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

Olaf Sporns, PhD- Professor, Department of Psychological and Brain Sciences, Indiana University, Bloomington, Bloomington, IN
When: July 10, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: After receiving an undergraduate degree in biochemistry, Olaf Sporns earned a PhD in neuroscience at Rockefeller University and then conducted postdoctoral work at The Neurosciences Institute in New York and San Diego. Currently he is a Distinguished Professor in the Department of Psychological and Brain Sciences at Indiana University in Bloomington. His main research area is theoretical and computational neuroscience, with a focus on complex brain networks. He has authored over 160 peer-reviewed publications as well as the recent books “Networks of the Brain” and “Discovering the Human Connectome”, both published by MIT Press. Sporns was awarded a John Simon Guggenheim Memorial Fellowship in 2011 and elected Fellow of the American Association for the Advancement of Science in 2013.

Alexander Lavin- Founder, CTO, Latent Sciences, Cambridge, MA
When: July 17th, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: Alexander Lavin is an AI researcher and SW engineer, specializing in Bayesian machine learning and probabilistic computation. He's the founder of Latent Sciences, a precision health startup developing solutions for neurodegenerative diseases, with a focus on Alzheimer's and Parkinson’s. Before Latent he was a Senior Research Engineer at both Vicarious and Numenta, building artificial general intelligence for robotics, and developing biologically-derived AI & ML algorithms, respectively. Lavin was previously a spacecraft engineer, building computational design and optimization algorithms for NASA and Blue Origin, and leading the development of a lunar rover. Lavin was a Forbes 30 Under 30 honoree in Science, is an AI advisor for NASA and several deep tech startups, and has published in top journals and conferences across AI/ML and neuroscience. In his free time, Alexander enjoys running, yoga, live music, and reading sci-fi and theoretical physics books.

Max Welling, PhD- Professor of Computer Science, Institute of Informatics, University of Amsterdam, Netherlands
When: July 24, 9:45-11:00 am
Title: Graph Nets: The Next Generation

Abstract: In this talk I will introduce the next generation of graph neural networks known as Natural Graph Networks and Mesh CNNs. GNNs have the property that they are invariant to permutations of the nodes in the graph. This turns out to be unnecessarily limited. In this work we develop new models that are more flexible in the sense that they do not have isotropic kernels but at the same time remain highly scalable. The Mesh-CNNs are developed to run messages over a graph (mesh) that represents a discretization of a (curved) surface. The Natural Graph Networks are designed to be more flexible convolutions on general graphs. Joint with Pim de Haan, Maurice Weiler, and Taco Cohen.

Biography: Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. He has a secondary appointment as a fellow at the Canadian Institute for Advanced Research (CIFAR). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. He is a founding board member of ELLIS. Max Welling is recipient of the ECCV Koenderink Prize in 2010. He directs the Amsterdam Machine Learning Lab (AMLAB), and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA). He has over 300 publications in machine learning and an h-index of 66.

Danielle Bassett, PhD - J Peter Skirkanich Professor, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
When: TBA
Title: TBA

Abstract: TBA

Biography: Dani Bassett's group studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Our broad goal is to isolate problems at the intersection of basic science, engineering, and clinical medicine that can be tackled using systems-level approaches. Recent examples include predicting the extent of learning from human brain networks, resolving the evolution of the neuronal synapse via genetic interaction networks, determining bulk material properties from mesoscale force networks, and isolating individual drivers of collective social behavior during evacuations. In these contexts, we seek to develop new mathematical methods for the principled characterization of temporally dynamic, spatially embedded, and multiscale networked systems, with the goal of predicting system behavior and designing perturbations to affect a specific outcome. A current focal interest of the group lies in network neuroscience. We develop analytic tools to probe the hard-wired pathways and transient communication patterns inside of the brain in an effort to identify organizational principles, to develop novel diagnostics of disease, and to design personalized therapeutics for rehabilitation and treatment of brain injury, neurological disease, and psychiatric disorders.

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