Introduction to AI and Machine Learning for Biomedical Research

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/

Expertise: medische beeldanalyse, computer-ondersteunde diagnose and beeldgestuurde interventies

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.

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.

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.

Walter Daelemans is professor of Artificial Intelligence and Natural Language Processing (NLP) at the University of Antwerp. He helped pioneer the statistical and machine learning revolution in NLP in the nineties with the development of Memory-Based Language Processing and with work on the methodology of machine learning for language processing. He was awarded EurAI and ACL fellowships for this work, and has published influential work on text mining and knowledge extraction from biomedical, clinical, and social media text, and on stylometry and author profiling. With currently 32 supervised PhDs graduated and more than 400 co-authored publications he is one of the most prolific NLP researchers in the Low Countries. In addition, he has been involved in the creation of high profile valorization results with popular open-source software such as TiMBL and Pattern, and has been instrumental in the creation of several spin-offs (textkernel, textgain, fluent.ai).

Dr. ir. Ilse Vermeulen is a Bio-engineer in Cell and Gene Biotechnology with a PhD in Medical Sciences from the Free University of Brussels (VUB), which she obtained in 2012. During her doctoral studies, Ilse focused on prediction models and epidemiological studies in the clinical biology of type I diabetes, which provided her with extensive experience in data processing and writing research articles for peer-reviewed journals.

For the past few years, Ilse has been working as a project manager at the University of Applied Sciences Leuven-Limburg (UCLL), where she was responsible for the respective focus lines "Environment & Health" and "Technology Enhanced Care".

In April 2022, Ilse joined the Research Group of Biomedical Data Sciences of Liesbet Peeters as a Staff Member and Project Manager to support the MS Data Alliance. Additionally, she leads the follow-up of other projects within the group, e.g. EBRAINS. Ilse is a vigorous creator, project enabler, and adept at transforming real-world data into real-world evidence.

Expertise:
Prediction models, stakeholder management, ...

Kris Laukens, who acquired his PhD in 2003, is now a Full Professor in bioinformatics at the University of Antwerp's Adrem Data Lab. His research primarily focuses on developing data science and AI methods to turn biomedical data into actionable insights, supporting a range of (pre-)clinical research projects through innovative data analysis and collaboration with hospitals and research institutes. Laukens founded BIOMINA in 2011, a multidisciplinary hub uniting computational research, life sciences, and clinical expertise, now recognized as a core facility of the University of Antwerp. Additionally, he leads the Tech Transfer consortium "Precision Medicine Technologies" (PreMeT), focusing on converting technology into economic and societal value. In 2022, he received the FWO FNRS AstraZeneca Award for his work on human immune response heterogeneity and he has been acknowledged as a top young innovator in Antwerpen. Further, Laukens has founded two successful spin-off companies. In ImmuneWatch BV, he works on AI technology to make T cell repertoire data actionable in clinical applications.

Education:

KULeuven MSc Eng Computersciences - Programmatuur 1998

KULeuven PhD Medical Sciences - Computuational Neurosciences 2007

KULeuven LRD Masterclass Hi-Tech Entrepreneurship 2012

Experience:

KULeuven Postdoc Computational Neurosciences 2007 - 2014

UCLL Parttime Research Coordinator Zorgzame IT + partime teaching IT 2013 - 2018

UCLL Head of Research & Expertise Center Digital Solutions 2018 - 2023

UCLL Coordinator Technology Enhanced Healthcare & Social Welfare 2023 - Present

KULeuven IOF Council Member 2021 - 2025

Expertise:

Machine Learning, AI, Textmining, AI4Health, Data Engineering, R, SQL, ...

Postdoctoral researcher at Ghent University

As a Business Development Manager for Medical Imaging Innovation I define and execute go-to-market strategies for the adoption and growth of AI/ML in the medical imaging space with customers and partners in EMEA.

I strongly believe in the power of analytics and that they can help businesses gain an edge. Based on my own experience I especially consider self-service analytics for domain experts as a key part of every analytics strategy in the foreseeable future with Machine Learning for researchers and data scientists as the other corner stone. Self-service analytics truly enable companies to scale by using their current employees' skill set and improve the collaboration between domain experts and data science teams.

I have extensive experience bringing ML and advanced time-series analytics to software platforms and further developing the broader vision, product strategy and GTM for advanced analytics.

Innovating on behalf of healthcare & life sciences customers in the EMEA public sector, through deep machine learning / AI expertise and a broad range of AWS cloud service offerings

Prior to joining AWS, I worked as a senior data scientist at McKinsey (specializing in leveraging cutting edge AI research to solve real-world problems on very short time scales), founded a healthcare AI startup, led brain-inspired AI research projects, contributed to a major proto-AGI project, and completed a PhD in the machine learning group at the University of Manchester.

Prof. Peter De Jaeger is director IT & data and the Chief Innovation Officer and leading RADar, the learning and innovation centre of AZ Delta. He holds a position at Hasselt University as a data science professor and a position as adjunct associate professor at University College Dublin. His working experience deals with project management, research & development, product development, clinical studies, data access/use/sharing.

After over 40 years of experience in the pharma/life sciences industry, Bart and his consulting company NeoDoma help with the development of a safe and efficient data ecosystem, allowing reuse of health data (real world data) for research purposes. Bart is the former senior director Health Information Sciences at Janssen Pharmaceutical Companies of Johnson and Johnson.