Keynote Speakers 

Razieh Nabi (Emory University) - Saturday, May 18, 2024

Talk Title: Using Causal and Counterfactual Thinking to Address Various Sources of Bias in Observational and Algorithmic Data

Abstract:This talk delves into the challenges of deriving valid causal conclusions from data, focusing on three main areas: confounding, missing data, and algorithmic unfairness. We begin by addressing the limitations of traditional methods for handling confounding due to unmeasured variables and introduce novel approaches that utilize data-adaptive machine learning algorithms to develop robust and efficient estimators. Next, we treat missing data as a causal inference issue, employing graphical models to draw parallels between methods for handling missing data and causal identification. This approach offers fresh insights into both fields. Lastly, we explore the ethical dimensions of algorithmic decision-making in critical areas like healthcare and criminal justice. We demonstrate how integrating causal inference with constrained optimization can address biases linked to sensitive attributes, ensuring fairness and rectifying historical injustices. Through these discussions, the talk highlights how causal and counterfactual reasoning can enhance the integrity and ethical standards of statistical analysis and decision-making, promoting more responsible data science practices.

Razieh Nabi graduated from the Johns Hopkins University in 2021 with a PhD in Computer Science, subsequently joining Emory University where she is currently a Rollins Assistant Professor in the Department of Biostatistics and Bioinformatics, with a secondary appointment in Computer Science. Dr. Nabi’s research is at the intersection of statistics, machine learning, and public health, where she focuses on developing innovative causal inference methodologies. Her work tackles critical challenges such as unmeasured confounders in observational studies, the curse of dimensionality, and biases in data—especially informative missing and censored data, and  those reflecting historical patterns of discrimination and inequality. Her methodologies have profound implications across various fields including social justice, public policy, and healthcare research.

Miguel Hernán (Harvard University) - Sunday, May 19, 2024

Talk Title: Immortal time and other misunderstandings in causal inference from observational data

Abstract: Biases that result in immortal time are responsible for some of the greatest historical blunders of data analyses for epidemiological and clinical research. Yet these biases disappear when observational data can be used to explicitly emulate a target trial. This lecture characterizes the biases with immortal time and discusses a framework to ensure that immortal time never happens again. 

Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. This work has shaped health policy and research methodology worldwide. Miguel is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health, where he has mentored dozens of trainees and students, and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers. Miguel has received many awards for his work, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the U.S. National Institutes of Health. He is Fellow of the American Association for the Advancement of Science and the American Statistical Association, Associate Editor of Annals of Internal Medicine, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association. He often serves on committees of the U.S. National Academies.”

Career Panels

Statistical Careers in Academia - Saturday, May 18, 2024 

Panelists: Claire Kelling, Carleton College; Linjun Zhang, Rutgers University; Joyce Fu, UC Riverside; Maria Montez-Rath, Phd, Stanford University; Sedigheh Mirzaei, M.S., Ph.D., Assistant Member at the Biostatistics Department of St. Jude Children's Research Hospital.

Overview: Embark on a journey into the world of Statistical Careers in Academia on Saturday, May 18, 2024. This event is tailor-made for those who aspire to make a profound impact through research, teaching, and scholarly pursuits in the field of statistics. Whether you're an undergraduate student contemplating graduate studies, a graduate student exploring academic career paths, or a seasoned professional considering a transition into academia, this event offers invaluable insights and guidance. Join us as renowned scholars and educators share their passion for statistics and illuminate the myriad opportunities within academia. Discover how statisticians contribute to cutting-edge research and mentor the next generation of scholars. Engage in stimulating discussions on topics ranging from innovative research methodologies to effective teaching strategies. Learn about the rewards and challenges of pursuing a career in academia, and gain practical advice on academic job searches, tenure-track positions, and professional development.

