{
	"event_id": "1100002",
	"eventinstance_id": "4088833",
	"calendar": {
		"id": 110,
		"title": "TRiO Center",
		"slug": "trio-center",
		"url": "https://events.ucf.edu/calendar/110/trio-center/"
	},
	"id": "4088833",
	"title": "Colloquium by Professor Abiy Tasissa, Tufts University",
	"subtitle": null,
	"description": "\u003Cp\u003EOur\u003Cspan\u003E\u0026nbsp\u003B\u003C/span\u003E\u003Ca href\u003D\u0022https://sciences.ucf.edu/math/colloquium/\u0022 target\u003D\u0022_blank\u0022\u003Ecolloquium\u003C/a\u003E\u003Cspan\u003E\u0026nbsp\u003B\u003C/span\u003Eseries offers a diverse platform for research scholars, faculty, students, and industry experts to share and exchange ideas, fostering discussion and networking across mathematics, statistics, and data science.\u003C/p\u003E\u000A\u003Cp\u003EDr. \u003Ca href\u003D\u0022https://math.tufts.edu/people/faculty/abiy\u002Dtasissa\u0022 target\u003D\u0022_blank\u0022\u003EAbiy Tasissa\u003C/a\u003E from Tufts University\u003Cspan\u003E\u0026nbsp\u003B\u003C/span\u003Ewill speak at this week\u0027s colloquium on \u0022\u003Cstrong\u003E\u003Cem\u003E\u003Cspan\u003ELearning geometry from anchored data\u003C/span\u003E\u003C/em\u003E\u003C/strong\u003E.\u0022\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003Cspan\u003E \u003C/span\u003E\u003C/strong\u003EMany learning problems require inferring global structure from incomplete, local, or relational data. We study this problem through the lens of anchors: selected points that provide partial geometric or relational information about the rest of the data, and serve as an inductive bias for learning. We develop this perspective in two settings. First, we consider representing data as convex combinations of local exemplars, where both the exemplars (anchors) and the representations are learned from data. This leads to sparse, structured representations and reveals connections to structured compressive sensing, as well as to neural architectures with built\u002Din geometric structure. Second, we study the problem of recovering point locations from partial distance measurements to anchors. Unlike standard localization and graph\u002Dbased methods, we do not assume distances between non\u002Danchor points, nor complete distance information among anchors. We present an optimization\u002Dbased approach for estimating global geometry under these minimal assumptions. We conclude by noting that anchors can serve a unifying framework for learning geometry and structure from partial information and highlight applications in resource\u002Dconstrained sensor networks, structure prediction, manifold learning, and interpretable deep learning.\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003ESpeaker Bio:\u003Cspan\u003E \u003C/span\u003E\u003C/strong\u003E\u003Cspan\u003EAbiy\u0026nbsp\u003BTasissa received a\u0026nbsp\u003BB.Sc. in Mathematics and an\u0026nbsp\u003BM.Sc. in Aeronautics and Astronautics from the Massachusetts Institute of Technology, and a Ph.D. in Applied Mathematics from Rensselaer Polytechnic Institute. He is currently an assistant professor in the Department of Mathematics at Tufts University. His research focuses on developing provable algorithms to estimate structures from incomplete distance data. He is also interested in provable algorithms for signal processing and statistical learning, structured deep learning, and applied linear algebra.\u003C/span\u003E\u003C/p\u003E",
	"location": "MSB 318: Mathematical Sciences Building, Room 318",
	"location_url": "https://www.ucf.edu/location/mathematical\u002Dsciences\u002Dbuilding/",
	"virtual_url": null,
	"registration_link": null,
	"registration_info": null,
	"starts": "Mon, 09 Mar 2026 15:30:00 -0400",
	"ends": "Mon, 09 Mar 2026 16:30:00 -0400",
	"ongoing": "False",
	"category": "Speaker/Lecture/Seminar",
	"tags": ["UCF SDMSS","UCF Statistics","UCF Mathematics"],
	"contact_name": "Christian Kümmerle",
	"contact_phone": null,
	"contact_email": "kuemmerle@ucf.edu",
	"url": "https://events.ucf.edu/event/4088833/colloquium-by-professor-abiy-tasissa-tufts-university/"
}