SPS-BSI Webinar: Machine learning paradigms towards single subject prediction

Date: 29 March 2024
Time: 1:00 PM ET (New York Time)
Presenter(s): Dr. Danilo Bzdok

Join us Friday, March 29th, 2024, at 1:00 PM ET for an exciting virtual talk by Dr. Danilo Bzdok entitled: “Machine learning paradigms towards single subject prediction” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.

Meeting information:

Meeting number: 2634 439 9615

Password: mSk8UghDA23 (67588443 from phones and video systems)

Join by phone:

+1-415-655-0002 US Toll

Access code: 2634 439 9615


The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.


Danilo Bzdok is a medical doctor and computer scientist with a dual background in systems neuroscience and machine learning algorithms. After medical training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland), and Harvard Medical School (USA), he completed one Ph.D. in brain-imaging neuroscience (Research Center Juelich, Germany, 2012) and one Ph.D. in computer science in machine learning statistics at INRIA Saclay and Neurospin (France, 2016). Danilo currently serves as Associate Professor at McGill’s Faculty of Medicine and as Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute, Montreal, Canada.