The NISS Distinguished Alumni Award is intended for former NISS postdocs or research associates who have had distinguished careers. Examples might include academics who have been promoted to full professor, and individuals who have had successful careers in business, or as leaders in industry or government. Only former NISS postdocs or research associates who left NISS more than 10 years prior to the awarding year are eligible for the award.
Nominations are being sought for the 2020 NISS Distinguished Alumni Award
To nominate yourself for the NISS Distinguished Alumni Award submit as one PDF document the following information to firstname.lastname@example.org by April 30, 2020:
- A cover letter limited to two pages highlighting your achievements. The letter should also include a statement concerning your willingness to attend the NISS reception at the Joint Statistical Meetings in Philadelphia, PA.
- Your up-to-date CV.
Send questions or comments to email@example.com
Haibo Zhou: Honoring his distinguished career, his contributions as a research biostatistician, especially in environmental statistics, outcome-dependent sampling, and reproductive epidemiology.
Matthias Schonlau: Honoring his distinguished career as a research statistician in both industry and academia, especially his contributions in the area of survey methodology.
Dr. Shanti Gomatam is a Mathematical Statistician at the U.S. Food and Drug Administration. The award recognizes Gomatam's significant contributions to the review and approval of safe and effective medical products.
Dr. Minge Xie is a Professor of Statistics at the Rutgers University. The award recognizes Xie's research on the foundations of statistics and his longstanding commitment to his students and profession.
Jiming Jiang: Honoring his distinguished career and excellence in leadership through research, through education and through example as a role model for the profession of statistics.
Laura Steinberg: Honoring her distinguished career and impact through her leadership in realizing a vision for the intersection of environmental science and statistics.