Tibor V. Varga is the Deputy Director of Copenhagen Health Complexity Center and an Associate Professor at the University of Copenhagen specializing in epidemiology and health equity. Tibor has experience using big data and AI to address public health challenges, developing predictive models for complex disease and investigating health disparities.
Varga’s current work focuses on AI, epidemiology, and health equity, emphasizing the importance of mitigating biases in data and algorithms to prevent healthcare inequalities. Algorithmic fairness and bias have moved beyond theoretical discussions to real-world applications (e.g., clinical risk prediction models, chatbots “informing” us every step of the way, algorithms that aid hiring, admission, or criminal sentencing processes), making it imperative for epidemiologists to grapple with these issues.
This keynote lecture will give an introduction to what machine learning and artificial intelligence mean and help the audience engage with algorithmic fairness challenges.
Kerstin Bach is a Professor at the Norwegian University of Science and Technology, the Research Director at the NorwAI Research Center , Deputy Head of the Data and Artificial Intelligence Unit, and a member of the Norwegian Open AI Lab.
Bach is a highly experienced researcher and has worked with AI across several disciplines and industries throughout her career. Her work primarily focuses on developing methods for applied artificial intelligence, including building systems that support complex, knowledge-intensive decisions using heterogeneous data sources. Her research spans several health-related projects, including the management of low back pain, physical activity, sleep, and mental health.
This keynote lecture will explore the past, present, and future of AI in health research.
Adam Hulman is an Associate Professor at Aarhus University and a Senior Researcher leading the Machine Learning & Clinical Prediction Lab at Steno Diabetes Aarhus, Aarhus University Hospital, Denmark, where he is leading the area of machine learning and clinical prediction. Hulman has more than a decade of experience working in diabetes epidemiology and is an Associate Editor of Diabetologia.
In his work, Hulman applies machine learning methods to multimodal data from epidemiological cohorts and the healthcare sector to provide clinical insights and more comprehensive profiling of disease risk. This keynote lecture will give examples of how machine learning can be used in epidemiology with a special focus on clinical prediction, as well as provide perspectives on how AI is perceived by patients and the general public.