Connection Day @ The Blackwell Inn - Monday, October 7
8:00 - 8:30 a.m. Morning Refreshments
8:30 - 9:45 a.m. Opening Session & Panel of Chairs
9:45 - 10:00 a.m. Break
10:00 - 11:15 a.m. Alumni Session 1
Presenters from Industry:
Session Chair: Matt (Jiangyong) Yin
Bio: Matt is the cofounder of fintech startup Jupiter Card. Previously, he was Head of Data Science at CapitalG, Alphabet's growth-stage VC fund.
Teri Tykodi Berliner
Title: A Statistician Among Electrical Engineers
Abstract: I will talk briefly about my year getting my MAS, and I'll discuss how I used my MAS at American Electric Power(AEP)
Bio: Teri received her Master of Applied Statistics (MAS) degree in 1985 and an Executive MBA from the Fisher College of Business in 2004. She had a 33-year career at American Electric Power and retired in 2018.
Dionne Swift
Title: Unleashing the Power of Statistics: A Statistician's Journey in Industry
Abstract: In today's data-driven world, the role of statisticians has become more crucial than ever. From analyzing complex datasets to making informed decisions, statisticians possess the skills and knowledge to unlock valuable insights that drive innovation across various industries. As a world leader in the research, product development, manufacture, and marketing of consumer products, a career in statistics at P&G offers the opportunities to tackle a wide array of complex problems and contribute to cutting-edge research. I will share my personal journey, experiences, and insights gained from applying statistical methods to real-world challenges and navigating the corporate world. I aim to provide insights into the skills and competencies required to thrive as a statistician in the industry, alongside the importance of staying up-to-date with emerging statistical methodologies, software tools, and technologies.
Bio: Dionne Swift is a Director-Principal Statistician in the Discovery & Innovation Platforms. She joined Procter and Gamble shortly after receiving her Ph.D. in Mathematical Statistics from the Ohio State University. Dionne has worked in conjunction with engineers and scientists in Corporate Research, Fabric & Home Care, Family Care and Beauty Care business units in areas of experimental design, test method development and validations (i.e., GR&R’s), statistical modeling and consumer research. Currently, she collaborates with scientists in the R&D Bioscience & Life Sciences Organizations to leverage existing/new life sciences capabilities for product innovation and discovery. Her current research interests include high-dimensional data analysis, statistical genomics, multi-omics integrative analysis, and Bayesian data analysis with application in genomics proteomics, and microbiome research.
Christopher Bush
Title: What I didn’t learn in graduate school
Abstract: As I was leaving OSU, my advisor reminded me that I had the education and skills that would allow me to have a good job and more or less have a good life. This was true, but moving into the workforce required some adjustments to my focus and I had many questions. How do I fit into the culture? How do I work with all these people from different disciplines? What is expected of me? How do I grow my career?
In this talk, I will share some of the most impactful advice and recommended types of training that I received from my managers and peers, and what I have passed on to my peers, colleagues and direct reports.
Bio: Chris Bush grew up in the Finger Lakes Region of New York State, completed his undergraduate studies at the Rochester Institute of Technology, and then moved with his wife to Columbus, Ohio for his graduate work. He completed his PhD in Statistics from The Ohio State University in 1994.
After leaving OSU he worked for two years as a trial statistician at Ross Products Division of Abbott Laboratories in Columbus. In 1997 he moved to Novartis Pharmaceuticals in NJ, and has been there ever since. While at Novartis he has held various roles, beginning as a trial statistician, then as the lead statistician for a series of products in the cardiovascular space that were submitted for regulatory approvals. Currently his is a group head for a team of statisticians who support late phase (post-approval) activities for the products in Novartis’ cardiovascular and renal portfolio.
Chris and his wife, Wandy, currently live outside of Bethlehem, Pennsylvania. They have two adult children, Cassandra and Jordan, who many may remember running around the basement hallway of Cockins Hall. Wandy has built and is running a gourmet garlic farm and is continually pestering Chris to retire and help her. Chris, has been resisting but the siren’s song of spending more time outside and the anticipated arrival of their first grandchild may soon win out.
Presenters from Academia:
Session Chair: Lin Fei
Bio: As a professor and researcher, Dr. Fei designs clinical and non-clinical studies, performs data analysis and presentation and examines mathematical modeling. His primary interests are probability and statistical theory applied to medical research, including distributional theory and optimization. His interest in this field stems from his mathematical and statistical training and quantitative research. One of his notable discoveries is the probabilistic relationship between sample size and discovery rate in clinical studies. He has been a researcher for over 29 years and began working with Cincinnati Children’s in 2012.
