{"693877":{"#nid":"693877","#data":{"type":"event","title":"PhD Defense | Structured Knowledge as a Lens for Trustworthy AI: Privacy, Unlearning, and Safety","body":[{"value":"\u003Cdiv\u003ETitle: Structured Knowledge as a Lens for Trustworthy AI: Privacy, Unlearning, and Safety\u003C\/div\u003E\u003Cdiv\u003EDate: Wednesday, November 18, 2026\u003C\/div\u003E\u003Cdiv\u003ETime: 10:00 AM \u2013 12:00 PM (ET)\u003C\/div\u003E\u003Cdiv\u003ELocation: Virtual via Microsoft Teams (link below). An in-person viewing room is also available at Coda C1215 Midtown (12th floor, Coda Building).\u003C\/div\u003E\u003Cdiv\u003EMeeting link: \u003Ca href=\u0022https:\/\/teams.microsoft.com\/meet\/2781943465376823?p=4kQ6wkrTY2G5qNmJSg\u0022 title=\u0022https:\/\/teams.microsoft.com\/meet\/2781943465376823?p=4kQ6wkrTY2G5qNmJSg\u0022\u003Ehttps:\/\/teams.microsoft.com\/meet\/2781943465376823?p=4kQ6wkrTY2G5qNmJSg\u003C\/a\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EMeeting ID: 278 194 346 537 682 3\u003C\/div\u003E\u003Cdiv\u003EPasscode: ma9KF2We\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ERongzhe Wei\u003C\/div\u003E\u003Cdiv\u003EMachine Learning PhD Candidate\u003C\/div\u003E\u003Cdiv\u003ESchool of Electrical and Computer Engineering\u003C\/div\u003E\u003Cdiv\u003EGeorgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ECommittee\u003C\/div\u003E\u003Cdiv\u003E1. Dr. Pan Li (Advisor), Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E2. Dr. Justin Romberg, Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E3. Dr. Juba Ziani, Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E4. Dr. Duen Horng (Polo) Chau, Computational Science and Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E5. Dr. Pin-Yu Chen, IBM Research\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAbstract\u003C\/div\u003E\u003Cdiv\u003EPrivacy, unlearning, and safety are often evaluated at the level of individual records, facts, or requests. This dissertation uses structured knowledge as a lens to examine how relationships among these units shape what AI systems reveal, retain, and enable. For privacy, it develops graph diffusion mechanisms and distribution-dependent analyses that account for data structure. For unlearning, it reveals overlooked privacy risks for minority data and shows how seemingly forgotten facts remain recoverable through related knowledge, motivating evaluations and methods that address these dependencies. For safety, it demonstrates how restricted knowledge can be assembled across seemingly benign queries and develops a defender that learns when to intervene as information accumulates. Together, these contributions show how accounting for relationships among data, knowledge, and interactions leads to more faithful evaluations and more effective methods for trustworthy AI.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cdiv\u003EStructured Knowledge as a Lens for Trustworthy AI: Privacy, Unlearning, and Safety\u003C\/div\u003E\u003Cp\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Rongzhe Wei - Machine Learning PhD Candidate - School of Electrical and Computer Engineering"}],"uid":"36518","created_gmt":"2026-11-09 12:57:26","changed_gmt":"2026-11-09 12:58:14","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-18T10:00:00-05:00","event_time_end":"2026-11-18T12:00:00-05:00","event_time_end_last":"2026-11-18T12:00:00-05:00","gmt_time_start":"2026-11-18 15:00:00","gmt_time_end":"2026-11-18 17:00:00","gmt_time_end_last":"2026-11-18 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda C1215 Midtown","extras":[],"related_links":[{"url":"https:\/\/teams.microsoft.com\/meet\/2781943465376823?p=4kQ6wkrTY2G5qNmJSg","title":""}],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"693809":{"#nid":"693809","#data":{"type":"event","title":"PhD Defense | Adaptive Subspace Tracking and Inverse Design for High-Dimensional Electromagnetics","body":[{"value":"\u003Cdiv\u003ETitle: Adaptive Subspace Tracking and Inverse Design for High-Dimensional Electromagnetics\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EDate: Thursday, November 19, 2026\u003C\/div\u003E\u003Cdiv\u003ETime: 2:00 PM EST\u003C\/div\u003E\u003Cdiv\u003ELocation:\u003C\/div\u003E\u003Cdiv\u003EIn person: CODA C1015 (Vinings)\u003C\/div\u003E\u003Cdiv\u003EVirtual: \u003Ca href=\u0022https:\/\/teams.microsoft.com\/meet\/2420063874953443?p=zH2mKpDkLDTZuzvr7Q\u0022 title=\u0022https:\/\/teams.microsoft.com\/meet\/2420063874953443?p=zH2mKpDkLDTZuzvr7Q\u0022\u003Ehttps:\/\/teams.microsoft.com\/meet\/2420063874953443?p=zH2mKpDkLDTZuzvr7Q\u003C\/a\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAlex Saad-Falcon\u003C\/div\u003E\u003Cdiv\u003EMachine Learning PhD Student\u003C\/div\u003E\u003Cdiv\u003ESchool of Electrical and Computer Engineering\u003C\/div\u003E\u003Cdiv\u003EGeorgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ECommittee\u003C\/div\u003E\u003Col\u003E\u003Cli data-list-item-id=\u0022ea96a93cf8e38bab85bf4d859af2e5405\u0022\u003E\u003Cdiv\u003EDr. Justin Romberg (Advisor), School of Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003C\/li\u003E\u003Cli data-list-item-id=\u0022e76d54b6cc2650ef565e5c2e781af6ce4\u0022\u003E\u003Cdiv\u003EDr. Mark Davenport, School of Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003C\/li\u003E\u003Cli data-list-item-id=\u0022e3580dd60c2c7a8e9ded8aca7430cb0c0\u0022\u003E\u003Cdiv\u003EDr. Saibal Mukhopadhyay, School of Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003C\/li\u003E\u003Cli data-list-item-id=\u0022e69ecfc04d1fce0af769ae55f0f61a18c\u0022\u003E\u003Cdiv\u003EDr. Nima Ghalichechian, School of Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003C\/li\u003E\u003Cli data-list-item-id=\u0022e977b81935816e109383d0effd9eb335b\u0022\u003E\u003Cdiv\u003EDr. Ryan Westafer, Georgia Tech Research Institute\u003C\/div\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAbstract\u003C\/div\u003E\u003Cdiv\u003EThis dissertation develops theory, algorithms, and systems for high-dimensional optimization problems in electromagnetics. The first part studies subspace tracking and principal component analysis under hardware constraints such as limited computation, scarce memory, or acquisition restricted to a few linear measurements per sample. For Gaussian data in dimension d, it shows that an Oja-type algorithm using 2k adaptive linear measurements per sample estimates the top-k principal subspace at the minimax error rate of a two-level spiked model, up to a constant factor, when the sample size is known in advance, while measurement designs fixed in advance have a lower bound larger by a factor of order d\/k at large sample sizes when the (k+1)-th eigenvalue is comparable to the k-th. It also develops regularized least squares algorithms on the Grassmann manifold for tracking time-varying subspaces. On the systems side, it describes AdaAFE-CIM, which uses subspace tracking for adaptive data compression in array processing hardware and reduces baseband power by 95.4% relative to full digitization in power-model simulations, together with an open-source end-to-end simulator for array processing under realistic hardware and channel effects. Finally, it presents inverse design methods for electromagnetic structures: a periodic level set function for gradient-free topology optimization, validated by fabrication and measurement for metasurfaces, and a neural adjoint method for reconfigurable antennas with exponentially large switch configuration spaces.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAdaptive Subspace Tracking and Inverse Design for High-Dimensional Electromagnetics\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Alex Saad-Falcon - Machine Learning PhD Student - School of Electrical and Computer Engineering Georgia Institute of Technology"}],"uid":"36518","created_gmt":"2026-11-05 19:13:35","changed_gmt":"2026-11-05 19:14:18","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-19T14:00:00-05:00","event_time_end":"2026-11-19T16:00:00-05:00","event_time_end_last":"2026-11-19T16:00:00-05:00","gmt_time_start":"2026-11-19 19:00:00","gmt_time_end":"2026-11-19 21:00:00","gmt_time_end_last":"2026-11-19 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CODA C1015 (Vinings)","extras":[],"related_links":[{"url":"https:\/\/teams.microsoft.com\/meet\/2420063874953443?p=zH2mKpDkLDTZuzvr7Q","title":""}],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"693808":{"#nid":"693808","#data":{"type":"event","title":"PhD Defense | Resource Pooling in Hyperconnected Logistics