Introduction to AI and Machine Learning for Biomedical Research

Dr. Axel-Jan Rousseau is an AI Expert for VAIA and a Postdoctoral Researcher at Hasselt University. Specializing in trustworthy computer vision, his work focuses on model calibration, ensuring AI systems provide reliable, statistically sound predictions.

He is also one of the coordinators for the Data Science track for FLAMES and develops AI-driven decision support systems for biomedical imaging and MS diagnosis. He holds a PhD in AI (2024) and Master's degrees in both AI and Industrial Engineering.

I am Christos Chatzichristos, currently a post-doctoral researcher at KU Leuven. My educational background revolves around electrical and computer engineering, with a specialization in Biomedical Applications and an emphasis on signal processing during both my Master's and Ph.D. studies. During my doctoral journey, I witnessed the profound impact of neural networks on the field of signal processing, marking the inception of my foray into the realm of AI applications. I hold a strong belief in fostering broad interdisciplinary collaborations, as I believe that research today cannot thrive in isolation within a single domain. Artificial intelligence stands as a potent tool to expedite healthcare research. However, to truly harness its potential, we must bridge the gap by facilitating healthcare professionals' understanding of fundamental AI concepts, just as they aid biomedical engineers in unraveling the mysteries of the human body. So, here's to using AI to accelerate healthcare research while ensuring that we all speak the same language – whether it's the language of algorithms or the language of anatomy!

Celine Vens is professor at KU Leuven campus Kulak and leading the machine learning and AI subgroup at itec. Her main research interest is the development of machine learning algorithms for applications in health and education. She focuses on non-standard supervised and semi-supervised learning tasks, such as multi-output prediction, time-to-event prediction and interaction prediction. Besides research, she is teaching several courses at the Faculty of Medicine.

Dr Dirk Valkenborg has an extensive academic and research background. He received his M.Sc. in Electrotechnical and Mathematical Engineering in 2004 from K.U.Leuven and his Ph.D. in Mathematics and Statistical Bioinformatics from the University of Hasselt in 2008, followed by an M.Sc. in Biostatistics in 2009. He worked as a postdoctoral researcher at K.U.Leuven and later joined VITO as a corporate scientist. He co-founded the Center for Proteomics at the University of Antwerp and focuses on statistical bioinformatics and data analysis in 'omics' research. Since 2015, he has been a professor at the University of Antwerp and is currently a professor in bioinformatics at Hasselt University, specializing in computational methodologies for 'omics' data analysis and data integrity.

Prof. Pieter Libin graduated in 2014 at Vrije Universiteit Brussel in Informatics and obtained his PhD in Computer Science at VUB in 2020. After his PhD, he held a postdoctoral position funded by the Flemish science foundation at Hasselt university, at the department of data science. Since October 2021, Pieter is active as an assistant professor at the AI lab of the Vrije Universiteit Brussel. His research involves the use of machine learning to support decision makers by combining machine learning techniques with realistic simulation models and concerns theoretical and applicational AI with a focus on reinforcement learning and Bayesian modeling. He has a broad experience in modeling and analyzing real-world systems ranging from virus diversity, epidemic emergencies, and renewable energy providers. Pieter is a member of the Jonge Academie, an association of young top researchers and artists with an engagement to policy, society, research, and the arts. Pieter is a board member of the Benelux Association for Artificial Intelligence.

MaryBeth Defrance is a fourth year PhD student at Ghent University. Her research focusses on fairness in AI, with a special interest in how to quantify it. She is keen to tackle this technological challenge from a societal perspective. She has had papers published in the top conferences of Computer Science such NeurIPS, ICLR, and FAccT. Besides research she is also active as a teaching assistant in the first bachelor year of engineering and gives an annual guest lecture on AI fairness for the students of computer science engineering.

Expertise: Fairness in AI

Yvan Saeys obtained his PhD in computer science from Ghent University. After spending time abroad at the University of the Basque Country (Spain) and the University of Lyon (France) he returned to Belgium and established the Data Mining and Modeling for Biomedicine (DAMBI) group at the VIB Center for Inflammation Research (IRC) in Gent. As of 2015, he is a professor at Ghent University and a principal investigator (group leader) at VIB, where he is heading an interdisciplinary research team of 21 people, consisting of mathematicians, computer scientists, engineers and bioinformaticians. The Saeys lab studies the design and application of novel data mining and machine learning techniques for high-dimensional single-cell omics data, including methods to model cell developmental trajectories and intercellular communication. At the methodological level, the lab studies the robustness and interpretability of machine learning models.

I'm Alexander and I'm a data science team lead at BioLizard. Our goal is to transform the biomedical data of our clients into actionable insights. Using data mining, data visualisation, bioinformatics pipeline development, software development, and the construction of ML and AI models, we support biotech and pharmaceutical companies and research institutes in their (pre)clinical research and development efforts.

Axel Faes is a postdoctoral researcher in Biomedical Data Sciences at Hasselt University, where he is technical machine learning lead and scientific coordinator of the Flanders AI Research Program’s Real World Evidence use case. He holds M.Sc. degrees in Computer Science Engineering and Artificial Intelligence from KU Leuven, where he also completed a PhD in Biomedical Sciences on finger movement decoding using tensor regression modeling. His work bridges computational neuroscience, brain-computer interfaces, and federated learning for multi-modal biomedical and health data, contributing to the development of innovative AI methods that advance biomedical research and clinical practice as part of #LeadingAIinHealth.

Dr. ir. Thomas De Cooman obtained his Master of Science in Computer Science from Ghent University and later earned his PhD in Engineering Science from KU Leuven, with a dissertation on Epileptic Seizure Detection in a Home Environment. His doctoral research focused on detecting epileptic seizures through heart rate analysis using signal processing and machine learning techniques. Following his PhD, he continued as a Postdoctoral Researcher at KU Leuven and later joined Cerence as a Research Scientist. Since 2022, he has been working as a Data Scientist at FibriCheck, where he is responsible for developing medical-grade deep learning applications for photoplethysmography (PPG) data.

Wouter Bogaert works as a civil engineer at Orsi Academy in Melle, one of the largest robotic surgery training centers in Europe and a center of expertise for all aspects of medical technology. He is also a doctoral researcher in computer science at ID-lab (Ghent University, IMEC), where his focus is primarily on natural language processing, computer-aided surgery, and multimodal systems.