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Computer Science

D-Index
63
Citations
18851
World Ranking
2724
National Ranking
1354

Research.com Recognitions

  • 2007 - Fellow of Alfred P. Sloan Foundation

Overview

Boaz Barak is a researcher affiliated with Harvard University in the United States. Their primary field of study is Computer Science, with a focus on several subfields including Artificial Intelligence, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics. Their research covers multiple topics, notably Quantum Computing Algorithms and Architecture, Quantum Information and Cryptography, Complexity and Algorithms in Graphs, Natural Language Processing Techniques, Quantum many-body systems, Stochastic Gradient Optimization Techniques, and Machine Learning and Algorithms.

The scientist has contributed to a range of notable papers over recent years. Some of the recent publications include:

  • Quantum optimization of maximum independent set using Rydberg atom arrays (2022, Science)
  • Noisy tensor completion via the sum-of-squares hierarchy (2022, Mathematical Programming)
  • Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage (2024, PRX Quantum)
  • Scaling Data-Constrained Language Models (2023, arXiv (Cornell University))
  • Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage (2021, arXiv (Cornell University))

Frequent co-authors collaborating with Boaz Barak include Chi-Ning Chou, Gal Kaplun, Xun Gao, Soonwon Choi, and Mikhail D. Lukin.

The researcher regularly publishes in the following venues:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Science
  • Mathematical Programming
  • PRX Quantum

One of the awards received by Boaz Barak is the Fellow of the Alfred P. Sloan Foundation, awarded in 2007.

Best Publications

  • Computational Complexity: A Modern Approach

    Sanjeev Arora;Boaz Barak

  • On the (im)possibility of obfuscating programs

    Boaz Barak;Oded Goldreich;Russell Impagliazzo;Steven Rudich

  • Computational Complexity: ADVANCED TOPICS

    Sanjeev Arora;Boaz Barak

  • Systematic comparison of single-cell and single-nucleus RNA-sequencing methods.

    Jiarui Ding;Xian Adiconis;Sean K. Simmons;Monika S. Kowalczyk

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

    Boaz Barak;Kamalika Chaudhuri;Cynthia Dwork;Satyen Kale

  • How to go beyond the black-box simulation barrier

    B. Barak

  • Subexponential Algorithms for Unique Games and Related Problems

    Sanjeev Arora;Boaz Barak;David Steurer

  • How to Compress Interactive Communication

    Boaz Barak;Mark Braverman;Xi Chen;Anup Rao

  • Universal Arguments and their Applications

    Boaz Barak;Oded Goldreich

  • Deep Double Descent: Where Bigger Models and More Data Hurt

    Preetum Nakkiran;Gal Kaplun;Yamini Bansal;Tristan Yang

  • Protecting Obfuscation against Algebraic Attacks

    Boaz Barak;Sanjam Garg;Yael Tauman Kalai;Omer Paneth

  • Universally composable protocols with relaxed set-up assumptions

    B. Barak;R. Canetti;J.B. Nielsen;R. Pass

  • Extracting Randomness Using Few Independent Sources

    Boaz Barak;Russell Impagliazzo;Avi Wigderson

  • Hypercontractivity, sum-of-squares proofs, and their applications

    Boaz Barak;Fernando G.S.L. Brandao;Aram W. Harrow;Jonathan Kelner

  • Constant-round coin-tossing with a man in the middle or realizing the shared random string model

    B. Barak

  • Public-key cryptography from different assumptions

    Benny Applebaum;Boaz Barak;Avi Wigderson

  • Leftover Hash Lemma, Revisited.

    Boaz Barak;Yevgeniy Dodis;Hugo Krawczyk;Olivier Pereira

  • 2-source dispersers for sub-polynomial entropy and Ramsey graphs beating the Frankl-Wilson construction

    Boaz Barak;Anup Rao;Ronen Shaltiel;Avi Wigderson

  • True Random Number Generators Secure in a Changing Environment

    Boaz Barak;Ronen Shaltiel;Eran Tromer

  • Derandomization in Cryptography

    Boaz Barak;Shien Jin Ong;Salil Vadhan

  • Rounding Semidefinite Programming Hierarchies via Global Correlation.

    Boaz Barak;Prasad Raghavendra;David Steurer

  • SGD on Neural Networks Learns Functions of Increasing Complexity

    Dimitris Kalimeris;Gal Kaplun;Preetum Nakkiran;Benjamin L. Edelman

Frequent Co-Authors

Sanjeev Arora
Sanjeev Arora Princeton University
David Steurer
David Steurer ETH Zurich
Amit Sahai
Amit Sahai University of California, Los Angeles
Yehuda Lindell
Yehuda Lindell Bar-Ilan University
Salil P. Vadhan
Salil P. Vadhan Harvard University
Avi Wigderson
Avi Wigderson Institute for Advanced Study
Oded Goldreich
Oded Goldreich Weizmann Institute of Science
Ran Canetti
Ran Canetti Boston University

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