Stony Brook University Applied Mathematics and Statistics

SUNY Korea Admissions Team hosted the 2023 SUNY Korea Experience Day on November 25, welcoming all prospective and admitted students. Prof. Hongshik Ahn provided an introduction about the AMS Department, and Prof. Young-Seon Lee gave a mock lecture on the topic of 'Essential Concepts of Integral Calculus' to the students. The Experience Day will be held every semester. All prospective students are welcome to the event. Hope to see many students next year again! Introduction of the AMS Department by Prof. Hongshik Ahn   Mock Lecture <Essential Concepts of Integral Calculus> by Prof. Young-Seon Lee

Fall 2023 AMS Seminars -> Seminar II, Hierarchical Bayesian Spatial Regression Models by Prof. Jaewoo Park from Yonsei University.     -> Seminar III, Accelerated Failure Time Modeling via Nonparametric Mixtures by Prof. Sangwook Kang from Yonsei University.   There will be more interesting seminars coming up next semester, so stay tuned! :) 

Speaker: Sangwook Kang   Location: B105 Date and Time: November 20 (Mon), 10:30 AM - 11:30 AM in KST About the Speaker: Dr. Sangwook Kang - (Current) Professor and Chair, Dept of Applied Statistics, Yonsei University -  Assistant/Associate Professor, Dept of Applied Statistics, Yonsei University -  Visiting Professor, Dept of Statistics, University of Connecticut -  Associate Director, Yonsei Institute of Data Science, Yonsei University -  Assistant Professor, Dept of Statistics, University of Connecticut -  Assistant Professor, Dept of Epidemiology and Biostatistics, University of Georgia Abstract An accelerated failure time (AFT) model assuming a log-linear relationship between failure time and a set of covariates can be either parametric or semi-parametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data. The semiparametric AFT model is more flexible and robust to departures from the distributional assumption than its parametric counterpart. However, the semiparametric AFT model is subject to producing biased results for estimating any quantities involving an intercept. Estimating an intercept requires a separate procedure. Moreover, a consistent estimation of the intercept requires stringent conditions. Thus, essential quantities such as mean failure times might not be reliably estimated using semiparametric AFT models, which can be naturally done in the framework of parametric AFT models. Meanwhile, parametric AFT models can be severely impaired by misspecifications. To overcome this, we propose a new type of the AFT model using a nonparametric Gaussian-scale mixture distribution. We also provide feasible algorithms to estimate the parameters and mixing distribution. The finite sample properties of the proposed estimators are investigated via an extensive stimulation study. The proposed estimators are illustrated using the well-known PBC dataset.

Speaker: Jaewoo Park   Location: B105 Date and Time: October 30 (Mon), 6:40 PM - 7:40 PM in KST RSVP HERE: https://forms.gle/SJeFVFSNsZRD2yfVA About the Speaker: Dr. Jaewoo Park -  (Current) Assistant Professor, Department of Applied Statistics, Yonsei University -  Ph.D. in Statistics, The Pennsylvania State University -  BA in Applied Statistics, Economics, Yonsei University Abstract Spatial regressions are widely used to study relationships between spatial variables in many scientific domains, such as ecology, epidemiology, and sociology. For a given spatial latent process, hierarchical structures are defined for building complex high-dimensional models from simple and low-dimensional building blocks. Bayesian approaches are popular for hierarchical spatial models due to their computational and inferential advantages. In this talk, I will review the Bayesian framework for hierarchical spatial regression models. I will show how such models can be applied to real datasets, such as confirmed COVID-19 cases in the United States.

Speaker: Thieu Nguyen   Location: B105 Date and Time: September 25 (Mon), 6:40 PM - 7:40 PM in KST RSVP HERE: https://forms.gle/GiLkAd6fvrdMSGH29 About the Speaker: Dr. Thieu Nguyen -  (Current) Institute of Mathematics, Vietnam Academy of Science And Technology, Hanoi, Vietnam -  Ph.D. in Applied Mathematics, University of Limoges, France -  MS in Mathematics, University of Limoges, France -  MS in Mathematics, Institute of Mathematics, Vietnam -  BS in Mathematics, Vietnam National University Abstract This talk serves as an introductory overview of generalized differentiation. We will present the concepts of generalized differentiation and their applications in optimization and optimal control. Firstly, we will revisit the definition of classical differentiability and explore some related results in optimization problems. Then, we will delve into nonsmooth techniques designed for scenarios where classical differentiation is inapplicable. Lastly, we will discuss the applications of these principles in optimal control and introduce some ongoing research problems.      