Candace Berrett
Title: Rethinking Spatial Confounding for the Spatial Linear Mixed Model
Abstract: In the last two decades, considerable research has been devoted to a phenomenon known as spatial confounding. Spatial confounding is thought to occur when there is multicollinearity between a covariate and the random effect in a spatial regression model. This multicollinearity is considered highly problematic when the inferential goal is estimating regression coefficients and various methodologies have been proposed to attempt to alleviate it. In this paper, we offer a novel perspective of synthesizing the work in the field of spatial confounding. We propose that at least two distinct phenomena are currently conflated with the term spatial confounding. We refer to these as the "analysis model'' and the "data generation'' types of spatial confounding. We show that these two issues can lead to different conclusions about whether spatial confounding exists and whether methods to alleviate it will improve inference. Our results also illustrate that in most cases, traditional spatial linear mixed models do help to improve inference on regression coefficients. Drawing on the insights gained, we offer a path forward for research in spatial confounding.
Bio: Candace Berrett earned her PhD in Statistics from The Ohio State University in 2010. She currently works at Brigham Young University in the Department of Statistics as Professor and Associate Chair. She is also currently serving as ISBA's EnviBayes chair and ASA's SBSS chair-elect-elect (2025 chair-elect). Her research interests focus on Bayesian models for spatially-dependent and environmental data.
Yufeng Liu
Title: Statistical Significance of Clustering for High Dimensional Data
Abstract: Clustering serves as a fundamental tool for exploratory data analysis, but a key challenge lies in determining the reliability of the clusters identified by these methods, differentiating them from artifacts resulting from natural sampling variations. In this talk, I will present statistical significance of clustering (SigClust) as a cluster evaluation tool for high dimensional data. To begin, we define a cluster as data originating from a single Gaussian distribution and frame the assessment of statistical significance of clustering as a formal testing procedure. Addressing the challenge of high-dimensional covariance estimation in SigClust, we employ a combination of invariance principles and a factor analysis model. I'll also discuss an enhanced SigClust using multidimensional scaling (MDS) on dissimilarity matrices. SigClust for hierarchical clustering will be presented as well. Simulations and real data, including cancer subtype analysis, validate SigClust's effectiveness in assessing clustering significance.
Bio: Dr. Yufeng Liu is currently professor in Department of Statistics and Operations Research, Department of Biostatistics, and Department of Genetics at University of North Carolina at Chapel Hill (UNC). He is also a member of the Lineberger Comprehensive Cancer Center at UNC. Dr. Liu performs research at the intersection of statistics, machine learning, optimization, and data science. His current research interests include statistical machine learning, high dimensional data analysis, dat integration, personalized medicine, bioinformatics, and e-commerce. Dr. Liu serves on the editorial boards for several statistics journals, including as Area Editor for Annals of Applied Statistics, and Associate Editor for Journal of American Statistical Association. He also served as an Associate Editor for several other journals, including Journal of the Royal Statistical Society: Series B (2013–2018), Canadian Journal of Statistics (2019–2022), Journal of Multivariate Analysis (2015–present), and Statistica Sinica (2011–2014). He received the CAREER Award from National Science Foundation in 2008, and Ruth and Phillip Hettleman Prize for Artistic and Scholarly Achievement in 2010, and the inaugural Leo Breiman Junior Award in 2017. Dr. Liu is currently an elected fellow at American Statistical Association (ASA), Institute of Mathematical Statistics (IMS), and an elected member of International Statistical Institute (ISI).
Matthias Katzfuss
Title: Generative modeling of spatial distributions via autoregressive Gaussian processes
Abstract: In many applications, including climate-model emulation and calibration, there is a need to learn the conditional distribution of a high-dimensional spatial field given a covariate vector, based on a small number of training samples. We propose a nonparametric Bayesian method that decomposes this challenging conditional density estimation task into a large series of univariate autoregressions that we model using heteroskedastic Gaussian processes with carefully chosen prior parameterizations. We describe scalable variational inference based on stochastic gradient descent. The resulting generative model can be used to sample from the learned distribution or transform existing fields as a function of the covariate vector. We provide numerical illustrations and comparisons.
Bio: Matthias Katzfuss obtained his PhD at The Ohio State University in 2011 under the direction of Noel Cressie. Matthias is currently a Professor in the Department of Statistics at University of Wisconsin–Madison. His research interests include computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications to environmental and satellite remote-sensing data. His research has been funded by NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. Matthias is a Fellow of the American Statistical Association (ASA) and the recipient of an NSF Career Award, a Fulbright Scholarship, and an Early Investigator Award from the ASA Section on Statistics and the Environment.
11:15 - 11:30 a.m. Break
11:30 a.m. - 12:45 p.m. Alumni Session 2
Presenters from Industry:
Session Chair: Abhijoy Saha
Bio: Abhijoy Saha is a Senior Advisor specializing in Oncology at Eli Lilly and Company, where he focuses on designing and optimizing late-phase clinical trials. His expertise spans developing advanced statistical methodologies and crafting innovative strategies to improve the efficiency of clinical research across the drug development lifecycle. He earned his Ph.D. in Statistics in 2019 under the supervision of Prof. Sebastian Kurtek.
