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

https://cornell.zoom.us/j/99574706503?pwd=ZFJhZit2R1NOWHAvNC9nOGk5SUtDZz09

Recordings of previous talks can be found here

Christos Davatzikos, PhD - Wallace T. Miller Sr. Professor of Radiology, Director of AI in Biomedical Imaging Laboratory and Director of Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA
When: October 2, 9:45-11:00 am
Title: Disentangling neurobiological heterogeneity, via semi-supervised clustering of imaging signatures: applications to neuropsychiatric and neurodegenerative diseases

Abstract: Machine learning has shown great promise in neuroimaging, over the past 15 years. This talk will focus on work taking place at Penn’s AIBIL laboratory, aiming to improve our understanding of neurobiological heterogeneity of neuropsychiatric and neurodegenerative diseases, via semi-supervised learning methods. A generative (CHIMERA) and a discriminative (HYDRA) approach are discussed, as well as their application to neuroimaging data revealing subtypes of schizophrenia and MCI/AD. Current work on extensions using multi-scale orthogonally-projective NMF, as a means for feature learning in conjunction with HYDRA, as well as a deep-learning approach utilizing GANs, is also presented. These methods are applied to large consortia of aging and schizophrenia, collectively including over 40,000 MRI scans.

Biography: Christos Davatzikos is the Wallace T. Miller Sr. Professor of Radiology, with secondary appointment in Electrical and Systems Engineering and joint appointments with the Bioengineering and Applied Math graduate groups at Penn. He received his undergraduate degree by the National Technical University of Athens, Greece, in 1989, and Ph.D. from Johns Hopkins University, in 1994. He joined the faculty at the Johns Hopkins School of Medicine as Assistant Professor (1995) and later Associate Professor (2001) of Radiology. In 2002 he moved to Penn to direct the Section for Biomedical Image Analysis, and in 2013 he established the Center for Biomedical Image Computing and Analytics. His interests are in the field of imaging informatics. In the past 15 years he has focused on the application of machine learning and pattern analysis methods to medical imaging problems, including the fields of computational neuroscience and computational neuro-oncology. He has worked on aging, Alzheimer's Disease, schizophrenia, brain development, and brain cancer. Dr. Davatzikos is an IEEE and AIMBE  Fellow, a Distinguished Investigator at the Academy of Radiology Research in the USA, and member of various editorial boards.

Brian Caffo, PhD - Professor, Department of Biostatistics, Johns Hopkins University, Baltimore, MD
When: October 16, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: Brian Caffo is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Dr. Caffo is a leading expert in statistics and biostatistics and is the recipient of the PECASE award, the highest honor given by the US Government for early career scientists and engineers. Along with Roger Peng and Jeff Leek, Dr. Caffo created the Data Science Specialization on Coursera. He leads the Johns Hopkins Data Science Lab (DaSL), a group based in the Johns Hopkins Bloomberg School of Public Health whose mission is to enhance data science thinking everywhere and make data science accessible to the entire world. The DsSL believe all people should be able to develop literacy, fluency and skill in data science so they can make sense of the data they encounter in their personal and professional lives. They recognize data science as a fundamentally human activity and focus our activities on helping people build data analyses for people.

Their goal is to

  • Teach people how to design, collect, interpret, and interact with data

  • Build a supportive environment for the people at Johns Hopkins who creatively use data to answer questions

  • Provide leadership on how people doing data science should be supported at Johns Hopkins and in academia, industry, and government

  • Build resources and products that help people learn and do data science

  • Conduct research into the theory and practice of data science

They have previously built massive online open courses in data science that have enrolled more than 8 million people around the world, published best selling books, widely-subscribed blogs, developed podcasts on data science, statistics, and academia, and have developed a software platform for interactive learning of statistics in R. We make our impact by combining cutting edge research in machine learning, artificial intelligence and statistics with a deep understanding of applications and an eye toward the human behavioral component of data analysis.

Rediet Abebe, PhD - Assistant Professor, Computer Science, University of California, Berkley, Berkley, CA
When: October 23, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor of Computer Science at the University of California, Berkeley. Abebe holds a Ph.D. in computer science from Cornell University as well as graduate degrees from Harvard University and the University of Cambridge. Her research is in the fields of artificial intelligence and algorithms, with a focus on equity and justice concerns. She co-founded and co-organizes Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary research initiative working to improve access to opportunity for historically disadvantaged communities. Abebe's research has informed policy and practice at the National Institute of Health (NIH) and the Ethiopian Ministry of Education. Abebe has been honored in the MIT Technology Reviews' 35 Innovators Under 35, ELLE, and the Bloomberg 50 list as a "one to watch." She has presented her research in venues including National Academy of Sciences, the United Nations, and the Museum of Modern Art. Abebe co-founded Black in AI, a non-profit organization tackling representation and inclusion issues in AI. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.

Marzyeh Ghassemi, PhD - Assistant Professor, Computer Science, University of California, Berkley, Berkley, CA
When: November 6, 9:45-11:00 am
Title: TBA

Abstract: TBA

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.

Danielle S. Bassett, PhD - J Peter Skirkanich Professor, Biomedical Engineering, University of Pennsylvania, Philadelphia, PA
When: November 20, 10-11:30 am
Title: TBA

Abstract: TBA

Biography: Dr. Danielle Bassett's group studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Their 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, they 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. They 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.

Pillavi 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.

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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