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 and accommodate the schedules of the world’s top scientists
Where: Broadcast to in-person conference rooms at Cornell University in Ithaca NY and Weill Cornell Medicine in New York, NY
Also open to remote attendees everywhere via Zoom
Konrad Kording, PhD - Professor, Department of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA
When: January 23rd, 3:15 - 4:30 pm
NYC location: ST8A-05 (Starr building, floor 8A)
Ithaca Location: Weill Hall 224
Title: Is most of medical machine learning wrong or misleading?
Abstract: The promise to convert large datasets into medical insights is driving the transition of medicine towards a data rich discipline. Consequently, many scientists focus on machine learning from such datasets. Countless papers are exciting, but very little has clinical impact. Here I argue that this is due to the way we do machine learning, and how common practices lead to non-replication or misleading interpretations of machine learning results. I will discuss ways of minimizing such problems.
Biography: Dr. Kording's (He/Him) is trying to understand how the world and in particular the brain works using data. Early research in the Kording lab focused on computational neuroscience and in particular movement. But as the approaches matured, the focus has more been on discovering ways in which new data sources as well as emerging data analysis can enable awesome possibilities. The current focus is on Causality in Data science applications - how do we know how things work if we can not randomize? But we are also very much excited about understanding how the brain does credit assignment. The kording lab style of working is transdisciplinary, we collaborate on virtually every project.
Danielle Bassett, PhD - J Peter Skirkanich Professor, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
When: TBA
NYC location: TBA
Ithaca Location: 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.
Ben Glocker, PhD - Reader in Machine Learning for Imaging, Faculty of Engineering, Department of Computing, Imperial College London, London UK
When: February 14th, 3:15 - 4:30 pm
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: Dr. Ben Glocker is a Reader in Machine Learning for Imaging, co-leading the Biomedical Image Analysis Group. He is also Adviser – Medical Image Analysis at HeartFlow and is leading the London-based HeartFlow-Imperial Research Team. He works as scientific adviser for Definiens and Kheiron Medical Technologies. His research is at the intersection of medical image analysis and artificial intelligence, aiming to build computational tools for improving image-based detection and diagnosis of disease.
Ulas Bagci, PhD - Principal Investigator and Assistant Professor, Center for Research in Computer Vision, University of Central Florida, Orlando, FL
When: March 5th, 3:15 - 4:30 pm
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: Prof. Bagci is a faculty member at the Center for Research in Computer Vision (CRCV), and the Assistant Professor in University of Central Florida (UCF). His research interests are Artificial intelligence, machine learning and their applications in biomedical and clinical imaging. Previously, he was a staff scientist and the lab co-manager at the NIH's Center for Infectious Disease Imaging (CIDI) Lab, department of Radiology and Imaging Sciences (RAD&IS). At NIH, Prof. Bagci has developed and implemented educational and scientific research initiatives, and mentored postdoctoral and postbaccalaureate fellows for quantitative image analysis in clinical and pre-clinical projects at the Clinical Center. Prof. Bagci had also been the leading scientist (image analyst) in biosafety/bioterrorism project initiated jointly by NIAID and IRF.
Prof. Bagci obtained his PhD degree from School of Computer Science, University of Nottingham (UK) in collaboration with Radiology department of University of Pennsylvania (with Prof. Udupa, MIPG). He has masters from Electrical Engineering and Computer Sciences and certificates of mastery from statistics, public health, and clinical trials. Prof. Bagci is senior member of IEEE and RSNA, and member of scientific organizations such as Society of Nuclear Medicine and Molecular Imaging (SNMMI), American Statistical Association (ASA), Royal Statistical Society (RSS), AAAS, and MICCAI. He has served as a program committee member for various conferences, and a regular reviewer for many prestigious journals in his fields and received best reviewer awards (most recently MICCAI 2016 Best Scientific Reviewer Award). Prof. Bagci is the recipient of many awards including NIH's FARE award (twice), RSNA Merit Certificates (5+ times), best paper awards, poster prizes, and several highlights in journal covers, media, and news. Prof. Bagci was co-chair of Image Processing Track of SPIE Medical Imaging Conference, 2017, and technical committee member of MICCAI 2018.
Julia Schnabel, PhD- Professor and Chair, Computational Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, UK
When: March 27th, 9:45 - 11:00 am
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: Julia Schnabel joined King's College London in July 2015 as Chair in Computational Imaging at the School of Biomedical Engineering and Imaging Sciences, where she is Director of the EPSRC Centre for Doctoral Training in Medical Imaging, which is jointly run by King’s College London and Imperial College London. She is School Lead for Research and Impact, and Co-Director of the NIHR funded Medtech and In vitro diagnostic Co-operative (MIC) in Cardiovascular Diseases.
Omer T Inan, PhD- Associate Professor, Technical Interest Groups: Bioengineering (Group Chair), Electronic Design and Applications, Georgia Tech, Atlanta, GA
When: April 16th, 3:15 - 4:30 pm
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: Omer T. Inan received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Stanford University in 2004, 2005, and 2009, respectively. He worked at ALZA Corporation in 2006 in the Drug Device Research and Development Group. From 2007-2013, he was chief engineer at Countryman Associates, Inc., designing and developing several high-end professional audio products. From 2009-2013, he was a visiting scholar in the Department of Electrical Engineering at Stanford. In 2013, he joined the School of ECE at Georgia Tech as an assistant professor. Dr. Inan is generally interested in designing clinically relevant medical devices and systems, and translating them from the lab to patient care applications. One strong focus of his research is in developing new technologies for monitoring chronic diseases at home, such as heart failure.
Rajesh Ranganath, PhD- Assistant Professor, Computer Science, Courant Institute, New York University, New York, NY
When: April 23rd, 3:15 - 4:30 pm
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: I am an Assistant Professor at the Courant Institute at NYU in Computer Science and at the Center for Data Science (affiliate). I am also part of the CILVR group. My research interests center on easy-to-use probabilistic inference, understanding the role of randomness and information in model building, and machine learning for healthcare. Before joining NYU, I completed my PhD at Princeton working with Dave Blei and my undergraduate at Stanford both in computer science. I have also spent time as a research affiliate at MIT’s Institute for Medical Engineering and Science.
Souptik Barua, PhD- Postdoctoral Fellow, Scalable Health Lab, Rice University, Houtson, TX
When: May 8th, 9:45 - 11:00 am
NYC location: TBA
Ithaca Location: TBA
Title: TBA
Abstract: TBA
Biography: I am currently a postdoctoral research scientist under Dr. Ashutosh Sabharwal in the Scalable Health Lab at Rice University. I obtained my Ph.D. in Electrical Engineering at Rice University under Prof. Arvind Rao and Prof. Ashok Veeraraghavan. My research is focused on applying ideas from machine learning, signal processing, and statistics, to discover clinically relevant information in medical data.
Alexander Lavin- Founder, CTO, Latent Sciences, Cambridge, MA
When: May 14th, 3:15-4:30 pm
NYC location: TBA
Ithaca Location: TBA
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.