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Spring 2022 NISS Virtual Academic Career Fair on May 18, 12-1:30pm ET

  • 1.  Spring 2022 NISS Virtual Academic Career Fair on May 18, 12-1:30pm ET

    Posted 05-15-2022 23:11
    Edited by Lingzhou Xue 05-15-2022 23:19


    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 hosted by NISS that will offer essential information about job opportunities for statisticians/data scientists in different academic environments.

    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?

    Department Chairs from the Department of Statistics at the University of Illinois Urbana-Champaign, Department of Biostatistics and Bioinformatics in the Rollins School of Public Health at Emory University, and Department of Statistics and Data Science in the Wharton School of the University of Pennsylvania will 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!

    Moderator: Xiufan Yu (Notre Dame), Speakers: Bo Li (Illinois), Robert  Krafty (Emory), Dylan Small (Penn)

    Each presenter will have 20 minutes to address the following general topics:

    • What advice do you give your new hires? 
    • How can a new hire seek a colleague who can provide good career advice? 
    • What should the priorities for new hires be – getting those publications sent out, perfecting your teaching, accepting service and committee assignments?
    • 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?


    Registration Process

    This virtual career fair is available (and free) to students/faculty at NISS Affiliate Institutions. To register for the virtual career fair, your organization needs to be a NISS affiliate. Please check the list of NISS affiliates to see if your organization is a NISS affiliate.

    If you qualify to register, please use your .edu, .gov, or .com email address when registering. The career fair will be conducted using Zoom. Please REGISTER TODAY (or at https://psu.zoom.us/webinar/register/WN_NaXojaDTRPa8w62rh-ozMg ).

    About the Speakers

    Dr. Bo Li is a Professor and Chair in the Department of Statistics at the University of Illinois Urbana-Champaign. She also holds a Richard and Margaret Romano Professorial Scholar and an Office of Risk Management & Insurance Research Faculty Scholar. She was also a Data Science Founder Professorial Scholar. Dr. Li received her Ph.D. in Statistics from Texas A&M University in 2006 and then became a Post-Doc at the National Center for Atmospheric Research before joining Purdue as an Assistant Professor in 2008. In 2013 she moved to the University of Illinois Urbana-Champaign. Dr. Li's research mainly focuses on spatial and spatio-temporal statistics and environmental statistics concerning climatology, atmospheric sciences, public health, forestry, and agriculture. Dr. Li has served on the editorial boards of several journals, including the Journal of the American Statistical Association, Journal of Agricultural, Biological and Environmental Statistics (JABES), and Environmetrics, and was a guest editor for a special issue in Statistica Sinica and a special issue in JABES. Her research has been funded by the NSF, NIH, NASA, and Sandia National Laboratories. Dr. Li was the recipient of the Young Investigator Award in the ASA Section on Statistics and the Environment and is a Fellow of the American Statistical Association. She was also the 2020 H. O. Hartley Award winner. 

    Dr. Robert Krafty has been Chair and Rollins Distinguished Professor of Biostatistics and Bioinformatics in the Rollins School of Public Health at Emory University since 2020, having previously served on the faculties of the Departments of Biostatistics and Statistics at the University of Pittsburgh and the Department of Statistical Science at Temple University's Fox School of Business.  His research includes the development of theory and methods for learning and conducting inference on high-dimensional time series, functional and signal data.   This methodological work is developed in conjunction with transdisciplinary collaborations to analyze heart rate variability, NIRS, EEG, physical activity recorded by wearable devices, and ecological momentary assessment data collected through mobile apps to monitor and treat mental, behavioral, and social health.

    Dr. Dylan Small received his Ph.D. in Statistics in 2002 from Stanford University with Tze Leung Lai as his thesis advisor.  Dylan is the Universal Furniture Professor of Statistics and Data Science in the Wharton School of the University of Pennsylvania and is currently the chair of the Department of Statistics and Data Science.  His research focuses on causal inference and applications of statistics to public health and public policy.  He was the founding editor of the journal Observational Studies.  Dylan has advised 28 Ph.D. students on their dissertations and mentored several undergraduates and postdoctoral fellows on research.  

    About the Moderator

    Dr. Xiufan Yu is an Assistant Professor of Statistics in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame. She received her Ph.D. in Statistics from the Pennsylvania State University in 2021 and her B.Sc. in Statistics from the University of Science and Technology of China in 2016. Her research interests include high-dimensional statistical inference, graphical models, statistical machine learning, and statistical modeling for multi-disciplinary applications in genetics and econometrics.



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    Lingzhou Xue
    The Pennsylvania State University
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