Automatic Generation of DAG Parallelism. - Ron Cytron; Michael Hind; Wilson C. Hsieh (PLDI, 1989)

Efficient loop-level parallelism in Ada. - Michael Hind; Edmond Schonberg (TRI-Ada, 1991)

Efficienty Loop-Level Parallelism in ADA. - Michael Hind (1991)

Loop Distribution with Multiple Exits. - Bor-Ming Hsieh; Michael Hind; Ron Cryton (SC, 1992)

Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers. - Michael G. Burke; Paul R. Carini; Jong-Deok Choi; Michael Hind (LCPC, 1994)

An Empirical Study of Precise Interprocedural Array Analysis. - Michael Hind; Michael G. Burke; Paul R. Carini; Samuel P. Midkiff (Sci. Program., 1994)

Flow-Sensitive Interprocedural Constant Propagation. - Paul R. Carini; Michael Hind (PLDI, 1995)

Using Regional Conferences to Mentor Student Development: A Case Study. - Michael Hind; Phil Pfeiffer (ACM SIGPLAN Notices, 1996)

Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses. - Michael Hind; Anthony Pioli (SAS, 1998)

The Jalapeño Dynamic Optimizing Compiler for Java. - Michael G. Burke; Jong-Deok Choi; Stephen J. Fink; David Grove; Michael Hind; Vivek Sarkar; Mauricio J. Serrano; Vugranam C. Sreedhar; Harini Srinivasan; John Whaley (Java Grande, 1999)

SIGAda '98: ACM/SIGAda International Conference (Report). - Michael Hind (ACM SIGPLAN Notices, 1999)

Efficient and precise modeling of exceptions for the analysis of Java programs. - Jong-Deok Choi; David Grove; Michael Hind; Vivek Sarkar (ACM SIGSOFT Softw. Eng. Notes, 1999)

Interprocedural pointer alias analysis. - Michael Hind; Michael G. Burke; Paul R. Carini; Jong-Deok Choi (ACM Trans. Program. Lang. Syst., 1999)

Optimizing Java Programs in the Presence of Exceptions. - Manish Gupta 002; Jong-Deok Choi; Michael Hind (ECOOP, 2000)

Which pointer analysis should I use? - Michael Hind; Anthony Pioli (ISSTA, 2000)

An Empirical Study of Selective Optimization. - Matthew Arnold; Michael Hind; Barbara G. Ryder (LCPC, 2000)

Adaptive optimization in the Jalapeño JVM. - Matthew Arnold; Stephen J. Fink; David Grove; Michael Hind; Peter F. Sweeney (OOPSLA, 2000)

Adaptive optimization in the Jalapeño JVM (poster session). - Matthew Arnold; Stephen Fink; David Grove; Michael Hind; Peter F. Sweeney (OOPSLA Addendum, 2000)

Traveling Through Dakota: Experiences with an Object-Oriented Program Analysis System. - Michael Hind; Anthony Pioli (TOOLS (34), 2000)

The Jalapeño virtual machine. - Bowen Alpern; C. Richard Attanasio; John J. Barton; Michael G. Burke; Perry Cheng; Jong-Deok Choi; Anthony Cocchi; Stephen J. Fink; David Grove; Michael Hind; Susan Flynn Hummel; Derek Lieber; et al. (IBM Syst. J., 2000)

NPIC - New Paltz interprocedural compiler. - Michael Hind (ACM SIGSOFT Softw. Eng. Notes, 2000)

Pointer analysis: haven't we solved this problem yet? - Michael Hind (PASTE, 2001)

Evaluating the effectiveness of pointer alias analyses. - Michael Hind; Anthony Pioli (Sci. Comput. Program., 2001)

Understanding the connectivity of heap objects. - Martin Hirzel; Johannes Henkel; Amer Diwan; Michael Hind (MSP/ISMM, 2002)

Online feedback-directed optimization of Java. - Matthew Arnold; Michael Hind; Barbara G. Ryder (OOPSLA, 2002)

Pointer Analysis in the Presence of Dynamic Class Loading. - Martin Hirzel; Amer Diwan; Michael Hind (ECOOP, 2004)

Using Hardware Performance Monitors to Understand the Behavior of Java Applications. - Peter F. Sweeney; Matthias Hauswirth; Brendon Cahoon; Perry Cheng; Amer Diwan; David Grove; Michael Hind (Virtual Machine Research and Technology Symposium, 2004)

Vertical profiling: understanding the behavior of object-priented applications. - Matthias Hauswirth; Peter F. Sweeney; Amer Diwan; Michael Hind (OOPSLA, 2004)

Virtual Machine Learning: Thinking like a Computer Architect. - Michael Hind (CGO, 2005)

