World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
8460
World Ranking
11467
National Ranking
4709

Research.com Recognitions

  • 2009 - ACM Distinguished Member

Overview

Michael Hind is affiliated with IBM in the United States and contributes to the field of Computer Science, with a primary focus on Artificial Intelligence. Their research spans several subfields including Artificial Intelligence, Health Informatics, Safety Research, Information Systems, and Sociology and Political Science.

The scientist's work addresses key topics within the domain of AI, such as Explainable Artificial Intelligence (XAI), Artificial Intelligence applications in Healthcare and Education, Adversarial Robustness in Machine Learning, Ethics and Social Impacts of AI, Privacy-Preserving Technologies in Data, COVID-19 Digital Contact Tracing, and Privacy, Security, and Data Protection.

Michael Hind has authored a number of papers in notable venues. Recent publications include:

  • "AI Explainability 360: Impact and Design," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Trust and Transparency in Contact Tracing Applications," 2020, arXiv (Cornell University)
  • "Quantitative AI Risk Assessments: Opportunities and Challenges," 2022, arXiv (Cornell University)
  • "A Methodology for Creating AI FactSheets," 2020, arXiv (Cornell University)
  • "Evaluating a Methodology for Increasing AI Transparency: A Case Study," 2022, arXiv (Cornell University)

Their frequent co-authors include John R. Richards and David Piorkowski, each with six collaborative works, Kush R. Varshney and Dennis Wei, each with four, and Pin-Yu Chen with three shared publications.

Michael Hind's research has appeared predominantly in the following publication venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Seton Hall Journal of Legislation and Public Policy

In recognition of professional contributions, Michael Hind was awarded the title of ACM Distinguished Member in 2009.

Best Publications

  • Pointer analysis: haven't we solved this problem yet?

    Michael Hind

  • The Jalapeño virtual machine

    B. Alpern;C. R. Attanasio;J. J. Barton;M. G. Burke

  • AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

    R. K. E. Bellamy;K. Dey;M. Hind;S. C. Hoffman

  • Adaptive optimization in the Jalapeno JVM

    Matthew Arnold;Stephen Fink;David Grove;Michael Hind

  • The Jalapeño dynamic optimizing compiler for Java

    Michael G. Burke;Jong-Deok Choi;Stephen Fink;David Grove

  • AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

    Rachel K. E. Bellamy;Kuntal Dey;Michael Hind;Samuel C. Hoffman

  • Adaptive optimization in the Jalapeño JVM

    Unknown

  • The Jikes research virtual machine project: building an open-source research community

    B. Alpern;S. Augart;S. M. Blackburn;M. Butrico

  • FactSheets: Increasing trust in AI services through supplier's declarations of conformity

    M. Arnold;R. K. E. Bellamy;M. Hind;S. Houde

  • One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

    Vijay Arya;Rachel K. E. Bellamy;Pin-Yu Chen;Amit Dhurandhar

  • Interprocedural pointer alias analysis

    Michael Hind;Michael Burke;Paul Carini;Jong-Deok Choi

  • Which pointer analysis should I use

    Michael Hind;Anthony Pioli

  • A Survey of Adaptive Optimization in Virtual Machines

    M. Arnold;S.J. Fink;D. Grove;M. Hind

  • Online feedback-directed optimization of Java

    Matthew Arnold;Michael Hind;Barbara G. Ryder

  • Vertical profiling: understanding the behavior of object-priented applications

    Matthias Hauswirth;Peter F. Sweeney;Amer Diwan;Michael Hind

  • Efficient and precise modeling of exceptions for the analysis of Java programs

    Jong-Deok Choi;David Grove;Michael Hind;Vivek Sarkar

  • Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers

    Michael G. Burke;Paul R. Carini;Jong-Deok Choi;Michael Hind

  • Method for characterizing program execution by periodic call stack inspection

    Matthew R. Arnold;Stephen J. Fink;David P. Grove;Michael J. Hind

  • Using hardware performance monitors to understand the behavior of java applications

    Peter F. Sweeney;Matthias Hauswirth;Brendon Cahoon;Perry Cheng

  • Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses

    Michael Hind;Anthony Pioli

  • Online Phase Detection Algorithms

    Priya Nagpurkar;Chandra Krintz;Michael Hind;Peter F. Sweeney

  • TED: Teaching AI to Explain its Decisions

    Michael Hind;Dennis Wei;Murray Campbell;Noel C. F. Codella

  • Increasing Trust in AI Services through Supplier's Declarations of Conformity

    Michael Hind;Sameep Mehta;Aleksandra Mojsilovic;Ravi Nair

  • Adaptive optimization in the Jalapeño JVM (poster session)

    Matthew Arnold;Stephen Fink;David Grove;Michael Hind

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