	A	B	C	D	E
1	Day 1 (10/18)		Speaker	Affiliation	Talk Title
2	9:00 AM	Session 1	chair: David Gleich
3			Jingmei Qiu	University of Delaware	Low rank Tensor Approximations to Kinetic Models
4			William Detmold	MIT	Tensor Computations in Lattice QCD
5			Joyce Ho	Emory University	Federated tensor learning and its application for healthcare
6			Ed Valeev	Virginia Tech	Automating symbolic manipulation and evaluation of data-sparse tensor algebra for quantum electronic structure
7	10:30 AM	break
8	11:00 AM	Session 2	chair: Aditya Devarakonda
9			Changwan Hong	MIT	Compiler Support for Structured Data
10			Jee Choi	University of Oregon	Linearized Tensor Format for Performance-Portable Sparse Tensor Computation
11			Toluwanimi Odemuyiwa	University of California, Davis	Extending Einsums to Support Graph Analytics: A BFS Example
12	12:00 PM	lunch
13	1:30 PM	Session 3	chair: Andrew Christleib
14			Cory Hauck	Oak Ridge National Laboratory	A semi-implicit, low-rank DG method for a kinetic model of radiation emission and absorption
15			Elizabeth Newman	Emory University	Optimal Matrix-Mimetic Tensor Algebras via Variable Projection
16			Kejun Huang	University of Florida	HOQRI: Higher-order QR Iteration for Scalable Tucker Decomposition
17			Huan He	University of Pennsylvania	Efficient Fine-tuning of pretrained machine learning models using Tensor Training
18	3:00 PM	break
19	3:15 PM	Session 4	chair: Ramki Kannan
20			Nandeeka Nayak	University of Illinois Urbana-Champaign	TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
21			Saday Sadayappan	University of Utah	Can tensor factorization help us shrink language models?
22			Scott Kovach	Stanford	Indexed Streams: A Formal Intermediate Representation for Fused Contraction Programs
23			Arvind Saibaba	North Carolina State University	Tensor methods for parametric low-rank kernel approximations
24	4:40 PM	poster session
25	5:45 PM	end day 1
26
27	Day 2 (10/19)
28	8:30 AM	Session 5	chair: Sara Pollock
29			Osman Malik	Lawrence Berkeley National Laboratory	Recent advances in sampling-based methods for tensor decomposition
30			Carmeliza Navasca	University of Alabama at Birmingham	Sampling Methods for the Canonical Polyadic Decomposition
31			Linjian Ma	University of Illinois at Urbana Champaign	Efficient tensor network contraction algorithms
32			Vivek Bharadwaj	UC Berkeley	Faster Implicit Leverage Sampling Algorithms for CP and Tensor-Train Decomposition
33	10:00 AM	break
34	10:30 AM	Session 6	char: Vishwas Rao
35			Alex Gittens	Rensselaer Polytechnic Institute	Faster Structured Tensor Decompositions via Sketching
36			Teresa Ranadive	Laboratory for Physical Sciences	Distributed Large-Scale All-at-Once Count Tensor Decomposition
37			Eric Phipps	Sandia National Laboratories	Streaming Generalized Canonical Polyadic Tensor Decompositions
38			Akwum Onwunta	Lehigh University	Tensor Train Approach to PDE-Constrained Optimization under Uncertainty
39	12:00 PM	lunch
40	1:30 PM	Session 7	chair: Piotr Luszczek
41			Matt Fishman	Flatiron Institute	Convenient development of general tensor network algorithms with ITensor
42			Avery Laird	University of Toronto	Automatically Translating Sparse Codes
43			Paul Kielstra	UC Berkeley	Tensor Butterfly Factorization (In Parallel!)
44			Mit Kotak	Massachusetts Institute of Technology	Optimizing Equivariant Tensor Products — the Computational Bottleneck of Symmetry-Equivariant Neural Networks
45	3:00 PM	end