The Department of Applied Mathematics and Statistics (AMS) at SUNY Korea seeks applicants for multiple tenure track and non-tenure-track faculty positions in the area of mathematics, applied mathematics or statistics at all levels, starting spring of 2024 or fall 2024. 

Congratulations to all of our proud class of Spring 2023!  Besides the students who attended today's SUNY Korea commencement, we would also like to express our congrats to all SUNY Korea AMS students graduating from Stony Brook New York Campus. Please join us in congratulating the class of Spring 2023 listed below. Master of Science Juan Kim Solha Park Su Hyun Shin Bachelor of Science Dajung Choi, Cum Laude Seung Won Han Yeji Kim, Magna Cum Laude Haejoon Lee, Summa Cum Laude Juyoung Lee, Cum Laude Leah Nabangi Ji Woo Park Seoyun Park, Magna Cum Laude Jihye Yang ​​​​​​​ Well done and wish you all the best on your new journey!!

Speaker: Jiangdong Ai  Venue: B103 Date and Time: May 22 (Mon), 6:30 PM - 7:30 PM in KST RSVP HERE: https://forms.gle/nLJsmjp8Y6JtaXG37 About the Speaker  -  (Current) Postdoctoral Research Fellow, SUNY Korea -  Postdoctoral Research Fellow, Royal Holloway, University of London, UK -  Ph.D. Candidate in Computer Science, Royal Holloway, University of London, UK -  BS in Math and Applied Math, Beijing University of Chemical Technology, China -  Research interest: Colorings of digraph; the generalization and improvement of some                              conclusions in undirected graphs in directed graphs; the proof or                              promotion or refutation of certain conjectures; for the basic properties                             of directed hypergraphs, including Hamiltonian, King, and so on. Abstract Graph theory is an area which connects the elegance of painting and the rigor of mathematics; It is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them.    

Speaker: Dr. Sehun Chun  Venue: B105 Date and Time: April 24 (Mon), 6:30 PM - 7:30 PM in KST RSVP HERE: https://forms.gle/xdcvA7r5baVyPoabA About the Speaker  -  (Current)  Assistant Professor of Applied Mathematics, UIC, Yonsei University, Incheon -  Residential Researcher, African Institute for Mathematical Sciences, Cape Town, South Africa -  Research Associate, Imperial College London, London, UK -  Research Assistant, Brown University, Providence, US -  Ph.D. in Applied Mathematics, Brown University -  M.S. in Applied Mathematics, Brown University & in Mathematics, Purdue University -  BS in Mech. & Aero Engineering, Seoul National University -  Research interest: Electrodynamics, Moving Frames, Curved surfaces, Electrophysiology, Cardiology, Neuroscience Abstract Recent advances in artificial intelligence (AI) have drawn unprecedented attention from every corner of our society. This presentation focuses on AI based on neural network algorithms. These algorithms derive from the study of a small region or function of the brain, particularly regarding vision. Their implications are almost limitless in the modern era but only a handful of applications survive, leaving other applications incomplete or inferior. Thus, we should pay attention to what is missing in AI algorithms compared to the real human brain before being overwhelmingly pessimistic about AI. Among the critical factors which will limit AI functionalities, we focus on the unique structure and distribution of neural fibers within a small volume of the brain, called the geometry of intelligence. The geometry of intelligence may explain the secret behind superior data processing efficiency and the mysterious entanglement with our physical world. The study of the geometry of intelligence can shed light on a possible breakthrough with respect to the current critical flaws of AI, or at least help us better understand the boundaries of AI for constructive and safe applications.   

Haejoon Lee has been admitted to the Ph.D. program in Robotics at the University of Michigan. Haejoon will be receiving 5-year guaranteed funding.  Haejoon will graduate in Spring 2023 with a bachelor's degree in applied mathematics and statistics and a minor in computer science.  Please join us in congratulating Haejoon Lee on the successful beginning of his Ph.D. program!

Speaker: Dr. Minsun Song  Venue: B105 Date and Time: March 29 (Wed), 1 PM - 2 PM in KST About the Speaker  -  (Current) Associate Professor, Dept of Statistics, Sookmyung Women's University -  Assistant Professor, Dept of Mathematics & Statistics, University of Nevada, Reno -  Postdoctoral Associate, Lewis-Sigler Institute for Integrative Genomics, Princeton University -  Postdoctoral Associate, National Cancer Institute, National Institute of Health -  Ph.D. in Statistics, University of Chicago -  M.S. &  B.S. in Statistics, Seoul National University -  Research interest: Statistical genetics and epidemiology, high dimensional data analysis, and dimension reduction Abstract We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as those measured in genome-wide associations studies. We also derive a new set of methodologies, called a genotype-conditional association test, shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and non-genetic contributions to the trait. Our proposed framework provides a substantially different approach to the problem from existing methods.    