Srinath Sampath
Title: Be Brave Enough To Suck At Something New: Reminiscences, Gratitude, And Lessons Learned
Abstract: From a largely clueless immigrant student in awe of America enrolled in the OSU Masters program in Statistics in 1992, who grew into a somewhat less clueless student still in awe of America and defending his PhD thesis in 2013, a mere 21 years later, here are fond memories, a boatload of gratitude, and lessons learned (for the young 'uns).
Bio: Dr. Srinath Sampath spent over 20 years in corporate America as an actuary, a trader, and a portfolio manager, managing institutional and private wealth portfolios over the years. [] earned his PhD in Statistics from OSU under the guidance of Dr. Joe Verducci. In 2016, [] founded PrepAccelerator, a test-prep company with its roots in Columbus and with a growing student base around the US. PrepAccelerator aspires to become a critical resource for teen learning, starting with test prep and then supporting them through their academic journey.
Jingjing Schneider
Title: Navigating Contemporary Challenges in Pharmaceutical Statistics: Error Rate Assessment in Confirmatory Platform Trials
Abstract: Platform trials are increasingly adopted for their ability to test multiple treatments simultaneously and adapt over time, enhancing efficiency and increasing the likelihood of participants receiving active treatments. However, this design presents significant statistical challenges due to potential outcome variability in the control arm. This presentation will:
• Introduce key statistical principles for platform trials, with a focus on defining different types of erroneous findings under the broader category of false approval rate.
• Evaluate the stability and conditional distribution of the number of false approvals, accounting for correlations introduced by a common control and the performance of the shared control.
The goal is to spark discussions among statisticians and drive innovation in drug development programs, advancing the application of statistical methods in clinical trials.
Bio: Jingjing graduated with a PhD in Statistics from our department in 2014. She has since built a career in the pharmaceutical industry. Currently, she is the Director of Biostatistics at BeiGene, where she leads the statistical function for Immunology and Inflammation. With over 10 years of experience, Jingjing has a proven track record of applying advanced statistical methodologies to drive drug development strategies and data-driven decision-making. Her areas of expertise include complex study design, dose optimization, multiplicity adjustment, and platform trials. Jingjing lives in Upper Arlington with her husband Grant and their three children. Outside of work, Jingjing enjoys reading, traveling, and discovering great food.
Nader Gemayel
Title: My Journey from OSU Statistics to JPMorgan Chase
Abstract: I will share some memories from my time at Ohio State and share ways in which the education and training I received at the Department of Statistics positioned me for career success. I will also talk about my career journey and what I enjoy about my job.
Bio: Nader Gemayel is an Applied AI/ML Director at JPMorgan Chase. He earned his Ph.D. in statistics at The Ohio State University in 2010 and joined the bank, where he has worked in a variety of model development roles.
Presenters from Academia:
Session Chair: Katie Thompson
Bio: Katie is an Associate Professor in the Dr. Bing Zhang Department of Statistics at the University of Kentucky. In 2013, she received her Ph.D. in Statistics from The Ohio State University under the advisement of Dr. Laura Kubatko, and her research interests include the development and application of statistical methodology to relevant topics in statistical genetics and healthcare settings.
Hengrui Luo
Title: Nonparametrics revisited: from rankings to tree-based regressions
Abstract: This talk covers recent progress in tree-based methods in statistical community. We'll start with a local ranking perspective towards decision tree regressions, which is a well-known nonparametric tool. Then we will focus on the power of feature selection using decision tree regressors, and attempt to explain how global ranking perspective helps to ensure consistency. With this new perspective, we propose a new rank-based divergence statistics that can be applied to feature selection applications, like symbolic regressions. We will conclude by pondering future research avenues in this fascinating intersection of ranking and tree-based models.
Bio: I am an Assistant Professor at the Department of Statistics, Rice University, I am also affiliated with LBNL.I was a Postdoc Researcher at the Computational Research Department, Lawrence Berkeley National Laboratory (LBNL) at Berkeley, I obtained my Ph.D. degree from the Department of Statistics, the Ohio State University at Columbus in 2020.
Brad Hartlaub
Title: A look back at the seasons passing and years rolling from 1986 to 2024
Abstract: This talk will include personal experience from 1986 until today with OSU faculty members and fellow graduate students. The reflections will begin with memories of faculty members, inside and outside of the classroom. Our cohort had some great times in Cockins Hall, but our experiences have gone well beyond the walls of a building. Some of those experiences will be shared. Building a statistics program at Kenyon College has been fun, challenging, and rewarding, so we will take a brief look at course development, undergraduate research, faculty collaboration, and mentoring. This will not be a typical colloquium talk, but we will touch on some ongoing research projects and possibilities.
Bio: Brad Hartlaub joined the Kenyon faculty in 1990. He is a nonparametric statistician and his research deals with rank-based tests for detecting interaction. He has served as the chief reader of the AP Statistics Program and is an active member of the American Statistical Association's Section on Statistics and Data Science Education.
