2013 SIGMOD Jim Gray Doctoral Dissertation Award
ACM SIGMOD is pleased to present the 2013 SIGMOD Jim Gray Doctoral Dissertation Award to Sudipto Das. Das completed his dissertation titled "Scalable and Elastic Transactional Data Stores for Cloud Computing Platforms" at the University of California, Santa Barbara. Das’ dissertation lays foundations for scalable transactional database services in multi-tenant cloud infrastructures. The thesis develops techniques for scalability based on static as well as dynamic partitioning (sharding) and elasticity by live migration. In combination the techniques achieve elastic scalability without sacrificing transactional consistency. The work is remarkable for its breadth of scope, its comprehensive implementation and thorough performance evaluation.
Sudipto Das is a Researcher in the Data Management, Exploration, and Mining (DMX) group at Microsoft Research (MSR). He is part of the eXtreme Computing Group (XCG), one of MSR's worldwide labs. He graduated from the Department of Computer Science, University of California Santa Barbara (UCSB) with a PhD in Fall 2011. He was advised by Professors Divy Agrawal and Amr El Abbadi during the course of my PhD as a member of the Distributed Systems Lab.
SIGMOD Jim Gray Doctoral Dissertation Honorable Mentions
ACM SIGMOD is also pleased to recognize Herodotos Herodotou and Wenchao Zhou for Honorable Mention for the 2013 SIGMOD Jim Gray Doctoral Dissertation Award. Herodotou completed his dissertation titled "Automatic Tuning of Data-Intensive Analytical Workloads" at Duke University. Zhou’s thesis is titled "Secure Time-aware Provenance for Distributed Systems" and the PhD was awarded by the University of Pennsylvania.
Herodotos Herodotou is a Senior Research SDE in the Data Management, Exploration and Mining (DMX) group, which is part of the eXtreme Computing Group (XCG) of Microsoft Research. He received his Ph.D. degree in Computer Science from Duke University in May 2012. His dissertation work titled "Automatic Tuning of Data-Intensive Analytical Workloads" presents a novel dynamic optimization approach that can form the basis for tuning workload performance automatically across different tuning scenarios and systems. His research interests are in large-scale Data Processing Systems and Database Systems. In particular, his work focuses on ease-of-use, manageability, and automated tuning of both centralized and distributed data-intensive computing systems. In addition, he is interested in applying database techniques in other areas like scientific computing, bioinformatics, and numerical analysis.
Wenchao Zhou is an assistant professor in the Computer Science Department at Georgetown University. He received the BSE degree in computer science from Tsinghua University in 2006, and the MSE and PhD degrees in computer science both from the University of Pennsylvania in 2009 and 2012 respectively. His research interests center on the use of data-centric and formal techniques towards ensuring safe and secure distributed systems. He is also interested in distributed data management and the application of database technologies to networked systems.