Dana Moshkovitz is an associate professor of Computer Science at UT Austin. Her research is in Theoretical Computer Science. Much of it focuses on the limitations of approximation algorithms and probabilistic checking of proofs.
Dana did her PhD at the Weizmann Institute in Israel. Her thesis co-won the Nessyahu Prize for best math PhD thesis in Israel in 2009, and part of this work was awarded the FOCS 2008 Best paper. Dana went on to spend a couple of years at Princeton University and the Institute of Advanced Study before joining MIT as an assistant professor. Dana is the recipient of the Jerome Saltzer teaching award of MIT EECS.
Subhash Khot, Dor Minzer, Dana Moshkovitz, Shmuel Safra. April 23 2018. Small Set Expansion in The Johnson Graph. Colloquium on Computational Complexity. Trier, Germany.
Dana Moshkovitz, Michal Moshkovitz. July 5 2018. Entropy Samplers and Strong Generic Lower Bounds For Space Bounded Learning. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
Dana Moshkovitz, Michal Moshkovitz. JUN 18 2017. Mixing Implies Lower Bounds For Space Bounded Learning. Electronic Colloquium on Computational Complexity.
Eden Chlamtáč, Pasin Manurangsi, Dana Moshkovitz, Aravindan Vijayaraghavan. JAN 16 2017. Approximation Algorithms for Label Cover and The Log-Density Threshold. Society for Industrial and Applied Mathematics.
Ofer Grossman, Dana Moshkovitz. OCT 9 2016. Amplification and derandomization without slowdown. IEEE. 770-779.