[Please Note: This session has already occurred. Go to the News Story for this event to read about what happened.]
Interested in pursuing a career as a statistician or data scientist at an academic institution? Perhaps you already have accepted an offer or will be on the market this coming year. Then you won’t want to miss this next career fair sponsored by NISS that will offer essential information about job opportunities for statisticians/data scientists in different academic environments.
During this academic career fair, you will hear from senior statisticians and data scientists who will be on hand to provide attendees with an inside look at the varying aspects of research, teaching and service that statisticians in these academic institutions get involved in and the career opportunities available for you to consider!
So if you have received an offer for this fall, what advice would you like to have as you start your first year in the job? What should your priorities be – getting those publications sent out, perfecting your teaching, accepting service and committee assignments?
How to answer these questions? We will ask several department heads, from different types of departments, to share their advice to their new hires.
Steve MacEachern, Department of Statistics, The Ohio State University
Bhramar Mukherjee, PhD, Department of Biostatistics, University of Michigan
Sujit Ghosh, PhD, Department of Statistics, North Carolina State University
Sharmistha Guha, Department of Statistics, Texas A&M University
Each presenter will give a brief overview of their institutions and discuss their opportunities, then we will transition to a panel discussion that will address the following general topics:
- What advice do you give your new hires?
- How can a new hire seek colleague who can provide good career advice?
- What are the potential distinguishing characteristics of candidates for a tenure-track/tenured faculty position in your institution?
- What advice would you give to potential job candidates this coming year?
- What advice would you give about how Ph.D. students or postdocs should prepare for the future?
A live Q&A Session will take place after the panel discussion.
About the Speakers
Steve MacEachern is a Distinguished Arts & Sciences Professor and Chair of the Department of Statistics at The Ohio State University. Steve MacEachern attended Carleton College, receiving a B.A. in Mathematics before moving on to the University of Minnesota for his graduate work. Upon graduation, he took a position at The Ohio State University where he is currently Professor of Statistics with a courtesy appointment in Psychology. He has spent leaves at Duke University and Carnegie Mellon University. He has served as President of the International Society for Bayesian Analysis and in a number of service roles for the American Statistical Association, including Program Chair for the 2012 Joint Statistical Meeting. Steve has worked on a variety of problems, ranging from the development and implementation of Bayesian methodology, to the creation of classical techniques in areas such as sampling and quality control, to applications in psychology and marketing. His research includes the development of models and inferential strategies, exploration of models’ properties, and the development of computational techniques. Recurring themes include robustness, the impact of model misspecification, and the gap between model and reality.
Professor Sujit Kumar Ghosh is currently a tenured Professor and Interim Department Head in the Department of Statistics at North Carolina State University (NCSU) in Raleigh, NC, USA. He has over 25 years of experience in conducting, applying, evaluating, and documenting statistical analysis of biomedical and environmental data. Prof. Ghosh is actively involved in teaching, supervising, and mentoring graduate students at the doctoral and master levels. He has supervised over 40 doctoral graduate students, he has served as a member of numerous other doctoral and master level committees. He was awarded the Cavell Brownie Mentoring Award at NCSU Statistics in 2014. Prof. Ghosh has also served as a statistical investigator and consultant for over 45 different research projects funded by various leading industries and federal agencies (e.g., BAYER, CDC, GSK, MERCK, NIH, NISS, NSF, SAS, U.S.EPA, USDA-NASS, among others). Prof. Ghosh has been regularly invited by several institutions and conference organizers around the world to present talks. He has given over 180 invited lectures, seminars at national and international meetings. He has also delivered several short courses and served as short-term visiting professor at several institutions in various countries (e.g., Greece, India, Italy, Singapore, Thailand, Turkey). Prof. Ghosh has published over 125 peer-reviewed journal articles in the various areas of statistics with applications in biomedical and environmental sciences, econometrics, and engineering. He has co-authored a book titled `Bayesian Statistical Methods published in 2019, which has been adapted as textbook by many leading institutions.
Prof. Ghosh received the International Indian Statistical Association (IISA) Young Investigator Award in 2008; was elected a Fellow of the American Statistical Association (ASA) in 2009; was elected as the President of the NC Chapter of ASA in 2013 and elected as the President of the IISA in 2017. He was the recipient of the prestigious Honorary Doctorate in Statistics at Thammasat University (Thailand) in 2015. He served as the Co-Director of Graduate Programs in Statistics at NCSU managing over 150 students annually during 2010-2013, the Project Director of a training program for undergraduates funded by the NSF during 2007-2013 and serving as the Principal Investigator of the NIH training program SIBS during 2019-2023. Furthermore, he has also served as the Program Director in the Division of Mathematical Sciences (DMS) within the Directorate of Mathematical and Physical Sciences (MPS) at NSF in 2013-2014. During 2014-2017 he also served as the Deputy Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), RTP, NC, an institute funded by the NSF. Link to Full Bio
Bhramar Mukherjee is the John D. Kalbfleisch Collegiate Professor and Chair of Biostatistics; Professor of Epidemiology and Global Public Health at University of Michigan (UM) School of Public Health. She also serves as the Associate Director for Quantitative Data Sciences at The University of Michigan Rogel Cancer Center. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation and analysis of high dimensional exposure data. She has co-authored more than 330 articles in statistics, biostatistics, medicine and public health. She is the founding director of the University of Michigan’s summer institute on Big Data.
Bhramar is a fellow of the American Statistical Association and the American Association for the Advancement of Science and is currently a Visiting By Fellow with Churchill College at the University of Cambridge in the UK. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond: including the Gertrude Cox Award, from the Washington Statistical Society in 2016, the L. Adrienne Cupples Award, from Boston University in 2020, and in 2021 the Distinguished Woman Scholar Award from Purdue University, the Janet L. Norwood award from University of Alabama at Birmingham, and most recently, the Sarah Goddard Power Award from the University of Michigan Academic Women’s Caucus. Bhramar and her team have been modeling the SARS-CoV-2 virus trajectory in India for the last two years which has been covered by major media outlets like Reuters, BBC, NPR, NYT, WSJ, Der Spiegel, Australian National Radio and the Times of India.
About the Moderator
Sharmistha Guha is an Assistant Professor (tenure-track) in the Department of Statistics at Texas A&M University. Previously, she was a postdoctoral fellow in the Department of Statistical Science at Duke University. She has earned her Ph.D. in Statistics from the University of California, Santa Cruz in 2019. Her research focus includes development of scalable Bayesian methods for object oriented data, supervised network data, and dimensionality reduction, where she draws motivation broadly from applications in neuroscience. She also develops methods in causal inference where observational data are spread over multiple files. She has been developing models for simultaneous Bayesian inference on probabilistic record linkage and causal effects. She has been a recipient of several awards, including the Leonard J. Savage Award (Honorable Mention) in 2021 for the best Bayesian dissertation for her work on Bayesian regression with networks.