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

Danielle S. Bassett, PhD - J Peter Skirkanich Professor, Biomedical Engineering, University of Pennsylvania, Philadelphia, PA
When: November 20, 10-11:15 am
Title: Building mental models of our networked world

Abstract: Human learners acquire not only disconnected bits of information, but complex interconnected networks of relational knowledge. The capacity for such learning naturally depends upon three factors: (i) the architecture of the knowledge network itself, (ii) the nature of our perceptive instrument, and (iii) the instantiation of that instrument in biological tissue. In this talk, I will walk through each factor in turn. l will begin by describing recent work assessing network constraints on the learnability of relational knowledge. I will then describe a computational model informed by the free energy principle, which offers an explanation of how such network constraints manifest in human perception. In the third section of the talk, I will describe how neural representations reflect network constraints. Throughout, I'll move from previously published work to unpublished data, and from the world outside to the world inside, before speculating on as-yet uncharted territory. 

Biography: Prof. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania, with appointments in the Departments of Bioengineering, Electrical & Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry. Bassett is also an external professor of the Santa Fe Institute. Bassett is most well-known for blending neural and systems engineering to identify fundamental mechanisms of cognition and disease in human brain networks. Bassett is currently writing a book for MIT Press entitled Curious Minds, with co-author Perry Zurn Professor of Philosophy at American University. Bassett received a B.S. in physics from Penn State University and a Ph.D. in physics from the University of Cambridge, UK as a Churchill Scholar, and as an NIH Health Sciences Scholar. Following a postdoctoral position at UC Santa Barbara, Bassett was a Junior Research Fellow at the Sage Center for the Study of the Mind. Bassett has received multiple prestigious awards, including American Psychological Association's ‘Rising Star’ (2012), Alfred P Sloan Research Fellow (2014), MacArthur Fellow Genius Grant (2014), Early Academic Achievement Award from the IEEE Engineering in Medicine and Biology Society (2015), Harvard Higher Education Leader (2015), Office of Naval Research Young Investigator (2015), National Science Foundation CAREER (2016), Popular Science Brilliant 10 (2016), Lagrange Prize in Complex Systems Science (2017), Erdos-Renyi Prize in Network Science (2018), OHBM Young Investigator Award (2020), AIMBE College of Fellows (2020). Bassett is the author of more than 300 peer-reviewed publications, which have garnered over 24,000 citations, as well as numerous book chapters and teaching materials. Bassett is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Bassett’s work has been supported by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Army Research Laboratory, the Office of Naval Research, the Department of Defense, the Alfred P Sloan Foundation, the John D and Catherine T MacArthur Foundation, the Paul Allen Foundation, the ISI Foundation, and the Center for Curiosity.

Pallavi Tiwari, PhD - Case Western Reserve University, Cleveland, OH
When: December 4, 9:45-11:00 am
Title: Radiomics and Radio-genomics: Opportunities for Precision Medicine

Abstract: In this talk, Dr. Tiwari will focus on her lab’s recent efforts in developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging. She will focus on applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She will also discuss current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment. Dr. Tiwari will conclude her talk with a discussion of some of the translational aspects of her work from a clinical perspective.

Biography: Dr. Pallavi Tiwari is an Assistant Professor of Biomedical Engineering and the director of Brain Image Computing Laboratory at Case Western Reserve University. She is also a member of the Case Comprehensive Cancer Center.   Her research interests lie in machine learning, data mining, and image analysis for personalized medicine solutions in oncology and neurological disorders. Her research has so far evolved into over 50 peer-reviewed publications, 50 peer-reviewed abstracts, and 9 patents (3 issued, 6 pending).  Dr. Tiwari has been a recipient of several scientific awards, most notably being named as one of 100 women achievers by Government of India for making a positive impact in the field of Science and Innovation.  In 2018, she was selected as one of Crain’s Business Cleveland Forty under 40.  In 2020, she was awarded the J&J Women in STEM (WiSTEM2D) scholar award in Technology. Her research is funded through the National Cancer Institute, Department of Defense, Johnson & Johnson, V Foundation Translational Award, Dana Foundation, State of Ohio, and the Case Comprehensive Cancer Center.

Marzyeh Ghassemi, PhD - Assistant Professor, Computer Science, University of California, Berkley, Berkley, CA
When: December 11, 9:45-11:00 am
Title: Don’t Expl-AI-n Yourself: Exploring "Healthy" Models in Machine Learning for Health

Abstract: Despite the importance of human health, we do not fundamentally understand what it means to be healthy. Health is unlike many recent machine learning success stories - e.g., games or driving - because there are no agreed-upon, well-defined objectives. In this talk, Dr. Marzyeh Ghassemi will discuss the role of machine learning in health, argue that the demand for model interpretability is dangerous, and explain why models used in health settings must also be "healthy". She will focus on a progression of work that encompasses prediction, time series analysis, and representation learning.

Biography: Dr. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She currently serves as a NeurIPS 2019 Workshop Co-Chair, and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). Previously, she was a Visiting Researcher with Alphabet's Verily and a post-doc with Dr. Peter Szolovits at MIT. Prior to her PhD in Computer Science at MIT, Dr. Ghassemi received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Professor Ghassemi has a well-established academic track record across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, EMBC, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, NVIDIA, Huffington Post. She was also recently named one of MIT Tech Review’s 35 Innovators Under 35.

Juan (Helen) Zhou, PhD - Associate Professor, Department of Medicine, National University of Singapore
When: February, 2021
Title: TBA

Abstract: TBA

Biography: Dr. Juan (Helen) Zhou is an Associate Professor and Principal Investigator of the Multimodal Neuroimaging in Neuropsychiatric Disorders Laboratory in the Center for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore (NUS). She also holds a joint appointment with Neuroscience and Behavioral Disorders Program at Duke-National University of Singapore Medical School, Singapore. Dr. Zhou serves as the Deputy Director, Center for Translational Magnetic Resonance Research MR operations at Yong Loo Lin School of Medicine. Her research focuses on the network-based vulnerability hypothesis in disease. Her lab studies the human neural bases of cognitive functions and the associated vulnerability patterns in aging and neuropsychiatric disorders using multimodal neuroimaging methods, psychophysical techniques, and machine learning approaches. Prior to joining Duke-NUS in 2011, Helen was an associate research scientist in the Child Study Centre (New York University). She did a two-year post-doctoral fellowship at the Memory and Aging Centre (Department of Neurology, University of California, San Francisco), from 2008 to 2010. Helen received her Bachelor degree in Computer Science with first class honour (First class, 3.5 years accelerated) in 2003 and her Ph.D. in Neuroimaging in 2008 from Nanyang Technological University, Singapore. She is the recipient of undergraduate scholarship from Ministry of Education, Singapore (1998-2003) and the nominee for Lee Kuan Yew Gold Medal and the Institution of Engineers Singapore Gold Medal, Singapore in 2004. Helen has published in a number of journals such as Neuron, Brain, PNAS, Neurology, NeuroImage, and Molecular Psychiatry and has been the recipient of research support from National Medical Research Council and Biomedical Research Council, Singapore as well as the Royal Society, UK. She serves as reviewers and editors for a number of journals (e.g. Editor for NeuroImage) and grants. She is the Council – Secretary and Program Committee Member of the Organization for Human Brain Mapping. She is a member of the Organization for Human Brain Mapping, Society for Neuroscience, International Society to Advance Alzheimer’s Research and Treatment, International Society of Magnetic Resonance in Medicine, and American Academy of Neurology.

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