Om ThakkarGraduate Student
Department of Computer Science
Email : omthkkr "at" bu.edu
Security group, and the Theoretical Computer Science group, at the Department of Computer Science, Boston University. My research is in privacy-preserving data analysis, with a specific focus on differential privacy and its applications to machine learning, deep learning, and adaptive data analysis. I am very fortunate to be advised by Dr. Adam Smith.
I completed the first 3.5 years of my Ph.D. in the Department of Computer Science and Engineering at
- I successfully defended my dissertation titled Advances in Privacy-Preserving Machine Learning on August 1, 2019.
- I was a Visiting Graduate Student in the Data Privacy program at Simons, Berkeley during Spring'19.
- Our paper titled Model-Agnostic Private Learning was accepted for an oral presentation at NeurIPS 2018.
- I worked with Úlfar Erlingsson on improving the utility of Differentially Private Stochastic Gradient Descent, while interning at Google Brain, Mountain View during Summer'18.
- Our paper titled Differentially Private Matrix Completion Revisited was accepted for a long talk in ICML 2018.
- Our paper titled Towards Practical Differentially Private Convex Optimization was accepted in S&P 2019.
- My most recent resume (last updated in August, 2019) can be found here.
- Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. Joint work with Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, and Blake Woodworth.
- Differentially Private Learning with Adaptive Clipping. Joint work with Galen Andrew, and Brendan McMahan.
- Towards Practical Differentially Private Convex Optimization. Abstract▼ Joint work with Roger Iyengar, Joseph P. Near, Dawn Song, Abhradeep Thakurta, and Lun Wang. In the 40th IEEE Symposium on Security and Privacy (S&P 2019).
- Model-Agnostic Private Learning. Abstract▼ Joint work with Raef Bassily, and Abhradeep Thakurta. In the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). Accepted for an oral presentation.
- Differentially Private Matrix Completion Revisited. Abstract▼ Joint work with Prateek Jain, and Abhradeep Thakurta. In the 35th International Conference on Machine Learning (ICML 2018). Presented as a long talk.
- Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. Abstract▼ Joint work with Ryan Rogers, Aaron Roth, and Adam Smith. In the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2016).
- Visiting Graduate Student in the Data Privacy program at the Simons Institute, Berkeley during Spring'19.
- Research Intern at Google Brain, Mountain View, CA during Summer 2018. Mentor: Úlfar Erlingsson.
- Visiting Student Researcher at University of California, Berkeley, CA during Fall 2017. Host: Dr. Dawn Song.
- Research Intern at Google, Seattle, WA during Summer 2017. Mentors: Brendan McMahan, and Martin Pelikan.
- Research Intern in the CoreOS: Machine Learning team at Apple, Cupertino, CA during Summer 2016.
- Towards Practical Differentially Private Convex Optimization
- @ the Future of Privacy Forum booth, Global Privacy Summit 2019, Washington, DC on May 3, 2019. (Coverage)
- @ the Privacy Tools Project meeting, Harvard on March 5, 2018.
- Model-Agnostic Private Learning,
- @ the 2019 IEEE North American School of Information Theory (NASIT), held at BU, on July 3, 2019. (Poster)
- @ the 2018 Open AIR: Industry Open House, BU on October 12, 2018. (Poster)
- Building Tools for Controlling Overfitting in Adaptive Data Analysis, @ the Adaptive Data Analysis workshop, Simons Institute, Berkeley on July 7, 2018.
- Differentially Private Matrix Completion Revisited
- @ the Mathematical Foundations of Data Privacy workshop, BIRS on May 2, 2018. (Talk video)
- @ the BU Data Science (BUDS) Day, Boston University on January 26, 2018. (Poster)
- @ the Privacy Tools Data Sharing workshop, Harvard University on December 12, 2017. (Poster)
- @ the Security Seminar, UC Berkeley on October 9, 2017.
- A brief introduction to Concentrated Differential Privacy, @ CSE Theory Seminar, Penn State on April 14, 2017.
- Max-Information, Differential Privacy, and Post-selection Hypothesis Testing
- @ INSR Industry Day, Penn State on April 24, 2017. (Poster)
- @ SMAC Talks, Penn State on December 2, 2016.
- @ CSE Theory Seminar, UCSD on November 7, 2016.
- @ CSE Theory Seminar, Penn State on October 14, 2016.
- Max-Information and Differential Privacy, @ CSE Theory Seminar, Penn State on May 5, 2016.
- The Stable Roommates Problem with Random Preferences, @ CSE Theory Seminar, Penn State on April 10, 2015.
- The Multiplicative Weights Update Method and an Application to Solving Zero-Sum Games Approximately, @ CSE Theory Seminar, Penn State on November 3, 2014.
- Teaching assistant:
- CMPSC 465 Data Structures and Algorithms, Spring 2017 @ Penn State.
- CMPSC 360 Discrete Mathematics for Computer Science, Spring 2015 @ Penn State.
- IT 114 Object Oriented Programming, Spring 2014 @ DA-IICT.
- IT 105 Introduction to Programming, Fall 2013 @ DA-IICT.
- Reviewer for JPC 2019, T-IFS 2019, JMLR 2018.
- Reviewer for NIST's The Unlinkable Data Challenge: Advancing Methods in Differential Privacy.
- External reviewer for NeurIPS 2019, IJCAI 2019, CCS (2018-2019), PETS (2017-2019), S&P (2017, 2019), ICML 2018, STOC (2016, 2018), ACSAC 2017, FOCS 2017, WABI 2015.
- Received a travel award for S&P 2019.
- Received a travel award for NeurIPS 2018.
- Received a travel award for ICML 2018.
- Received a GSO Conference Travel Grant for Summer 2018.
- Received a travel award for FOCS 2014.
- Report on Node-differentially Private Algorithms for Graph Statistics. It includes joint work with Ramesh Krishnan.