About the Panelists

	Claire Kelling is an Assistant Professor of Statistics at Carleton College, and completed her Dual PhD in Statistics and Social Data Analytics from Penn State. Claire’s research lies at the intersection of data analytics, criminology, public health, and political science. Her goal is to encourage and facilitate evidence-based practice and policy on crime and policing using tools for analyzing complex data in statistics and data science.
	Linjun Zhang is an Assistant Professor in the Department of Statistics, at Rutgers University. He received my Ph.D. in Statistics at University of Pennsylvania in 2019 where he was advised by Professor T. Tony Cai. His current research interests include machine learning theory (especially deep learninig), high dimensional statistical inference, unsupervised learning and privacy-preserving data analysis.
	Yingzhuo (Joyce) Fu, assistant professor of teaching in the Department of Statistics, earned her Ph.D. in statistics at University of California, Riverside. Previously worked as a data scientist in MarketShare, LA then taught Data Science in NYU Shanghai. Her core teaching philosophy is that intrinsic motivation brings out the best learning experience. Her research interests are in the areas of data mining, change-point detection for discrete data with various applications in network surveillance, digital marketing, consumer behavior analysis with big data.
	Maria Montez-Rath is a senior biostatistician and director of the Biostatistics Core of the Division of Nephrology at Stanford University where she has been collaborating with faculty and fellows since 2010 to study a variety of research questions relevant to kidney disease. She completed her PhD in Biostatistics from Boston University in 2008 focusing on methods for modeling interaction effects in studies involving populations with high levels of comorbidity, such as persons on dialysis. She is a senior biostatistician and director of the Biostatistics Core of the Division of Nephrology at Stanford University where she has been collaborating with faculty and fellows since 2010 to study a variety of research questions relevant to kidney disease. Her methodological interests are mainly data-driven and include the handling of missing data, survival analysis with an emphasis on models for time-varying covariates and competing risks, methods for analyzing epidemiologic studies, analysis of correlated data and comparative effectiveness studies, as well as data visualization.
	Sedigheh (Sadie) Mirzaei, M.S., Ph.D., is an Assistant Member at the Biostatistics Department of St. Jude Children's Research Hospital. Previously (2016-18), she was a postdoctoral research fellow of the Centre for Quantitative at Duke-NUS in Singapore (for 6 months) and the Biostatistics and Bioinformatics Branch of NICHD, NIH (for about 1.5 years). She completed her bachelor’s and master’s degrees in statistics at the Isfahan University of Technology and her PhD in Statistics at the Indian Statistical Institute in Kolkata. Since joining St. Jude, her primary research focus has been on the long-term effects of childhood cancer treatment, enhancing methodological rigor in designing, conducting, analyzing, and reporting cutting-edge survivorship research conducted at St. Jude. This includes biostatistical methodology research focused on developing innovative and practical statistical methods to address research challenges, particularly those for analyzing chronic health conditions (CHCs) in cohort studies. She is keenly interested in understanding the timing and occurrence of CHCs, recurrence, or late adverse events, particularly in childhood cancer survivorship research. In addition to her expertise in adaptive trials, sequential multiple-assignment randomized trials (SMART) designs, and large dataset analysis. She has been the Co-Investigator (co-PI) of several grants funded by the National Cancer Institute (NCI) and the Department of Defense (DOD). She is chair of the Epidemiology/Biostatistics/Global Outcome Working Group in the Cancer Control & Survivorship Program (CCSP) of the St. Jude Comprehensive Cancer Center.

Statistical Careers in Industry and Government - Sunday, May 19, 2024

Panelists: Will Nicholson, Senior Quantitative Analyst at Centiva Capital; Kathleen LD Maley, MA, Vice President of Analytics Products at Experian; Christy Chuang-Stein, Former Pfizer; Luca Sartore, Senior Research Associate at NASS/NISS; Chaoyu Yu, Data Scientist at YouTube 

Overview: Join us for an enlightening exploration into Statistical Careers in Industry and Government on Sunday, May 19, 2024. This event is designed to shed light on the diverse array of opportunities available for statisticians in both the private sector and government agencies. The NISS Graduate Student Network has put together this career panel for graduate students who may be contemplating your career path who may be curious about the role statistics play in various sectors. Throughout this session, esteemed speakers from leading industries and government bodies will share their insights, experiences, and expertise. Learn about the pivotal role statisticians play in shaping policies, driving business decisions, and solving real-world problems. Gain valuable advice on career development, skill enhancement, and navigating the job market in the statistical field. Engage in thought-provoking discussions, network with fellow attendees, and discover the endless possibilities that await in statistical careers. Whether your passion lies in data analysis, predictive modeling, or policy formulation, this event promises to inspire and inform. 

