constraint solving techniques more intelligent and human-aware.

Willem Waegeman is an associate professor at Ghent University, and a member of the research unit Knowledge-based Systems (KERMIT) of the Department of Data Analysis and Mathematical Modelling. His main interests are machine learning and bioinformatics. Specific interests include multi-target prediction problems, uncertainty quantification, sequence models and deep learning.

Willem Waegeman is an author of more than 100 papers of peer-reviewed journals and conferences, and his work has won several prizes. In recent years he has served on the program committees of leading conferences in his field (ICML, NIPS, ECML/PKDD, AAAI, AISTATS, IJCAI, etc.).

Since 2008 he is lecturing a machine learning course in Ghent. Since 2014 he is also lecturing several introductory math courses in the first bachelor (> 500 students per year). Willem Waegeman is currently supervising eight PhD-students.

Prof. Steven Latré, is VP R&D AI and Algorithms and is leading the algorithmic research at imec, with a particular focus on machine learning. imec is the worldwide leading R&D hub for nano-electronics and digital technologies. He leads an interdisciplinary team of 1,000 researchers at the intersection of AI accelerators, advanced sensors and machine learning. This results in application domains such as compute and connectivity systems, life science technologies, automotive, greentech and smart industries. Next to this, he is also a part-time professor at the University of Antwerp.