Ashwin Machanavajjhala (original) (raw)
| Bio | Publications (DBLP) | Students | Teaching | Tutorials |
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News: I am spending 50% of my time as Cheif Scientist of Tumult Labs.
- [Feb 2022] Yuchao Tao's Tumult Labs internship work on "Benchmarking Differentially Private Synthetic Data Generation Algorithms" will be presented as a spotlight talk AAAI-PPAI 2022
- [Dec 2021] "IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy" with Cehnghong Wang, Johes Bater and Kartik Nayak accepted with shepherding to SIGMOD 2022
- [Dec 2021] "R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys " with Yuchao Tao, Ke Yi, Wei Dong and Juanru Fang, accepted to SIGMOD 2022
- [May 2021] Our SIGMOD 2011 paper titled "No Free Lunch in Differential Privacy" awarded the ACM SIGMOD 2021 Test of Time award
Funding:
- NSF Collaborative Research: SaTC: CORE: Medium: #2016393: "Quicksilver: A Write-oriented, Private, Outsourced Database Management System"
- NSF RAPID: #2029853: "Poirot: From Contact Tracing to Private Exposure Detection"
- DARPA Brandeis and SPAWAR N66001-15-C-4067: "System-P: A Data Analytics Engine With Customizable Privacy and Optimized Utility"
- NSF CIF21 DIBBs #1443014: "An Integrated System for Public/Private Access to Large-Scale, Confidential Social Science Data"
- NSF TWC:Medium: Collaborative: #1408982: "Re[DP]: Realistic Data Mining Under Differential Privacy"
- NSF CAREER Award #1253327: "PROTEUS: A Practical and Rigorous Toolkit for Privacy" Bio:
Ashwin Machanavajjhala is an Associate Professor in the Department of Computer Science, Duke University, and co-founder of Tumult Labs. His primary research interests lie in algorithms for privacy preserving data analytics with a focus on differential privacy. He is an ACM Distinguished Member, a recipient of the ACM SIGMOD 2021 Test of Time and IEEE ICDE 2017 Influential Paper awards, and the NSF Faculty Early CAREER award in 2013. In collaboration with the US Census Bureau, he is credited with developing the first real world deployment of differential privacy. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.
Postdocss:
- Amir Gilad
- Johes Bater
Students:
- Yuchao Tao
- David Pujol
- Chenghong Wang
- Shweta Patwa Alumni:
- Ios Kotsogiannis (PhD 2020) (→ Snap Inc.)
- Nisarg Raval (PhD 2019) (→ LinkedIn)
- Xi He (PhD 2018) (→ U Waterloo, Asst. Prof.)
- Yan Chen (PhD 2018) (→ Google)
- Ben Stoddard (MS) (→ Google)
- Kiron Lebeck(BS) (→ PhD candidate at U Washington)
- Bharat Chelapalli (MS) (→ Amazon)
Prospective Students:I am interested in research into privacy preserving data analysis, especially differential privacy, and algortihmic fairness. Please read these representative papers before contacting me:
- Differential Privacy: Kotsogiannis et al PrivateSQL: A Differentially Private SQL Query Engine VLDB 2019 and Zhang et al Ektelo: A Framework for Defining Differentially-Private Computations SIGMOD 2018
- Secure Computation: Bater et al Shrinkwrap: Differentially-Private Query Processing in Private Data Federations VLDB 2019.
- Fairness: Kuppam et al Fair Decision Making using Privacy-Protected Data
Teaching:
- "CompSci 590: Privacy and Fairness in Data Science" with Brandon Fain, Fall 2018, Fall 2021
- "CompSci 290: Everything Data", co taught with Jun Yang,Spring 2015,Spring 2014, Spring 2019
- "CompSci 590.03: Privacy in a Mobile-Social World", Fall 2016,Fall 2013, Fall 2012
- "CompSci 590.02: Algorithms for Big Data", Fall 2015,Spring 2013 Tutorials/Talks:
- "Practical Security and Privacy for Database Systems", (with Xi He, Jennie Rogers, Johes Bater, Chenghong Wang, Xiao Wang), SIGMOD 2021
- "Differential Privacy in the Wild: A tutorial on current practices & open challenges", (with Xi He, Michael Hay), VLDB/SIGMOD 2017, Part 1 (slides,video), Part 2 (slides,video)
- "Entity Resolution", (with L. Getoor) KDD Conference Aug 2013, Very Large Databases Conference, August 2012, AAAI Conference, August 2012
- "Privacy in Data Publishing: Tutorial (part Iand part II)", (with J. Gehrke) IEEE ICDE 2010, IEEE Symposium on Security and Privacy, 2009