Stony Brook University Applied Mathematics and Statistics

Hyerin Choi who is double majoring in AMS has been selected to National Institute for Mathematical Sciences as the Industrial Mathematics Undergraduate Trainee for Summer 2021. Hyerin will get the opportunity to practice solving industrial mathematics problems and experience industrial mathematics culture proliferation activities.

We are pleased to announce that Professor Young-Seon Lee from the AMS Department will offer Summer 2021 Math Boot Camp! This camp can be beneficial to students who want to improve their math skills and math placement scores prior to the Math Placement Exam. This camp also provides a brief review of mathematical concepts, which is useful to prepare for SBU math courses.  It is a first-come-first-serve basis. Do not miss this great opportunity! Please find the details about the Math Boot Camp from the poster, and if you are interested in registering for the Math Boot Camp this Summer, please feel free to contact YounJu Baek via younju.baek@sunykorea.ac.kr.  

Professor Young-Seon Lee has been selected as a recipient of the National Research Foundation research grant funded by the Ministry of Science and ICT. He will serve as a principal investigator for the research project entitled “Assessing and minimizing parameter uncertainty in mathematical models of cardiac cells” for two years and nine months (2021.06.01 ~ 2024.02.29) Professor Ky Tran has been selected as a recipient of the National Research Foundation research grant funded by the Ministry of Science and ICT. He will serve as a principal investigator for the research project entitled “Asymptotic behaviors, control, and applications of stochastic hybrid systems” for two years and nine months (2021.06.01 ~ 2024.02.29).  

Speaker: Dr. Yinfei Kong  Place: Online via Zoom. Zoom ID: 912 6831 4669, Passcode: 686304 Time: Fri, 05/14/2021 - 11:00 About the speaker Dr. Yinfei Kong is an Associate Professor at the Department of Information Systems and Decision Sciences, College of Business and Economics, California State University, Fullerton. He received his Ph.D. degree from the University of Southern California in 2016.  He is particularly interested in deep learning, big data analytics, and applications in business and health care. (Web-page: https://sites.google.com/site/yinfeikong/; Email: yikong@fullerton.edu) Abstract We operationalize an intersectionality conceptual framework using a novel statistical approach and with these efforts improve estimation of disparities in access to treatment beyond race. We analyzed a sample of 941,286 treatment episodes collected in 2015, 2016, and 2017 in the United States from the Treatment Episodes Data Survey (TEDS-A). We also analyzed a subset of TEDS data from California (N=188,637) and Maryland (N=184,276), states with the largest sample of episodes. We conducted a retrospective subgroup analysis using a two-step approach called virtual twins. In step 1, we trained a classification model that gives the probability of waiting (one day or more). In step 2, we identified the subgroups with higher probability difference of waiting due to race. We tested three classification models for step 1 and identified that random forest was the classification model for step 1. Findings suggested that the following factors can define the subgroup more vulnerable to racial disparities: services setting, referral source, living arrangement, prior episodes, medication-assisted opioid treatment, and frequency of using the primary drug.    

Speaker: Prof. Emilio Vilches  Place: Online via Zoom. Zoom ID: 518 570 2306 / Passcode: 096577 Time: Fri, 04/16/2021 - 11:00 About the speaker Emilio Vilches, Institute Engineering Sciences, Universidad de O’Higgins Abstract Differentialequations with nonsmooth componentsoccur in various situations. For example, they arise in mechanical systems if the effects of dry friction are included in the model or happen in the case of impacts. They are present in electrical circuits or biological systems if nonsmooth characteristics are used to represent switches. This seminar aims to introduce Moreau’s Sweeping Process, which is a class of nonsmooth dynamical systems involving normal cones to time-dependent moving sets. We review basic results, numerical methods, and practical applications in nonsmooth mechanics and electrical circuits. The current state of research and the main challenges will be discussed. This work has been supported by ANID-Chile under project Fondecyt de Iniciación 11180098.    

Professor Hyunwook Koh has been selected as a recipient of the National Research Foundation research grant funded by the Ministry of Science and ICT. He will serve as a principal investigator for the research project entitled "Data-driven adaptive mediation analyses for human microbiome studies" for three years (2021.3 - 2024.2).

Speaker: Hyunwook Koh, AMS SUNY Korea  Place: Online via Zoom. Zoom ID: 485 595 6497 / Passcode: 504784 Time: Fri, 11/20/2020 - 16:30 Abstract  The human microbiome is the totality of all microbes inhabiting in different organs (e.g., gut, mouth, skin) of the human body. The roles of the microbiome on human health or disease have been increasingly studied by the recent advance in high-throughput sequencing technologies. For example, the microbiome perturbation has been associated with a variety of health or disease status (e.g., obesity, diabetes, cancer, brain disorder), medical interventions (e.g., antibiotic or probiotic use) and behavioral or environmental factors (e.g., diet, residence, smoking, birth mode). However, the high complexity of the microbiome data (e.g., high-dimensionality, compositionality, zero-inflation, phylogenetic correlations) makes the downstream data analysis challenging, and thus there is a strong need for more sophisticated statistical methods. In this seminar, we study the human microbiome from the very beginnings of the subject to recent statistical methods (e.g., a- and b-diversity analysis). Then, we further discuss the potential and promise in statistical method development.

