Kush R. Varshney
IBM Research AI
T. J. Watson Research Center
1101 Kitchawan Road
Yorktown Heights, NY 10598
(914)-945-1628

Publications and Patents

Books and Booklets

B5. Trustworthy Machine Learning. Kush R. Varshney. Independently Published, Chappaqua, NY, USA, 2022.

B4. AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning. Trisha Mahoney, Kush R. Varshney, and Michael Hind. O'Reilly Media, Sebastopol, CA, USA, 2020.

Book Chapters

B6. Human-Centered Explainable AI (XAI): From Algorithms to User Experiences. Q. Vera Liao and Kush R. Varshney. Human-Centered Machine Learning. Rebecca Fiebrink, Marco Gillies, and Gonzalo Ramos, editors. Cambridge University Press, 2024.

B3. Learning Interpretable Classification Rules with Boolean Compressed Sensing. Dmitry M. Malioutov, Kush R. Varshney, Amin Emad, and Sanjeeb Dash. Transparent Data Mining for Big and Small Data, p. 95-121. Tania Cerquitelli, Daniele Quercia, and Frank Pasquale, editors. Springer, Cham, Switzerland, 2017.

B2. Legislative Prediction with Political and Social Network Analysis. Jun Wang, Kush R. Varshney, and Aleksandra Mojsilović. Encyclopedia of Social Network Analysis and Mining, p. 804-811. Reda Alhajj and Jon Rokne, editors. Springer, Heidelberg, Germany, 2014.

B1. Automatic Fingerprint Matching Systems. Kush R. Varshney. Glimpses of Systems Theory and Novel Applications: Felicitation Volume in Honour of Professor Raj Kumar Varshney, p. 149-164. Harjinder Singh Sekhon, Rajendra Kumar Varshney, Prabhat Kumar, Jag Mohan Singh, and Rajendra Prasad, editors. Navin Press, Aligarh, India, 2005.

Journal and Magazine Articles

J47. Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, Joan Byamugisha, Maria Chang, Pierre Dognin, Eitan Farchi, Ndivhuwo Makondo, Aleksandra Mojsilovic, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Inkit Padhi, Orna Raz, Jesus Rios, Prasanna Sattigeri, Moninder Singh, Siphiwe Thwala, Rosario A. Uceda-Sosa, and Kush R. Varshney. IEEE Internet Computing, vol. 28, no. 5, September/October 2024.

J46. Künstliche Intelligenz: Benchmarks für Generative Sprachmodelle im Überblick. Andreas Christian and Kush R. Varshney. iX Magazin für Professionelle Informationstechnik, May 2024.

J45. A Synergistic Future for AI and Ecology. Barbara A. Han, Kush R. Varshney, Shannon LaDeau, Ajit Subramaniam, Kathleen C. Weathers, and Jacob Zwart. Proceedings of the National Academy of Sciences of the United States of America, vol. 120, issue 38, p. e2220283120, September 2023.

J44. Humble AI. Bran Knowles, Jason D’Cruz, John Richards, and Kush R. Varshney. Communications of the ACM, vol. 66, no. 9, p. 73-79, September 2023.

J43. Skin Tone Analysis for Representation in Educational Materials (STAR-ED) Using Machine Learning. Girmaw Abebe Tadesse, Celia Cintas, Kush R. Varshney, Peter Staar, Chinyere Agunwa, Skyler Speakman, Justin Jia, Elizabeth Bailey, Ademide Adelekun, Jules B. Lipoff, Ginikanwa Onyekaba, Jenna C. Lester, Veronica Rotemberg, James Zou, and Roxana Daneshjou. npj Digital Medicine, vol. 6, p. 151, August 2023.

J42. The Incentive Gap in Data Work in the Era of Large Models. Katy Ilonka Gero, Payel Das, Pierre Dognin, Inkit Padhi, Prasanna Sattigeri, and Kush R. Varshney. Nature Machine Intelligence, vol. 5, issue 6, p. 565-567, June 2023.

J41. Human-Centered Explainability for Life Sciences, Healthcare and Medical Informatics. Sanjoy Dey, Prithwish Chakraborty, Bum Chul Kwon, Amit Dhurandhar, Mohamed Ghalwash, Fernando J. Suarez Saiz, Kenney Ng, Daby Sow, Kush R. Varshney, and Pablo Meyer. Patterns, vol. 3, issue 5, p. 100493, May 2022.

