{"692469":{"#nid":"692469","#data":{"type":"event","title":"ISyE Seminar - Judy Jin (University of Michigan)","body":[{"value":"\u003Ch2\u003EAI-Enabled In-Situ Quality Control: Learning Beyond the Known\u003C\/h2\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EModern manufacturing generates increasingly rich in-situ sensing data, creating new opportunities for AI to enable automated and intelligent quality control decisions. However, conventional quality control using supervised learning relies on abundant labeled data and assumes that future product defects or process faults resemble those known during training. In practice, new defect types emerge, while abnormal conditions may be rarely observed or completely unknown. These challenges are particularly important for in-situ quality control, where defects must be detected or correctly classified for real-time decision-making, including newly emerging defects with limited or unavailable labels. Moreover, for latent defects that cannot be directly inspected online, defects must instead be predicted from indirect process-sensing signals. This requires mapping process-signal changes to possible defects despite scarce or unavailable defect training samples. This talk explores how advances in AI can address these challenges and enable more adaptive and intelligent in-situ quality control and decision-making for smart manufacturing.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EDr. Judy Jin is the A. Galip Ulsoy Collegiate Professor of Engineering and Professor of Industrial and Operations Engineering at the University of Michigan. Her research lies at the intersection of data science and quality engineering, with a focus on synergistically integrating engineering models, AI, and advanced quality control methods to improve system design and operational performance. She has served as PI\/Co-PI on more than $20 million in federally and industry-funded research. Her work has received numerous honors, including 18 Best Paper Awards, the S.M. Wu Research Implementation Award from SME, the Forging Achievement Award from FIERF, the NSF CAREER Award, and the NSF PECASE Award.\u003Cbr\u003E\u003Cbr\u003EDr. Jin currently serves as Editor-in-Chief of IISE Transactions. She has also served as Vice President of INFORMS, Chair of the INFORMS Quality, Statistics and Reliability Section, and President of the IISE Quality Control and Reliability Engineering Division. She is a Fellow of ASME, IISE, and INFORMS.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAI-Enabled In-Situ Quality Control: Learning Beyond the Known\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"AI-Enabled In-Situ Quality Control: Learning Beyond the Known "}],"uid":"36870","created_gmt":"2026-09-09 18:51:49","changed_gmt":"2026-09-17 13:38:13","author":"bjones434","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-20T11:00:00-05:00","event_time_end":"2026-11-20T12:00:00-05:00","event_time_end_last":"2026-11-20T12:00:00-05:00","gmt_time_start":"2026-11-20 16:00:00","gmt_time_end":"2026-11-20 17:00:00","gmt_time_end_last":"2026-11-20 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 360","extras":[],"hg_media":{"681144":{"id":"681144","type":"image","title":"Judy Jin","body":"\u003Cp\u003EJudy Jin\u003C\/p\u003E","created":"1789407369","gmt_created":"2026-09-14 17:36:09","changed":"1789407369","gmt_changed":"2026-09-14 17:36:09","alt":"Judy Jin2","file":{"fid":"265505","name":"Judy-Jin.jpg","image_path":"\/sites\/default\/files\/2026\/09\/14\/Judy-Jin.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/14\/Judy-Jin.jpg","mime":"image\/jpeg","size":81518,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/14\/Judy-Jin.jpg?itok=xMCp9Gjo"}}},"media_ids":["681144"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"692581":{"#nid":"692581","#data":{"type":"event","title":"ISYE Statistics Seminar - Alon Kipnis","body":[{"value":"\u003Cdiv dir=\u0022ltr\u0022\u003E\u003Cdiv\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: The Sharp Minimax Risk for High-Dimensional Uniformity Testing and Applications to Model Calibration\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;Testing whether high-dimensional categorical data follow a specified distribution is a fundamental problem in statistics, learning, and theoretical computer science.