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D-Index & Metrics

Computer Science

D-Index
62
Citations
36970
World Ranking
2819
National Ranking
1392

Overview

Kunal Talwar is affiliated with Apple in the United States and has contributed extensively to the field of computer science, with a focus on privacy-preserving technologies, cryptography, and optimization techniques. Their research output spans multiple subfields, including artificial intelligence, management science and operations research, statistics and probability, computer networks and communications, and computational theory and mathematics.

Their work covers a wide range of topics such as:

  • Privacy-Preserving Technologies in Data
  • Cryptography and Data Security
  • Stochastic Gradient Optimization Techniques
  • Internet Traffic Analysis and Secure E-voting
  • Complexity and Algorithms in Graphs
  • Adversarial Robustness in Machine Learning
  • Advanced Bandit Algorithms Research

Talwar has published numerous papers, frequently appearing in venues like arXiv (Cornell University) and the Leibniz-Zentrum für Informatik (Schloss Dagstuhl). Among recent publications are:

  • Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation, 2020, arXiv (Cornell University)
  • Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses, 2020, arXiv (Cornell University)
  • Information-Theoretic Single-Server PIR in the Shuffle Model, 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling, 2020, arXiv (Cornell University)
  • Private Adaptive Gradient Methods for Convex Optimization, 2021, arXiv (Cornell University)

Their frequent coauthors include Vitaly Feldman, Hilal Asi, Audra McMillan, Jason M. Altschuler, and Tomer Koren, highlighting collaborative research efforts across various specialized areas. Talwar's work has contributed to major publication venues such as the SIAM Journal on Computing and the SIAM Journal on Mathematics of Data Science, further emphasizing a broad engagement with theoretical and applied aspects of computer science.

Throughout their career, Talwar has focused on developing methods related to privacy amplification, stochastic gradient methods, and information-theoretic approaches to secure computation. The blend of topics and collaborations indicates a research agenda centered on enhancing data privacy and optimization in machine learning and cryptographic contexts.

Best Publications

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Deep Learning with Differential Privacy

    Martin Abadi;Andy Chu;Ian Goodfellow;H. Brendan McMahan

  • Mechanism Design via Differential Privacy

    F. McSherry;K. Talwar

  • Quincy: fair scheduling for distributed computing clusters

    Michael Isard;Vijayan Prabhakaran;Jon Currey;Udi Wieder

  • Spectral Graph Theory and its Applications

    D.A. Spielman

  • A tight bound on approximating arbitrary metrics by tree metrics

    Jittat Fakcharoenphol;Satish Rao;Kunal Talwar

  • The complexity of pure Nash equilibria

    Alex Fabrikant;Christos Papadimitriou;Kunal Talwar

  • Learning Differentially Private Recurrent Language Models

    H. Brendan McMahan;Daniel Ramage;Kunal Talwar;Li Zhang

  • Detecting format string vulnerabilities with type qualifiers

    Umesh Shankar;Kunal Talwar;Jeffrey S. Foster;David Wagner

  • Privacy, accuracy, and consistency too: a holistic solution to contingency table release

    Boaz Barak;Kamalika Chaudhuri;Cynthia Dwork;Satyen Kale

  • Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

    Nicolas Papernot;Martín Abadi;Úlfar Erlingsson;Ian J. Goodfellow

  • An Approximate Truthful Mechanism for Combinatorial Auctions with Single Parameter Agents

    Aaron Archer;Christos H. Papadimitriou;Kunal Talwar;Éva Tardos

  • On the geometry of differential privacy

    Moritz Hardt;Kunal Talwar

  • Adversarially Robust Generalization Requires More Data

    Ludwig Schmidt;Shibani Santurkar;Dimitris Tsipras;Kunal Talwar

  • Adversarially Robust Generalization Requires More Data

    Ludwig Schmidt;Shibani Santurkar;Dimitris Tsipras;Kunal Talwar

  • Scalable Private Learning with PATE

    Nicolas Papernot;Shuang Song;Ilya Mironov;Ananth Raghunathan

  • The Limits of Two-Party Differential Privacy.

    Andrew McGregor;Ilya Mironov;Toniann Pitassi;Omer Reingold

  • Analyze gauss: optimal bounds for privacy-preserving principal component analysis

    Cynthia Dwork;Kunal Talwar;Abhradeep Thakurta;Li Zhang

  • Bypassing the embedding: algorithms for low dimensional metrics

    Kunal Talwar

  • Heuristics for Vector Bin Packing

    Rina Panigrahy;Kunal Talwar;Lincoln Uyeda;Udi Wieder

  • Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • Analyze Gauss: optimal bounds for privacy-preserving PCA

    Cynthia Dwork;Kunal Talwar;Abhradeep Thakurta;Li Zhang

Frequent Co-Authors

Anupam Gupta
Anupam Gupta Carnegie Mellon University
Frank McSherry
Frank McSherry Materialize, Inc.
Kamal Jain
Kamal Jain Microsoft (United States)
Li Zhang
Li Zhang Google (United States)
Vitaly Feldman
Vitaly Feldman Apple (United States)
Ilya Mironov
Ilya Mironov Google (United States)
Cynthia Dwork
Cynthia Dwork Harvard University
Abhradeep Thakurta
Abhradeep Thakurta Google (United States)
Satish Rao
Satish Rao University of California, Berkeley

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