J40. A Human-Centered Methodology for Creating AI FactSheets. John Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilović, and Kush R. Varshney. Bulletin of the Technical Committee on Data Engineering, vol. 44, issue 4, p. 47–58, December 2021.

J39. Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, and Kush R. Varshney. Entropy, vol. 23, issue 12, p. 1571, November 2021.

J38. Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. Lu Cheng, Kush R. Varshney, and Huan Lu. Journal of Artificial Intelligence Research, vol. 71, p. 1137-1181, August 2021.

J37. 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 Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. Journal of Machine Learning Research, vol. 21, issue 130, p. 1-6, June 2020.

J36. 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 Mojsilović, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, Darrell Reimer, John Richards, Jason Tsay, and Kush R. Varshney. IBM Journal of Research and Development, vol. 63, issue 4/5, p. 6, July/September 2019.

J35. 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 Mojsilović, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. IBM Journal of Research and Development, vol. 63, issue 4/5, p. 4, July/September 2019.

J34. Fairness GAN: Generating Datasets with Fairness Properties Using a Generative Adversarial Network. Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, and Kush R. Varshney. IBM Journal of Research and Development, vol. 63, issue 4/5, p. 3, July/September 2019.

J33. Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration. Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, and Francesca Rossi. IBM Journal of Research and Development, vol. 63, issue 4/5, p. 2, July/September 2019.

J32. 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 Mojsilović, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. IEEE Software, vol. 36, issue 4, p. 76-80, July-August 2019.

J31. Confronting Data Sparsity to Identify Potential Sources of Zika Virus Spillover Infection Among Primates. Barbara A. Han, Subhabrata Majumdar, Flavio P. Calmon, Benjamin S. Glicksberg, Raya Horesh, Abhishek Kumar, Adam Perer, Elisa B. von Marschall, Dennis Wei, Aleksandra Mojsilović, and Kush R. Varshney. Epidemics, vol. 27, p. 59–65, June 2019.

J30. Trustworthy Machine Learning and Artificial Intelligence. Kush R. Varshney. ACM XRDS Magazine, vol. 25, issue 3, p. 26-29, Spring 2019.

J29. A Big Data Approach to Computational Creativity: The Curious Case of Chef Watson. Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schörgendorfer and Yi-Min Chee. IBM Journal of Research and Development, vol. 63, issue 1, p. 7, January-February 2019.

J28. Distribution-Preserving k-Anonymity. Dennis Wei, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. Statistical Analysis and Data Mining, vol. 11, issue 6, p. 253-270, December 2018.

J27. Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis. Flavio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. IEEE Journal of Selected Topics in Signal Processing, vol. 12, issue 5, p. 1106-1119, October 2018.

J26. How to Foster Innovation: A Data-Driven Approach to Measuring Economic Competitiveness. Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao, Chandra Reddy, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, p. 11, November-December 2017.

J25. Dataflow Representation of Data Analyses: Towards a Platform for Collaborative Data Science. Evan Patterson, Robert McBurney, Hollie Schmidt, Ioana Baldini, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, p. 9, November-December 2017.

J24. Real-Time Understanding of Humanitarian Crises via Targeted Information Retrieval. Kien T. Pham, Prasanna Sattigeri, Amit Dhurandhar, Arpith C. Jacob, Maja Vukovic, Patrice Chataigner, Juliana Freire, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, p. 7, November-December 2017.

J23. Understanding the Ecospace of Philanthropic Projects. Hemank Lamba, Mary E. Helander, Moninder Singh, Nizar Lethif, Anuradha Bhamidipaty, Salman Baset, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, p. 6, November-December 2017.

J22. Effectiveness of Peer Detailing in a Diarrhea Program in Nigeria. Yumeng Tao, Debarun Bhattacharjya, Aliza R. Heching, Aditya Vempaty, Moninder Singh, Felix Lam, Jason Houdek, Mohammed Abubakar, Ahmad Abdulwahab, Tiwadayo Baraimoh, Nnenna Ihebuzor, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, p. 1, November-December 2017.

J21. On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products. Kush R. Varshney and Homa Alemzadeh. Big Data, vol. 5, issue 3, p. 246-255, September 2017.

J20. Signal Processing for Social Good. Kush R. Varshney. IEEE Signal Processing Magazine, vol. 34, issue 3, p. 112, 108, May 2017.

