How to Be a Good Mentor

About the Speakers

Nick Horton is a Beitzel Professor in Technology and Society (Statistics and Data Science) in the Mathematics and Statistics Department at Amherst College. He has taught a variety of courses in statistics, data science, and related fields, including probability, mathematical statistics, regression and design of experiments. Nick is passionate about improving quantitative and data literacy for students with a variety of backgrounds (for introductory statistics) as well as engagement and mastery of higher-level concepts and capacities to undertake research (for the upper-level courses). Nick founded the mentoring program within the American Statistical Association Section on Statistics and Data Science Education. As an applied biostatistician and data scientist, Dr. Horton’s work is based squarely within the mathematical, statistical, and computational sciences, but spans other fields in order to ensure that research is conducted on a sound footing. His statistical methodological research focuses on the development of approaches to account for multivariate response models, longitudinal studies and missing data. He has also participated in several data science education initiatives through the National Academies, where he serves as co-chair of the Committee on Applied and Theoretical Statistics (CATS). Dr. Horton received a Sc.D. from Harvard School of Public Health (1999) and an A.B. from Harvard College (1987).

Reneé H. Moore is a Research Professor, Director of the Biostatistics Scientific Collaboration Center (BSC), and Director of Diversity, Equity & Inclusion for the department of Epidemiology and Biostatistics at the Dornsife School of Public Health. Most recently, Dr. Moore was Associate Professor-Research in the Department of Biostatistics and Bioinformatics at the Rollins School of Public Health of Emory University and the Director of the Biostatistics Collaboration Core. She received a B.S. in mathematics and completed the secondary mathematics education program from Bennett College, and her PhD in Biostatistics from Emory University. After completing her doctoral degree, Dr. Moore spent six years as an Assistant Professor at the University of Pennsylvania, Perelman School of Medicine, with a primary appointment in the Department of Biostatistics and Epidemiology and a secondary appointment in the Department of Psychiatry. Dr. Moore taught physicians, was the lead statistician in the data coordinating center for a multi-site randomized clinical trial of sleep apnea, and was the biostatistician in the Center for Weight and Eating Disorders, joining a group of researchers investigating interventions for the prevention and treatment of obesity in children, adolescents, and adults. Dr. Moore was also an Associate Professor-Teaching in North Carolina State University's Department of Statistics. Dr. Moore’s research interests are in the design, conduct, and analysis of clinical trials and statistical applications to obesity, sleep apnea, and health disparities. She has a wealth of experience as a biostatistician collaborating with clinicians, public health practitioners, and scientists. Dr. Moore also dedicates much of her time to training the next generation of collaborative biostatisticians and users of statistics. Dr. Moore has been very active in ENAR, ASA, especially the Committee on Minorities in Statistics, and is a Fellow of the ASA.

Daniel Jeske is the Vice Provost of Academic Personnel and a Professor in the department of statistics at the University of California, Riverside (USA).  He served a 7-year term as the Statistics Department Chair at UCR.  As Vice Provost he leads the team at UCR that facilitates the recruitment and advancement of academic employees and provides training and assistance to those employees navigating academic policies. He is an elected fellow of the American Statistical Association (ASA) and the current Past-Editor-in-Chief of the ASA's flagship journal, The American Statistician.  He is the current President of the International Society of Business and Industrial Statistics (ISBIS), a society of the International Statistical Institute (ISI). His research interests have included classification and prediction methodologies, longitudinal data modeling, statistical process control methodologies, biostatistics applications, and reliability modeling.

Joel Dubin completed his Master’s Degree in Applied Statistics at Villanova University in 1993 and went on to work at Veteran Affairs Health Services and Research in Houston, Texas; and at the University of Texas M.D. Anderson Cancer Center, also in Houston. Joel received his PhD in Statistics from the University of California at Davis in 2000, after which he worked as an assistant professor at the Yale University Division of Biostatistics, now the Department of Biostatistics, forging several collaborations with researchers in public health and medicine. Dr. Dubin arrived as an associate professor at the University of Waterloo in 2005, with a joint appointment in the Department of Statistics and Actuarial Science, and the Department of Health Studies and Gerontology, the latter which is now the School of Public Health Sciences. His primary research interest is in the area of methodological development in longitudinal data analysis, with an emphasis on multivariate longitudinal data. Methods pursued for this type of data include the correlation of different longitudinal outcomes over time using curve-based methods, and incorporating lags and derivatives of the curves. Joel is also interested in change point and latent response models for longitudinal data, as well as prediction models, including the consideration of similarity to improve prediction accuracy. He works in a variety of application areas, including nephrology, cancer, smoking cessation, intensive care, electronic health records, nutrition, aging, youth health, and environmental issues.

Moderator

Y. Samuel Wang is currently an assistant professor at Cornell in the Department of Statistics and Data Science. He was previously a principal researcher (post-doc) at the University of Chicago’s Booth School of Business working with Mladen Kolar, and he completed his PhD in Statistics at the University of Washington under the supervision of Mathias Drton. Dr. Wang’s interests include thinking about problems where the goal is to discover interpretable structure which underlies the data generating process. This includes problems in the areas of causal discovery, graphical models, and mixed membership models. In many cases, the methods are tailored for the high-dimensional setting where the number of variables considered may be large when compared to the number of observed samples. Dr. Wang’s applied interests vary, his primary research areas are graphical models and causal discovery. Dr. Wang received his undergraduate degree in both applied math and economics at Rice University, and he worked as a management consultant for two years before embarking on his PhD studies.

Event Organizers:

Sumanta Basu, Cornell University
Esra Kurum, University of California, Riverside
Piaomu Liu, Bentley University
Kevin Lee, Western Michigan University