Josh Alman

Josh Alman is an Assistant Professor of Computer Science at Columbia University, affiliated with the Theory Group. His research focuses on algorithm design, complexity theory, and algebraic methods in computer science. Previously, he was a Rabin Postdoc in Theoretical Computer Science at Harvard University (2020–2023), and completed his PhD in Computer Science at MIT (2016–2019), advised by Ryan Williams and Virginia Vassilevska Williams. His work bridges foundational questions in algorithms and complexity, with notable contributions to matrix multiplication, lower bounds, and applications of algebraic tools.

He advises PhD students at Columbia, including Hengjie Zhang, Kevin Yeo, and Hantao Yu. His notable awards include the Best Paper Award at SOSA 2023, the Machtey Award for Best Student Paper at FOCS 2019, and the Best Student Paper Award at CCC 2019.

Alman’s research interests span theoretical computer science, with a focus on algebraic and combinatorial approaches to problems in algorithms, complexity, and machine learning. His recent work explores the limits of fast algorithms for fundamental tasks (e.g., matrix multiplication, kernel density estimation) and their implications for broader computational models.

Education

• PhD in Computer Science, MIT (2019), advised by Ryan Williams and Virginia Vassilevska Williams
• M.S./B.S. in Computer Science, Stanford University (2014–2016)

Key Research Themes

• Lower bounds for arithmetic circuits and matrix rigidity
• Algebraic methods for algorithm design
• Fine-grained complexity and its applications
• Machine learning theory and attention mechanisms