J19. Decision Making with Quantized Priors Leads to Discrimination. Lav R. Varshney and Kush R. Varshney. Proceedings of the IEEE, vol. 105, issue 2, p. 241-255, February 2017.

J18. Associative Algorithms for Computational Creativity. Lav R. Varshney, Jun Wang, and Kush R. Varshney. Journal of Creative Behavior, vol. 50, issue 3, p. 211-223, September 2016.

J17. Olfactory Signal Processing. Kush R. Varshney and Lav R. Varshney. Digital Signal Processing, vol. 48, p. 84-92, January 2016.

J16. Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond. Kush R. Varshney, Dennis Wei, Karthikeyan Natesan Ramamurthy, and Aleksandra Mojsilović. ACM Journal of Data and Information Quality, vol. 6, issue 2-3, p. 5, June 2015.

J15. Targeting Villages for Rural Development Using Satellite Image Analysis. Kush R. Varshney, George H. Chen, Brian Abelson, Kendall Nowocin, Vivek Sakhrani, Ling Xu, and Brian L. Spatocco. Big Data, vol. 3, issue 1, p. 41-53, March 2015.

J14. Optimal Grouping for Group Minimax Hypothesis Testing. Kush R. Varshney and Lav R. Varshney. IEEE Transactions on Information Theory, vol. 60, issue 10, p. 6511-6521, October 2014.

J13. Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers. Kush R. Varshney. IEEE Journal of Selected Topics in Signal Processing, vol. 8, issue 4, p. 576-585, August 2014.

J12. Collaborative Kalman Filtering for Dynamic Matrix Factorization. John Z. Sun, Dhruv Parthasarathy, and Kush R. Varshney. IEEE Transactions on Signal Processing, vol. 62, issue 14, p. 3499-3509, July 15, 2014.

J11. Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, Autofocusing, Moving Targets, and Compressed Sensing. Müjdat Çetin, Ivana Stojanović, N. Özben Önhon, Kush R. Varshney, Sadegh Samadi, W. Clem Karl, and Alan S. Willsky. IEEE Signal Processing Magazine, vol. 31, issue 4, p. 27-40, July 2014.

J10. Practical Ensemble Classification Error Bounds for Different Operating Points. Kush R. Varshney, Ryan J. Prenger, Tracy L. Marlatt, Barry Y. Chen, and William G. Hanley. IEEE Transactions on Knowledge and Data Engineering, vol. 25, issue 11, p. 2590-2601, November 2013.

J9. Sales-Force Performance Analytics and Optimization. Moritz Baier, Jorge E. Carballo, Alice J. Chang, Yingdong Lu, Aleksandra Mojsilović, M. Jonathan Richard, Moninder Singh, Mark S. Squillante, and Kush R. Varshney. IBM Journal of Research and Development, vol. 56, issue 6, November/December 2012.

J8. Generalization Error of Linear Discriminant Analysis in Spatially-Correlated Sensor Networks. Kush R. Varshney. IEEE Transactions on Signal Processing, vol. 60, issue 6, p. 3295-3301, June 2012.

J7. Bayes Risk Error is a Bregman Divergence. Kush R. Varshney. IEEE Transactions on Signal Processing, vol. 59, issue 9, p. 4470-4472, September 2011.

J6. Business Analytics Based On Financial Time Series. Kush R. Varshney and Aleksandra Mojsilović. IEEE Signal Processing Magazine, vol. 28, issue 5, p. 83-93, September 2011.

J5. Linear Dimensionality Reduction for Margin-Based Classification: High-Dimensional Data and Sensor Networks. Kush R. Varshney and Alan S. Willsky. IEEE Transactions on Signal Processing, vol. 59, issue 6, p. 2496-2512, June 2011.

J4. Classification Using Geometric Level Sets. Kush R. Varshney and Alan S. Willsky. Journal of Machine Learning Research, vol. 11, p. 491-516, February 2010. (software)

J3. Postarthroplasty Examination Using X-Ray Images. Kush R. Varshney, Nikos Paragios, Jean-François Deux, Alain Kulski, Rémy Raymond, Phillipe Hernigou, and Alain Rahmouni. IEEE Transactions on Medical Imaging, vol. 28, issue 3, p. 469-474, March 2009. (movies)

J2. Quantization of Prior Probabilities for Hypothesis Testing. Kush R. Varshney and Lav R. Varshney. IEEE Transactions on Signal Processing, vol. 56, issue 10, p. 4553-4562, October 2008. (software)