\u0026nbsp;We derive an expression for the asymptotic minimax risk in terms of the number of categories, the sample size, and the separation between the alternative class and the uniform distribution null. This result settles an open problem related to identity and uniformity testing in computer science and nonparametric hypothesis testing on distributions in mathematical statistics.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EThe sharp characterization enables comparison among competing tests at the level of exact constants rather than asymptotic rates, revealing differences invisible under standard sample-complexity analyses.\u0026nbsp;Interestingly, commonly used chi-squared and collision statistics are asymptotically minimax under fixed sample sizes but fail to retain this property under Poisson sampling. We derive a new statistic that is asymptotically minimax in both settings.\u0026nbsp;The proof combines ideas from signal detection in white noise\u0026nbsp;with a new conditional central limit theorem that overcomes the de-Poissonization challenge.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAs a practical consequence, the sharp constant answers a longstanding design question in calibration testing:\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EHow many bins should one use when testing calibration using the probability integral transform\u003C\/strong\u003E?\u003C\/div\u003E\u003Cdiv\u003EWe derive an explicit formula for the largest number of bins that guarantees a prescribed minimax risk, replacing heuristic bin selection by a statistically optimal design rule.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EThis talk is partly based on the following work, which received the best non-student paper award in an\u0026nbsp;AISTATS 2026 workshop.\u003C\/div\u003E\u003Cdiv\u003EA. Kipnis, \u0022Calibrating the Calibration Tester: Optimal Binning and Minimax Calibration Testing for Continuous Predictive Models\u0022,\u0026nbsp;\u003Cem\u003ETowards Trustworthy Predictions: Theory and Applications of Calibration for Modern AI\u0026nbsp;@ AISTATS 2026\u003C\/em\u003E (\u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fopenreview.net%2Fforum%3Fid%3Ddy7XNC3W0g\u0026amp;data=05%7C02%7Cstatseminarseries%40isye.gatech.edu%7C9ebe7f175dc64f41f90008df125ceb18%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639249862490750398%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=bxxTY8NVzHEo9fKT4%2Bt44d%2FH6NN4Z3N%2FuHrmGsvmJ1Y%3D\u0026amp;reserved=0\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022Original URL: https:\/\/openreview.net\/forum?id=dy7XNC3W0g. Click or tap if you trust this link.\u0022\u003Ehttps:\/\/openreview.net\/forum?id=dy7XNC3W0g\u003C\/a\u003E)\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E:\u0026nbsp;Alon Kipnis is a Senior Lecturer (Assistant Professor) at the Efi Arazi School of Computer Science, Reichman University, Israel. He received the Ph.D. in Electrical Engineering from Stanford University in 2017, and was a Koret Foundation Postdoctoral Fellow in Statistics at Stanford University from 2018 to 2021.\u0026nbsp;His research focuses on mathematical statistics, information theory, signal processing, and machine learning.\u0026nbsp;\u003C\/div\u003E\u003Cp\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe Sharp Minimax Risk for High-Dimensional Uniformity Testing and Applications to Model Calibration\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"The Sharp Minimax Risk for High-Dimensional Uniformity Testing and Applications to Model Calibration"}],"uid":"36868","created_gmt":"2026-09-14 13:59:05","changed_gmt":"2026-09-14 21:53:00","author":"mferrick3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-09-30T11:00:00-04:00","event_time_end":"2026-09-30T12:00:00-04:00","event_time_end_last":"2026-09-30T12:00:00-04:00","gmt_time_start":"2026-09-30 15:00:00","gmt_time_end":"2026-09-30 16:00:00","gmt_time_end_last":"2026-09-30 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 1502 ","extras":[],"hg_media":{"681142":{"id":"681142","type":"image","title":"Alon Kipnis","body":null,"created":"1789394934","gmt_created":"2026-09-14 14:08:54","changed":"1789394934","gmt_changed":"2026-09-14 14:08:54","alt":"Alon Kipnis","file":{"fid":"265503","name":"kipnis-13831.jpg","image_path":"\/sites\/default\/files\/2026\/09\/14\/kipnis-13831.