Ryan McKenna, Joie Wu, Arisa Tajima, Brett Mullins, Siddhant Pradhan, and Cecilia Ferrando have recently won the first prize in the National Institute of Standards and Technology (NIST) Differential Privacy Temporal Map Challenge. The challenge seeks new tools with which to push the boundaries of current technologies for de-identifying data […]
Cen Wang‘s paper “NIM: Modeling and Generation of Simulation Inputs via Generative Neural Networks” won Best Contributed Theoretical Paper Honorable Mention at WSC 2020! Authors: Cen Wang, Emily Herbert, Peter J. Haas
Two papers from DREAM Lab won the Best Demonstration and Best Demonstration Runner-up awards at VLDB 2020. Matteo Brucato, Miro Mannino, Azza Abouzied, Peter J. Haas, Alexandra Meliou won the 2020 VLDB Best Demonstration award for their work on “sPaQLTooLs: A Stochastic Package Query Interface for Scalable Constrained Optimization“. Anna […]
Alexandra Meliou wins ACM SIGSOFT Distinguished Artifact Award at ICSE 2020. Causal Testing: Understanding Defects’ Root Causes. Authors: Brittany Johnson, Yuriy Brun, Alexandra Meliou.
College of Information and Computer Sciences (CICS) doctoral candidate Anna Fariha has recently been awarded a 2020 Microsoft Research Dissertation Grant for her proposal, “Enhancing Usability and Explainability of Data Systems.” Fariha’s work focuses on reducing the usability gap between non-expert users and complex data systems. Her thesis aims to […]
Gerome Miklau’s PODS paper from 2010 will be awarded the test-of-time award at PODS 2020. This work was done by lab alumni Chao Li, Michael Hay, along with Andrew McGregor. “Optimizing Linear Counting Queries under Differential Privacy” Authors: Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor
Peter J. Haas is a co-PI of a National Science Foundation Grant entitled “Simulation and Decision-Analysis Algorithms for Integrated Modeling of Diseases: A healthy lives for all approach”
Peter J. Haas and Alexandra Meliou have received a grant from the National Science Foundation to study In-Database Prescriptive Analytics under Uncertainty.
Ryan McKenna has recently won the National Institute of Standards and Technology’s (NIST) Differential Privacy Synthetic Data Challenge Match #3. The competition tests participants’ ability to identify and develop practical methods for creating differentially private synthetic data sets. The challenges include empirical evaluation of synthetic data generation on common tasks […]
Two papers from DREAM Lab have been recognized as 2018 ACM SIGMOD Research Highlights. Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau were recognized with a 2018 SIGMOD Research Highlight Award for their work on “Ektelo: A Framework for Defining Differentially-Private Computations“. Brian […]