麻豆AV

Short Bio:
Danilo Bzdok is a medical doctor and computer scientist with a dual background in systems neuroscience and machine learning algorithms. After training at RWTH Aachen University (Germany), Universit茅 de Lausanne (Switzerland), and Harvard Medical School (USA), he completed one Ph.D. in cognitive neuroscience (Research Center Juelich, Germany) and one Ph.D. in computer science in machine learning statistics at INRIA Saclay and Neurospin (France). Danilo currently serves as Associate Professor at 麻豆AV's Faculty of Medicine and as Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute, Montreal, Canada, including cross-appointments at the McConnell Brain Imaging Center, Montreal Neurological Institute, Ludmer Centre for Neuroinformatics and Mental Health, and the School of Computer Science at 麻豆AV. His interdisciplinary research activity centers on narrowing knowledge gaps in the brain basis of human-defining types of thinking, with a special focus on the higher association cortex in health and disease.

Research Summary:
There is now increasing momentum in data sharing, open access, and data collection consortia that build richly annotated "big data" repositories for brain and behavior. This unprecedented data setting creates a rapidly growing potential to provide new answers to old questions on human brain organization and its disturbances in brain disease. Dr. Bzdok will take the opportunity to explore, formalize, and predict brain phenotypes of hidden population variation by capitalising on heterogeneous data sources to tackle open questions in systems neuroscience in a way that also paves new ways for precision medicine in brain health.

His research group is dedicated to such interdisciplinary challenges in a domain-agnostic approach (especially high- but also low-level cognitive processes) leveraging several recently emerged population datasets (such as UK Biobank, HCP, CamCAN, ABCD) across levels of observation (brain structure and function, consequences from brain lesion, or common-variant genetics) using a broad toolkit of bioinformatic methods (machine-learning, high-dimensional statistics, and probabilistic Bayesian hierarchical modeling).

Future Work:
The key ambition is to bring closer neuroscience and learning predictive patterns from data. Due to the complexity of the patterns that need to be detected in life experience, neural processing, and genomics, human intuition alone may not be sufficient to provide explicit, fine-detailed brain mechanisms. His combination of backgrounds allows identifying pressing questions in medical imaging and health, reframing them as machine learning problems, and translating new insight into biomedicine. His research team is focused on data-guided analysis techniques for large datasets from a systems neuroscience perspective. He believes that strong interdisciplinarity, with an equal footing in research object and research method, is a prerequisite for forward progress in quantitative neuroscience and personalized medicine.