Brad was selected as a fellow of the American Statistical Association in 2006 and won the Waller Distinguished Teaching Career Award in 2024 . He has served the College as chair of the Department of Mathematics, chair of the Division of Natural Sciences, and an Associate Provost. He enjoys working with students on undergraduate research projects.
Emily L. Kang
Title: Fueling Curiosity: A Buckeye’s Journey of Growth Through Teaching, Learning, and Discovery
Abstract: The journey through graduate study is more than an academic pursuit—it's a transformative process of personal and professional growth. In this talk, I will reflect on how my experience in The Ohio State University’s Statistics program fostered curiosity, encouraged borrowing strength from mentors and peers, and inspired a commitment to excellence through teaching, learning, and discovery. I’ll also take a moment to acknowledge and celebrate the invaluable guidance of my professors and the camaraderie of fellow Buckeye graduates.
Bio: Emily received her M.S. in Statistics in 2006 and Ph.D. in Statistics in 2009, both from The Ohio State University. She is currently a Professor of Statistics in the Division of Statistics and Data Science at the University of Cincinnati. Her research focuses on developing statistical and machine learning methods for large-scale data with complex dependence structures, driven by applications in remote sensing, climate science, engineering, and the biological sciences.
12:45 - 2:15 p.m. Lunch
2:15 - 3:30 p.m. Alumni Session 3
Presenters from Government/Lab/Nonprofit:
Session Chair: Dave Jeppesen
Bio: Dave Jeppesen graduated in 1995 with a Master of Applied Statistics degree. He began his career at Capital One in Virginia starting as a statistician and ending as the Deputy Chief Risk Officer. He next went to Barclays Bank in London England as the UK Chief Marketing Officer. After two years in London, he moved back home to Idaho where he worked at Blue Cross of Idaho as the Executive VP of strategy, marketing and sales. He ended his career serving for five years as a member of Idaho Governor Brad Little’s Cabinet as the Director of Health and Welfare, the states largest agency. Dave retired in January 2024 and is married with four children and three grandchildren.
William Guthrie
Title: Identifying and Addressing Type III and Type IV Errors in Forensic Science Applications
Abstract: A critical aspect in the application of statistical methods to any type of real-world problem is the need to understand the problem in detail and to identify (or develop) and implement statistical methods or models that provide a practical solution. For about the last ten years, statisticians at NIST have been working with forensic scientists and other stakeholders in the justice system on the application of statistics to different types of scientific evidence, including DNA, footwear and tire tread impressions, properties of glass, fire debris, and identification of controlled substances. Since these types of forensic evidence have all been used for decades, however, statistical methods and models have already been proposed for the analysis of many of these types of evidence. Closer examination of the solutions provided by some of these methods, however, has turned up previously unrecognized Type III or Type IV errors [Mitroff and Silvers, 2009]. This talk explores two potential instances of Type III and IV errors identified by staff in the NIST Statistical Engineering Division and offers some tools and practices that could better address the needs of the forensic community when analyzing the different types of evidence these methods address.
Bio: William Guthrie received a B.A. degree in mathematics from Case Western Reserve University in Cleveland, OH, in 1987 and an M.S. degree in statistics from The Ohio State University in Columbus, OH, in 1990.
He worked as a mathematical statistician in the Statistical Engineering Division at the National Institute of Standards and Technology (NIST) in Gaithersburg, MD from 1989 to 2024. In 2015 he became the Chief of the Statistical Engineering Division and retired from federal service in June 2024, though his work at NIST continues now as a NIST Associate.
While at NIST he collaborated with scientists and engineers on applied research in a wide range of areas including forensics science, semiconductor and microelectronics applications, building materials and fire research, and chemical science. His statistical interests include uncertainty assessment, Bayesian statistics, design of experiments, calibration, modern regression methods, and statistical computation.
Over the course of his career he was awarded Gold, Silver, and Bronze Medals from the Department of Commerce, the NIST Allen V. Astin Measurement Science Award, and a Department of Energy Secretarial Honor Award. He is a Fellow of the American Statistical Association.
Nancy McMillan
Title: AI in Practice: Tools and Transitions
Abstract: Data analysis has been a headline worthy story since the early 2000’s, and for many years now “Data Scientist” has been “the sexist job of the 21st century”. Fast forward 20 years and businesses of all sizes and across nearly every market have data scientists that are asked to develop AI models to generate value, improve performance, and generally help their company stay competitive. The skills to be a data scientist are data analytics, experimentation, and programming. Although statisticians think of our field as data analytics, and we are certainly trained in experimentation, most data scientists are not statisticians. In fact, with the rise of data science and AI, statistics is getting pushed toward a narrow range of analytics, focused more on validation and confirmation and less on discovery, insights, and decision making. This talk explores these trends.
Bio: Nancy McMillan currently serves as Data Science Research Leader within Battelle’s Health Research & Analytics Business Line. For a diverse set of federal government clients, she is currently leading development of a large language model (LLM) based biocuration acceleration pipeline and user tool, development of pipelines, analytics, and visualizations of electronic initial case reporting data, and development of analytical methods for achieving abbreviated new drug application (ANDA) approval for an agile drug manufacturing technology.
