"I love Thomas Steinke"

2022-06-12 10:13:31

Thomas Steinke I am a Research Scientist in the Brain Team at Google Research. Previously I was at IBM Research - Almaden. I completed my PhD in Computer Science at Harvard University in 2016, where I was extremely fortunate to be advised by Salil Vadhan. Before that, I was at the University of Canterbury in my hometown of Christchurch, New Zealand. My research interests include principled approaches to data privacy (specifically, differential privacy), its connections to learning and generalization (particularly in the adaptive setting), and pseudorandomness. For more details, see my CV and (old) research statement. Email: ResearchSee also my google scholar profile and DBLP page.Debugging Differential Privacy: A Case Study for Privacy Auditing with Florian Tramer, Andreas Terzis, Shuang Song, Matthew Jagielski, and Nicholas Carlini, 2022.Public Data-Assisted Mirror Descent for Private Model Training with Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Vinith M. Suriyakumar, Om Thakkar, and Abhradeep Thakurta, ICML 2022.A Private and Computationally-Efficient Estimator for Unbounded Gaussians with Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, and Jonathan Ullman, COLT 2022.Hyperparameter Tuning with Renyi Differential Privacy with Nicolas Papernot, ICLR 2022.PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes with Peter Grünwald and Lydia Zakynthinou, COLT 2021.Privately Learning Subspaces with Vikrant Singhal, NeurIPS 2021.The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise with Zeyu Ding, Daniel Kifer, Sayed M. Saghaian N. E., Yuxin Wang, Yingtai Xiao, and Danfeng Zhang, 2021Leveraging Public Data for Practical Private Query Release with Terrance Liu, Giuseppe Vietri, Jonathan Ullman, and Zhiwei Steven Wu, ICML 2021.The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation with Peter Kairouz and Ziyu Liu, ICML 2021.Evading the Curse of Dimensionality in Unconstrained Private GLMs with Shuang Song, Om Thakkar, and Abhradeep Thakurta, AISTATS 2021. Multi-Central Differential Privacy 2020.The Discrete Gaussian for Differential Privacy with Clément Canonne and Gautam Kamath, NeurIPS 2020.Reasoning About Generalization via Conditional Mutual Information with Lydia Zakynthinou, COLT 2020.New Oracle-Efficient Algorithms for Private Synthetic Data Release with Giuseppe Vietri, Grace Tian, Mark Bun, and Steven Wu, ICML 2020.Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation with Mark Bun, NeurIPS 2019.Private Hypothesis Selection with Mark Bun, Gautam Kamath, and Zhiwei Steven Wu, NeurIPS 2019.A Hybrid Approach to Privacy-Preserving Federated Learning with Stacey Truex, Nathalie Baracaldo, Ali Anwar, Heiko Ludwig, Rui Zhang, and Yi Zhou, AISec 2019.Towards Instance-Optimal Private Query Release with Jaroslaw Blasiok, Mark Bun, and Aleksandar Nikolov, SODA 2019.The Limits of Post-Selection Generalization with Kobbi Nissim, Adam Smith, Uri Stemmer, and Jonathan Ullman, NeurIPS 2018.Composable and Versatile Privacy via Truncated CDP with Mark Bun, Cynthia Dwork, and Guy N. Rothblum, STOC 2018.Calibrating Noise to Variance in Adaptive Data Analysis with Vitaly Feldman, COLT 2018. Tight Lower Bounds for Differentially Private Selection with Jonathan Ullman, FOCS 2017. Generalization for Adaptively-chosen Estimators via Stable Median with Vitaly Feldman, COLT 2017. Subgaussian Tail Bounds via Stability Arguments with Jonathan Ullman, 2017. Upper and Lower Bounds for Privacy and Adaptivity in Algorithmic Data Analysis PhD Thesis, Harvard University 2016. Exposed! A Survey of Attacks on Private Data with Cynthia Dwork, Adam Smith, and Jonathan Ullman, Annual Review of Statistics and Its Application 2017. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds with Mark Bun, TCC 2016-B. Make Up Your Mind: The Price of Online Queries in Differential Privacy with Mark Bun and Jonathan Ullman, SODA 2017. Robust Traceability from Trace Amounts with Cynthia Dwork, Adam Smith, Jonathan Ullman, and Salil Vadhan, FOCS 2015. Algorithmic Stability for Adaptive Data Analysis with Raef Bassily, Kobbi Nissim, Adam Smith, Uri Stemmer, and Jonathan Ullman, STOC 2016. Between Pure and Approximate Differential Privacy with Jonathan Ullman, TPDP 2015 & Journal of Privacy and Confidentiality 2017. Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness with Mark Bun, RANDOM 2015. Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery with Jonathan Ullman, COLT 2015. Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs with Salil Vadhan and Andrew Wan, RANDOM 2014 & Theory of Computing 2017. Pseudorandomness for Regular Branching Programs via Fourier Analysis with Omer Reingold and Salil Vadhan, RANDOM 2013. Pseudorandomness for Permuatation Branching Programs Without the Group Theory ECCC 2012. Learning Hurdles for Sleeping Experts with Varun Kanade, ITCS 2012 & Transactions on Computing Theory 2014. Hierarchical Heavy Hitters with the Space Saving Algorithm with Michael Mitzenmacher and Justin Thaler, ALENEX 2012. Constructive Notions of Compactness in Apartness Spaces MSc Thesis, University of Canterbury 2011.A Rigorous Extension of the Schonhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic with Raazesh Sainudiin, CCA 2010 & Reliable Computing 2013.Olds and News Gautam Kamath, Seša Slavković, Adam Smith, Jon Ullman, and I are organizing a Workshop on Differential Privacy and Statistical Data Analysis at The Fields Institute in Toronto 25-29 July 2022. I recently moved to Google.Check out DifferentialPrivacy.org.I mentored Vikrant Singhal and Lydia Zakynthinou as 2020 IBM summer interns.Clément Canonne has joined the IBM Almaden theory group as a Goldstine postdoctoral fellow.I mentored Lydia Zakynthinou as a 2019 IBM summer intern. Marco Gaboardi, Jun Sakuma, and I are organizing a workshop on Differential Privacy and its Applications in Japan in mid 2020 8-12 November 2021 October 31 to November 4, 2022.See here for some open problems in differential privacy, including two from me.For Spring 2019, I was visiting the Simons Institute for the Theory of Computing at UC Berkeley for the Data Privacy: Foundations and Applications program.Mark Bun, Cynthia Dwork, Toniann Pitassi, Guy Rothblum, Kunal Talwar, and I organized a workshop on the Mathematical Foundations of Data Privacy in April/May 2018 in Banff, Canada. Steinke is a german name meaning little stone. (Stein means stone or rock and -ke is a diminutive suffix.) The "correct" pronounciation is, approximately, Shteyn-keh, but I sometimes am lazy and use the anglicized pronounciation of Styne-key. I honestly don't care how it is pronounced, as long as it is vaguely recognizable and does not smell bad. My preferred name is Thomas and my preferred pronouns are he/him/his, but Dr. Steinke and they/them/their are also acceptable.Last updated February 2022 by Thomas Steinke.