Networks","body":[{"value":"\u003Cdiv\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Resource Pooling in Hyperconnected Logistics Networks\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: Monday, November 16, 2026\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 2:00 PM \u2013 4:00 PM ET\u003C\/div\u003E\u003Cdiv\u003ELocation: Virtual via Microsoft Teams\u003C\/div\u003E\u003Cdiv\u003EMeeting link: \u003Ca href=\u0022https:\/\/teams.microsoft.com\/meet\/297219506494396?p=jBxsWTUiavcTjnCxMG\u0022 id=\u0022OWAbbc97ae9-d333-8f4b-1878-b2d702d83f3a\u0022 title=\u0022https:\/\/teams.microsoft.com\/meet\/297219506494396?p=jBxsWTUiavcTjnCxMG\u0022\u003Ehttps:\/\/teams.microsoft.com\/meet\/297219506494396?p=jBxsWTUiavcTjnCxMG\u003C\/a\u003E (Meeting ID: 297 219 506 494 396, Passcode: cp9zJ6Ty)\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EYujia Xu\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003EMachine Learning PhD Student\u003C\/div\u003E\u003Cdiv\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/div\u003E\u003Cdiv\u003EGeorgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003E1 Dr. Benoit Montreuil (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E2 Dr. Walid Klibi, Centre of Excellence for Supply Chain Innovation and Transportation, Kedge Business School\u003C\/div\u003E\u003Cdiv\u003E3 Dr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E4 Dr. Valerie Thomas, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E5 Dr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003EHyperconnected logistics networks comprise interconnected hubs and organizations whose resources can be shared, so that capacity committed at one location can serve demand arising elsewhere in the network. Resource pooling therefore extends capacity planning under uncertainty beyond individual facilities and firms to include the coordination of shared resources and the discovery of outside capacity. This dissertation develops stochastic optimization, decomposition, and learning methods for resource-pooling decisions, from deploying known resources to expanding the pool beyond the network. We first formulate the dynamic deployment of pooled workers and autonomous mobile robots across urban parcel hubs as a rolling-horizon two-stage stochastic program that respects worker shift and location preferences, and solve large-scale instances with an accelerated Benders decomposition and clustering-based relax-and-fix heuristics. Then we design battery swapping and charging networks that support long-haul truck electrification by keeping swapping close to freight demand while allowing charging to shift to grid-suitable hubs. We model this design problem as a multi-period, two-stage stochastic location\u2013inventory\u2013fleet program and solve it with a Lov\u00e1sz-cut Benders decomposition that exploits the submodularity of the battery recourse function. Lastly, we introduce open capacity pooling, in which a disrupted network discovers capacity held by previously unengaged providers through a closed loop of coordination, search, and admission under a limited budget of requests for quotation. Each request targets a capacity gap remaining after incumbent firms coordinate through the alternating direction method of multipliers (ADMM) without sharing their operating models, and is directed to a provider ranked by a machine-learning layer built on directory records and large language model readings of provider self-descriptions. We evaluate the proposed methods in computational studies grounded in real-world data from industry partners and public sources. The findings show how resource pooling helps logistics networks respond to uncertain demand and disruptions by coordinating shared resources and incorporating capacity from outside providers.