About
I am a Staff Research Scientist at Google in the Algorithms and Optimization group. I received my PhD in computer science from the Georgia Institute of Technology, where I was advised by Dana Randall. Prior to that, I studied computer science and mathematics at the University of Kentucky. I am the recipient of a FOCS 2020 Best Paper Award, NSF Graduate Research Fellowship, and Barry Goldwater Scholarship. My research interests broadly include algorithms, discrete mathematics, machine learning, and optimization.
Preprints
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GIST: Greedy Independent Set Thresholding for Diverse Data Summarization
Matthew Fahrbach, Srikumar Ramalingam, Morteza Zadimoghaddam, Sara Ahmadian, Gui Citovsky, Giulia DeSalvo
Manuscript
Journal Articles
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Edge-Weighted Online Bipartite Matching
Matthew Fahrbach, Zhiyi Huang, Runzhou Tao, Morteza Zadimoghaddam
Journal of the ACM 69(6): 45:1-45:35 (2022)
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Coefficients and Roots of Peak Polynomials
Sara Billey, Matthew Fahrbach, Alan Talmage
Experimental Mathematics 25(2): 165-175 (2016)
Conference Papers
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Practical Performance Guarantees for Pipelined DNN Inference
Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu
ICML 2024 · Spotlight
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PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
ICML 2024 · Spotlight
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Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng
NeurIPS 2023 · Spotlight
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Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
ICML 2023
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Learning Rate Schedules in the Presence of Distribution Shift
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
ICML 2023
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Sequential Attention for Feature Selection
Taisuke Yasuda, MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
ICLR 2023
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Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Matthew Fahrbach, Gang Fu, Mehrdad Ghadiri
NeurIPS 2022
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A Fast Minimum Degree Algorithm and Matching Lower Bound
Robert Cummings, Matthew Fahrbach, Animesh Fatehpuria
SODA 2021
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Edge-Weighted Online Bipartite Matching
Matthew Fahrbach, Zhiyi Huang, Runzhou Tao, Morteza Zadimoghaddam
FOCS 2020 · Best Paper Award
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Faster Graph Embeddings via Coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang
ICML 2020
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Slow Mixing of Glauber Dynamics for the Six-Vertex Model in the
Ordered Phases
Matthe Fahrbach, Dana Randall
RANDOM 2019
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Non-monotone Submodular Maximization with Nearly
Optimal Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam
ICML 2019
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Submodular Maximization with Nearly Optimal Approximation,
Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam
SODA 2019
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Graph Sketching Against Adaptive Adversaries Applied to the
Minimum Degree Algorithm
Matthew Fahrbach, Gary Miller, Richard Peng, Saurabh Sawlani, Junxing Wang, Shen Chen Xu
FOCS 2018
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Nearly Tight Bounds for Sandpile Transience on the Grid
David Durfee, Matthew Fahrbach, Yu Gao, Tao Xiao
SODA 2018
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Analyzing Boltzmann Samplers for Bose–Einstein Condensates with Dirichlet Generating Functions
Megan Bernstein, Matthew Fahrbach, Dana Randall
ANALCO 2018
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Approximately Sampling Elements with Fixed Rank in Graded Posets
Prateek Bhakta, Ben Cousins, Matthew Fahrbach, Dana Randall
SODA 2017