Nancy managed the Health Analytics Division from 2017-2023, a team of approximately 100 data scientists that supports Battelle’s contract research business. She has a long history of collaborative work across Battelle bringing statistics and machine learning to Battelle’s deep capability in biology, chemistry, and material science. As a researcher and Project Management Professional, Nancy has worked and published on environmental exposure and risk assessment; transportation safety benefits; quantitative risk assessment related to chemical, biological, radiological and nuclear (CBRN) terrorism; bio surveillance; and bioinformatics.
Brian Williams
Title: Reflections on a Career of Statistical Contributions to National Security Science
Abstract: This presentation reflects on the author's educational and career experience as a statistician focusing on contributions to uncertainty quantification in support of decisions pertinent to the nation's national security. Applications to recapitalization of fielded military systems, stockpile stewardship, and nuclear explosion monitoring are discussed.
Bio: Dr. Brian J. Williams is a scientist in the Statistical Sciences Group (CCS-6) at Los Alamos National Laboratory. Dr. Williams has contributed to the development and implementation of statistical methods for the design and analysis of physical and simulation experiments, with a focus on data-driven tuning of physical and empirical models and solutions to inverse problems. He has three years of experience initiating and managing uncertainty quantification deliverables for a consortium of national laboratories, universities, and industrial partners. Most recently, he has developed open-source software tools for the Low Yield Nuclear Monitoring program that fuse signatures from multiple sensor modalities to improve the characterization of device parameters for explosive events. These tools have been integrated into the development environment supporting the nation’s treaty monitoring mission.
3:30 - 3:45 p.m. Break
3:45-4:30 p.m. Speed Session
4:30 - 5:00 p.m. Break
5:00 - 6:30 p.m. Poster Session & Drink Reception
6:30 - 8:30 p.m. Golden Anniversary Banquet
7:00 - 7:15 p.m. Presentation of Pickleball Tournament and Virtual 5.0k Winners
Pickleball Tournament
First Place: Seth Adarkwah Yiadom, Biostatistics PhD Student, and Ryan Loney, Statistics MS Student
Second Place: Alan Gan, Statistics PhD Student, and David Sivakoff, Current Faculty
Third Place: Allen Chen, Statistics PhD (2016), and Matthias Katzfuss, Statistics PhD (2011)
5.0K Virtual Race
First Place (21:29): Andrea Kuhn, Statistics MS Student
Second Place (23:12): Staci Hepler, Biostatistics PhD (2023)
Third Place (23:22): Alyssa (Farmer) Antonini, Biostatistics PhD 2023
7:15 - 7:30 p.m. Presentation of Alumni Awards
Tommy Wright
Gary Koch
Remarks delivered virtually by Gary Koch: I very much appreciate being one of the recipients of the inaugural alumni awards, as part of The Ohio State University Department of Statistics 50th Anniversary Celebration. My academic exposure to statistics began at The Ohio State University with an undergraduate set of courses taught by Professor Ransom Whitney. During the first of these courses in fall 1961, he identified opportunities for part-time hourly activities with the statistics laboratory, and I enthusiastically proceeded with them accordingly. At that time, the statistics laboratory was located on the 3rd floor of university hall prior to its renovation, and my activities there included manual calculations, key punching data cards, and card sorting on a tempermental device that was located on the 4thfloor (which had been identified as a no-access zone). The compensation was $1.00 per hour for 10-15 hours per week, although tuition per quarter at that time was $100.00 and very good meals could be obtained at cafeterias for $0.75. My enriching experiences with the statistics laboratory identified statistics as the discipline for my PhD graduate school program with the University of North Carolina at Chapel Hill and subsequently my professional activities there for statistical teaching, practice, and research. Since my graduation from The Ohio State University, the Department of Statistics has had 50 years of outstanding growth and leadership and I expect these trends to continue for the foreseeable future.
7:30 - 8:30 p.m. Keynote Address - Tommy Wright, PhD
Bio: Since joining the U. S. Bureau of the Census in January 1996 as a research mathematical statistician, Tommy Wright has served as Chief of the Center for Statistical Research and Methodology (formerly Statistical Research Division). The Center's researchers engage in collaborative work applying known statistical methods and in research for new and better statistical methods motivated by practical problems in data collection, processing, analysis, and dissemination. Between 1979 and 1996, he was a research staff member of the Mathematical Science Section at Oak Ridge National Laboratory where his research focused on probability sampling and estimation, the design of sample surveys, and elementary applied probability and combinatorics. He has over 30 years of undergraduate /graduate teaching experience in statistics and mathematics at Knoxville College; University of Tennessee-Oak Ridge Graduate Program; University of Tennessee, Knoxville; and Georgetown University. He was an ASA/NSF/Census Research Fellow (1993-1996) pursuing research into using probability sampling methods to improve the constitutionally required decennial census count. Currently, he is actively engaged in problems related to the Census Bureau's mission to provide quality data that helps leaders and decision makers maintain our nation's representative form of democracy. Dr. Wright was born and grew up in Birmingham, Alabama. He received the M.S. and Ph.D. in statistics from The Ohio State University, the M.S. in mathematics from the University of Tennessee, and the B.S. in mathematics from Knoxville College. His broad contributions in collaborative research (author of one book, editor of another, and author of over 40 papers in statistics and mathematics journals), teaching, and service have led to professional recognition: (I) Elected Member, International Statistical Institute (1989) and (ii) Fellow, American Statistical Association (1995).
