Stony Brook University Applied Mathematics and Statistics

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.  

Prof. Suil O is organizing a workshop on Spectral Graph Theory from October 7th to October 9th. This workshop is partially supported by the National Research Foundation of Korea and SUNY Korea.  If you are interested in the workshop, please feel free to join us. It is open to the public. You may find the detailed information from the workshop homepage from the link HERE.

A warm welcome from SBU AMS Department for the Fall 2022 SUNY Korea visiting students!

Prof. Hyunwook Koh in AMS is looking for a research assistant who will work on statistical methods for human microbiome studies. The research topics can include (but are not limited to) 1) causal inference, and mediation analysis; 2) repeated measures analysis; 3) machine learning; 4) web-based software development; 5) collaborative studies and data analysis. If you are interested, please send your CV and transcripts to Prof. Koh (hyunwook.koh@stonybrook.edu). Please find the attachment for more details.

<Dr. Hongshik Ahn, Juan Kim, Solha Park, Gu Won, Dr. Myoungshic Jhun> Please join us in congratulating the class of Spring 2022! We have a total of 12 students graduating this Spring 2022. 3 students attended the commencement ceremony at SUNY Korea on June 17, and the other 9 students attended the commencement at Stony Brook University New York Campus on May 20. Master's Degree - Won Gu, Suea Kwon Bachelor's Degree - Seung Won Byun, Hakyung Cha, Nilson Chapagain, Crispen Chisina, Juan Kim, Seowon Lee, Nara Park, Solha Park, Kushal Sedhai, Hyunju Tahk AMS Department wishes the best to all the AMS graduates on their new journey!

1 SUNY Korea AMS graduate student (Gu Won) and 3 undergraduate students (Nilson Chapagain, Seowon Lee, and Solha Park) have been awarded honor selections from SBU AMS Department. This honoring ceremony is to celebrate the students who have performed outstanding performances throughout the semesters.  Please join us in congratulating the students!

  Prof. Suil O has been carrying out research works with students at the Incheon Academy of Science and Arts as part of the STEAM activity since 2019. The research work that had been done in 2020 has been recently published online in a mathematics journal in May 2022. Click the following link: https://www.tandfonline.com/doi/full/10.1080/03081087.2022.2076799

Speaker: Dr. Wonil Chung  Place: Online via Zoom, Zoom ID: 938 0290 9321 / Passcode: ams Time: Thu, 05/26/2022 - 16:00 About the speaker  Dr. Wonil Chung is an Assistant Professor in Statistics at Soongsil University. Before that, he was a research associate at Harvard T.H. Chan School of Public Health and received his Ph.D. degree in Biostatistics from the University of North Carolina at Chapel Hill (UNC) and MS and BS degrees in Statistics from Seoul National University. His research interests are in Biostatistics, Statistical Genetics, Big Data, Statistical Learning and Deep Learning. Abstract Although Type 2 diabetes (T2D) have been known as one of the important risk factors for the severity and mortality of COVID-19, the effect of T2D and its genetic susceptibility on COVID-19 are largely unknown. We analyzed the population-based cohort data of 459,188 individuals from UK Biobank with COVID-19 test results, individuals’ hospitalization data and death-related records during the period from March 11, 2020 to December 20, 2021. First, we investigated the association of T2D, and its genetic susceptibility with COVID-19 infection using multivariable logistic regression model. To capture overall genetic susceptibility for T2D, we computed polygenic risk scores (PRS) based on summary statistics from UK Biobank. In the multivariable logistic models, we found that the odds ratio (OR) of T2D was 1.555 (P= 3.49*10-86) and OR of PRS for T2D with one-unit (=standard deviation) increase in PRS was 1.064 (P=3.11*10-12), indicating the roles of T2D-related genetics in the pathogenesis of COVID-19 infection. Next, we performed multivariable Cox proportional hazard models to investigate the effect of T2D patients infected with COVID-19 on the survival times. The estimated survival curves and pairwise log-rank tests showed that the estimated hazard for COVID-19 infected T2D patients were 4.67 times (P=9.88*10-324) and 2.58 times (P=6.20*10-231) higher than individuals without COVID-19 infection and T2D, respectively and the hazard ratio (HR) of PRS for T2D with one-unit increase in PRS was 1.088 (P=4.76*10-14). Furthermore, we found the mortality of COVID-19 infected T2D patients was dramatically increased compared to T2D patients not infected with COVID-19 and the mortality of individuals with high genetic susceptibility for T2D was increased as well.