Michael Hind

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 36

Top Topics

Case Based Reasoning

Top Venues

IBM J. Res. Dev.

MLCN/DLF/iMIMIC@MICCAI

Publications

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
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR (2024)
David Piorkowski, Michael Hind, John T. Richards
Quantitative AI Risk Assessments: Opportunities and Challenges. CoRR (2022)
David Piorkowski, John T. Richards, Michael Hind
Evaluating a Methodology for Increasing AI Transparency: A Case Study. CoRR (2022)
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
AI Explainability 360: Impact and Design. AAAI (2022)
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. CoRR (2021)
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
AI Explainability 360 Toolkit. COMAD/CODS (2021)
John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic, Kush R. Varshney
A Human-Centered Methodology for Creating AI FactSheets. IEEE Data Eng. Bull. 44 (4) (2021)
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. PERSUASIVE (2021)
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
AI Explainability 360: Impact and Design. CoRR (2021)
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
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
Stacy Hobson, Michael Hind, Aleksandra Mojsilovic, Kush R. Varshney
Trust and Transparency in Contact Tracing Applications. CoRR (2020)
Michael Hind, Dennis Wei, Yunfeng Zhang
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness. CoRR (2020)
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
AI explainability 360: hands-on tutorial. FAT* (2020)
John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic
A Methodology for Creating AI FactSheets. CoRR (2020)
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
Experiences with Improving the Transparency of AI Models and Services. CHI Extended Abstracts (2020)
Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
TED: Teaching AI to Explain its Decisions. AIES (2019)
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
Promoting Distributed Trust in Machine Learning and Computational Simulation. IEEE ICBC (2019)
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
Experiences with Improving the Transparency of AI Models and Services. CoRR (2019)
Ravi Kiran Raman, Kush R. Varshney, Roman Vaculín, Nelson Kibichii Bore, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Michael Hind
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation. ICASSP (2019)
Michael Hind
Explaining explainable AI. XRDS 25 (3) (2019)
Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations. IEEE ICBC (2019)
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
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36 (4) (2019)
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
FactSheets: Increasing trust in AI services through supplier's declarations of conformity. IBM J. Res. Dev. 63 (4/5) (2019)
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
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63 (4/5) (2019)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR (2019)
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
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR (2019)
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images. CoRR (2018)
Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach. CoRR (2018)
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
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network. CoRR (2018)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
TED: Teaching AI to Explain its Decisions. CoRR (2018)
Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, Kush R. Varshney
Increasing Trust in AI Services through Supplier's Declarations of Conformity. CoRR (2018)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
Teaching Meaningful Explanations. CoRR (2018)
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
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR (2018)
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images. MLCN/DLF/iMIMIC@MICCAI (2018)
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
The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations. ACM Trans. Program. Lang. Syst. 38 (4) (2016)
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
META: Middleware for Events, Transactions, and Analytics. IBM J. Res. Dev. 60 (2-3) (2016)
Michael Hind
SIGPLAN research highlights annual report. ACM SIGPLAN Notices 49 (4S) (2014)
Michael Hind
CACM research highlights annual report. ACM SIGPLAN Notices 48 (4S) (2013)
Michael Hind
Addressing the disconnect between the good and the popular. ACM SIGPLAN Notices 43 (11) (2008)
Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder
A Loop Correlation Technique to Improve Performance Auditing. PACT (2007)