High-level real-time programming in Java. - David F. Bacon; Perry Cheng; David Grove; Michael Hind; V. T. Rajan; Eran Yahav; Matthias Hauswirth; Christoph M. Kirsch; Daniel Spoonhower; Martin T. Vechev (EMSOFT, 2005)

The Jikes Research Virtual Machine project: Building an open-source research community. - Bowen Alpern; Steve Augart; Stephen M. Blackburn; Maria A. Butrico; Anthony Cocchi; Perry Cheng; Julian Dolby; Stephen J. Fink; David Grove; Michael Hind; Kathryn S. McKinley; Mark F. Mergen; et al. (IBM Syst. J., 2005)

A Survey of Adaptive Optimization in Virtual Machines. - Matthew Arnold; Stephen J. Fink; David Grove; Michael Hind; Peter F. Sweeney (Proc. IEEE, 2005)

The Need for a Whole-System View of Performance. - Matthias Hauswirth; Peter F. Sweeney; Amer Diwan; Michael Hind (Stud. Inform. Univ., 2005)

Online Phase Detection Algorithms. - Priya Nagpurkar; Chandra Krintz; Michael Hind; Peter F. Sweeney; V. T. Rajan (CGO, 2006)

Online performance auditing: using hot optimizations without getting burned. - Jeremy Lau; Matthew Arnold; Michael Hind; Brad Calder (PLDI, 2006)

A Loop Correlation Technique to Improve Performance Auditing. - Jeremy Lau; Matthew Arnold; Michael Hind; Brad Calder (PACT, 2007)

Using hpm-sampling to drive dynamic compilation. - Dries Buytaert; Andy Georges; Michael Hind; Matthew Arnold; Lieven Eeckhout; Koen De Bosschere (OOPSLA, 2007)

Dynamic compilation: the benefits of early investing. - Prasad A. Kulkarni; Matthew Arnold; Michael Hind (VEE, 2007)

Fast online pointer analysis. - Martin Hirzel; Daniel von Dincklage; Amer Diwan; Michael Hind (ACM Trans. Program. Lang. Syst., 2007)

Addressing the disconnect between the good and the popular. - Michael Hind (ACM SIGPLAN Notices, 2008)

CACM research highlights annual report. - Michael Hind (ACM SIGPLAN Notices, 2013)

SIGPLAN research highlights annual report. - Michael Hind (ACM SIGPLAN Notices, 2014)

META: Middleware for Events, Transactions, and Analytics. - Matthew Arnold; David Grove; Benjamin Herta; Michael Hind; Martin Hirzel; Arun Iyengar; Louis Mandel; Vijay A. Saraswat; Avraham Shinnar; Jérôme Siméon; Mikio Takeuchi; Olivier Tardieu; et al. (IBM J. Res. Dev., 2016)

The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations. - Stephen M. Blackburn; Amer Diwan; Matthias Hauswirth; Peter F. Sweeney; José Nelson Amaral; Tim Brecht; Lubomír Bulej; Cliff Click; Lieven Eeckhout; Sebastian Fischmeister; Daniel Frampton; Laurie J. Hendren; et al. (ACM Trans. Program. Lang. Syst., 2016)

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images. - Noel C. F. Codella; Chung-Ching Lin; Allan Halpern; Michael Hind; Rogério Schmidt Feris; John R. Smith (MLCN/DLF/iMIMIC@MICCAI, 2018)

Teaching Meaningful Explanations. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2018)

Increasing Trust in AI Services through Supplier's Declarations of Conformity. - Michael Hind; Sameep Mehta; Aleksandra Mojsilovic; Ravi Nair; Karthikeyan Natesan Ramamurthy; Alexandra Olteanu; Kush R. Varshney (CoRR, 2018)

Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach. - Ravi Kiran Raman; Roman Vaculín; Michael Hind; Sekou L. Remy; Eleftheria Kyriaki Pissadaki; Nelson Kibichii Bore; Roozbeh Daneshvar; Biplav Srivastava; Kush R. Varshney (CoRR, 2018)

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; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Jacquelyn Martino; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; et al. (CoRR, 2018)

Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network. - Nelson Kibichii Bore; Ravi Kiran Raman; Isaac M. Markus; Sekou L. Remy; Oliver Bent; Michael Hind; Eleftheria Kyriaki Pissadaki; Biplav Srivastava; Roman Vaculín; Kush R. Varshney; Komminist Weldemariam (CoRR, 2018)

TED: Teaching AI to Explain its Decisions. - Michael Hind; Dennis Wei; Murray Campbell; Noel C. F. Codella; Amit Dhurandhar; Aleksandra Mojsilovic; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (AIES, 2019)

Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation. - Ravi Kiran Raman; Kush R. Varshney; Roman Vaculín; Nelson Kibichii Bore; Sekou L. Remy; Eleftheria Kyriaki Pissadaki; Michael Hind (ICASSP, 2019)

