Michael Hind

90 papers A* 5A 10B 5C 1Misc 2Journal 48Unranked 17
YearRankTypeTitle / Venue / Authors
2026 J jnl
LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface.
CoRR
Michael Hind, Basel Shbita, Bo Wu, Farhan Ahmed, Chad DeLuca, Nathan Fulton, David D. Cox, Dan Gutfreund
2025 J jnl
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources.
CoRR
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, John T. Richards, Sean Rooney, Dhaval Salwala, Seshu Tirupathi, Peter Urbanetz, Kush R. Varshney, Inge Vejsbjerg, Mira L. Wolf-Bauwens
2025 J jnl
Agentic AI Needs a Systems Theory.
CoRR
Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Matthew Riemer, Djallel Bouneffouf, John T. Richards, Amit Dhurandhar, Elizabeth M. Daly, Michael Hind, Prasanna Sattigeri, Dennis Wei, Ambrish Rawat, Jasmina Gajcin, Werner Geyer
2025 J jnl
Developing a Risk Identification Framework for Foundation Model Uses.
CoRR
David Piorkowski, Michael Hind, John T. Richards, Jacquelyn Martino
2025 — conf
Granite Guardian: Comprehensive LLM Safeguarding.
NAACL (Industry Track)
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, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
2024 J jnl
BenchmarkCards: Large Language Model and Risk Reporting.
CoRR
Anna Sokol, Nuno Moniz, Elizabeth Daly, Michael Hind, Nitesh V. Chawla
2024 J jnl
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations.
CoRR
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici
2024 J jnl
Granite Guardian.
CoRR
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, Mark Purcell, Michael Desmond, Qian Pan, Zahra Ashktorab, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
2024 J jnl
Usage Governance Advisor: from Intent to AI Governance.
CoRR
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
2022 A* conf
AI Explainability 360: Impact and Design.
AAAI
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2022 J jnl
Evaluating a Methodology for Increasing AI Transparency: A Case Study.
CoRR
David Piorkowski, John T. Richards, Michael Hind
2022 J jnl
Quantitative AI Risk Assessments: Opportunities and Challenges.
CoRR
David Piorkowski, Michael Hind, John T. Richards
2021 J jnl
A Human-Centered Methodology for Creating AI FactSheets.
IEEE Data Eng. Bull.
John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic, Kush R. Varshney
2021 — conf
AI Explainability 360 Toolkit.
COMAD/CODS
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2021 J jnl
AI Explainability 360: Impact and Design.
CoRR
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2021 B conf
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users.
PERSUASIVE
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
2021 J jnl
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users.
CoRR
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
2020 J jnl
A Methodology for Creating AI FactSheets.
CoRR
John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic
2020 J jnl
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.
J. Mach. Learn. Res.
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2020 — conf
AI explainability 360: hands-on tutorial.
FAT*
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2020 J jnl
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness.
CoRR
Michael Hind, Dennis Wei, Yunfeng Zhang
2020 — conf
Experiences with Improving the Transparency of AI Models and Services.
CHI Extended Abstracts
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
2020 J jnl
Trust and Transparency in Contact Tracing Applications.
CoRR
Stacy Hobson, Michael Hind, Aleksandra Mojsilovic, Kush R. Varshney
2019 — conf
A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations.
IEEE ICBC
Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
2019 J jnl
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.
IBM J. Res. Dev.
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, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
2019 Misc conf
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation.
ICASSP
Ravi Kiran Raman, Kush R. Varshney, Roman Vaculín, Nelson Kibichii Bore, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Michael Hind
2019 J jnl
Experiences with Improving the Transparency of AI Models and Services.
CoRR
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
2019 J jnl
Explaining explainable AI.
XRDS
Michael Hind
2019 J jnl
FactSheets: Increasing trust in AI services through supplier's declarations of conformity.
IBM J. Res. Dev.
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, Jason Tsay, Kush R. Varshney
2019 J jnl
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.
CoRR
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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
2019 — conf
Promoting Distributed Trust in Machine Learning and Computational Simulation.
IEEE ICBC
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
2019 C conf
TED: Teaching AI to Explain its Decisions.
