What: Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions

Why: To reduce academia’s carbon footprint, accommodate the schedules of the world’s top scientists and maintain social distancing

Who: All are welcome!

Where: Zoom Webinar

Recordings of previous talks can be found here

Juan (Helen) Zhou, PhD - Associate Professor, Department of Medicine, National University of Singapore
When: February 26, 9:45-11:00 am
Title: Mapping multimodal brain network changes in neurological disorders: a longitudinal perspective


The spatial patterning of each neurodegenerative disease relates closely to a distinct structural and functional network in the human brain. This talk will mainly describe how brain network-sensitive neuroimaging methods such as resting-state fMRI and diffusion MRI can shed light on brain network dysfunctions associated with pathology and cognitive decline from preclinical to clinical stage of neurological disorders. I will first present our findings from two independent datasets on how amyloid and cerebrovascular pathology influence brain functional networks cross-sectionally and longitudinally in individuals with mild cognitive impairment and dementia. Evidence on longitudinal functional network organizational changes in healthy older adults and the influence of APOE genotype will be presented. In the second part, I will describe our work on how different pathology influences brain structural network and white matter microstructure. I will also touch on some new data on how individual-level brain network integrity contributes to behavior and disease progression using multivariate or machine learning approaches. These findings underscore the importance of studying selective brain network vulnerability instead of individual region and longitudinal design. Further developed with machine learning approaches, multimodal network-specific imaging signatures will help reveal disease mechanisms and facilitate early detection, prognosis and treatment search of neuropsychiatric disorders.


Dr. Juan Helen ZHOU is an Associate Professor at the Center for Sleep and Cognition, and the Deputy Director, Center for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore (NUS). She is also affiliated with Duke-NUS Medical School. Her laboratory studies selective brain network-based vulnerability in neuropsychiatric disorders using multimodal neuroimaging and machine learning approaches. She received Bachelor degree and Ph.D. from School of Computer Science and Engineering, Nanyang Technological University, Singapore. Dr. Zhou was an associate research scientist at Department of Child and Adolescent Psychiatry, New York University. She did a post-doctoral fellowship at the Memory and Aging Center, University of California, San Francisco, and in the Computational Biology Program at Singapore-MIT Alliance. Dr. Zhou is currently the Council Member and a previous Program Committee member of the Organization of Human Brain Mapping. She serves as an Editor of multiple journals including Human Brain Mapping, NeuroImage, and Communications Biology.

Pascal Notin- PhD Student, Oxford Applied and Theoretical Machine Learning Group, Department of Computer Science, University of Oxford
When: March 19, 9:45-11:00 am
Title: Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning


Quantifying the pathogenicity of protein variants in human disease-related genes would have a profound impact on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational methods could support the large-scale interpretation of genetic variants. However, prior methods have relied on training supervised models which -- due to learning from sparse and variable quality labels -- have been considered insufficiently reliable. 

In this talk I will present an approach we developed to predict the clinical significance of protein variants in a fully unsupervised manner, directly learning from the natural distribution of proteins in evolutionary data. Our model EVE (Evolutionary model of Variant Effect) not only outperforms computational approaches that rely on labelled data, but also performs on par with high-throughput assays which are increasingly used as strong evidence for variant classification. We predict the pathogenicity of 11 million variants across 1,081 disease genes, and assign high-confidence reclassification for 72k Variants of Unknown Significance. Our work suggests that models of evolutionary information can provide a strong source of independent evidence for variant interpretation and that the approach will be widely useful in research and clinical settings.

Biography: Pascal Notin is a Ph.D. student in the Oxford Applied and Theoretical Machine Learning Group, part of the Computer Science Department at the University of Oxford, under the supervision of Yarin Gal.

His research interests lie at the intersection of Bayesian Deep Learning, Generative models and Computational biology. The current focus of his work is to develop methods to quantify and leverage uncertainty in models for structured representations (e.g., sequences, graphs) with applications in biology and medicine. He has several years of applied machine learning experience developing AI solutions, primarily within the healthcare and pharmaceutical industries (e.g., disease prediction, clinical trials excellence, Real World Evidence analytics). Prior to coming to Oxford, he was a Senior Manager at McKinsey & Company in the New York and Paris offices, where he was leading cross-disciplinary teams on fast-paced analytics engagements. He obtained a M.S. in Operations Research from the IEOR department at Columbia University, and a B.S. and M.S. in Applied Mathematics from Ecole Polytechnique.

Emily S. Finn, PhD - Assistant Professor, Department of Psychological and Brain Sciences, Dartmouth College
When: April 9, 9:45-11:00 am
Title: Is it time to put rest to rest?

Abstract: TBA

Biography: Dr. Emily S. Finn is an assistant professor in the Department of Psychological and Brain Sciences at Dartmouth College, where she directs the Functional Imaging and Naturalistic Neuroscience (FINN) Lab. Her work focuses on individual variability in brain activity and behavior, especially as it relates to appraisal of ambiguous information under naturalistic conditions. She received a PhD in Neuroscience from Yale, and completed a postdoc at the National Institute of Mental Health.

Mihaela van der Schaar, PhD - Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge
When: May 7, 9:45-11:00 am
Title: Revolutionizing Healthcare: Understanding and guiding clinical decision making using machine learning

Abstract: TBA

Biography: Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA. Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI. Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

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