\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EResource Pooling in Hyperconnected Logistics Networks\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Yujia Xu - Machine Learning PhD Student - H. Milton Stewart School of Industrial and Systems Engineering"}],"uid":"36518","created_gmt":"2026-11-05 19:11:24","changed_gmt":"2026-11-05 19:12:42","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-16T14:00:00-05:00","event_time_end":"2026-11-16T16:00:00-05:00","event_time_end_last":"2026-11-16T16:00:00-05:00","gmt_time_start":"2026-11-16 19:00:00","gmt_time_end":"2026-11-16 21:00:00","gmt_time_end_last":"2026-11-16 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"https:\/\/teams.microsoft.com\/meet\/297219506494396?p=jBxsWTUiavcTjnCxMG (Meeting ID: 297 219 506 494 396, Passcode: cp9zJ6Ty)","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"691535":{"#nid":"691535","#data":{"type":"event","title":"Machine Learning Seminar Series Fall 2026 | Measuring Cultural Variation: from Twitter to LLMs","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E People\u0027s language use reflects their culture. As a result, LLMs should adapt to the cultures of their many users, but they struggle to do so. Today\u0027s LLMs are multilingual but not multicultural: they speak fluent Hindi or Mandarin yet reflect the (often stereotypical) American perspectives implicit in their training data.\u0026nbsp; We first demonstrate how language can be used to quantify cultural variation, analyzing Twitter texts to quantify variation in individualism and collectivism across US counties. We then present a benchmark to assess how well LLMs understand cultural variation\u2014a set of questions that should be answered differently across countries\u2014and demonstrate that LLMs struggle to respond appropriately. Achieving truly global generative AI requires not just linguistic diversity, but LLMs that adapt to their multicultural users.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003ELyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group builds chatbots and language analysis tools for psychological and medical research.\u0026nbsp; They study the drivers of physical and mental well-being and their cultural variation, and develop socially sensitive AI-based tutors and coaches.\u003C\/p\u003E\u003Ch4\u003E\u0026nbsp;\u003C\/h4\u003E\u003Ch4\u003E\u003Cstrong\u003EFor CODA guest access, please contact \u003C\/strong\u003E\u003Ca href=\u0022mailto:shatcher8@gatech.edu\u0022\u003E\u003Cstrong\u003Eshatcher8@gatech.edu\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003Eat least 2 business days prior to the event.\u003C\/strong\u003E\u003C\/h4\u003E\u003Ch4\u003E\u0026nbsp;\u003C\/h4\u003E\u003Ch5\u003EZoom link: \u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F91607375625%3Fpwd%3DybCsFPzknA8VqYZaSqvenqlWbrvyzx.1\u0026amp;data=05%7C02%7Cchrista.ernst%40research.gatech.edu%7C584f9253922649cf4a6508df34f0dd56%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639287881280250418%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=Q9YiiewMNH1%2BTvaz5CFWHEdeUJhYPQ0XsEXcjUEHuZs%3D\u0026amp;reserved=0\u0022 target=\u0022_blank\u0022 title=\u0022Original URL: https:\/\/gatech.zoom.us\/j\/91607375625?pwd=ybCsFPzknA8VqYZaSqvenqlWbrvyzx.1. Click or tap if you trust this link.\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/91607375625?pwd=ybCsFPzknA8VqYZaSqvenqlWbrvyzx.1\u003C\/a\u003E\u003C\/h5\u003E\u003Ch6\u003EMeeting ID: 916 0737 5625\u003Cbr\u003EPasscode: 038828\u003C\/h6\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAll Seminars Held 12pm - 1pm in CODA\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Featuring | Lyle Ungar - Professor of Computer and Information Science at the University of Pennsylvania"}],"uid":"27863","created_gmt":"2026-08-07 14:25:42","changed_gmt":"2026-11-03 19:30:27","author":"Christa Ernst","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-11T12:00:00-05:00","event_time_end":"2026-11-11T13:00:00-05:00","event_time_end_last":"2026-11-11T13:00:00-05:00","gmt_time_start":"2026-11-11 17:00:00","gmt_time_end":"2026-11-11 18:00:00","gmt_time_end_last":"2026-11-11 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CODA Building 9th floor Atrium \u0026 Virtual","extras":[],"groups":[{"id":"322011","name":"College of Computing Events"},{"id":"1278","name":"College of Sciences"},{"id":"545781","name":"Institute for Data Engineering and Science"},{"id":"142761","name":"IRIM"}],"categories":[],"keywords":[{"id":"9167","name":"machine learning"},{"id":"187023","name":"go-data"},{"id":"654","name":"College of Computing"},{"id":"188087","name":"go-irim"},{"id":"193860","name":"Artifical Intelligence"}],"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":[{"value":"\u003Cp\u003E\u003Cstrong\u003EFor CODA guest access, please contact \u003C\/strong\u003E\u003Ca href=\u0022mailto:shatcher8@gatech.edu\u0022\u003E\u003Cstrong\u003Eshatcher8@gatech.edu\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E at least 2 business days prior to the event.