Martin Hirzel, Daniel von Dincklage, Amer Diwan, Michael Hind
Fast online pointer analysis. ACM Trans. Program. Lang. Syst. 29 (2) (2007)
Prasad A. Kulkarni, Matthew Arnold, Michael Hind
Dynamic compilation: the benefits of early investing. VEE (2007)
Dries Buytaert, Andy Georges, Michael Hind, Matthew Arnold, Lieven Eeckhout, Koen De Bosschere
Using hpm-sampling to drive dynamic compilation. OOPSLA (2007)
Priya Nagpurkar, Chandra Krintz, Michael Hind, Peter F. Sweeney, V. T. Rajan
Online Phase Detection Algorithms. CGO (2006)
Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder
Online performance auditing: using hot optimizations without getting burned. PLDI (2006)
Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney
A Survey of Adaptive Optimization in Virtual Machines. Proc. IEEE 93 (2) (2005)
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
The Jikes Research Virtual Machine project: Building an open-source research community. IBM Syst. J. 44 (2) (2005)
Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind
The Need for a Whole-System View of Performance. Stud. Inform. Univ. 4 (1) (2005)
Michael Hind
Virtual Machine Learning: Thinking like a Computer Architect. CGO (2005)
David F. Bacon, Perry Cheng, David Grove, Michael Hind, V. T. Rajan, Eran Yahav, Matthias Hauswirth, Christoph M. Kirsch, Daniel Spoonhower, Martin T. Vechev
High-level real-time programming in Java. EMSOFT (2005)
Martin Hirzel, Amer Diwan, Michael Hind
Pointer Analysis in the Presence of Dynamic Class Loading. ECOOP (2004)
Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind
Vertical profiling: understanding the behavior of object-priented applications. OOPSLA (2004)
Peter F. Sweeney, Matthias Hauswirth, Brendon Cahoon, Perry Cheng, Amer Diwan, David Grove, Michael Hind
Using Hardware Performance Monitors to Understand the Behavior of Java Applications. Virtual Machine Research and Technology Symposium (2004)
Martin Hirzel, Johannes Henkel, Amer Diwan, Michael Hind
Understanding the connectivity of heap objects. MSP/ISMM (2002)
Matthew Arnold, Michael Hind, Barbara G. Ryder
Online feedback-directed optimization of Java. OOPSLA (2002)
Michael Hind, Anthony Pioli
Evaluating the effectiveness of pointer alias analyses. Sci. Comput. Program. 39 (1) (2001)
Michael Hind
Pointer analysis: haven't we solved this problem yet? PASTE (2001)
Michael Hind, Anthony Pioli
Which pointer analysis should I use? ISSTA (2000)
Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney
Adaptive optimization in the Jalapeño JVM. OOPSLA (2000)
Matthew Arnold, Michael Hind, Barbara G. Ryder
An Empirical Study of Selective Optimization. LCPC (2000)
Michael Hind
NPIC - New Paltz interprocedural compiler. ACM SIGSOFT Softw. Eng. Notes 25 (1) (2000)
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
The Jalapeño virtual machine. IBM Syst. J. 39 (1) (2000)
Michael Hind, Anthony Pioli
Traveling Through Dakota: Experiences with an Object-Oriented Program Analysis System. TOOLS (34) (2000)
Manish Gupta, Jong-Deok Choi, Michael Hind
Optimizing Java Programs in the Presence of Exceptions. ECOOP (2000)
Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F. Sweeney
Adaptive optimization in the Jalapeño JVM (poster session). OOPSLA Addendum (2000)
Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar
Efficient and precise modeling of exceptions for the analysis of Java programs. ACM SIGSOFT Softw. Eng. Notes 24 (5) (1999)
Michael Hind, Michael G. Burke, Paul R. Carini, Jong-Deok Choi
Interprocedural pointer alias analysis. ACM Trans. Program. Lang. Syst. 21 (4) (1999)
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
The Jalapeño Dynamic Optimizing Compiler for Java. Java Grande (1999)
Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar
Efficient and Precise Modeling of Exceptions for the Analysis of Java Programs. PASTE (1999)
Michael Hind
SIGAda '98: ACM/SIGAda International Conference (Report). ACM SIGPLAN Notices 34 (12) (1999)
Michael Hind, Anthony Pioli
Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses. SAS (1998)
Michael Hind, Phil Pfeiffer
Using Regional Conferences to Mentor Student Development: A Case Study. ACM SIGPLAN Notices 31 (7) (1996)
Paul R. Carini, Michael Hind
Flow-Sensitive Interprocedural Constant Propagation. PLDI (1995)
Michael G. Burke, Paul R. Carini, Jong-Deok Choi, Michael Hind
Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers. LCPC (1994)
Michael Hind, Michael G. Burke, Paul R. Carini, Samuel P. Midkiff
An Empirical Study of Precise Interprocedural Array Analysis. Sci. Program. 3 (3) (1994)
Bor-Ming Hsieh, Michael Hind, Ron Cryton
Loop Distribution with Multiple Exits. SC (1992)
Michael Hind, Edmond Schonberg
Efficient loop-level parallelism in Ada. TRI-Ada (1991)
Ron Cytron, Michael Hind, Wilson C. Hsieh
Automatic Generation of DAG Parallelism. PLDI (1989)