Program

Abstract: 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, and evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation and quarantine, as well as summarizing the epidemiological characteristics of the cases. The results show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan. I will next present the estimated transmission rates in USA and other countries and intervention effects using social distancing, test-trace-isolate strategies. I will present the analysis results of >500,000 participants of the HowWeFeel project on symptoms and health conditions in US, and discuss the risk factors of the epidemic. I will discuss estimation of the proportion of asymptomatic and pre-symptomatic cases and the probability of resurgence. Preliminary survey results on the impact of COVID-19 on US undergraduate education will also be discussed. I will provide several takeaways and discuss priorities.

Bio: Xihong Lin is former chair and Professor of the Department of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University, and Associate Member of the Broad Institute of Harvard and MIT.

Dr. Lin’s research interests lie in development and application of scalable statistical and computational methods for analysis of massive health and genomic data. Examples include analysis of 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 complex observational study data.

Dr. Lin’s theoretical and computational statistical research lies in scalable statistical inference for big data, including statistical methods for testing a large number of complex hypotheses, statistical inference for large covariance matrices, prediction models using high-dimensional data, causal inference, and cloud-based scalable statistical computing. 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 National Cancer Institute. She is 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 an elected member of the National Academy of Medicine. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award, and the 2017 COPSS FN David Award. She is an elected fellow of American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Lin is the former Chair of the COPSS (2010-2012). She is the founding chair of the US Biostatistics Department Chair Consortium. She is the founding co-chair of the Young Researcher Workshop of East-North American Region (ENAR) of International Biometric Society. She co-launched the Section of Statistical Genetics and Genomics of the American Statistical Association (ASA). She is the former Coordinating Editor of Biometrics, and the founding co-editor of Statistics in Biosciences.

Abstract: To combat COVID-19, clinicians and scientists all need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. The first challenge is quantity. For example, nearly 2.7K new papers are published at PubMed per day. This knowledge bottleneck causes significant delay in the development of vaccines and drugs for COVID-19. The second challenge is quality due to the rise and rapid, extensive publications of preprint manuscripts without pre-publication peer review. Many research results about coronavirus from different research labs and sources are redundant, complementary or event conflicting with each other. Let's consider drug repurposing as a case study. Besides the long process of clinical trial and biomedical experiments, another major cause for the long process is the complexity of the problem involved and the difficulty in drug discovery in general. The current clinical trials for drug re-purposing mainly rely on symptoms by considering drugs that can treat diseases with similar symptoms. However, there are too many drug candidates and too much misinformation published from multiple sources. In addition to a ranked list of drugs, clinicians and scientists also aim to gain new insights into the underlying molecular cellular mechanisms on Covid-19, and which pre-existing conditions may affect the mortality and severity of this disease. To tackle these two challenges, we have developed a novel and comprehensive knowledge discovery framework, COVID-KG, to accelerate scientific discovery and build a bridge between clinicians and biology scientists.

COVID-KG starts by reading existing papers to build multimedia knowledge graphs (KGs), in which nodes are entities/concepts and edges represent relations involving these entities, extracted from both text and images. Given the KGs enriched with path ranking and evidence mining, COVID-KG answers natural language questions effectively. Using drug repurposing as a case study, for 11 typical questions that human experts aim to explore, we integrate our techniques to generate a comprehensive report for each candidate drug. Preliminary assessment by expert clinicians and medical school students show our generated reports are informative and sound.

Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is also an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She is the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Bosch, IBM, Disney).

Jian Pei is a professor at Simon Fraser University. His research interest is on data science, data mining, database systems, information retrieval, and applications, such as health informatics. He has published prolifically in his areas and also actively served the communities. He was very lucky to obtained his Ph.D. degree under Professor Jiawei Han’s supervision from the same university in 2002.

Chengshan Xiao is the Chandler Weaver Professor and Chair of the Department of Electrical and Computer Engineering at Lehigh University. He is a Fellow of the IEEE and a Fellow of the Canadian Academy of Engineering. Previously, he was a Program Director at the USA National Science Foundation, a Senior Engineer at Nortel Networks, and faculty member at several universities in China, Canada and USA. Dr. Xiao is an elected member of the Board of Governors of the IEEE Communications Society. Previously, he served as the Editor-in-Chief of the IEEE Transactions on Wireless Communications.