Closing Events @ TDAI Ideation Zone, Pomerene Hall - Tuesday, October 8
9:30 a.m. Alumni Career Panel
Moderator: Andrew Richards
Bio: Andrew Richards is a 2021 graduate of our PhD program. He has since worked at OSU as a Visiting Assistant Professor and then as an Assistant Professor of Teaching Practice in our department. Prior to OSU he has years of experience in banking, insurance, asset management and government roles.
Session Chair: Glenn Hofmann
Bio: Glenn Hofmann is an influential C-level leader in Data and AI, who is known for building and managing successful organizations within the complex business, cultural, regulatory and technology realities of large corporations. He builds collaborative cultures and trust quickly across all levels,
inspiring globally dispersed teams and stakeholders to deliver their best work.
From 2016 to 2023 he was Chief Analytics Officer at New York Life, a Fortune 100 company. He has substantial experience both with data ecosystems (infrastructure, governance, modeling, MDM, etc.) and AI ecosystems (use case prioritization, AI-specific infrastructure, practical deployment of AI into production, governance, training, value generation).
He has a Ph.D. in Statistics from The Ohio State University and an MBA from the Wharton Business School. Glenn was recognized by DataIQ as one of the “100 Most Influential People in Data” in 2022 and 2023, and by OnCon as a “Top 100 Data and Analytics Professional”.
Panelists:
Po-Hsu (Allen) Chen
Bio: Dr. Chen is a Research Statistician and Data Scientist whose work mainly focus on extracting useful information and structures from large, high-dimensional datasets. Since joining Battelle in August of 2016, I have utilized my skills in statistical modeling and programming to successfully analyze large complex high dimensional data in fields such as health care, biomedical research, and environmental dilution. At OSU, my PhD work focused on developing a new sequential design optimization algorithm using calibrated computer simulators. In addition, I have developed a framework using calibrated predictors that can help injection molding manufacturers to identify the processing conditions that optimize processes with several conflicting performance measures.
Revathi Subramanian
Bio: Revathi Subramanian, is a Managing Director, Global Data Science Executive for Accenture Operations and leads a world class team of Data Scientists and Engineers addressing complex problems in Procurement, Infrastructure, Digital Marketing etc. using advanced analytics.
Earlier, she was SVP, Data Science at CA Technologies where she successfully launched various eCommerce 3D Secure Fraud, Security, and IT Management analytical product suites. As Director, R&D in SAS Institute Inc. she conceptualized, built, and implemented SAS Fraud Management (aka Raptor) solution with sophisticated Machine Learning / AI / Neural Network based fraud detection and advanced analytic solutions for Tax Under-filing, Collections, Network Intrusion Detection etc. For her pioneering work, she was awarded the “CEO award of excellence” by the CEO of SAS Institute in 2011. At HNC Software, as Executive Director, Product Engineering, Revathi spearheaded ProfitMax, a real-time transaction decision system that predicted credit risk, attrition risk, revenue and profit.
Revathi has an impressive track record of building world class highly successful AI-ML based businesses for multiple companies. Here at Accenture, Revathi is the Global Fraud AI Capability Lead for Center of Advanced AI and leads a team that has developed 40+ AI and Gen AI based solutions in Accenture with over 50+ successful deployments in highly complex environments. She has spearheaded product thinking and conceptualization of core components such as the scoring engine and feature libraries that span across industries and areas have helped to deliver a significant ROI and great business value. These solutions address diverse areas with an impact on billions of $ of revenue
Revathi has a strong presence as a leader in the industry and is knows as an excellent AI Technologist with 25+ seminal patents and a recognized data science expert with a book to her credit: http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470494395.html.
Revathi holds M.S. in Statistics degree from Ohio State University and a B.Sc. degree in Mathematics from Ethiraj College in Chennai, India. She serves as a trustee on the board of Indian Fine Arts Academy, San Diego, instrumental in sponsoring the top-notch artistes. Revathi has a number of other interests – she is an accomplished classical vocalist, community volunteer, avid gardener, and crafter with many interests.
Kevin Manouchehri
Bio: Kevin is a data science and statistics manager at Lubrizol, where he has worked since completing his Master's at OSU in 2019. He and his team create statistical and molecular models to predict and optimize chemical performance and also provide data science consulting and translating services.