  (From left to right) Dr. Suil O, Dr. Tan Cao Professor Suil O and Professor Tan Cao were awarded tenure as Associate Professors in the Department of Applied Mathematics and Statistics of SUNY Korea, effective February 15, 2023. Please join us in congratulating this great news! 

(From left to right) Dr. Suil O, Dr. Myoungshic Jhun, Ji Hun Kim, Hyo Jung Jang, Dr. Young-Seon Lee, Dr. Tan Cao Congratulations to all of our proud class of Fall 2022! We had a special photo time with our MS graduating students, Ji Hun Kim and Hyo Jung Jang. Besides the students who attended today's SUNY Korea commencement, we would also like to express our congrats to all SUNY Korea AMS students graduating from Stony Brook New York Campus. Please join us in congratulating the class of Fall 2022 listed below. Master of Science Hyo Jung Jang Ji Hun Kim Bachelor of Science Minyoung Cho Minsung Ku Junyoung Lee, Cum Laude Yoobin Lee Kyungbae Min, Summa Cum Laude Eunseo Shin, Cum Laude Boin Suh, Cum Laude Well done and wish you all the best on your new journey!!

Speaker: Dr. Won Chang  Place: Online via Zoom, Zoom ID: 936 1134 3585 / Passcode: sunykorea Date and Time: Fri, 11/18/2022, 16:00 - 17:00 About the Speaker  - Associate Professor in the Division of Statistics and Data Science at the Department of Mathematical Sciences University of Cincinnati - Postdoctoral Scholar: University of Chicago, 2014-2016 - Ph.D.: Pennsylvania State University, 2014 (Statistics) - M.S.: Korea University Seoul, 2009 (Statistics) - B.S.: Korea University Seoul, 2007 (Statistics) Abstract Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. In this talk, I will introduce the basic framework for calibration and discuss how the input parameters can be estimated while accounting for data-model discrepancy using Bayesian inference. Some scientific applications including disease modeling and climate change projection will be discussed as well.    

Speaker: Dr. Sungho Won  Place: Online via Zoom, Zoom ID: 929 2121 8579 / Passcode: sunykorea Date and Time: Fri, 10/21/2022, 16:00 - 17:00 About the Speaker  Dr. Sungho Won is a Professor in the Graduate School of Public Health at the Seoul National University. Before that, Dr. Won has worked as an Assistant Professor in the Dept of Applied Statistics at Chung-Ang University, and as a Postdoctoral Fellow in the Dept of Biostatistics at Harvard School of Public Health. Dr. Won received his Ph.D. degree in Biostatistics from Case Western Reserve University, MS degree in Statistics and BS degree in Biology Education from Seoul National University. His research interests are in Statistical Genetics, Bioinformatics, Linear Models, and Missing Data.  Abstract Recent investigations have revealed that the human microbiome plays an essential role in the occurrence of type 2 diabetes (T2D). However, despite the importance of understanding the involvement of the microbiota throughout the body in T2D, most studies have focused specifically on the intestinal microbiota. Extracellular vesicles (EVs) have been recently found to provide important evidence regarding the mechanisms of T2D pathogenesis, as they act as key messengers between intestinal microorganisms and the host. Herein, we explored microorganisms potentially associated with T2D by tracking changes in microbiota-derived EVs from patient urine samples collected three times over four years. Mendelian randomization analysis was conducted to evaluate the causal relationships among microbial organisms, metabolites, and clinical measurements to provide a comprehensive view of how microbiota can influence T2D. We also analyzed EV-derived metagenomic (N = 393), clinical (N = 5032), genomic (N = 8842), and metabolite (N = 574) data from a prospective longitudinal Korean community-based cohort. Our data revealed that GU174097_g, an unclassified Lachnospiraceae, was associated with T2D (β = -189.13; p = 0.00006), and it was associated with the ketone bodies acetoacetate and 3-hydroxybutyrate (r = -0.0938 and -0.0829, respectively; p = 0.0022 and 0.0069, respectively). Furthermore, a causal relationship was identified between acetoacetate and HbA1c levels (β = 0.0002; p = 0.0154). GU174097_g reduced ketone body levels, thus decreasing HbA1c levels and the risk of T2D. Taken together, our findings indicate that GU174097_g may lower the risk of T2D by reducing ketone body levels.