  Professor Tan Cao, Professor Suil O, and Professor Myoungshic Jhun have been selected as a recipient of the National Research Foundation research grant funded by the Ministry of science and ICT. Prof. Cao’s project is on “Optimal Control of the Sweeping Process and Its Applications” for 3 years, Prof. O’s project is on “Factors and eigenvalues of graphs” for 3 years and Prof. Jhun’s project is on “On the Use of Adaptive Nearest Neighbors for Classification and Regression” for 2 years.

 In the summer of 2018, Professor Suil O gave a special mini-lecture about graph theory to students at Incheon Academy of Science and Arts as a part of the STEAM activity for the first-grade students in the school. With some of the students, he carried out a research work from March 2019 to February 2020 as for the second-grade students.   The research work has been published online in a mathematics journal this year. He is going to carry out research work with some students in the school from the late of April 2020 again.  

  [SUNY Korea] Strength of AMS, SUNY Korea | Stony Brook AMS Chair Joseph Mitchell Check out the interview video   https://youtu.be/F27fNGpSb-Y   Do you want to know about Applied Mathematics & Statistics in SUNY Korea? http://sunykorea.ac.kr/page/sbu201095   Sign up for SUNY Korea's biweekly newsletter https://page.stibee.com/subscriptions...

Speaker: Il Memming Park, Department of Neurology and Behavior, Stony Brook University Place: B203 Time: Mon, 09/23/2019 - 19:00 Abstract How does the brain represent information and process it? By controlling and analyzing the input to and output from the brain -- the sensory stimulus and the behavior -- we have made great advances in modeling cognitive computation which is often well described by simple mathematical models such as a network of noisy leaky integrators and comparators. However, despite extensive efforts, how the brain implements those computations as a biophysical system is still largely unknown. One of the main obstacles is the subsampling problem: there are many (hundreds to hundreds of millions of) neurons across brain areas involved in transforming sensory information to producing the behavior, however, our experimental technology has been limited to observing neural signals from a small fraction of neurons in a handful of areas at the same time. This vast subsampling has limited our ability to infer the physical implementation of cognitive computation in the brain. We had to heavily rely on theoretical principles, intuition, and imagination as to how they might be implemented by a complex network of spiking neurons. Fortunately, recent advances in neural recording technology is allowing us access to several orders of magnitude more neurons at a high temporal precision. This is opening up new opportunities to directly infer the neural implementations: how external and internal information is represented in the population, and how it is transformed over time and across areas. Even for the simplest cognitive processes, such a bottom-up approach has not been successful so far in uncovering the underlying neural dynamics. In this presentation, I lay a principled approach that can tackle the subsampling problem by exploiting the low-dimensional structure of tasks and neural variability. This new approach to studying neural codes and computation is possible through advances in statistical and machine learning techniques aimed at extracting interpretable, i.e., scientifically useful, mathematical models. We have developed interpretable probabilistic models and Bayesian inference algorithms suitable for reverse engineering neural computation from large-scale population data.  

  Dr. Joseph Mitchell, the Chair of Applied Mathematics and Statistics at Stony Brook University, visited SUNY Korea to give a special speech in Fall 2019 commencement. Please visit the link below to find the special speech by Dr. Joseph Mitchell to our first AMS graduated students at SUNY Korea. http://ams.sunykorea.ac.kr/node/378  

Speaker: Tan Cao, AMS SUNY Korea  Place: B203 Academic Building Time: Mon, 11/25/2019 - 18:40 Abstract Optimal control theory is a branch of applied mathematics that deals with finding a control law for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in both science and engineering. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the moon with minimum fuel expenditure. Or the dynamical system could be a nation's economy, with the objective to minimize unemployment; the controls in this case could be fiscal and monetary policy.  Optimal control is a mathematical optimization method for deriving optimal policies and is an extension of the calculus of variations, dating back to the formulation of Bernoull's brachistochrone problem more than 300 years ago. Optimal control can also be seen as a control strategy in control theory.  Bernoulli's Challenge In the June 1696 issue of Acta Eruditorum, Bernoulli posed the following challenge:  “Given two points A and B in the vertical plane, for a moving particle m, how to find the path AmB descending along which by its own gravity and beginning to be urged from the point A, it may in the shortest time reach B?” This problem, which sounds much better in Latin in its English translation, is the so-called brachistochrone problem, named by combining the two Greek words for the ‘’shortest” (brachistos) and “time” (chronos). In this AMS seminar talk, we will review some basic knowledge of Calculus and discuss an approach how to solve the Brachistochrone Problem.   

Speaker: Yongtang Shi, Nankai University  Place: Academic Building B203 Time: Mon, 10/28/2019 - 18:40 Abstract Ramsey theory is an old and difficult area in extremal combinatorics. In this talk, we will introduce Ramsey numbers and Gallai-Ramsey Numbers of graphs. Especially, Gallai-Ramsey Number of complete graphs with four vertices will be presented. Disjoint work with Henry Liu, Colton Magnant, Akira Saito, and Ingo Schiermeyer.  

Speaker: Hyeuk Kim, Division of Big Data and Management Engineering, Hoseo University Place: B207 Time: Tue, 10/01/2019 - 18:40 Abstract A particle matter is the important issue in control and reduction of pollutants in Korea nowadays. Many atmospheric scientists have tried to predict a particle matter for a long time. In this presentation, we introduce a data mining approach to predict PM2.5 and use a special data source such as a satellite.