Jaret Mrochek
Bio: I’m originally from Hilliard Ohio. I graduated from Ohio State in SP21 with a degree in Data Analytics and the specialization in Business Analytics. I started my career in Data Science as an Associate Data Scientist at KnitWell (formerly Ascena) and recently moved to CoverMyMeds in January ‘24.
10:45 - 11:00 a.m. Break
11:00 a.m. - 12:30 p.m. Panel Session 1, "Celebration of Women in Statistics and Data Science at Ohio State"
Moderator: Asuman Turkmen
Bio: Asuman Turkmen is a Professor in the Department of Statistics at OSU. She earned her PhD in Statistics from Auburn University in 2008. Her research interests lie in multivariate statistical methods that deal with robust estimation and in statistical genetics, specifically, examining potential sources of missing heritability and proposing research strategies to shed light on the underlying genetic architecture of complex diseases for more effective disease prevention or treatment. Outside of her academic work, Dr. Turkmen is passionate about contributing to initiatives that encourage and uplift women and underprivileged groups pursuing careers in statistics and data science.
Session Chair: Jessica Kohlschmidt
Panelists
Angela Dean
Bio: Angela Dean joined the statistics faculty in 1980 and retired in 2011. She is now Professor Emerita and a member of the Emeritus Academy at Ohio State. Angela is an elected Fellow of the ASA and the IMS, an elected member of ISI, and a Fellow of the RSS.
Angela’s primary research focus is in experimental design which is the science of determining the most efficient use of resources for answering a set of experimental questions. Her work has been supported by NSF, and she has published over 100 papers and has advised or co-advised 18 Ph.D. students. She is coauthor (with D.T. Voss and D. Draguljic) of the textbook "Design and Analysis of Experiments” (2nd ed, 2017). Angela co-organised the first and third of the conference series “Design and Analysis of Experiments” (DAE), which is aimed at young researchers and is now planning its 13th event. She served on the DAE steering committee for 8 years and has been active in the ASA Section for Physical and Engineering Sciences, serving as Program Chair in 2006 and as Section Chair in 2012.
Laura Kubatko
Bio: Laura Kubatko joined the statistics faculty in 2006, and holds a joint appointment in the Department of Evolution, Ecology, and Organismal Biology at Ohio State. Her other appointments include Adjunct Research Scientist at Lovelace Respiratory Research Institute in Albuquerque, New Mexico, Faculty Affiliate of the Initiative in Population Research at Ohio State, Faculty Affiliate of the Battelle Center for Mathematical Medicine at Nationwide Children's Hospital, and Faculty Affiliate for Translational Data Analytics at Ohio State. She has served as an Associate Editor for Systematic Biology since 2007, as an Associate Editor for Evolution from 2008 - 2010, and as Section Editor for BMC Evolutionary Biology since September 2016. Her work is supported by several grants from the National Science Foundation.
Yoonkyung Lee
Bio: Yoonkyung Lee is a Professor in the Departments of Statistics and of Computer Science and Engineering (by courtesy) at the Ohio State University. She received her PhD from the University of Wisconsin at Madison and joined Ohio State in 2002. She was a faculty co-director of the Translational Data Analytics Institute, Ohio State from 2020–2022. Her research areas are statistical learning and multivariate analysis with a focus on classification, ranking, dimensionality reduction, and kernel methods. She is generally interested in problems at the intersection of statistics and machine learning. She is an elected Fellow of the American Statistical Association and an associate editor of Statistical Analysis and Data Mining, Chemometrics and Intelligent Laboratory Systems, and Journal of Machine Learning Research.
Shili Lin
Bio: Shili Lin joined the Statistics Department faculty in 1995. Prior to that, she was the Neyman Visiting Assistant Professor in the Department of Statistics at the University of California at Berkeley, from 1993-1995. Lin’s research interests lie in the development and application of statistical methods to multi-omics data from cell lines, populations, and family samples. The most notable features of such data include ultra-high dimensionality, complex dependency, sparsity, heterogeneity, and incompleteness. Lin is an active member of and serves the statistical profession in various capacities, including editorial service for various journals (e.g. current Associate Editor (AE) of Biometrics and former AE of the Journal of the American Statistical Association), severing as panel members in NIH Study Sections, and serving professional organizations such as the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the International Statistical Institute (ISI). She also served the Caucus for Women in Statistics as President in 2018, and is currently serving in the Board of Directors of the Canadian Statistical Sciences Institute. Lin is the co-founder and president of the Florence Nightingale Day for Statistics and Data Science, a 501(c)(3) organization. Lin is a Fellow of the ASA, of the IMS, and of the American Association for the Advancement of Science (AAAS), and an elected member of the ISI.