J1. Sparse Representation in Structured Dictionaries with Application to Synthetic Aperture Radar. Kush R. Varshney, Müjdat Çetin, John W. Fisher III, and Alan S. Willsky. IEEE Transactions on Signal Processing, vol. 56, issue 8, p. 3548-3561, August 2008. (software)

Conference Papers

C152. Racial and Neighborhood Disparities in Legal Financial Obligations in Jefferson County, Alabama. Óscar Lara-Yejas, Aakanksha Joshi, Andrew Martinez, Leah Nelson, Skyler Speakman, Krysten Thompson, Yuki Nishimura, Jordan Bond, and Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, San Jose, CA, October 2024.

C151. Decolonial AI Alignment: Openness, Viśeṣa-Dharma, and Including Excluded Knowledges. Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, San Jose, CA, October 2024.

C150. Individual Fairness in Graphs Using Local and Global Structural Information. Yonas Sium, Qi Li, and Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, San Jose, CA, October 2024.

C149. ComVas: Contextual Moral Values Aligned System. Inkit Padhi, Pierre Dognin, Jesús M. Ríos Aliaga, Ronny Luss, Swapnaja Achintalwar, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, and Djallel Bouneffouf. International Joint Conference on Artificial Intelligence, Jeju, South Korea, August 2024.

C148. Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa. Victor Akinwande, Megan MacGregor, Celia Cintas, Ehud Karavani, Dennis Wei, Kush R. Varshney, and Pablo Nepomnaschy. International Joint Conference on Artificial Intelligence, Jeju, South Korea, August 2024.

C147. Towards Healthy AI: Large Language Models Need Therapists Too. Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, and Kush R. Varshney. NAACL Workshop on Trustworthy Natural Language Processing, Mexico City, Mexico, June 2024.

C146. Evaluating the Impact of Skin Tone Representation on Out-of-Distribution Detection Performance in Dermatology. Assala Benmalek, Celia Cintas, Girmaw Abebe Tadesse, Roxana Daneshjou, Kush R. Varshney, and Cherifi Dalila. IEEE International Symposium on Biomedical Imaging, Athens, Greece, May 2024.

C145. Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. Ioana Baldini, Chhavi Yadav, Payel Das, and Kush R. Varshney. ACL Workshop on Trustworthy Natural Language Processing, Toronto, Canada, July 2023.

C144. Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Amit Dhurandhar, and Juan E. Gilbert. ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL, June 2023.

C143. Trustworthy AI and the Logics of Intersectional Resistance. Bran Knowles, Jasmine Fledderjohann, John T. Richards, and Kush R. Varshney. ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL, June 2023.

C142. Foundation Model Platforms and Bottom-of-the-Pyramid Innovation. Kush R. Varshney. ICLR Workshop on Practical Machine Learning for Developing Countries, Kigali, Rwanda, May 2023.

C141. What Is Missing in IRM Training and Evaluation? Challenges and Solutions. Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, and Sijia Liu. International Conference on Learning Representations, Kigali, Rwanda, May 2023.

C140. A Banal Account of a Safety-Creativity Tradeoff. Kush R. Varshney and Lav R. Varshney. IUI Workshop on Designing for Safety in Human-AI Interactions, Sydney, Australia, March 2023.

C139. Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, and Payel Das. AAAI Conference on Artificial Intelligence, Washington, DC, February 2023.

C138. Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, and Yiming Ying. AAAI Conference on Artificial Intelligence, Washington, DC, February 2023.

C137. Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre Dognin, and Kush R. Varshney. Advances in Neural Information Processing Systems, New Orleans, LA, November–December 2022.

C136. On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, and Moninder Singh. Advances in Neural Information Processing Systems, New Orleans, LA, November–December 2022.

C135. The Empathy Gap: Why AI Can Forecast Behavior But Cannot Assess Trustworthiness. Jason R. D’Cruz, William Kidder, and Kush R. Varshney. AAAI Fall Symposium Series Symposium on Thinking Fast and Slow and Other Cognitive Theories in AI, Arlington, VA, November 2022.

C134. Deciding Fast and Slow: The Role of Cognitive Biases in AI-Assisted Decision-Making. Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, and Richard Tomsett. ACM Conference on Computer-Supported Cooperative Work and Social Computing, November 2022.

