- Home
- Best Scientists - Computer Science
- Toniann Pitassi

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
48
Citations
9,683
169
World Ranking
4024
National Ranking
175

2018 - ACM Fellow For contributions to research and education in the fields of computational and proof complexity

- Algorithm
- Algebra
- Combinatorics

The scientist’s investigation covers issues in Discrete mathematics, Combinatorics, Mathematical proof, Proof complexity and Pigeonhole principle. He has researched Discrete mathematics in several fields, including Search problem and Propositional proof system. His research integrates issues of Decision problem and Field in his study of Combinatorics.

His Mathematical proof research includes elements of Propositional calculus and Polynomial. His Pigeonhole principle research includes themes of Resolution and Exponential function. His work in Degree addresses subjects such as Polynomial, which are connected to disciplines such as Function and Communication complexity.

- Fairness through awareness (1211 citations)
- Differential privacy under continual observation (423 citations)
- The reusable holdout: Preserving validity in adaptive data analysis (199 citations)

The scientist’s investigation covers issues in Discrete mathematics, Combinatorics, Mathematical proof, Proof complexity and Communication complexity. His Discrete mathematics study integrates concerns from other disciplines, such as Function, Computational complexity theory and Exponential function. His work deals with themes such as Nondeterministic algorithm, Rank and Constant, which intersect with Combinatorics.

The various areas that he examines in his Mathematical proof study include Resolution and Pigeonhole principle. The concepts of his Proof complexity study are interwoven with issues in Algorithm, Polynomial and Algebraic number. His Communication complexity research is multidisciplinary, incorporating elements of Worst-case complexity and Partition.

- Discrete mathematics (55.51%)
- Combinatorics (37.29%)
- Mathematical proof (25.00%)

- Discrete mathematics (55.51%)
- Mathematical proof (25.00%)
- Communication complexity (20.76%)

His main research concerns Discrete mathematics, Mathematical proof, Communication complexity, Theoretical computer science and Proof complexity. Toniann Pitassi performs integrative study on Discrete mathematics and Bounded function in his works. His Mathematical proof study frequently involves adjacent topics like Calculus.

His work focuses on many connections between Communication complexity and other disciplines, such as Partition, that overlap with his field of interest in Independent set. The Theoretical computer science study combines topics in areas such as Efficient algorithm and Directed acyclic graph. His Proof complexity research focuses on Field and how it connects with Rank, Prime and Open problem.

- Learning Adversarially Fair and Transferable Representations (119 citations)
- Learning Adversarially Fair and Transferable Representations (81 citations)
- Flexibly Fair Representation Learning by Disentanglement (79 citations)

- Algebra
- Algorithm
- Combinatorics

Toniann Pitassi focuses on Discrete mathematics, Communication complexity, Feature learning, Proof complexity and Artificial intelligence. His study in Discrete mathematics is interdisciplinary in nature, drawing from both Field, Degree and Rank. His Communication complexity research incorporates elements of PSPACE, State and Partition, Combinatorics.

His Feature learning research is multidisciplinary, relying on both Transfer of learning, Theoretical computer science and Key. His Proof complexity study contributes to a more complete understanding of Mathematical proof. Toniann Pitassi has included themes like Complexity class, Algebraic number and Conjecture in his Mathematical proof study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Fairness through awareness

Cynthia Dwork;Moritz Hardt;Toniann Pitassi;Omer Reingold.

conference on innovations in theoretical computer science **(2012)**

2021 Citations

Differential privacy under continual observation

Cynthia Dwork;Moni Naor;Toniann Pitassi;Guy N. Rothblum.

symposium on the theory of computing **(2010)**

634 Citations

The reusable holdout: Preserving validity in adaptive data analysis

Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi.

Science **(2015)**

312 Citations

Learning Adversarially Fair and Transferable Representations

David Madras;Elliot Creager;Toniann Pitassi;Richard S. Zemel.

international conference on machine learning **(2018)**

290 Citations

Preserving Statistical Validity in Adaptive Data Analysis

Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi.

symposium on the theory of computing **(2015)**

288 Citations

Combining Component Caching and Clause Learning for Effective Model Counting.

Tian Sang;Fahiem Bacchus;Paul Beame;Henry A. Kautz.

theory and applications of satisfiability testing **(2004)**

274 Citations

Simplified and improved resolution lower bounds

P. Beame;T. Pitassi.

foundations of computer science **(1996)**

254 Citations

Exponential lower bounds for the pigeonhole principle

Toniann Pitassi;Paul Beame;Russell Impagliazzo.

Computational Complexity **(1993)**

206 Citations

Lower bounds for cutting planes proofs with small coefficients

Maria Luisa Bonet;Toniann Pitassi;Ran Raz.

Journal of Symbolic Logic **(1997)**

192 Citations

Pan-Private Streaming Algorithms

Cynthia Dwork;Moni Naor;Toniann Pitassi;Guy N. Rothblum.

international conference on supercomputing **(2010)**

187 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

University of Washington

University of California, San Diego

University of Toronto

University of Toronto

Stanford University

University of California, San Diego

Harvard University

Princeton University

Apple (United States)

Max Planck Institute for Intelligent Systems

University of California, Santa Barbara

University of Maryland, College Park

Stanford University

Chinese Academy of Sciences

Pacific Northwest National Laboratory

University of Vienna

University of Queensland

University of Massachusetts Medical School

University of Minnesota

University of Florida

École Polytechnique Fédérale de Lausanne

University of Barcelona

Baylor College of Medicine

Technical University of Munich

Princeton University

University of Chicago

Something went wrong. Please try again later.