Scholar
Di Wang
Google Scholar ID: gn2qlUoAAAAJ
Google Research
Citations & Impact
Citations
994
H-index
15
i10-index
20
Publications
20
Co-authors
11
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Information is insufficient.
Research Experience
- Joined Google Research at Mountain View in July 2019. From Spring 2018 to Summer 2019, he was a postdoc in the Algorithms & Randomness Center at Georgia Tech with Richard Peng. In Fall 2017, he was a research fellow at the Simons Institute for the program 'Bridging Continuous and Discrete Optimization'.
Education
- Finished his PhD in Computer Science at UC Berkeley in summer 2017, advised by Satish Rao. Before attending Berkeley, he received a B.S. in Computer Science from Cornell University, advised by Robert Kleinberg.
Background
- Research interests are in the design of efficient algorithms, especially for large-scale optimization problems and graph problems that arise broadly in applications from machine learning, data analysis, and operations research. His work draws on a broad range of numerical and discrete tools from combinatorics, optimization, and graph theory, leading to not only stronger theoretical guarantees but also practical improvements for computational challenges arising in practice.
Miscellany
- Links to some courses and resources such as Dan Spielman's course on spectral graph theory, 'The Design of Approximation Algorithms' by David P. Williamson and David B. Shmoys, Stanford Network Analysis Platform (SNAP), 'Networks, Crowds, and Markets' by David Easley and Jon Kleinberg, Sanjeev Arora's course on learning theory, and Local Graph Clustering.