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/14\/kipnis-13831.jpg","mime":"image\/jpeg","size":15563,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/14\/kipnis-13831.jpg?itok=iqDz1dMH"}}},"media_ids":["681142"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"692192":{"#nid":"692192","#data":{"type":"event","title":"ISyE Seminar - Omar El Housni (Cornell Tech)","body":[{"value":"\u003Ch2\u003ETwo-sided Assortment Optimization\u003C\/h2\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003ETwo-sided matching platforms, including labor markets, dating apps, accommodation services, and ridesharing systems, must make matching decisions in the presence of choice congestion and strategic platform design challenges. When agents have correlated preferences, popular options can attract too much demand, reduce overall efficiency, and lead to poor market outcomes. In this\u0026nbsp;talk, I will present a framework for two-sided assortment optimization that studies how a platform should decide which options to display to agents and in what order, with the goal of improving matching performance. The main focus will be on maximizing the expected number of matches under general choice models. I will describe several natural classes of platform policies, ranging from static simultaneous displays to fully adaptive sequential policies, and compare their power through adaptivity gap results. I will also discuss polynomial-time approximation algorithms for computing near-optimal policies, and then briefly discuss the revenue-maximization version of the problem, where matches generate pair-dependent rewards. This\u0026nbsp;talk\u0026nbsp;is based on joint works with Alfredo Torrico, Ulysse Hennebelle, and Mohammadreza Ahmadnejadsaein.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EOmar El Housni is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell Tech and Cornell University. He is a Field Member of the Center of Applied Mathematics at Cornell. He is also an Amazon Scholar. His research focuses on decision-making under uncertainty where he aims to develop optimization models and design robust and efficient algorithms to address a wide range of operational problems, including revenue management problems such as assortment optimization and online matchings. Omar has spent time as a research scientist at Amazon and Uber where he contributed to the design and implementation of data-driven optimization models for matching and retailing platforms. Omar holds a PhD in Operations Research from Columbia University and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris). His work has been recognized by INFORMS George Nicholson award and his current research is supported by NSF.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ETwo-sided Assortment Optimization\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Two-sided Assortment Optimization "}],"uid":"36870","created_gmt":"2026-09-02 13:00:42","changed_gmt":"2026-09-14 17:54:33","author":"bjones434","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-10-02T11:00:00-04:00","event_time_end":"2026-10-02T12:00:00-04:00","event_time_end_last":"2026-10-02T12:00:00-04:00","gmt_time_start":"2026-10-02 15:00:00","gmt_time_end":"2026-10-02 16:00:00","gmt_time_end_last":"2026-10-02 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 1502","extras":[],"hg_media":{"681146":{"id":"681146","type":"image","title":"Omar El Housni","body":null,"created":"1789408448","gmt_created":"2026-09-14 17:54:08","changed":"1789408448","gmt_changed":"2026-09-14 17:54:08","alt":"Omar El Housni","file":{"fid":"265507","name":"el-housni-13725.jpg","image_path":"\/sites\/default\/files\/2026\/09\/14\/el-housni-13725.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/14\/el-housni-13725.jpg","mime":"image\/jpeg","size":75907,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/14\/el-housni-13725.jpg?itok=g7EorVKL"}}},"media_ids":["681146"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"692148":{"#nid":"692148","#data":{"type":"event","title":"ISyE Seminar - Eugene Feinberg (Stony Brook University)","body":[{"value":"\u003Ch2\u003EInfinite-State Partially Observable Markov Decision Processes with Applications to Inventory Control\u003C\/h2\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EThis talk describes the progress in analysis and optimization of Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs) with infinite state spaces and possibly noncompact action sets. We shall also discuss applications to inventory control and to controlled linear Gaussian systems.