\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"693605":{"#nid":"693605","#data":{"type":"event","title":"PhD Defense | Optimization Driven Machine Learning for Healthcare","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;Optimization Driven Machine Learning for Healthcare\u003Cbr\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;Monday, November 9, 2026\u003Cbr\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;4 PM (ET)\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;George Tower 1502\u003Cbr\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EHimadri S. Pandey\u003C\/strong\u003E\u003Cbr\u003EMachine Learning PhD Candidate\u003Cbr\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Col\u003E\u003Cli data-list-item-id=\u0022ea8d9f1a68a41bf4f2bf221e86123a613\u0022\u003EDr. Gian-Gabriel P. Garcia (Advisor), Industrial and Systems Engineering, University of Washington\u003C\/li\u003E\u003Cli data-list-item-id=\u0022ee4e40b597df1b37e487366b3df2e22ad\u0022\u003EDr. Jing Li, Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/li\u003E\u003Cli data-list-item-id=\u0022eea8871db1a81f162107be291a1ee6fbb\u0022\u003EDr. Lauren N. Steimle, Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/li\u003E\u003Cli data-list-item-id=\u0022ee5269688a13149beeaaf9d1fdd3c2cad\u0022\u003EDr. Kai Wang, Computational Science and Engineering, Georgia Institute of Technology\u003C\/li\u003E\u003Cli data-list-item-id=\u0022e9ba88f4deb4a3abf817ea992d03a8dbf\u0022\u003EDr. Landon Lempke, School of Medicine, Virginia Commonwealth University\u003C\/li\u003E\u003C\/ol\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine learning has shown great promise for improving healthcare through more accurate diagnosis, risk prediction, and personalized care. However, accurate predictions alone do not guarantee better decisions. In practice, healthcare providers must make decisions with limited time, staffing, and resources. Yet these constraints are often overlooked during model development. This dissertation investigates how optimization can be integrated into machine learning to develop decision-making tools that explicitly account for these operational constraints. It addresses three interconnected questions: When is individualized information worth collecting? What information should we prioritize when resources are limited? And who should receive scarce interventions, and when?\u003C\/p\u003E\u003Cp\u003EThe first part of this dissertation examines the value of information in clinical decision-making, developing a framework to quantify how individualized baseline testing changes diagnostic decisions in sports-related concussion assessment. The second part develops an operationally constrained machine learning framework that jointly selects diagnostic assessments and trains interpretable prediction models while accounting for uncertain administration times, clinical knowledge, and operational requirements. The third part introduces a mixed-integer optimization framework for ranked feature selection that optimizes feature rankings simultaneously across multiple resource budgets, accounting for feature acquisition costs and the predictive value of each successive feature set. The final part addresses the dynamic allocation of scarce healthcare resources among pediatric patients whose clinical conditions evolve over time. It proposes a multi-action restless multi-armed bandit framework and a decision-focused learning approach that combines neural networks and long-run resource planning to coordinate clinical interventions under resource constraints.\u003C\/p\u003E\u003Cp\u003ETogether, these contributions advance a shift in healthcare AI from \u201cWhat can we predict?\u201d to \u201cWhat actions should we take and for whom, given the resources available?