XIHONG LIN

Professor and Former Chair, Department of Biostatistics

Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health

Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University

Associate Member of the Broad Institute of Harvard and MIT

 

LEARNING FROM COVID-19 DATA IN WUHAN, USA, AND THE WORLD ON TRANSMISSION, HEALTH OUTCOMES, AND INTERVENTIONS

COVID-19 is an emerging respiratory infectious disease that has become a pandemic. In this talk, I will first provide a historical overview of the epidemic in Wuhan. I will then provide the analysis results of 32,000 lab-confirmed COVID-19 cases in Wuhan to estimate the transmission rates using Poisson Partial Differential Equation based transmission dynamic models. This model is also used to evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation, and quarantine. I will present the results of the epidemiological characteristics of the cases. The results show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan. I will next present transmission regression models for estimating transmission rates in the USA and other countries, as well as factors including intervention effects using social distancing, test-trace-isolate strategies that affect transmission rates. I will present the analysis results of >500,000 participants of the HowWeFeel project on symptoms and health conditions in the US, and discuss the risk factors of the epidemic. I will discuss the estimation of the proportion of undetected cases, including asymptomatic, pre-symptomatic cases, and mildly symptomatic cases, the chances of a resurgence in different scenarios, and the factors that affect transmissions. I will provide several takeaways and discuss priorities.

 

Biography:

Dr. Lin is an elected member of the National Academy of Medicine . She received the 2002 Mortimer Spiegelman Award from the American Public Health Association and the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award and the 2017 COPSS FN David Award . She is an elected fellow of the American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Lin’s research interests lie in the development and application of statistical and computational methods for the analysis of massive data from the genome, exposome, and phenome, and scalable statistical inference and learning for big genomic, epidemiological, and health data. Examples include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and electronic health records, whole-genome variant functional annotations, genes and environment, multiple phenotype analysis, risk prediction, integrative analysis of different types of data, causal mediation analysis and causal inference, analysis of epidemiological and complex observational study data. Her theoretical and computational statistical research includes statistical methods for testing a large number of complex hypotheses, causal inference, statistical inference for large covariance matrices, prediction models using high-dimensional data, cloud-based statistical computing, and statistical methods for epidemiological studies.

Dr. Lin’s statistical methodological research has been supported by the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (OIA) (R35) (2015-2022) from the National Cancer Institute (NCI). She is the contact PI of the Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute , and the multiple PI of the U19 grant on Integrative Analysis of Lung Cancer Etiology and Risk from NCI. She is also the contact PI of the T32 training grant on interdisciplinary training in statistical genetics and computational biology . She is the former contact PI of the Program Project (PO1) on Statistical Informatics in Cancer Research from NCI.

Dr. Lin is the former Chair of the COPSS (2010-2012) and a former member of the Committee of Applied and Theoretical Statistics (CATS) of the National Academy of Science. She co-launched the new Section of Statistical Genetics and Genomics of the American Statistical Association and served as a former section chair. She is the former Coordinating Editor of Biometrics and the founding co-editor of Statistics in Biosciences. She has served on a large number of committees of many statistical societies, and numerous NIH and NSF review panels.

BRENDEN LAKE

Assistant Professor of Psychology and Data Science, NYU

LEARNING THROUGH THE EYES OF A CHILD

Young children have meaningful expectations about the world around them. What is the origin of this early knowledge? How much can be explained through generic learning mechanisms applied to sensory data, and how much requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to new datasets and progress in deep learning. We train large-scale neural networks through the eyes of a single developing child, using longitudinal baby headcam videos (Sullivan et al., 2020, PsyArxiv). Our results show how high-level visual representations emerge from a subset of one baby’s experience, through only self-supervised learning.

 

Biography

Brenden builds computational models of everyday cognitive abilities, focusing on problems that are easier for people than they are for machines. The human mind is the best-known solution to a diverse array of difficult computational problems: learning new concepts, learning new tasks, understanding scenes, learning a language, asking questions, forming explanations, amongst many others. Machines also struggle to simulate other facets of human intelligence, including creativity, curiosity, self-assessment, and commonsense reasoning.

In this broad space of computational challenges, Brenden’s work has addressed a range of questions: How do people learn a new concept from just one or a few examples? How do people act creatively when designing new concepts? How do people learn qualitatively different forms of structure? How do people ask questions when searching for information?

By studying these distinctively human endeavors, there is potential to advance both cognitive science and machine learning. In cognitive science, building a computational model is a test of understanding; if people outperform all existing algorithms on certain types of problems, we have more to understand about how people solve them. In machine learning, these cognitive abilities are both important open problems as well as opportunities to reverse engineer human solutions. By studying human solutions to difficult computational problems, Brenden aims to better understand humans and to build machines that learn in more powerful and more human-like ways.

 

Follow Brenden on Twitter: @LakeBrenden

 

DSI & CCMB Data Wednesday Seminar Series

The Data Science Initiative (DSI) joins the Center for Computational and Molecular Biology (CCMB ) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science. Please check our events page for more information on these and other events of interest.

Desmond Upton Patton, PhD
Associate Professor of Social Work; Associate Dean of Curriculum Innovation and Academic Affairs
Columbia University School of Social Work

Desmond Upton Patton’s research uses qualitative and computational data collection methods to examine the relationship between youth and gang violence and social media; how and why violence, grief, and identity are expressed on social media; and the real-world impact these expressions have on well-being for low-income youth of color. He studies the ways in which gang-involved youth conceptualize threats on social media, and the extent to which social media shapes and facilitates youth and gang violence.

The Decoding Disparities Lecture Series

The Decoding Disparities Lecture Series is sponsored by The Warren Alpert Medical School of Brown University and the Brown School of Public Health to examine health inequity and to outline steps toward a more equitable and just health care system.

The series is supported by The Paul Levinger Professorship Pro Tem in the Economics of Health Care. This lecture was established in 1987 to honor the memory of Paul Levinger by a gift from his wife, Ruth N. Levinger, on behalf of the Levinger family. The Levingers’ daughter and son-in-law, Bette Levinger Cohen and John M. Cohen ’59, MD were instrumental in Mrs. Levinger’s decision to make this gift.

Continuing Medical Education Credit

This live activity is approved for AMA PRA Category 1 Credit™

Physicians: To be eligible to claim CME credit, please register for this event at cme-learning.brown.edu