AIES
Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
2019 J jnl
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning.
CoRR
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2019 J jnl
Think Your Artificial Intelligence Software Is Fair? Think Again.
IEEE Softw.
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, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
2018 J jnl
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.
CoRR
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, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
2018 — conf
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images.
MLCN/DLF/iMIMIC@MICCAI
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith
2018 J jnl
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images.
CoRR
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith
2018 J jnl
Increasing Trust in AI Services through Supplier's Declarations of Conformity.
CoRR
Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, Kush R. Varshney
2018 J jnl
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network.
CoRR
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
2018 J jnl
TED: Teaching AI to Explain its Decisions.
CoRR
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2018 J jnl
Teaching Meaningful Explanations.
CoRR
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2018 J jnl
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach.
CoRR
Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
2016 J jnl
META: Middleware for Events, Transactions, and Analytics.
IBM J. Res. Dev.
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, Wei Zhang
2016 J jnl
The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations.
ACM Trans. Program. Lang. Syst.
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, Michael Hind, Antony L. Hosking, Richard E. Jones, Tomas Kalibera, Nathan Keynes, Nathaniel Nystrom, Andreas Zeller
2014 J jnl
SIGPLAN research highlights annual report.
ACM SIGPLAN Notices
Michael Hind
2013 J jnl
CACM research highlights annual report.
ACM SIGPLAN Notices
Michael Hind
2013 — ed.
Proceedings of the third ACM SIGPLAN X10 Workshop, X10 2013, Seattle, Washington, USA, June 20, 2013
X10@PLDI
Michael Hind, David Grove
2009 A* ed.
Proceedings of the 2009 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2009, Dublin, Ireland, June 15-21, 2009
PLDI
Michael Hind, Amer Diwan
2008 J jnl
Addressing the disconnect between the good and the popular.
ACM SIGPLAN Notices
Michael Hind
2007 B conf
A Loop Correlation Technique to Improve Performance Auditing.
PACT
Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder
2007 B conf
Dynamic compilation: the benefits of early investing.
VEE
Prasad A. Kulkarni, Matthew Arnold, Michael Hind
2007 J jnl
Fast online pointer analysis.
ACM Trans. Program. Lang. Syst.
Martin Hirzel, Daniel von Dincklage, Amer Diwan, Michael Hind
2007 A conf
Using hpm-sampling to drive dynamic compilation.
OOPSLA
Dries Buytaert, Andy Georges, Michael Hind, Matthew Arnold, Lieven Eeckhout, Koen De Bosschere
2006 A conf
Online Phase Detection Algorithms.
CGO
Priya Nagpurkar, Chandra Krintz, Michael Hind, Peter F. Sweeney, V. T. Rajan
2006 A* conf
Online performance auditing: using hot optimizations without getting burned.
PLDI
Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder
2005 J jnl
A Survey of Adaptive Optimization in Virtual Machines.
Proc. IEEE
Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney
2005 Misc conf
High-level real-time programming in Java.
EMSOFT
David F. Bacon, Perry Cheng, David Grove, Michael Hind, V. T. Rajan, Eran Yahav, Matthias Hauswirth, Christoph M. Kirsch, Daniel Spoonhower, Martin T. Vechev
2005 B ed.
Proceedings of the 1st International Conference on Virtual Execution Environments, VEE 2005, Chicago, IL, USA, June 11-12, 2005
VEE
Michael Hind, Jan Vitek
2005 J jnl
The Jikes Research Virtual Machine project: Building an open-source research community.
IBM Syst. J.
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, J. Eliot B. Moss, Ton Anh Ngo, Vivek Sarkar, Martin Trapp
2005 J jnl
The Need for a Whole-System View of Performance.
Stud. Inform. Univ.
Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind
2005 A conf
Virtual Machine Learning: Thinking like a Computer Architect.
CGO
Michael Hind
2004 A conf
Pointer Analysis in the Presence of Dynamic Class Loading.
ECOOP
Martin Hirzel, Amer Diwan, Michael Hind
2004 — conf
Using Hardware Performance Monitors to Understand the Behavior of Java Applications.