Promoting Distributed Trust in Machine Learning and Computational Simulation. - Nelson Kibichii Bore; Ravi Kiran Raman; Isaac M. Markus; Sekou L. Remy; Oliver Bent; Michael Hind; Eleftheria Kyriaki Pissadaki; Biplav Srivastava; Roman Vaculín; Kush R. Varshney; Komminist Weldemariam (IEEE ICBC, 2019)

A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations. - Ravi Kiran Raman; Roman Vaculín; Michael Hind; Sekou L. Remy; Eleftheria Kyriaki Pissadaki; Nelson Kibichii Bore; Roozbeh Daneshvar; Biplav Srivastava; Kush R. Varshney (IEEE ICBC, 2019)

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2019)

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; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (CoRR, 2019)

Explaining explainable AI. - Michael Hind (XRDS, 2019)

FactSheets: Increasing trust in AI services through supplier's declarations of conformity. - Matthew Arnold; Rachel K. E. Bellamy; Michael Hind; Stephanie Houde; Sameep Mehta; Aleksandra Mojsilovic; Ravi Nair; Karthikeyan Natesan Ramamurthy; Alexandra Olteanu; David Piorkowski; Darrell Reimer; John T. Richards; et al. (IBM J. Res. Dev., 2019)

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. - Rachel K. E. Bellamy; Kuntal Dey; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Jacquelyn Martino; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; et al. (IBM J. Res. Dev., 2019)

Think Your Artificial Intelligence Software Is Fair? Think Again. - Rachel K. E. Bellamy; Kuntal Dey; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; John T. Richards; et al. (IEEE Softw., 2019)

Experiences with Improving the Transparency of AI Models and Services. - Michael Hind; Stephanie Houde; Jacquelyn Martino; Aleksandra Mojsilovic; David Piorkowski; John T. Richards; Kush R. Varshney (CHI Extended Abstracts, 2020)

AI explainability 360: hands-on tutorial. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (FAT*, 2020)

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness. - Michael Hind; Dennis Wei; Yunfeng Zhang (CoRR, 2020)

Trust and Transparency in Contact Tracing Applications. - Stacy Hobson; Michael Hind; Aleksandra Mojsilovic; Kush R. Varshney (CoRR, 2020)

A Methodology for Creating AI FactSheets. - John T. Richards; David Piorkowski; Michael Hind; Stephanie Houde; Aleksandra Mojsilovic (CoRR, 2020)

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

AI Explainability 360 Toolkit. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (COMAD/CODS, 2021)

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. - Tim Draws; Zoltán Szlávik; Benjamin Timmermans; Nava Tintarev; Kush R. Varshney; Michael Hind (PERSUASIVE, 2021)

A Human-Centered Methodology for Creating AI FactSheets. - John T. Richards; David Piorkowski; Michael Hind; Stephanie Houde; Aleksandra Mojsilovic; Kush R. Varshney (IEEE Data Eng. Bull., 2021)

AI Explainability 360: Impact and Design. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (AAAI, 2022)

Evaluating a Methodology for Increasing AI Transparency: A Case Study. - David Piorkowski; John T. Richards; Michael Hind (CoRR, 2022)

Quantitative AI Risk Assessments: Opportunities and Challenges. - David Piorkowski; Michael Hind; John T. Richards (CoRR, 2022)

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. - Swapnaja Achintalwar; Adriana Alvarado Garcia; Ateret Anaby-Tavor; Ioana Baldini; Sara E. Berger; Bishwaranjan Bhattacharjee; Djallel Bouneffouf 001; Subhajit Chaudhury; Pin-Yu Chen; Lamogha Chiazor; Elizabeth M. Daly; Rogério Abreu de Paula; et al. (CoRR, 2024)

BenchmarkCards: Large Language Model and Risk Reporting. - Anna Sokol; Nuno Moniz; Elizabeth Daly; Michael Hind; Nitesh V. Chawla (CoRR, 2024)

Usage Governance Advisor: from Intent to AI Governance. - Elizabeth M. Daly; Sean Rooney; Seshu Tirupathi; Luis Garcés-Erice; Inge Vejsbjerg; Frank Bagehorn; Dhaval Salwala; Christopher Giblin; Mira L. Wolf-Bauwens; Ioana Giurgiu; Michael Hind; Peter Urbanetz (CoRR, 2024)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 2024)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)

AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources. - Frank Bagehorn; Kristina Brimijoin; Elizabeth M. Daly; Jessica He; Michael Hind; Luis Garcés-Erice; Christopher Giblin; Ioana Giurgiu; Jacquelyn Martino; Rahul Nair; David Piorkowski; Ambrish Rawat; et al. (CoRR, 2025)

Developing a Risk Identification Framework for Foundation Model Uses. - David Piorkowski; Michael Hind; John T. Richards; Jacquelyn Martino (CoRR, 2025)