{
	"event_id": "1155600",
	"eventinstance_id": "4223699",
	"calendar": {
		"id": 1684,
		"title": "Mathematics Department Calendar",
		"slug": "mathematics-department-calendar",
		"url": "https://events.ucf.edu/calendar/1684/mathematics-department-calendar/"
	},
	"id": "4223699",
	"title": "Colloquium by Professor Longxiu Huang, Michigan State University",
	"subtitle": null,
	"description": "\u003Cp\u003EOur \u003Ca href\u003D\u0022https://sciences.ucf.edu/sdmss/colloquium/\u0022 target\u003D\u0022_blank\u0022\u003EColloquium\u003C/a\u003E series 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\u003EProfessor \u003Ca href\u003D\u0022https://directory.natsci.msu.edu/directory/Profiles/Person/102984\u0022 target\u003D\u0022_blank\u0022\u003ELongxiu Huang\u003C/a\u003E from Michigan State University will speak at this week\u0027s colloquium on \u0022\u003Cstrong\u003E\u003Cem\u003ESame Noise, Different Place: Local Geometry of CUR\u003C/em\u003E\u003C/strong\u003E.\u0022\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C/strong\u003E\u0026nbsp\u003BLow\u002Drank approximation is a staple of modern data science, and the singular value decomposition settles one version of the problem completely: it gives the smallest possible error for a given rank. But that is only half the question. The other half \u002D\u002D what should the low\u002Drank factors actually be? \u002D\u002D the SVD answers with abstract directions that blend every row and column of the data. CUR decompositions answer it differently. They build the approximation out of genuine columns and rows of the matrix itself, so every factor points at something real: a gene, a sensor, a customer, a wavelength.\u003C/p\u003E\u000A\u003Cp\u003EClassical CUR theory tells us how badly noise can hurt, with perturbation bounds controlled by the size of the noise. Those bounds are sharp as worst\u002Dcase statements, but they see a perturbation only through its norm. In this talk I will describe a complementary, local picture: a perturbation expansion showing that the Fr\u0026eacute\u003Bchet derivative of the rank\u002Dtruncated CUR map is an oblique tangent\u002Dspace projector determined entirely by the selected rows and columns. The leading\u002Dorder recovery error therefore depends on where the noise sits relative to the sample, not merely on how large it is: perturbations invisible to the selected rows and columns are removed to first order, so two corruptions of identical magnitude can produce wildly different errors.\u003C/p\u003E\u000A\u003Cp\u003EAfter illustrating the theory on real data, I will close with the practical moral. Because the sample is yours to choose, robustness to the noise you can anticipate is something you design, not something you inherit. Time permitting, I will also discuss the tensor case.\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003ESpeaker Bio:\u003C/strong\u003E Longxiu Huang is an Assistant Professor in the Department of Computational Mathematics, Science and Engineering and the Department of Mathematics at Michigan State University. She received her Ph.D. in Mathematics from Vanderbilt University in 2019, advised by Akram Aldroubi, with a thesis on dynamical sampling. She was subsequently an Assistant Adjunct Professor in the Department of Mathematics at UCLA, working with Deanna Needell. Her research sits at the interface of applied harmonic analysis and randomized numerical linear algebra. She works on sampling theory, low\u002Drank matrix and tensor recovery, and CUR\u002Dtype decompositions \u0026mdash\u003B with a recurring emphasis on methods that are fast, provably accurate, and interpretable in terms of the original data. She is a core member of TEMPEST, the recently funded NSF Science and Technology Center for turbulence research headquartered at Michigan State University.\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, 05 Oct 2026 15:30:00 -0400",
	"ends": "Mon, 05 Oct 2026 16:30:00 -0400",
	"ongoing": "False",
	"category": "Speaker/Lecture/Seminar",
	"tags": ["UCF SDMSS"],
	"contact_name": "HanQin Cai",
	"contact_phone": null,
	"contact_email": "hqcai@ucf.edu",
	"url": "https://events.ucf.edu/event/4223699/colloquium-by-professor-longxiu-huang-michigan-state-university/"
}