Menu
Menu
Duke Kunshan Healthcare Big Data Training Program is a one-week immersion program broadly introducing undergraduate students to data analytics through a health lens. Hosted by the Global Health Program at Duke Kunshan University, the Healthcare Big Data Training Program welcomes talented rising sophomores, juniors, and seniors intrigued by the applications of big data technologies in the healthcare industry, such as drug discovery, health monitoring, diagnostics, and precision medicine. We aim to stimulate curiosity in the field of big data through this rigorous yet approachable and fun program. By the end of the program, students will not only be equipped with essential techniques for managing and analyzing quantitative and qualitative data but will also be exposed to advanced machine-learning methodologies. Along the way, students will develop a working proficiency in several data analysis tools.
Students applying to this program should be motivated to learn cutting-edge data analytics and have a strong interest in becoming a researcher in health sciences or pursuing a career in the healthcare industry.
This unique experience serves as an entrée into the healthcare data analytics world as it is not heavily focused on math, algorithm, or computing but rather on the practical applications of machine learning, data mining, and statistics, as well as a variety of qualitative methodology in life and health sciences and healthcare industry. Duke Kunshan professors in global health and guests from the healthcare industry, in conjunction with post-graduate student workers, will teach the program.
“Big Data” refers to a large amount of information being monitored, collected, digitized, standardized, analyzed, and modeled. The amount of data human beings generate every day is unbelievably large. Many of these data are about our health – blood pressure, heart rate, body temperature, body mass index, stress, and brain activity. In a world where information is readily available and easily accessible electronically, there are unprecedented opportunities to apply big data in the life science and healthcare industry to solve complex health problems. Big data is revolutionizing the field of health sciences and the healthcare industry and has been applied almost everywhere in health and biomedical research, including but not limited to drug discovery, health monitoring, disease diagnostics and management, and precision medicine. Students will learn how to apply rudimentary and intermediate methods in machine learning, data mining, and statistics for solving real-world health problems. The highly innovative course will equip students with the tools and abilities needed to embark on an exciting academic and professional pathway to health and biomedical sciences.
Dr. Chenkai Wu is Assistant Professor of Global Health and Director of Graduate Studies, Global Health at Duke Kunshan University. Previously, he was a faculty in the Department of Epidemiology and Community Health at New York Medical College. Dr. Wu’s main research interests include theory, measurement, epidemiology, and clinical implications of frailty, promotion of healthy aging and longevity, and implications of machine learning and big data in clinical practice. Since 2016, Dr. Wu has written two book chapters and published nearly 40 peer-reviewed papers in the fields of epidemiology, gerontology, and cardiovascular disease. Dr. Wu has led or participated in research projects funded by external sources, such as Ministry of Science and Technology of China, Bill & Melinda Gates Foundation, National Institute of Health, and World Heart Federation. His work has been featured in over 100 major national and international media, including the Wall Street Journal, the Guardian, the National Public Radio, and Harvard Business Review.
Dr. Lijing Yan is Professor (with tenure) of Global Health, the Head of Non-communicable Chronic Diseases (NCDs) Research at Duke Kunshan University since July 2014. She is also affiliated with Duke University, Northwestern University, Peking University, Wuhan University, and The George Institute for Global Health. Dr. Yan graduated from Peking University (BA in Sociology) and the University of California, Berkeley (MPH and PhD). Her main areas of research are cardio-metabolic disease prevention and control and healthy aging. She has published over 120 peer-reviewed scientific papers some of which in leading medical journals such as JAMA, the Lancet, and Circulation. Her Scopus citation H-index was 30, Google Scholar H-index 48 and i10-index 76.
Dr. Ming Li received his B.S. degree in communication engineering from Nanjing University, China, in 2005 and his M.S. degree from Chinese Academy of Sciences in 2008. He joined the Signal Analysis and Interpretation Laboratory (SAIL) at USC in 2008 and received his Ph.D. in Electrical Engineering in May 2013. He is Associate Professor at Electrical and Computer Engineering of Duke Kunshan University, an adjunct professor at Wuhan University and a research scholar at Duke University. His research interests are in the areas of speech processing and multimodal behavior signal analysis. He has published more than 120 academic papers and served as the member of IEEE speech and language processing technical committee, APSIPA speech and language processing technical committee. He serves as the area chair of speaker and language recognition for Interspeech 2016, Interspeech 2018 and Interspeech 2020. Dr. Li has won multiple renowned awards in the area of speech recognition.
Dr. Qian Long is Associate Professor in the Global Health program at Duke Kunshan University. Dr. Long’s research interest and experience centers on health equity in relation to health systems development (with a focus on health financing and health services organization and delivery), including maternal health, tuberculosis control, and non-communicable diseases management in poor areas and among vulnerable groups of China and other low- and middle-income countries. Dr. Long has good knowledge of epidemiology, qualitative research, and systematic review.
Dr. Nisa is scientific agenda is centered around identifying what works to helps us lead healthier and more sustainable lives. Her research program focuses on the effectiveness of behavioral interventions to promote healthy and sustainable living – eg. how to best promote energy savings, reduce food waste, increase cancer screening or blood donation. She translates behavioral science into practice to tackle these critical challenges, and to respond to calls for better informed policies. In order to do so, she uses a variety of methodological tools including (1) lab studies testing small-scale psychologically-driven interventions; (2) large field experiments testing how to scale-up behavioral interventions in natural settings; and (3) evidence-based policy evaluation, based on meta-analyses of randomized controlled trials and quasi-experiments.
scientific agenda is centered around identifying what works to helps us lead healthier and more sustainable lives. Her research program focuses on the effectiveness of behavioral interventions to promote healthy and sustainable living – eg. how to best promote energy savings, reduce food waste, increase cancer screening or blood donation. She translates behavioral science into practice to tackle these critical challenges, and to respond to calls for better informed policies. In order to do so, she uses a variety of methodological tools including (1) lab studies testing small-scale psychologically-driven interventions; (2) large field experiments testing how to scale-up behavioral interventions in natural settings; and (3) evidence-based policy evaluation, based on meta-analyses of randomized controlled trials and quasi-experiments.