\u201d\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOptimization Driven Machine Learning for Healthcare\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Himadri S. Pandey - Machine Learning PhD Candidate - H. Milton Stewart School of Industrial and Systems Engineering"}],"uid":"36518","created_gmt":"2026-10-29 13:35:02","changed_gmt":"2026-10-29 13:36:00","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-09T16:00:00-05:00","event_time_end":"2026-11-09T18:00:00-05:00","event_time_end_last":"2026-11-09T18:00:00-05:00","gmt_time_start":"2026-11-09 21:00:00","gmt_time_end":"2026-11-09 23:00:00","gmt_time_end_last":"2026-11-09 23:00:00","rrule":null,"timezone":"America\/New_York"},"location":"George Tower 1502","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"693561":{"#nid":"693561","#data":{"type":"event","title":"PhD Defense | Decentralized Learning of Interdependencies for Multi-Component Dynamic Systems","body":[{"value":"\u003Cdiv\u003E\u003Cstrong\u003ETitle: \u003C\/strong\u003EDecentralized Learning of Interdependencies for Multi-Component Dynamic Systems\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EDate: \u003C\/strong\u003E10th\u0026nbsp;Nov 2026\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ETime: \u003C\/strong\u003E10:00 to 12:00 EST\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ELocation: \u003C\/strong\u003EVirtual via Zoom \u2014 \u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F98959804477%3Fpwd%3DEvx56C26tohvh3a6Q4HQzyA2861uAz.1\u0026amp;data=05%7C02%7Cshatcher8%40gtvault.onmicrosoft.com%7C2a49ce90c0d046a9d6f608df345e325f%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639287251319613230%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=fGoXPHqeSNkBIEBWcsma%2FtT7Vm4%2F5q%2Fh43jhWF%2BwZtQ%3D\u0026amp;reserved=0\u0022 title=\u0022Original URL: https:\/\/gatech.zoom.us\/j\/98959804477?pwd=Evx56C26tohvh3a6Q4HQzyA2861uAz.1. Click or tap if you trust this link.\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/98959804477?pwd=Evx56C26tohvh3a6Q4HQzyA2861uAz.1\u003C\/a\u003E\u0026nbsp;(Meeting ID: 989 5980 4477, \u0026nbsp;Passcode: 070732)\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EAyush Mohanty\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003EMachine Learning PhD Student\u003C\/div\u003E\u003Cdiv\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/div\u003E\u003Cdiv\u003EGeorgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003E1 Prof. Nagi Gebraeel (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E2 Prof. Pascal Van Hentenryck, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E3 Prof. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E4 Prof. Aaron Stebner, \u0026nbsp;George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E5 Prof. Jeff Shamma, The Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003EModern industrial systems comprise multiple interconnected subsystems whose temporal dynamics are often interdependent, making it essential to understand how changes in one subsystem influence others. Learning these interdependencies is particularly challenging in decentralized environments where raw data cannot be shared, and individual subsystems operate proprietary models that cannot be modified. This dissertation develops decentralized learning frameworks for characterizing cross-client temporal interdependencies in both linear and nonlinear dynamical systems while preserving the autonomy of individual client models. For linear systems, we establish a federated Granger causality framework that enables the discovery of unknown cross-client interdependencies. We further use this federated Granger causality\u0026nbsp;framework to investigate the propagation of estimation uncertainty and its implications for interdependency learning. For nonlinear systems, the dissertation introduces graph attention-based learning with a predefined candidate graph structure, establishing theoretical relationships between learned attention coefficients and Jacobian-based interdependencies. It further develops a complementary framework for learning nonlinear interdependencies without requiring a predefined graph skeleton, where the downstream task of root cause analysis is explored. The proposed methods are evaluated on synthetic and industrial datasets, demonstrating their utility for decentralized prediction and interdependency characterization.