\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EEugene A. Feinberg received MS in Applied Mathematics and Computer Engineering from Moscow University of Transportation, Russia, in 1976 and Ph.D. in Probability and Statistics from Vilnius University, Lithuania, in 1979. Currently he is Distinguished Professor at the Department of Applied Mathematics and Statistics of Stony Brook University.\u003Cbr\u003E\u003Cbr\u003EHis research interests include stochastic models of operations research, probability theory, real analysis, Markov Decision Processes, and applications of operations research and statistics to engineering, biology, and medicine. He has published more than 100 papers and edited the Handbook on Markov Decision Processes. His research has been partially supported by the National Science Foundation, Office of Naval Research, National Institute of Health, New York Office of Science, Technology and Academic Research, and private industry. He has served as a Council Member of the INFORMS Applied Probability Society and on several editorial boards. He is a fellow of INFORMS.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EInfinite-State Partially Observable Markov Decision Processes with Applications to Inventory Control\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Infinite-State Partially Observable Markov Decision Processes with Applications to Inventory Control"}],"uid":"36870","created_gmt":"2026-09-01 13:17:13","changed_gmt":"2026-09-10 22:27:03","author":"bjones434","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-10-16T11:00:00-04:00","event_time_end":"2026-10-16T12:00:00-04:00","event_time_end_last":"2026-10-16T12:00:00-04:00","gmt_time_start":"2026-10-16 15:00:00","gmt_time_end":"2026-10-16 16:00:00","gmt_time_end_last":"2026-10-16 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 1502","extras":[],"hg_media":{"681137":{"id":"681137","type":"image","title":"Eugene Feinberg","body":"\u003Cp\u003EEugene Feinberg\u003C\/p\u003E","created":"1789079191","gmt_created":"2026-09-10 22:26:31","changed":"1789079191","gmt_changed":"2026-09-10 22:26:31","alt":"Eugene Feinberg","file":{"fid":"265497","name":"Eugene-Feinberg.jpg","image_path":"\/sites\/default\/files\/2026\/09\/10\/Eugene-Feinberg.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/10\/Eugene-Feinberg.jpg","mime":"image\/jpeg","size":297630,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/10\/Eugene-Feinberg.jpg?itok=JUB0cw_a"}}},"media_ids":["681137"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"692468":{"#nid":"692468","#data":{"type":"event","title":"ISyE Seminar - Jian Kang (University of Michigan)","body":[{"value":"\u003Ch2\u003EModern Gaussian Processes for Neuroimaging Data Analysis\u003C\/h2\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003ERecent advances in neuroimaging have produced massive and heterogeneous datasets, ranging from fMRI with high spatial resolution to EEG with high temporal resolution, characterized by complex spatiotemporal correlations and substantial inter-subject variability. Traditional regression models and Gaussian process (GP) approaches with fixed parametric kernels often fail to model such complex data effectively while maintaining scalability and interpretability. This talk introduces a family of modern Bayesian GP frameworks that integrate deep kernel learning, neural network priors, and geometric modeling for large-scale neuroimaging analysis. An example is the Deep Kernel Learning Process (DKLP), which embeds deep neural networks within GP priors to learn data-adaptive covariance structures directly from imaging data. DKLP provides a unified modeling foundation for image-on-scalar, scalar-on-image, and image-on-image regression, supported by theoretical guarantees and efficient posterior computation. Applications to fMRI data from the Adolescent Brain Cognitive Development (ABCD) study reveal reproducible cortical activation patterns associated with cognitive ability, while analyses of EEG-based brain\u2013computer interface data demonstrate robust neural decoding under high noise. I will also discuss scalable heat-kernel GPs on manifolds and thresholded GP\u2013based spatially varying neural network priors, which together expand the scope of Bayesian inference for complex neuroimaging data.