{
	"event_id": "1106809",
	"eventinstance_id": "4104293",
	"calendar": {
		"id": 2269,
		"title": "UCF Events",
		"slug": "ucf-events",
		"url": "https://events.ucf.edu/calendar/2269/ucf-events/"
	},
	"id": "4104293",
	"title": "Colloquium by Professor Alexander Katsevich, University of Central Florida",
	"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\u003EOur own Professor \u003Ca href\u003D\u0022https://sciences.ucf.edu/math/person/alexander\u002Dkatsevich/\u0022 target\u003D\u0022_blank\u0022\u003EAlexander Katsevich\u003C/a\u003E will speak at this week\u0027s colloquium on \u0022\u003Cstrong\u003E\u003Cem\u003EHigh\u002Ddimensional Laplace asymptotics up to the concentration threshold\u003C/em\u003E\u003C/strong\u003E.\u0022\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003Cspan\u003E\u0026nbsp\u003B\u003C/span\u003E\u003C/strong\u003EHigh\u002Ddimensional Laplace\u002Dtype integrals are a basic tool for approximating difficult quantities across mathematics, statistics, physics, chemistry, and engineering. They arise in posterior normalization and expectation formulas in Bayesian statistics, in partition functions and free energies in statistical physics, in saddle\u002Dpoint and fluctuation calculations in field\u002Dtheoretic models, and in molecular simulation and theoretical chemistry. A major obstacle, however, is that in many modern problems both the large parameter lambda and the dimension d grow, and the classical rigorous theory no longer applies in these regimes.\u003C/p\u003E\u000A\u003Cp\u003EIn this talk I will describe recent results that substantially extend the reach of rigorous Laplace asymptotics. Until now, explicit high\u002Ddimensional expansions with remainder bounds were essentially limited to the Gaussian\u002Dapproximation regime, which requires d^2/lambda \u002D\u0026gt\u003B 0. Our results go far beyond that barrier and remain valid essentially all the way up to the natural concentration threshold d/lambda \u002D\u0026gt\u003B 0. In other words, they identify the true high\u002Ddimensional range in which one can still obtain explicit asymptotic formulas with rigorous error control. This closes a major gap between formal practice and rigorous theory, and represents a significant advance on a century\u002Dold problem of extending Laplace asymptotics to high dimension.\u003C/p\u003E\u000A\u003Cp\u003EThe results have several important consequences. They provide explicit higher\u002Dorder formulas for logarithms of Laplace integrals, yielding rigorous versions of corrections that in physics are often interpreted as fluctuation or loop terms. They also give constructive approximations to the underlying concentrating probability measures, leading to practical approximations for posterior expectations, marginal likelihoods, and approximate sampling schemes. As a result, they are directly relevant to uncertainty quantification and Bayesian inverse problems, while also placing a broad class of calculations in statistical physics, chemistry, and related areas on a firmer mathematical foundation.\u003C/p\u003E\u000A\u003Cp\u003EThis is joint work with Anya Katsevich, Department of Statistical Science, Duke University.\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003ESpeaker Bio:\u003Cspan\u003E \u003C/span\u003E\u003C/strong\u003EAlexander Katsevich is a Professor of Mathematics at the University of Central Florida whose research focuses on inverse problems, microlocal analysis, tomography, and medical imaging. He earned his Ph.D. in Mathematics from Kansas State University in 1994 and held a postdoctoral fellowship at Los Alamos National Laboratory before joining UCF in 1996. He is the recipient of the 2016 Marcus Wallenberg Prize.\u0026nbsp\u003B\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, 13 Apr 2026 10:30:00 -0400",
	"ends": "Mon, 13 Apr 2026 11:30:00 -0400",
	"ongoing": "False",
	"category": "Speaker/Lecture/Seminar",
	"tags": ["UCF SDMSS","UCF Statistics","UCF Mathematics"],
	"contact_name": "Suyeon Kang",
	"contact_phone": null,
	"contact_email": "suyeon.kang@ucf.edu",
	"url": "https://events.ucf.edu/event/4104293/colloquium-by-professor-alexander-katsevich-university-of-central-florida/"
}