C133. Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust. Bran Knowles, Jason D’Cruz, John T. Richards, and Kush R. Varshney. ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Arlington, VA, October 2022.

C132. Differentially Private SGDA for Minimax Problems. Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, and Yiming Ying. Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, August 2022.

C131. Causal Feature Selection for Algorithmic Fairness. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, and Kush R. Varshney. ACM SIGMOD/PODS International Conference on Management of Data, Philadelphia, PA, June 2022.

C130. Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning. Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, and Kush R. Varshney. IEEE International Symposium on Biomedical Imaging, Kolkata, India, March 2022.

C129. 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 Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei and Yunfeng Zhang. Conference on Innovative Applications of Artificial Intelligence, February 2022.

C128. Uncertainty Quantification 360. Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang. ACM India Joint International Conference on Data Science and Management of Data, p. 333–335, January 2022.

C127. CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, and Kush R. Varshney. Advances in Neural Information Processing Systems, December 2021.

C126. Blockchain and the Scientific Method. James A. Evans, Kweku Opoku-Agyemang, Krishna Ratakonda, Kush R. Varshney, and Lav R. Varshney. ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems, November 2021.

C125. Out-of-Distribution Detection and Fairness Assessment in Dermatology. Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, and Kush R. Varshney. KDD Outlier Detection and Description Workshop, August 2021.

C124. An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness. Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, and Reginald E. Bryant. KDD Workshop on Measures and Best Practices for Responsible AI, August 2021.

C123. A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques. Renzhe Yu, Subhro Das, Sairam Gurajada, Kush R. Varshney, Hari Raghavan and Carlos X. Lastra-Anadon. ACL-IJCNLP Workshop on NLP for Positive Impact, August 2021.

C122. Biomedical Interpretable Entity Representations. Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, and Kush R. Varshney. Findings of ACL: ACL-IJCNLP, August 2021.

C121. Treatment Effect Estimation Using Invariant Risk Minimization. Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, and Amit Dhurandhar. IEEE International Conference on Acoustics, Speech, and Signal Processing, p. 5005-5009, June 2021.

C120. Beyond Reasonable Doubt: Improving Fairness in Budget-Constrained Decision Making Using Confidence Thresholds. Michiel Bakker, Duy Patrick Tu, Krishna Gummadi, Alex ‘Sandy’ Pentland, Kush R. Varshney, and Adrian Weller. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, May 2021.

C119. Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences with Attention. Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian Castellanos, Arvind Varsani, and Kush R. Varshney. ICLR Workshop on Machine Learning for Preventing and Combating Pandemics, May 2021.

C118. Fairly Estimating Socioeconomic Status Under Costly Feature Acquisition. Ritika Brahmadesam and Kush R. Varshney. ICLR Workshop on Practical Machine Learning for Developing Countries, May 2021.

C117. Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney. International Conference on Learning Representations, May 2021.

C116. Automated Meta-Analysis in Medical Research: A Causal Learning Perspective. Lu Cheng, Dmitriy Katz-Rogozhnikov, Kush R. Varshney, and Ioana Baldini. ACM Conference on Health, Inference, and Learning Workshop, April 2021.

C115. Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, and Michael Hind. International Conference on Persuasive Technologies, April 2021.

C114. Automated Evaluation of Representation in Dermatology Educational Materials. Girmaw Abebe Tadesse, Hannah Kim, Roxana Daneshjou, Celia Cintas, Kush R. Varshney, Ademide Adelekun, Jules Lipoff, Ginikanwa Onyekab, Veronica Rotemberg, and James Zou. AAAI Workshop on Trustworthy AI for Healthcare, February 2021.

C113. Exploring the Efficacy of Generic Drugs in Treating Cancer. Ioana Baldini, Mariana Bernagozzi, Sulbha Aggarwal, Mihaela Bornea, Saksham Chawla, Joppe Geluykens, Dmitriy A. Katz-Rogozhnikov, Pratik Mukherjee, Smruthi Ramesh, Sara Rosenthal, Jagrati Sharma, Kush R. Varshney, Catherine Del Vecchio Fitz, Pradeep Mangalath, and Laura B. Kleiman. AAAI Conference on Artificial Intelligence, February 2021.

C112. AI Explainability 360 Toolkit. Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. ACM India Joint International Conference on Data Science and Management of Data, p. 376-379, Bangalore, India, January 2021.

