Join us on October 20, 2023, from 12:30 PM to 2 PM ET for an engaging and informative virtual panel featuring distinguished alumni from our NISS Academic Affiliates!
The NISS Academic Affiliate program fosters collaboration between academia and industry to advance the field of statistics and data science. This webinar will showcase the remarkable journeys and career paths of our alumni who have leveraged their education in statistics to make a significant impact in various sectors. Our panel of esteemed alumni will share their personal experiences, challenges, and triumphs as they navigated the transition from academic life to the professional world. They will discuss how their statistical education prepared them for the dynamic and ever-evolving landscape of data science and statistics.
Lili Wang, Data Scientist, Youtube Trust & Safety and Payment Risk
Received PhD Biostatistics, MS Biostatistics, University of Michigan (2016), MS Molecular Biology, University of Michigan (2014)
Ben Seiyon Lee, Assistant Professor, Department of Statistics at George Mason University
Received PhD, Statistics, Pennsylvania State University, 2020
Christian Dueñas is a Data Scientist at Red Bull
Received MS in Statistics from University of California, Riverside, 2023
Rebecca Kurtz-Garcia, Smith College
Received PhD, Statistics, University of California, Riverside, 2023
Career Trajectories: Learn how these alumni transitioned from their academic studies to fulfilling careers in diverse industries, such as healthcare, finance, technology, and more.
Challenges and Opportunities: Gain insights into the challenges they faced and the opportunities they seized during their professional journeys.
Impact of Statistical Education: Discover the pivotal role statistical education played in shaping their careers and enabling them to solve real-world problems.
Advice for Aspiring Data Scientists: Get valuable advice and tips for students and early-career professionals looking to excel in the field of statistics and data science.
This alumni panel is an excellent opportunity for students, educators, and professionals interested in the statistical and data science fields to gain inspiration and insights from those who have successfully navigated similar paths. Whether you're a student contemplating your career choices or a seasoned professional seeking to stay updated with industry trends, this panel discussion promises to be enlightening and thought-provoking.
Don't miss this chance to connect with NISS Academic Affiliate alumni and broaden your understanding of the limitless possibilities that a statistical education can offer! Mark your calendars for October 20, 2023, at 12:00 PM ET, and register now to secure your spot for this enlightening webinar. We look forward to having you join us for this engaging discussion!
About the Speakers
Lili Wang is a data scientist at YouTube, working on multiple exciting projects related to Youtube's Trust & Safety and Payment Risk. Before joining Youtube, she was a Data Scientist at Google where she designed and built a platform to evaluate payments risk, worked on experiment design and data analysis, and developed statistical methods for payments risk. For her educational background, she was a research assistant for the University of Michigan Kidney Epidemiology and Cost Center (UM-KECC) from 2015 to 2020 and an intern for Sanofi in 2019. She graduated with a Ph.D. in Biostatistics from the University of Michigan and was advised by Prof. Douglas E. Schaubel and Prof. Peter Xuekun Song. Her research interests include developing statistical methods and software for dependent event data analysis, infectious disease transmissions, causal inference, and machine learning.
Ben Seiyon Lee is an Assistant Professor in the Department of Statistics at George Mason University. Ben Lee’s research interests include (1) computational methods for modeling high-dimensional spatial/Spatiotemporal data; (2) statistical methods and algorithms for calibrating complex computer models; and (3) interdisciplinary research in the environmental sciences. Lee's most exciting project was calibrating a hydrological computer model on flash floods and inland flooding in central Pennsylvania. His research goals included finding out how global warming affects the severity of inland floods and how those projections affect flood zones and insurance. So, he designed a hydrological computer model to project future inland flooding hazards. To aid him in his research, he studied data on the streamflow heights (water levels) in Selinsgrove, PA, and temperature inputs from high-quality climate models. He analyzed his data using the Sequential Monte Carlo to calibrate hydrological models and further assessed future hazards and risks based on climate change scenarios. He teaches survival analysis (STAT668) and alternative regression methods (STAT676) for the department. He's involved in many organizations such as student seminar co-chair, faculty advisor to the graduate student association, and liaison to the National Institute of Statistical Science (NISS).
Christian Dueñas is a Data Scientist at Red Bull where he works on various projects related to growth opportunities within the company. Most of his daily tasks involve performing data analysis, statistical modeling and web scraping. He began as an intern in the Summer of 2022, and landed a full-time data scientist position after finishing graduate school. He is an alumnus of the University of California, Riverside, where he obtained a B.S. (2021) and an M.S. (2023) in Statistics. During his time at UCR, Christian also worked as researcher for the United States Department of Agriculture. He performed the statistical analyses for two RNA-seq experiments, both of which became peer-reviewed publications in scientific journals. He was also the co-president of UCR’s undergraduate statistical club, HiSS, and led student-based projects regarding the effect of COVID-19 on undergraduates in the statistics department.
About the Moderator
Rebecca Kurtz-Garcia, is an assistant professor of mathematics and statistical and data sciences at Smith College, Department of Statistical & Data Sciences. She earned her M.S. degree in statistics at Ball State University and her Ph.D. in applied statistics from the University of California, Riverside. She has worked on a variety of projects related to time series, reliability analysis, biostatistics, sports analysis and fiscal policy. Her current research is on robust variance estimation for dependent multivariate data, which is often a critical component in hypothesis-testing procedures. Common settings with dependent data include economic indicators, steady state simulations, environmental metrics and other time series applications. Rebecca is also one of the founding members of the NISS Graduate Student Network.
NISS Academic Affiliates Committee
Piaomu Liu, Bentley University
Analisa Flores, UC Riverside
Vivian Li, UC Riverside
Samuel Wang, Cornell University
Sharmistha Guha, Texas A&M University