Presented by the Clinical and Translational Science Institute’s BERD Program and the Department of Biostatistics and Data Science, the BERD Methods Conference aims to:
- Catalyze the discussion and development of novel tools and methods for employing prediction modeling techniques in clinical and translational research.
November 18th, 2022
11:00 AM - 4:00 PM EST
No cost to attend
Learn more about the 2022 BERD Methods Conference Speakers!
"Selective Inference for Exploratory Data Analysis"presented by: Daniela Witten, PhD, MS.
Professor, Biostatistics and Statistics, University of Washington
Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. Daniela is the recipient of an NIH Director's Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, a Simons Investigator Award in Mathematical Modeling of Living Systems, the Spiegelman Award from the American Public Health Association for a statistician under age 40 who has made outstanding contributions to statistics for public health, and the Leo Breiman Award for contributions to the field of statistical machine learning. She is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute.
In her free time Dr. Witten enjoys running, eating good food, and exploring the beautiful Pacific Northwest.
"Statistical Hypothesis Testing versus Machine-learning Binary Classification: Distinctions and Guidelines" presented by: Jingyi Jessica Li, PhD.
Professor, Department of Statistics - University of California, Los Angeles
Jingyi Jessica Li is an Associate Professor in the Department of Statistics at UCLA. Prior to joining UCLA in 2013, Jessica obtained her Ph.D. degree from UC Berkeley. Jessica and her students focus on developing statistical and computational methods motivated by important questions in biomedical sciences and abundant information in big genomic and health-related data. Jessica is the recipient of the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), the MIT Technology Review 35 Innovators Under 35 China (2020), and the Harvard Radcliffe Fellowship (2022).
A fun fact about me is that my research direction is what I wanted to do when I was in high school. Besides academics, I love music, reading, and yoga.
"Deep ECG Learning for Cardiovascular Disease Risk Prediction" presented by: Oguz Akbilgic, PhD.
Associate Professor, Cardiovascular Medicine & Biomedical Informatics
Oguz Akbilgic is an Associate Professor at the Cardiology and Center for Biomedical Informatics as well as an Associate Director for Epidemiological Cardiology Research Center (EPICARE). As a biomedical informaticist, he has expertise in both AI methodology and AI applications in clinical decision making. His lab focuses on developing AI models for cardiovascular disease risk prediction, early identification of Parkinson's Disease, surgery outcome prediction as well as remote applications of such AI models on wearables data.
In his free time, Dr. Akbilgic loves to watch and play soccer.
"Using Algorithms to Diagnose Human Errors"presented by: Ziad Obermeyer, MD.
Associate Professor, Health Policy and Management, UC Berkeley
Ziad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. His research and teaching focus on machine learning as a tool for improving decision making in health. He continues to practice emergency medicine in underserved communities.
"Joint Activity Design: Design patterns and Evaluations Strategies for Integrating AI/ML into Human-Machine Clinical Teams" presented by: Michael Rayo, PhD, MS.
Assistant Professor, Department of Integrated Systems Engineering and Core Faculty, The Ohio State University
Mike Rayo, PhD is an Assistant Professor in the Department of Integrated Systems Engineering and Core Faculty at the Translational Data Analytics Institute at The Ohio State University and a Scientific Advisor for patient safety at The Ohio State University Wexner Medical Center. He is the director of the Cognitive Systems Engineering Laboratory (CSEL) and the Symbiotic Healthcare Design Laboratory. His research and design work focuses on technology-mediated coordination to facilitate resilient system performance. His current projects include Systemic Contributor and Adaptation Diagramming (SC/A/D), Joint Activity Design/Testing/Monitoring, alarm design and management, team coordination, visual analytics, computerized decision support, and interpersonal communication.
A fun fact about me is that I am hopelessly addicted to music. I graduated with a B.A. in Music Performance, keep picking up new instruments, and was once, a long time ago, the Case Western Reserve University Dance Champion.
"Accelerated Oblique Random Survival Forests: Faster and More Interpretable" presented by: Byron Jaeger, PhD.
Assistant Professor, Biostatistics and Data Science, WFUSOMI grew up in a science family where my dad, a physicist, would take us to conferences and practice giving his talks to us in the hotel. When I went to college, dad steered me away from physics (not enough funding) and encouraged me to learn math. I went on to get a degree in biostatistics and, while I was doing my dissertation, my advisor told me “Statistical methods need software!” almost every day, so I’ve kind of grown hyper focused on programming and statistical software.
A fun fact about me is that in 2020 my friends and I started doing weekly online game nights. I enjoy catching up with them, playing our favorite games (Rocket League is a crowd favorite), and generally feeling like we are all back in grad school.
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