The Disruptive Role of Data and AI in the Life Sciences

Title: Integrative Approaches to Single-Cell Genomics for Personalized Medicine

Bio: Dr. Keles obtained her Ph.D. in Biostatistics from the University of California at Berkeley. After a year-long postdoctoral appointment at UC Berkeley, she joined the Department of Biostatistics and Medical Informatics and the Department of Statistics at the University of Wisconsin, Madison. She has twenty years of experience in developing statistical and computational methods for genomics, including serving as an ENCODE PI, and pioneering foundational statistical models for leveraging multi-mapping reads in high throughput sequencing data analysis (ChIP-seq, Hi-C).

Her research interests span developing statistical and computational methods for denoising and signal extraction from sequencing data and modeling of high dimensional data. Her computational approaches led to fundamental contributions on how GATA factors mediate transcriptional regulation in HSPCs and erythroid cells. Dr. Keles is an elected fellow of the American Statistical Association.

Title: All the data from everywhere all at once: data integration, AI imaginaries, and their unpredictable outcomes

Bio: Klaus Hoeyer is professor of Medical Science and Technology Studies at the Centre for Medical Science and Technology Studies, University of Copenhagen. His research focuses on the links between policy, practice and experience in relations to medical research and clinical practice. In recent years, he has focused mainly on what he calls intensified data sourcing in healthcare and how it interacts with and changes the health services. This research is primarily financed by the European Research Council.

Title: The automation of science

Bio: Ross D. King did his PhD on applying machine learning to predicting protein structure at the Turing Institute in Glasgow. He has joint positions at Chalmers Institute of Technology, and the University of Cambridge. He is one of the most experienced machine learning researchers in Europe.

His main research interest is the interface between computer science and science. He originated the idea of a ‘Robot Scientist’: integrating AI and laboratory robotics to physically implement closed-loop scientific discovery. His Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge. His Robot Scientist ‘Eve’ is currently searching for drugs against neglected tropical diseases, and COVID.  His other core research interest is DNA computing.

Title: Molecular simulation and machine learning to delineate target binding of therapeutic oligonucleotides

Bio: Alexander Schliep is the chair for Medical Bioinformatics at the Faculty of Health Sciences Brandenburg at Brandenburg Technical University Cottbus-Senftenberg, with a secondary appointment at the University of Gothenburg and member of the WASP (Wallenberg AI, Autonomous Systems and Software Program) research collegium.

His current research program is focused on machine learning and algorithmics for analyses of genomic data and nucleic acid-based therapeutics, including machine learning for pan-genome graph analyses, efficient analysis algorithms for large genomic datasets, and federated, privacy-preserving methods for training ML/AI models for oligonucleotide therapeutics.

Title: Visual Analytics for Enhancing Quality and Trust in Genome-wide Expression Clustering and Annotation

Bio: Prof. Dr. Andreas Kerren received his PhD degree in Computer Science from Saarland University, Saarbrücken, Germany. In 2008, he achieved his habilitation (docent competence) from Växjö University, Sweden.

Dr. Kerren is currently a Full Professor of Information Visualization, Linköping University (LiU) and Linnaeus University (LNU), Sweden. He holds the Chair of Information Visualization at LiU and is head of the research group Information and Software Visualization at LNU. In addition, he is an ELLIIT professor supported by the Excellence Center at Linköping–Lund in Information Technology and key researcher of the Linnaeus University Centre for Data Intensive Sciences and Applications.

His main research interests include several areas of information visualization and visual analytics, especially visual network analytics, text visualization, and the use of visual analytics for explainable AI.

He has been editorial board member of a number of journals such as Information Visualization or Computer Graphics Forum, has served as organizer/program chair at numerous conferences such as IEEE VISSOFT 2013/2018 or GD 2018, and has edited a number of successful books on human-centered visualization. Dr. Kerren has published more than 200 peer-reviewed papers, articles, and book chapters.

Title: Improved protein structure prediction by adding noise at inference

Bio: Björn Wallner has been a faculty member at Linköping University since 2011 and a Professor in Bioinformatics since 2019. He obtained his Ph.D. in Bioinformatics from Stockholm University in 2005 and did postdoctoral work at the University of Washington from 2006-2008.

His research interests include protein structure prediction, protein-protein interactions, protein function, disorder, flexibility, and dynamics.

Title: Incorporating Stability Into Flow Matching

Bio: Christopher Iliffe Sprague is a postdoctoral researcher at KTH Royal Institute of Technology and SciLifeLab, working with Arne Elofsson and Hossein Azizpour on deep learning approaches for protein-protein interactions. He earned his PhD in robotics from KTH, where he focused on developing efficient and trustworthy AI for critical robotic systems.

Currently, as a postdoc, Christopher is leveraging his robotics expertise to enhance inductive biases in deep generative models, particularly for equilibrium generation tasks like molecular docking. His research interests include flow-based generative models, hybrid dynamical systems, and drug design

Title: Linguistic Diversity Through the Prism of Biodiversity

Bio: Harald Hammarström is Professor of General Linguistics at
Uppsala University. He has a background in both Computer Science and
Linguistics.

He has a very broad linguistic interest spanning all
areas of the world but specializing in minority languages in Papua,
Africa and South America. His research activities span from
documentary fieldwork in Papua, Indonesia, classical linguistic
analytic work, typological databases, and NLP for lesser-known
languages.

He is currently focussing on large-scale empirical and
computational approaches to linguistic diversity, genealogical/areal
relationships and language universals.