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EDr. Jian Kang is Professor and Associate Chair for Research in the Department of Biostatistics at the University of Michigan. His research lies at the intersection of Bayesian statistics, machine learning, and artificial intelligence, with applications in neuroimaging, brain\u2013computer interfaces, omics, and precision medicine. He has published more than 175 papers in leading statistics, machine learning, and biomedical journals. Dr. Kang has served as an Associate Editor for several premier statistical journals, including the Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS) and Biometrics. He is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He currently serves as Chair of the ASA Section on Statistics in Imaging.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EModern Gaussian Processes for Neuroimaging Data Analysis\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Modern Gaussian Processes for Neuroimaging Data Analysis "}],"uid":"36870","created_gmt":"2026-09-09 18:46:28","changed_gmt":"2026-09-09 23:08:24","author":"bjones434","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-09-25T11:00:00-04:00","event_time_end":"2026-09-25T12:00:00-04:00","event_time_end_last":"2026-09-25T12:00:00-04:00","gmt_time_start":"2026-09-25 15:00:00","gmt_time_end":"2026-09-25 16:00:00","gmt_time_end_last":"2026-09-25 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 1502","extras":[],"hg_media":{"681113":{"id":"681113","type":"image","title":"Jian Kang","body":null,"created":"1788995256","gmt_created":"2026-09-09 23:07:36","changed":"1788995256","gmt_changed":"2026-09-09 23:07:36","alt":"Jian Kang","file":{"fid":"265472","name":"kang-13747.jpg","image_path":"\/sites\/default\/files\/2026\/09\/09\/kang-13747.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/09\/kang-13747.jpg","mime":"image\/jpeg","size":428535,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/09\/kang-13747.jpg?itok=r9JNvQuS"}}},"media_ids":["681113"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"692380":{"#nid":"692380","#data":{"type":"event","title":"Georgia Statistics Day 2026","body":[{"value":"\u003Ch2\u003EGathering Minds Across Georgia: Promoting Interdisciplinary Statistics Research\u003C\/h2\u003E\u003Cp\u003EThe H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology is pleased to welcome you to Atlanta, Georgia for Georgia Statistics Day 2026.\u003C\/p\u003E\u003Cp\u003EGeorgia Statistics Day is an annual event that promotes interdisciplinary statistics research across the University of Georgia, the Georgia Institute of Technology, and Emory University, with the venue rotating among the participating institutions. The 2026 event will feature a keynote lecture, two semi-plenary lectures, parallel research sessions, a student poster session, and ample opportunity for exchange between academia and industry.\u003C\/p\u003E\u003Cp\u003EWe are honored to have Prof. Jianqing Fan, Frederick L. Moore \u201918 Professor of Finance and Professor of Statistics, Machine Learning, and Operations Research and Financial Engineering at Princeton University, as our keynote speaker. The semi-plenary speakers are Prof. Sivaraman Balakrishnan from Carnegie Mellon University and Prof. Mladen Kolar from the University of Southern California.\u003C\/p\u003E\u003Cp\u003EThis one-day workshop brings together faculty, students, and industry researchers from across Georgia and the Southeast for invited talks, interdisciplinary exchange, mentoring, and networking. Registration and poster submissions are open through September 28, 2026.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/sites.gatech.edu\/gsd2026\/\u0022\u003EGeorgia Statistics Day 2026 website\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Statistics Day 2026\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Statistics Day 2026"}],"uid":"27764","created_gmt":"2026-09-08 14:40:49","changed_gmt":"2026-09-08 14:56:19","author":"Scott Jacobson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-10-05T08:00:00-04:00","event_time_end":"2026-10-05T17:30:00-04:00","event_time_end_last":"2026-10-05T17:30:00-04:00","gmt_time_start":"2026-10-05 12:00:00","gmt_time_end":"2026-10-05 21:30:00","gmt_time_end_last":"2026-10-05 21:30:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower, Georgia Tech","extras":[],"related_links":[{"url":"https:\/\/sites.gatech.edu\/gsd2026\/","title":"Georgia Statistics Day 2026 website"}],"groups":[{"id":"660346","name":"Master of Science in Analytics"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194682","name":"Workshop"},{"id":"1789","name":"Conference\/Symposium"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/user\/908\/contact\u0022\u003EMonike Welch\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}