Virginia Vassilevska Williams

Virginia Vassilevska Williams is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). Her research focuses on combinatorial and graph-theoretic tools in computational domains, including shortest paths, pattern detection, fine-grained complexity, and computational social choice.

• Education: B.S. in Mathematics and Eng. and Applied Science from Caltech (2003), Ph.D. in Computer Science from Carnegie Mellon University (2008), postdoc at the Institute for Advanced Study (2008-2009), and Computing Innovations Fellow at UC Berkeley (2009-2011).

Her work includes foundational contributions to fine-grained complexity, such as conditional lower bounds for problems in P, and algorithmic innovations in cycle detection, spanners, and approximate counting. She has also explored computational issues in social choice, including election manipulation and voting rule analysis.

Recent publications highlight advancements in sparse graph algorithms, distributed cycle detection, and diameter approximation, leveraging matrix multiplication and quantum computing techniques. Her research emphasizes bridging theoretical insights with practical computational challenges.

Scientific contributions include the

• Computing Innovations Fellowship

and co-organizing key workshops like TCS For All and Simons Institute programs. She actively advises Ph.D. students and has mentored numerous graduates now at institutions like UCSD, INSAIT, and Boston University.