Titel: Unraveling the secrets of nature’s high-performance fiber

Bio: Hedvig Kjellström is a Professor in the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology, Sweden, and also affiliated with Swedish University of Agricultural Sciences, Swedish e-Science Research Centre, and Max Planck Institute for Intelligent Systems, Germany. She received an MSc in Engineering Physics and a PhD in Computer Science from KTH in 1997 and 2001, respectively, and thereafter worked at the Swedish Defence Research Agency, before returning to a faculty position at KTH. Her present research focuses on methods for enabling artificial agents to interpret human and animal behavior. These ideas are applied in the study of human aesthetic bodily expressions such as in music and dance, modeling and interpreting human communicative behavior, and the understanding of animal behavior and experiences. In order to accomplish this, methods are developed for agents to perceive the world and build representations of it through vision.

Hedvig has received several prizes for her research, including the 2010 Koenderink Prize for fundamental contributions in computer vision. She has written around 150 papers in the fields of computer vision, machine learning, robotics, information fusion, cognitive science, speech, and human-computer interaction. She is mostly active within computer vision, where she is an Editor-in-Chief for CVIU, a Program Chair for CVPR 2025, and regularly serves as Area Chair for the major conferences. 

Title: Characterization and visualization of cardiac spectral imaging data

Bio: Ingrid Hotz is a professor at Linköping University in Sweden, leading the Scientific Visualization group in the Department of Science and Technology. She holds an M.S. in theoretical physics from Ludwig Maximilian University, Munich, and a Ph.D. in computer science from the University of Kaiserslautern. Hotz has held research positions at the Institute for Data Analysis and Visualization (IDAV) at UC Davis, the Zuse Institute Berlin, and the German Aerospace Center (DLR). Since 2015, she has been a professor at Linköping University and was named the Dr. Ram Kumar IISc Distinguished Visiting Chair Professor at the Indian Institute of Science in 2022.

Her research focuses on scientific visualization and topological data analysis, aiming to develop advanced visual analysis tools for complex datasets across various fields, including engineering, physics, chemistry, and medicine. She integrates methods from computer science and mathematics, such as computer graphics and computational topology, with a participatory design approach to ensure practical and relevant solutions.

Title: Anonymization of Data in Precision Medicine Research

Bio: Minh Vu is a postdoctoral researcher at IceLab, Umeå University, working under the supervision of Martin Rosvall and Beatrice Melin. Their research focuses on anonymizing and visualizing biobank data to facilitate meaningful insights and discoveries in disease understanding and treatment development.

Title: Molecular simulation and machine learning to delineate target binding of therapeutic oligonucleotides

Bio: Pär Matsson is Professor of Pharmacokinetics at the Sahlgrenska Academy, University of Gothenburg, and Scientific Director of OligoNova Hub – the SciLifeLab infrastructure for development of therapeutic oligonucleotides.

His research is centered on elucidating the molecular mechanisms of cellular and subcellular drug disposition, and how they influence therapeutic effect. A particular focus is on the cellular disposition and effects of non-traditional drug modalities, including therapeutic oligonucleotides and targeted protein degraders (PROTACs).

Title: Mapping the Nexus of Biodiversity, Climate Change, Human Society and Health using Large Language Models and other Data Science Approaches

Bio: Sonja Aits leads the “Cell Death, Lysosomes and Artificial Intelligence” group at the Faculty of Medicine, Lund University, which works at the intersection of data, life and sustainability science. Her group develops computational tools such as large language models for scientific text mining and computer vision models for histology and high-content microscopy and uses them to identify biological pathways and disease mechanisms in humans and other species as well as intervention strategies that promote human health, biodiversity and sustainability.

Sonja helps coordinate AI Lund, Lund University’s umbrella organization for AI-related research, outreach and education, and the profile area “Nature-based future solutions”. In addition, she serves as study director of the COMPUTE research school, where she has developed the PhD course program “AI in Medicine and Life Science”. She is also deeply engaged in open education and public outreach.

Title: AI in the Health Care Sector – Legal Challenges

Bio: Stanley Greenstein (Jur. Dr.) is an Associate Professor (Docent) in Law and Information Technology, Faculty of Law, Stockholm University. He is Chairman of the Board, Swedish Law and Informatics Research Institute (IRI, https://irilaw.org/), Chairman of the Board, Foundation for Legal Information (Stiftelsen för rättsinformation) and a Digital Futures faculty member (https://www.digitalfutures.kth.se/about/).

Stanley’s primary academic focus is cross-disciplinary in nature and addresses the regulation of emerging digital technologies, especially artificial intelligence (AI). His teaching, research and participation in externally funded projects has revolved around the subject areas of ethics, data protection, sustainability and legal design.

Title: Characterization and visualization of cardiac spectral imaging data

Bio: Tino Ebbers is a professor of physiological measurements at Linköping University, with a focus on cardiac imaging, modeling, and simulation. He holds an MSc in Electrical Engineering from the University of Twente and a PhD in Biomedical Engineering from Linköping University. After completing his PhD, he worked at Philips Medical Systems before returning to academia. He also served as a visiting professor at the University of California, San Francisco.

With over 100 publications in leading scientific journals and numerous contributions to international conferences, Tino Ebbers has made a significant impact through his multidisciplinary approach to merging technical research with clinical applications. He is best known for pioneering 4D flow MRI, a breakthrough technology now widely used to study cardiovascular blood flow in both research and clinical settings. His contributions have greatly advanced cardiovascular imaging and led to major innovations in the diagnosis, treatment, and management of cardiovascular diseases.