\u003C\/div\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EDecentralized Learning of Interdependencies for Multi-Component Dynamic Systems\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Ayush Mohanty - Machine Learning PhD Student - H. Milton Stewart School of Industrial and Systems Engineering"}],"uid":"36518","created_gmt":"2026-10-27 19:13:47","changed_gmt":"2026-10-27 19:15:04","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-10T10:00:00-05:00","event_time_end":"2026-11-10T12:00:00-05:00","event_time_end_last":"2026-11-10T12:00:00-05:00","gmt_time_start":"2026-11-10 15:00:00","gmt_time_end":"2026-11-10 17:00:00","gmt_time_end_last":"2026-11-10 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"https:\/\/gatech.zoom.us\/j\/98959804477?pwd=Evx56C26tohvh3a6Q4HQzyA2861uAz.1 (Meeting ID: 989 5980 4477,  Passcode: 070732)","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"691559":{"#nid":"691559","#data":{"type":"event","title":"IRIM Fall 2026 Seminar |  Restoring Dexterity in Telerobotics: An Engineer\u2019s Journey Into Neuroscience","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E The human body can perform dexterous manipulation across a variety of environments. However, some environments present challenges due to limitations in function, distance, and scale. In many of these cases, access can be restored through the use of a telerobot. Dexterous manipulation via a telerobot is feasible only if it can effectively extend the user\u2019s inherent sensorimotor control scheme to the distal environment. In this talk, I will discuss research from our lab that explores the intricate relationship between touch perception, sensorimotor control, and telerobotics in both healthy and impaired individuals. Specifically, I will focus on our efforts to 1) restore \u201cneurally efficient\u201d control in upper-limb prosthetics, 2) enhance skill learning and skill transfer in robotic minimally invasive surgical training, and 3) assess and augment touch perception in healthy and impaired sensorimotor systems. Throughout, I will emphasize the need for increased collaboration between engineers and neuroscientists to deepen our understanding of these long-standing societal challenges and move closer to long-term solutions.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EJeremy D. Brown is the John C. Malone Associate Professor in the Department of Mechanical Engineering at Johns Hopkins University and the Solomon H. Snyder Department of Neuroscience at the Johns Hopkins School of Medicine. He is also a member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare. Prior to joining Hopkins, Jeremy was a Postdoctoral Research Fellow at the University of Pennsylvania in the Haptics Research Group, which is part of Penn\u2019s General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. He received his M.S. and Ph.D. degrees in Mechanical Engineering at the University of Michigan. He also holds B.S. degrees in Applied Physics and Mechanical Engineering from Morehouse College and the University of Michigan, respectively, as a graduate of the Atlanta University Center\u2019s Dual Degree Engineering Program. Jeremy directs the Haptics and Medical Robotics (HAMR) Laboratory, whose research focuses on human-robot interaction and haptic feedback, with applications in upper-limb prosthetics, robot-assisted surgery, and rehabilitation. By integrating human perception, motor control, and biomechanics, his work advances the design of intuitive and effective human-machine interfaces. He has been honored to receive several awards, including the NIH R21 Trailblazer Award, the IEEE RAS Technical Committee on Haptics Early CAREER Award, the NSF CRII Award, the NSF CAREER Award, and the Sloan Foundation Fellowship. He was also named a scholar to the NIH funded Interdisciplinary Rehabilitation Engineering Career Development Program (IREK-12). He is a member of the Institute of Electrical and Electronics Engineers\u2019 (IEEE) Robotics and Automation Society, the American Society of Mechanical Engineers (ASME), and the National Society of Black Engineers (NSBE). Brown\u2019s work has appeared in several peer-reviewed journals and news outlets, including Nature Scientific Reports, the Journal of Neuroengineering and Rehabilitation, IEEE Transactions on Haptics, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Systems and Rehabilitation Engineering, the IEEE Transactions on Medical Robotics and Bionics, Forbes, and The New York Times.