{"690844":{"#nid":"690844","#data":{"type":"event","title":"PhD Defense by Benjamin Cobb","body":[{"value":"\u003Cp\u003ETitle: Novel Tensor Factorization Algorithms and Applications\u003C\/p\u003E\u003Cp\u003EDate: Thursday, July 2nd (7\/2), 2026\u003C\/p\u003E\u003Cp\u003ETime: 12:00-2:00pm ET\u003C\/p\u003E\u003Cp\u003ELocation: Coda C1315 (Grant Park)\u003C\/p\u003E\u003Cp\u003EZoom:\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/95337401341?pwd=UA3HGBasOpWtv4JIanQFoBKtyovP9y.1\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/95337401341?pwd=UA3HGBasOpWtv4JIanQFoBKtyovP9y.1\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBenjamin Cobb\u003C\/p\u003E\u003Cp\u003EComputer Science Ph.D. Candidate\u003C\/p\u003E\u003Cp\u003ESchool of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.ben-cobb.com\/\u0022\u003Ehttps:\/\/www.ben-cobb.com\/\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECommittee:\u003C\/p\u003E\u003Cp\u003EDr. Richard W. Vuduc (co-advisor), CSE, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Haesun Park (co-advisor), CSE, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Edmond Chow, CSE, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Grey M. Ballard, CS, Wake Forest University\u003C\/p\u003E\u003Cp\u003EDr. Ramakrishnan Kannan, Discrete Algorithms, Oak Ridge National Laboratory\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EAs our capacity to collect and generate data continues to outpace our capacity to store and analyze said data, low-rank tensor factorizations offer a solution to alleviate this issue for tensorized data. The process of computing low-rank tensor factorizations is computationally expensive and requires efficiently leveraging computational resources to enable feasibility. This\u0026nbsp;thesis\u0026nbsp;proposes several novel algorithms for low-rank tensor factorizations to enable interpretable analysis and compression of massive multiway datasets. To this end, we focus on three overarching themes: efficiently leveraging computational resources to compute large-scale tensor decompositions, efficiently enforcing nonnegativity constraints to improve interpretability, and incorporating additional information into the factorization to improve solution quality.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EWe start by proposing the Fused In-place Sequentially Truncated Higher Order Singular Value Decomposition (FIST-HOSVD) algorithm as the first in-place method for computing the dense Tucker decomposition, increasing the problem size that can be factorized by up to 3x. We demonstrate the effectiveness of the proposed FIST-HOSVD algorithm by decreasing the auxiliary memory consumption by over 135x when computing the dense Tucker decomposition on two combustion simulation compression applications. We then provide an in-depth study of several state-of-the-art methods for Nonnegative Matrix Factorization (NMF). As part of this, we provide a comprehensive survey of Nonnegative Least Squares (NNLS) solvers used to enforce the nonnegativity of the factors. In doing so, we propose a Fast Active-Set Thresholding NNLS (FAST-NNLS) solver which outperforms existing NNLS methods for broad classes of problems. We then introduce a GPU-accelerated Hierarchical NMF K-Means initialization method for large-scale protein clustering on commodity-grade hardware. We then propose the Low Rank Approximations with Constraints at Exascale (LORACX) framework for computing large-scale distributed NMF. We demonstrate that LORACX yields unprecedented performance and scalability by achieving 0.67 exaflops in double-precision on 8,192 nodes of the Frontier supercomputer when computing NMF of a 2.1 petabyte matrix. We then extend the NMF objective function to incorporate additional multiway data, culminating in a Joint Nonnegative Coupled Matrix-Tensor Factorization (Joint-NCMTF) framework. We demonstrate that the proposed Joint-NCMTF method yields improved clustering quality and additional dimensions of insight relative to traditional matrix-based methods.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ENovel Tensor Factorization Algorithms and Applications\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Novel Tensor Factorization Algorithms and Applications"}],"uid":"27707","created_gmt":"2026-06-22 13:58:16","changed_gmt":"2026-06-22 13:58:52","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-07-02T12:00:00-04:00","event_time_end":"2026-07-02T14:00:00-04:00","event_time_end_last":"2026-07-02T14:00:00-04:00","gmt_time_start":"2026-07-02 16:00:00","gmt_time_end":"2026-07-02 18:00:00","gmt_time_end_last":"2026-07-02 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda C1315 (Grant Park)","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}