Posts classified under: Workshops

BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
5/11/23 1:30PM-2:50PM
MSOB X303 (ZOOM LINK BELOW)
Lexin Li, Ph.D.
Professor of Biostatistics at the Department of Biostatistics and Epidemiology, and Helen Wills Neuroscience Institute, of the University of California, Berkeley

Title: Statistical Neuroimaging Analysis: An Overview

Abstract:

Understanding the inner workings of human brains, as well as their connections with neurological disorders, is one of the most intriguing scientific questions. Studies in neuroscience are greatly facilitated by a variety of neuroimaging technologies, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging, positron emission tomography (PET), among many others. The size and complexity of medical imaging data, however, pose numerous challenges, and call for constant development of new statistical methods. In this talk, I give an overview of a range of neuroimaging topics our group has been investigating, including imaging tensor analysis, brain connectivity network analysis, multimodality analysis, and imaging causal analysis. I also illustrate with a number of specific case studies.

Bio:

Lexin Li, Ph.D., is a Professor of Biostatistics at the Department of Biostatistics and Epidemiology, and Helen Wills Neuroscience Institute, of the University of California, Berkeley. His research interests include neuroimaging analysis, network data analysis, high dimensional regressions, dimension reduction, machine learning, and biomedical applications. He is a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS), and an Elected Member of the International Statistical Institute (ISI).

Zoom link: https://stanford.zoom.us/j/92124459914? pwd=cFpJYXVLOExUVjMzZkNsYXA0b0RxUT09&from=addon Meeting ID: 943 2440 5118
Password: 366430

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Honoring Richard Olshen on his 80th Birthday
BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
5/4/23 1:30PM-2:50PM
MSOB X303

Featured Speakers: Lu Tian, Trevor Hastie, Brad Betts, and Brad Efron with introductory and concluding remarks by Sylvia Plevritis

Talks from 1:30-2:40 at MSOB 303
Cake and other refreshments to follow at DBDS Lounge

Professor Richard Allen Olshen began his illustrious career at Yale University under the guidance of L.J. Savage, where he completed his Ph.D in Statistics in 1966. He first came to Stanford in 1967. In 1975 he went to UC San Diego, where he was on the faculty in the Math Department until 1989, at which point he returned to Stanford first in the Department of Health Research and Policy and ultimately the Department of Biomedical Data Science. During his career he made the transition from mathematical statistics to cutting-edge applications. He has made strong contributions to tree-structured learning, gait analysis, digital radiography, and is currently working on problems in molecular genetics. The four speakers will highlight some of his research accomplishments during his storied career.

BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
4/27/23 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Su-In Lee
Professor, Paul G. Allen School of Computer Science &
Engineering University of Washington

Abstract: In the first part of the talk, I will go over a number of research work done by my lab on the topics of explainable AI applied to biomedical problems, which exemplifies how it addresses new scientific questions, make new biological discoveries from data, make informed clinical decisions, and even open new research directions in biomedicine.

In the second part of the talk, I will show you that explainable AI needs to evolve and improve to solve real-world problems in computational biology and medicine by having a deep dive into our cancer pharmacogenomics project led by our Ph.D. student Joseph Janizek in collaboration with Prof. Kamila Naxerova at Harvard Medical School.

Bio:

Prof. Su-In Lee is a Paul G. Allen Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory. Before joining the UW in 2010, Lee was a Visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. She has received the National Science Foundation CAREER Award and been named an American Cancer Society Research Scholar. She has received generous grants from the National Institutes of Health, the National Science Foundation, and the American Cancer Society.

Zoom link: https://stanford.zoom.us/j/94324405118? pwd=WnR3Y1dqK3plYWREN0RNVjRlNnhEUT09&from=addon

Meeting ID: 943 2440 5118 Password: 366430

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BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
4/20/23 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Mark van der Laan
Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics
University of California, Berkeley

Title:

Targeted Learning and Causal Inference for Integrating Real World Evidence into the Drug Approval Process and Safety Analysis

Abstract:

Targeted Learning represents a general multi-step roadmap for accurately translating the real world into a formal statistical estimation problem, and a corresponding template for construction of optimal machine learning based estimators of any desired target causal estimand combined with formal statistical inference. It is flexible by being able to incorporate high dimensional and diverse data sources. To optimize finite sample performance, it can be tailored towards the precise experiment and statistical estimation problem in question, while being theoretically grounded, optimal, and benchmarked. We provide a motivation, explanation, and overview of targeted learning; the key role of super-learning, the Highly Adaptive Lasso; and discuss SAP construction based on targeted learning. Specifically, we discuss recent theoretical advances on the Higher order Spline Highly Adaptive Lasso. We also discuss a Sentinel and FDA RWE demonstration project of targeted learning.

Zoom link: https://stanford.zoom.us/j/94324405118? pwd=WnR3Y1dqK3plYWREN0RNVjRlNnhEUT09&from=addon
Meeting ID: 943 2440 5118
Password: 366430

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