Daniel Dadush

Daniel Dadush is a part-time Professor at Utrecht University and a senior researcher at Centrum Wiskunde & Informatica (CWI), where he leads the Networks & Optimization group. His research spans lattice algorithms, integer programming, convex optimization, and discrepancy theory, with a focus on theoretical and algorithmic advancements.

• PhD in Algorithms, Combinatorics, and Optimization (ACO) from Georgia Tech (2012)
• Simons Postdoctoral Fellow at Courant Institute, NYU (2012-2014)

His work bridges discrete and continuous optimization, exemplified by breakthroughs like Strongly Polynomial Algorithms for Linear Programming (STOC 2024) and Interior Point Methods Are Not Worse Than Simplex (FOCS 2022). Recent publications emphasize randomized algorithms, integrality gaps, and high-dimensional geometry.

Scientific Awards:

• ERC Starting Grant (2019-2024)
• NWO Veni Grant (2015-2018)
• Van Dantzig Prize (2020)
• A.W. Tucker Prize for Best Thesis (2015)
• INFORMS Optimization Society Student Paper Prize (2011)

He mentors PhD students and postdocs, including Ben Bals, Samarth Tiwari, and Sophie Huiberts, and co-organizes major conferences like ISMP 2027 and Dutch Day on Optimization. His teaching includes courses on Interior Point Methods and Learning-Augmented Algorithms.