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAll Seminars Held on Wednesdays 12:15 - 1:15pm\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Featuring |  Jeremy D. Brown - John C. Malone Associate Professor, Department of Mechanical Engineering, Johns Hopkins University"}],"uid":"27863","created_gmt":"2026-08-07 17:40:54","changed_gmt":"2026-10-26 18:41:36","author":"Christa Ernst","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-18T12:15:00-05:00","event_time_end":"2026-11-18T13:15:00-05:00","event_time_end_last":"2026-11-18T13:15:00-05:00","gmt_time_start":"2026-11-18 17:15:00","gmt_time_end":"2026-11-18 18:15:00","gmt_time_end_last":"2026-11-18 18:15:00","rrule":null,"timezone":"America\/New_York"},"location":"Klaus 1116 E\u0026W","extras":["free_food"],"related_links":[{"url":"https:\/\/hamr.lcsr.jhu.edu\/","title":"PI Website"}],"groups":[{"id":"1278","name":"College of Sciences"},{"id":"142761","name":"IRIM"}],"categories":[],"keywords":[{"id":"188087","name":"go-irim"},{"id":"108031","name":"College of Engineering; Coulter Department of Biomedical Engineering"},{"id":"78681","name":"medical robotics"},{"id":"172970","name":"go-neuro"},{"id":"187582","name":"go-ibb"}],"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":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:christa.ernst@research.gatech.edu\u0022\u003Echrista.ernst@research.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"691537":{"#nid":"691537","#data":{"type":"event","title":"Machine Learning Seminar Series Fall 2026 | Session VII","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E TBA\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E Enric Boix-Adsera is an assistant professor of statistics and data science. His parents are originally from Barcelona, and he grew up mostly in Princeton, New Jersey. Before coming to Wharton, he earned a doctorate in electrical engineering and computer science from MIT.\u003C\/p\u003E\u003Cp\u003EBoix-Adsera studies the learning processes of artificial intelligence models, such as ChatGPT, with the goal of improving their efficiency and reliability.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch4\u003E\u0026nbsp;\u003Cstrong\u003EFor CODA guest access, please contact \u003C\/strong\u003E\u003Ca href=\u0022mailto:shatcher8@gatech.edu\u0022\u003E\u003Cstrong\u003Eshatcher8@gatech.edu\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E at least 2 business days prior to the event.\u003C\/strong\u003E\u003C\/h4\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAll Seminars Held 12pm - 1pm in CODA\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Featuring | Enric Boix, Wharton School at University of Pennsylvania \u2013 Department of Statistics and Data Science"}],"uid":"27863","created_gmt":"2026-08-07 14:31:32","changed_gmt":"2026-08-13 14:18:23","author":"Christa Ernst","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-12-02T12:00:00-05:00","event_time_end":"2026-12-02T13:00:00-05:00","event_time_end_last":"2026-12-02T13:00:00-05:00","gmt_time_start":"2026-12-02 17:00:00","gmt_time_end":"2026-12-02 18:00:00","gmt_time_end_last":"2026-12-02 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CODA Building 9th floor Atrium \u0026 Virtual","extras":[],"related_links":[{"url":"https:\/\/eboix.github.io\/","title":"PI Website"}],"groups":[{"id":"322011","name":"College of Computing Events"},{"id":"1278","name":"College of Sciences"},{"id":"545781","name":"Institute for Data Engineering and Science"},{"id":"142761","name":"IRIM"}],"categories":[],"keywords":[{"id":"9167","name":"machine learning"},{"id":"654","name":"College of Computing"},{"id":"187023","name":"go-data"},{"id":"188087","name":"go-irim"}],"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":[{"value":"\u003Cp\u003E\u003Cstrong\u003EFor CODA guest access, please contact \u003C\/strong\u003E\u003Ca href=\u0022mailto:shatcher8@gatech.edu\u0022\u003E\u003Cstrong\u003Eshatcher8@gatech.edu\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E at least 2 business days prior to the event.\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}