About the Panelists

	Will Nicholson is a Senior Quantitative Analyst at Centiva Capital. He is an experienced quantitative researcher with a focus on developing systematic trading strategies in global equities (primarily focused on the US). He has experience contributing to every aspect of the research process from idea generation to implementation and risk management. He has an extensive background in statistics, dating back to before the terms "data science" and "machine learning" were in common parlance. He completed his PhD in statistics at Cornell University. His research focused on the application of regularization methods to a wide variety of high-dimensional applied financial problems, with a particular emphasis on multivariate time series. He still actively engage in research whenever he has the time and in addition to regularization in time series, he has a particular interest in random forests and is always on the lookout for interesting and challenging problems. Over the course of his academic and industry career, he has developed novel, computationally efficient forecasting and prediction methods that result in substantial improvements in accuracy over conventional methods. He is an expert R programmer with considerable experience in both academic and industry settings.
	Chaoyu Yu is a data scientist at YouTube, where he leads the development of online experimentation methodologies and tools there. Before joining YouTube, he worked as a data scientist at Google shopping ads and commerce. Chaoyu received his PhD in Biostatistics from the University of Washington, advised by Peter Hoff and Mathias Drton. His dissertation topic was on adaptive statistical inference procedures and phylogenetic tree inferences.
	Kathleen Maley is Vice President of Analytics Products at Experian, where she leverages nearly 20 years of deep expertise in business intelligence, investigative analytics, predictive modeling, and optimization to maximize the impact of business-centered solutions. Kathleen is an analytics thought-leader who charts the vision and course for a modern analytics strategy, and has held various executive roles across the banking industry. An experienced model developer, investigative analyst, and P&L owner, Kathleen now teaches others how to harness the power of data for actionable insights and measurable ROI. Kathleen is a member of the International Institute for Analytics’ expert network, a published writer, and frequent speaker at both public and private events. She holds an AB in Mathematics from Bryn Mawr College and an MA in Applied Statistics from University of Michigan. Previously, Kathleen taught high school mathematics and statistics in Costa Rica, Mexico, and China.
	Christy Chuang-Stein is an independent statistical consultant with 30 years of experience in the pharmaceutical industry. She was Vice President, Head of the Statistical Research and Consulting Center (SRCC) when she retired from Pfizer in 2015. As the Head of the SRCC, Christy led a group of expert statistical consultants in providing strategic consultation to all teams that could benefit from the use of statistical thinking at Pfizer. In addition, Christy and her team collaborated broadly with scientists, both internally and externally. Christy grew up in Taiwan. She attended the National Taiwan University with a major in mathematics. She studied statistics at the University of Minnesota under the guidance of Professors Kinley Larntz and Stephen Fienberg. Her first post-graduate appointment was to teach statistics and provide consulting services at the Cancer Center of the University of Rochester. It was during her work at the Cancer Center that Christy developed her strong interest in statistical applications to biomedical research. That interest led her to join the pharmaceutical industry in 1985. Christy is a Fellow of the American Statistical Association with more than 145 publications including several book chapters and two books. Christy is a repeat recipient of Drug Information Association’s Donald E. Francke Award for Overall Excellence in Journal Publishing and Thomas Teal Award for Excellence in Statistics Publishing. Christy is a founding editor of the journal Pharmaceutical Statistics and has served on several editorial boards. Christy was a vice president of the American Statistical Association (ASA, 2009-2011). She received ASA’s Founders’ Award in 2012 and the Distinguished Achievement Award of the International Chinese Statistical Association in 2014.
	Luca Sartore is a Senior Research Associate for the National Institute of Statistical Science (NISS), working with the National Agricultural Statistical Service (NASS). He has been involved with the estimation and calibration of the US Census of Agriculture. He worked on modelling livestock, yield, and acreage for major agricultural commodities using various data sources, and he has also developed methodologies for assessing uncertainties. His contribution on the automation of analytical systems has focused on machine learning, artificial intelligence, and high-performance computing. He received his master in Statistics from the Ca’ Foscari University of Venice (Italy) and Ph.D. from the University of Padua (Italy). After his Ph.D., he joined the European Center of Living Technologies as a postdoc researching evolutionary algorithms in AI for one year in Venice (Italy). Since 2013, he has maintained several packages on the Comprehensive R-Archive Network (CRAN), one of which is currently used for production at NASS.

Organizing Committee

NISS Graduate Student Network Committee Members

Executive Committee: Grad Student Leadership

GSN Steering Committee: Faculty Members

About the NISS Graduate Student Network

The National Institute of Statistical Sciences (NISS) is a national organization that works on issues related to information and quantitative analysis. The goal of the NISS Graduate Student Network (GSN) is to create connections among graduate students from different academic institutions within the NISS Affiliates Program. Under this network, activities are organized to help students tackle challenges of graduate programs and help with their future careers. Students can share their experiences regarding their programs or an internship they did, and webinars and workshops can be organized around topic(s) of interest to them. In addition, we hope that this network will be beneficial throughout the student's career even after they graduate from their programs!