Virtual Machine Research and Technology Symposium
Peter F. Sweeney, Matthias Hauswirth, Brendon Cahoon, Perry Cheng, Amer Diwan, David Grove, Michael Hind
2004 A conf
Vertical profiling: understanding the behavior of object-priented applications.
OOPSLA
Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind
2002 A conf
Online feedback-directed optimization of Java.
OOPSLA
Matthew Arnold, Michael Hind, Barbara G. Ryder
2002 — conf
Understanding the connectivity of heap objects.
MSP/ISMM
Martin Hirzel, Johannes Henkel, Amer Diwan, Michael Hind
2001 J jnl
Evaluating the effectiveness of pointer alias analyses.
Sci. Comput. Program.
Michael Hind, Anthony Pioli
2001 — conf
Pointer analysis: haven't we solved this problem yet?
PASTE
Michael Hind
2000 — conf
Adaptive optimization in the Jalapeño JVM (poster session).
OOPSLA Addendum
Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F. Sweeney
2000 A conf
Adaptive optimization in the Jalapeño JVM.
OOPSLA
Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney
2000 — conf
An Empirical Study of Selective Optimization.
LCPC
Matthew Arnold, Michael Hind, Barbara G. Ryder
2000 J jnl
NPIC - New Paltz interprocedural compiler.
ACM SIGSOFT Softw. Eng. Notes
Michael Hind
2000 A conf
Optimizing Java Programs in the Presence of Exceptions.
ECOOP
Manish Gupta, Jong-Deok Choi, Michael Hind
2000 J jnl
The Jalapeño virtual machine.
IBM Syst. J.
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, Vassily Litvinov, Mark F. Mergen, Ton Ngo, James R. Russell, Vivek Sarkar, Mauricio J. Serrano, Janice C. Shepherd, Stephen E. Smith, Vugranam C. Sreedhar, Harini Srinivasan, John Whaley
2000 — conf
Traveling Through Dakota: Experiences with an Object-Oriented Program Analysis System.
TOOLS (34)
Michael Hind, Anthony Pioli
2000 A conf
Which pointer analysis should I use?
ISSTA
Michael Hind, Anthony Pioli
1999 — conf
Efficient and Precise Modeling of Exceptions for the Analysis of Java Programs.
PASTE
Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar
1999 J jnl
Efficient and precise modeling of exceptions for the analysis of Java programs.
ACM SIGSOFT Softw. Eng. Notes
Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar
1999 J jnl
Interprocedural pointer alias analysis.
ACM Trans. Program. Lang. Syst.
Michael Hind, Michael G. Burke, Paul R. Carini, Jong-Deok Choi
1999 J jnl
SIGAda '98: ACM/SIGAda International Conference (Report).
ACM SIGPLAN Notices
Michael Hind
1999 — conf
The Jalapeño Dynamic Optimizing Compiler for Java.
Java Grande
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
1998 B conf
Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses.
SAS
Michael Hind, Anthony Pioli
1996 J jnl
Using Regional Conferences to Mentor Student Development: A Case Study.
ACM SIGPLAN Notices
Michael Hind, Phil Pfeiffer
1995 A* conf
Flow-Sensitive Interprocedural Constant Propagation.
PLDI
Paul R. Carini, Michael Hind
1994 J jnl
An Empirical Study of Precise Interprocedural Array Analysis.
Sci. Program.
Michael Hind, Michael G. Burke, Paul R. Carini, Samuel P. Midkiff
1994 — conf
Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers.
LCPC
Michael G. Burke, Paul R. Carini, Jong-Deok Choi, Michael Hind
1992 A conf
Loop Distribution with Multiple Exits.
SC
Bor-Ming Hsieh, Michael Hind, Ron Cryton
1991 — conf
Efficient loop-level parallelism in Ada.
TRI-Ada
Michael Hind, Edmond Schonberg
1991 — —
Efficienty Loop-Level Parallelism in ADA.
Michael Hind
1989 A* conf
Automatic Generation of DAG Parallelism.
PLDI
Ron Cytron, Michael Hind, Wilson C. Hsieh