Deb Rumsey
Bio: Dr. Deborah Rumsey is a Fellow of the American Statistical Association. Her main interest is Statistics Education. She is one of the founding members of CAUSE, the Consortium of Undergraduate Statistics Education and was the conference designer and first program chair for USCOTS: The United States Conference on Teaching Statistics. Deborah has written 7 books, including Statistics 1 and 2 for Dummies, Statistics Workbook for Dummies, Probability for Dummies, and Introductory Business Statistics: A Future Leader's Guide.
Elizabeth Stasny
Bio: When I arrived at OSU, I was the 3.5th woman in the department. OSU was the only place I interviewed where I would not be the only, or one of just two women on the faculty. I was the first woman on the faculty to have a baby while on the faculty. I am delighted that women now have a much larger presence in the department, as well as in the discipline.
My love of teaching led me to work for my PhD at Carnegie Mellon University; there, I discovered how much fun research is too. My proudest accomplishments in my career are the PhD and master's degree students I advised. Our graduates are such an impressive group! It has been an honor and a joy to work with them.
Xinyi Xu
Bio: Xinyi Xu received her PhD from the University of Pennsylvania and joined the statistics faculty in 2005. She is currently a Professor and Vice Chair for Graduate Studies. Xinyi also serves as an associate editor for Bayesian Analysis and has been actively involved in various committees of the American Statistical Association and the International Society for Bayesian Analysis. Her research focuses on developing Bayesian methodologies for statistical inference in high-dimensional data analysis, with specific interests in Bayesian hierarchical modeling, model selection, model averaging, predictive density estimation, and causal inference. Her work has been supported by multiple grants from the NSF and NIH.
12:30 - 2:00 p.m. Lunch
2:00 - 3:00 p.m. Ruth Keogh (live stream from IDWSDS)
3:00 - 4:00 p.m. Panel Session 2, Alum/OSU Employees
Moderator & Session Chair: Kellie Archer
Bio:
Panelists
Jonathan Baker
Bio: Jonathan Baker joined the statistics faculty in 2013 after almost two decades of employment as faculty member or chair at his previous institution. He serves as course coordinator for STAT 1450 (Introduction to the Practice of Statistics) and is also the department's transfer credit coordinator. He enjoys working with part-time lecturers on instructional matters and serves as a resource for graduate students as well. Jonathan is an Advocate per the University’s Advocates & Allies for Gender Diversity program. He serves as a faculty mentor to football student-athletes and members of the Louis Stokes Alliances for Minority Participation and Young Scholars Program for College Success. Dr. Baker is also a proud alumnus of this department.
Lai Wei
Bio: I am a Clinical Associate Professor in the Department of Biomedical Informatics within the College of Medicine and serve as the Division Lead for the Clinical Trials Division at the Center for Biostatistics in The Ohio State University. I received my Ph.D. in Statistics from OSU's Department of Biostatistics in the spring of 2008. With over 16 years of experience in collaborative research with cancer clinicians and researchers at the College of Medicine and Wexner Medical Center, I specialize in clinical trial design, including adaptive designs and sample size re-estimation. As a biostatistician, I contribute to early-phase oncology clinical trial design, implementation, oversight, and data analysis, as well as adaptive laboratory experiments. I have authored over 160 publications and serve as a biostatistical reviewer for the Clinical Scientific Review Committee. Additionally, I serve on the Ovarian Task Force of NCI’s Gynecologic Cancer Steering Committee, where I provide guidance on phase II/III clinical trial designs.
John Draper
Bio: John Draper is an Associate Professor of Clinical Operations and Business Analytics in the Fisher College of Business at The Ohio State University. He has a PhD and MS in statistics from Ohio State as well as BS degrees in mathematics and statistics from Florida State University. His extensive experience in education at Ohio State includes courses statistical theory, business analytics, engineering, operations, six sigma, statistical computing/data science, and sports analytics at both the graduate and undergraduate level as well as biostatistics courses for graduate-level students in the College of Public Health and Dentistry. He currently serves as the Co-academic Director of the Specialized Masters of Business Analytics and the Academic Director of the Executive Education Master Black Belt.
His professional experiences include roles as a statistical consultant as well as positions at Battelle Memorial Institute as well as developing educational resources for Cengage Learning and McGraw-Hill. Draper is a member of the American Statistical Association, and the recipient of the Thomas E. and Jean D. Powers Award for Outstanding Teaching Associate. In 2021, he received the Bostic-Georges Faculty Pace-Setters Award for contributions to assist with the transition to online learning.
Draper is a proud alum of The Ohio State University Marching Band (2003-07) and the Florida State University Marching Chiefs (1999-2003). He has also worked closely with the OSUMB in halftime show design since 2006.
Jiae Kim
Bio: Jiae Kim joined the Department of Marketing and Logistics at Fisher as a visiting assistant professor. Prior to arriving at Ohio State, she was a visiting assistant professor at the Department of Statistics at Indiana University, whose interests include statistical learning with specific emphasis on classification, dimension reduction, non-linear classification methods with kernels, and their applications to quantitative marketing research. Jiae earned her PhD in Statistics from Ohio State in 2020.