Om Thakkar
Research ScientistMountain View, CA, USA
Email : omthkkr "at" google.com




Short Bio
I am a Research Scientist at Google, working in the team of Françoise Beaufays. 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.Before joining Google, I graduated with a Ph.D. in Computer Science from Boston University (BU) in 2019. I was very fortunate to be advised by Dr. Adam Smith. At BU, I was a part of the Security group, and the Theoretical Computer Science group. I completed the first 3.5 years of my Ph.D. in the Department of Computer Science and Engineering at
News
- Our paper titled Evading the Curse of Dimensionality in Unconstrained Private GLMs has been accepted to appear at AISTATS 2021.
- Three papers accepted to appear in PPML 2020, with the work on Training Production Language Models without Memorizing User Data accepted for an oral presentation.
- Our paper titled Privacy Amplification via Random Check-Ins has been accepted to appear at NeurIPS 2020.
- Three papers accepted to appear in TPDP 2020, with the work on Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems accepted for an oral presentation.
- Our paper titled Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis has been accepted to appear in AISTATS 2020.
- I joined Google, in the team of Françoise Beaufays, on September 30, 2019.
- 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.
Resume
- My most recent resume (last updated in January, 2021) can be found here.
Manuscripts
- Training Production Language Models without Memorizing User Data.
Swaroop Ramaswamy*, Om Thakkar*, Rajiv Mathews, Galen Andrew, Brendan McMahan, and Françoise Beaufays. (In order of contribution) *Equal contribution.
- Understanding Unintended Memorization in Federated Learning.
Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, and Françoise Beaufays. (In order of contribution)
- Differentially Private Learning with Adaptive Clipping.
Om Thakkar, Galen Andrew, and Brendan McMahan. (In order of contribution)
Patents
- Server Efficient Ehnancement of Privacy in Federated Learning. Om Thakkar, Peter Kairouz, Brendan McMahan, Borja Balle, and Abhradeep Thakurta. Filed US Patent 63/035,559.
Publications
Papers available here may be subject to copyright, and are intended for personal, non-commercial use only. Unless specifically indicated, all publications have authors listed in the alphabetical order of last names (as per the convention in theoretical computer science).- Evading the Curse of Dimensionality in Unconstrained Private GLMs.
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta. Accepted to appear at the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021).
- Privacy Amplification via Random Check-Ins.
Abstract▼ Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, and Abhradeep Thakurta. In the 34th Conference on Neural Information Processing Systems (NeurIPS 2020).
- Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.
Abstract▼ Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, and Blake Woodworth. In the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).
- Advances in Privacy-Preserving Machine Learning.
Ph.D. Thesis, BU, September 2019. Supervisor: Dr. Adam Smith.
- Towards Practical Differentially Private Convex Optimization.
Abstract▼ Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang. In the 40th IEEE Symposium on Security and Privacy (S&P 2019).
- Model-Agnostic Private Learning.
Abstract▼ Raef Bassily, Om Thakkar, 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▼ Prateek Jain, Om Thakkar, 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▼ Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar. In the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2016).
Workshop Publications
- Training Production Language Models without Memorizing User Data.
Swaroop Ramaswamy*, Om Thakkar*, Rajiv Mathews, Galen Andrew, Brendan McMahan, and Françoise Beaufays. (In order of contribution) In the Privacy Preserving Machine Learning (PPML) 2020 workshop. Accepted for an oral presentation. *Equal contribution.
- Privacy Amplification via Random Check-Ins.
Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, and Abhradeep Thakurta. In the Theory and Practice of Differential Privacy (TPDP) 2020 workshop.
- Understanding Unintended Memorization in Federated Learning.
Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, and Françoise Beaufays. (In order of contribution) In the Theory and Practice of Differential Privacy (TPDP) 2020 workshop, and the Privacy Preserving Machine Learning (PPML) 2020 workshop.
- Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.
Shuang Song, Om Thakkar, and Abhradeep Thakurta. In the Theory and Practice of Differential Privacy (TPDP) 2020 workshop, and the Privacy Preserving Machine Learning (PPML) 2020 workshop. Accepted for an oral presentation at TPDP 2020.
Internships and Research Visits
- 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.
Talks and Poster Presentations
- Towards Training Provably Private Models via Federated Learning in Practice @ the Workshop on Federated Learning and Analytics 2020 (Google) on July 29, 2020.
- Privacy Amplification via Random Check-Ins @ the Ph.D. Intern Research Conference 2020 (Google) on July 22, 2020.
- 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
- 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.
Professional Activities
- Program committee member for TPDP 2020.
- Reviewer for JPC 2019, T-IFS 2019, JMLR 2018.
- Reviewer for NIST's The Unlinkable Data Challenge: Advancing Methods in Differential Privacy.
- Reviewer for PETS (2017-2021), NeurIPS (2019-2020), IJCAI 2019, CCS (2018-2019), S&P (2017, 2019), ICML 2018, STOC (2016, 2018), ACSAC 2017, FOCS 2017, WABI 2015.
Recent Awards
- 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 registration award for FOCS 2014.
Miscellaneous
- Report on Node-differentially Private Algorithms for Graph Statistics. It includes joint work with Ramesh Krishnan.