PhdFit
Resume-aware faculty matching

Find professors who actually fit you

Review faculty evidence in public, then use the workspace to turn your background into a shortlist, outreach, and meeting prep.

Profile-awarePaper evidenceSix agents
Mahesh Viswanathan

Mahesh Viswanathan

· Professor

University of Illinois Urbana-Champaign · Computer Science

Active 1975–2026

h-index39
Citations6.4k
Papers27148 last 5y
Funding$2.0M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

Faculty page
See your match with Mahesh Viswanathan — sign in to PhdFit.Sign in

About

Mahesh Viswanathan is a professor at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer and Information Science from the University of Pennsylvania, obtained in 2000. His research areas include Programming Languages, Formal Methods, Software Engineering, and Theory and Algorithms. He has taught a variety of courses related to computer science, such as Discrete Structures, Intro to Computer Systems, Algorithms, Logic in Computer Science, Formal Models of Computation, and Applied Machine Learning. His work focuses on advancing understanding in these fields, contributing to the development of computing education and research in formal methods and algorithms.

Research topics

  • Computer Science
  • Computer Security
  • Data Mining
  • Mathematics
  • Statistics
  • Theoretical computer science
  • Algorithm

Selected publications

  • Deciding Differential Privacy for Programs with Finite Inputs and Outputs

    2020 · 23 citations

    Senior authorCorresponding

    Differential privacy is a de facto standard for statistical computations over databases that contain private data. Its main and rather surprising strength is to guarantee individual privacy and yet allow for accurate statistical results. Thanks to its mathematical definition, differential privacy is also a natural target for formal analysis. A broad line of work develops and uses logical methods for proving privacy. A more recent and complementary line of work uses statistical methods for findin…

    DOI

  • Proof Blocks

    2022-07-07 · 20 citations

    preprintOpen access

    In this software tool paper we present Proof Blocks, a tool which enables students to construct mathematical proofs by dragging and dropping prewritten proof lines into the correct order. We present both implementation details of the tool, as well as a rich reflection on our experiences using the tool in courses with hundreds of students. Proof Blocks problems can be graded completely automatically, enabling students to receive rapid feedback. When writing a problem, the instructor specifies the…

    PublisherOA PDFDOI

  • Sound Dynamic Deadlock Prediction in Linear Time

    Proceedings of the ACM on Programming Languages · 2023-06-06 · 13 citations

    articleOpen accessSenior author

    Deadlocks are one of the most notorious concurrency bugs, and significant research has focused on detecting them efficiently. Dynamic predictive analyses work by observing concurrent executions, and reason about alternative interleavings that can witness concurrency bugs. Such techniques offer scalability and sound bug reports, and have emerged as an effective approach for concurrency bug detection, such as data races. Effective dynamic deadlock prediction, however, has proven a challenging task…

    PublisherOA PDFDOI

  • Dynamic Race Detection with O(1) Samples

    Proceedings of the ACM on Programming Languages · 2023-01-09 · 7 citations

    articleOpen accessSenior author

    Happens before-based dynamic analysis is the go-to technique for detecting data races in large scale software projects due to the absence of false positive reports. However, such analyses are expensive since they employ expensive vector clock updates at each event, rendering them usable only for in-house testing. In this paper, we present a sampling-based, randomized race detector that processes only constantly many events of the input trace even in the worst case. This is the first sub-linear t…

    PublisherOA PDFDOI

  • Deciding Differential Privacy of Online Algorithms with Multiple Variables

    2023-11-15 · 5 citations

    article

    We consider the problem of checking the differential privacy of online randomized algorithms that process a stream of inputs and produce outputs corresponding to each input. This paper generalizes an automaton model called DiP automata [10] to describe such algorithms by allowing multiple real-valued storage variables. A DiP automaton is a parametric automaton whose behavior depends on the privacy budget ∈. An automaton A will be said to be differentially private if, for some D, the automaton is…

    PublisherDOI

Recent grants

Frequent coauthors

Labs

  • Siebel School of Computing and Data SciencePI

Education

  • Ph.D., Computer Science

    University of Illinois at Urbana-Champaign

    2000

  • M.S., Computer Science

    University of Illinois at Urbana-Champaign

    1996

  • B.S., Electrical and Electronics Engineering

    University of Madras

    1992

Awards & honors

  • Celebration of Excellence 2021
  • Celebration of Excellence 2022
  • Celebration of Excellence 2023
  • Celebration of Excellence 2024
  • Celebration of Excellence 2025

Similar researchers at University of Illinois Urbana-Champaign

  • Resume-aware match score
  • Save to shortlist
  • AI-drafted outreach

See your match with Mahesh Viswanathan

PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.

Sign in to PhdFitHow it works
  • Free to start
  • No credit card
  • 30-second signup