C111. Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning. William Ogallo, Skyler Speakman, Victor Akinwande, Kush R. Varshney, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam, Claire-Helene Mershon, and Nosa Orobaton. American Medical Informatics Association Annual Symposium, Chicago, IL, November 2020.

C110. Fairness of Classifiers Across Skin Tones in Dermatology. Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, and Kush R. Varshney. International Conference on Medical Image Computing and Computer Assisted Intervention, Lima, Peru, October 2020.

C109. Trust and Transparency in Contact Tracing Applications. Stacy Hobson, Michael Hind, Aleksandra Mojsilović, and Kush R. Varshney. KDD Workshop on Fragile Earth: Data Science for a Sustainable Planet, San Diego, CA, August 2020.

C108. Tutorial on Human-Centered Explainability for Healthcare. Prithwish Chakraborty, Bum Chul Kwon, Sanjoy Dey, Amit Dhurandhar, Daniel Gruen, Kenney Ng, Daby Sow, and Kush R. Varshney. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 3547–3548, San Diego, CA, August 2020.

C107. On the Equivalence of Bi-Level Optimization and Game-Theoretic Formulations of Invariant Risk Minimization. Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, and Amit Dhurandhar. ICML Workshop on Inductive Biases, Invariances and Generalization in RL, Vienna, Austria, July 2020.

C106. Invariant Risk Minimization Games. Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, and Amit Dhurandhar. International Conference on Machine Learning, Vienna, Austria, July 2020.

C105. Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R. Varshney. International Conference on Machine Learning, Vienna, Austria, July 2020.

C104. Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health. William Ogallo, Skyler Speakman, Victor Akinwande, Kush R. Varshney, Aisha Walcott-Bryant, Charity Wayua, and Komminist Weldemariam. International Joint Conference on Artificial Intelligence–Pacific Rim International Conference on Artificial Intelligence, Yokohama, Japan, July 2020.

C103. Characterization of Overlap in Observational Studies. Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, and Kush R. Varshney. International Conference on Artificial Intelligence and Statistics, Palermo, Italy, June 2020.

C102. Preservation of Anomalous Subgroups on Variational Autoencoder Transformed Data. Samuel C. Maina, Reginald E. Bryant, William Ogallo, Kush R. Varshney, Skyler Speakman, Celia Cintas, Aisha Walcott-Bryant, Robert-Florian Samoilescu, and Komminist Weldemariam. IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, May 2020.

C101. DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning. Michiel Bakker, Duy Patrick Tu, Humberto Riverón Valdés, Krishna Gummadi, Kush R. Varshney, Adrian Weller and Alex ‘Sandy’ Pentland. ICLR Workshop on Towards Trustworthy ML, Addis Ababa, Ethiopia, April 2020.

C100. Experiences with Improving the Transparency of AI Models and Services. Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilović, David Piorkowski, John Richards, and Kush R. Varshney. ACM CHI Conference on Human Factors in Computing Systems, Honolulu, HI, April 2020.

C99. Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines. Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, and Kush R. Varshney. ACM Conference on Health, Inference, and Learning, p. 19–29, Toronto, Canada, April 2020.

C98. On Mismatched Detection and Safe, Trustworthy Machine Learning. Kush R. Varshney. Conference on Information Sciences and Systems, Princeton, NJ, March 2020.

C97. Event-Driven Continuous Time Bayesian Networks. Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, and Dharmashankar Subramanian. AAAI Conference on Artificial Intelligence, New York, NY, February 2020.

C96. A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications. Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, and Laura B. Kleiman. Conference on Innovative Applications of Artificial Intelligence, New York, NY, February 2020.

C95. Data Augmentation for Discrimination Prevention and Bias Disambiguation. Shubham Sharma, Yunfeng Zhang, Jesús M. Ríos Aliaga, Djallel Bouneffouf, Vinod Muthusamy, and Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, New York, NY, February 2020.

C94. Joint Optimization of AI Fairness and Utility: A Human-Centered Approach. Yunfeng Zhang, Rachel K. E. Bellamy, and Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, New York, NY, February 2020.

C93. Fair Enough: Improving Fairness in Budget-Constrained Decision Making Using Confidence Thresholds. Michiel Bakker, Humberto Riverón Valdés, Duy Patrick Tu, Krishna Gummadi, Kush R. Varshney, Adrian Weller and Alex ‘Sandy’ Pentland. AAAI Workshop on Safe Artificial Intelligence, New York, NY, February 2020.

C92. How Data Scientists Work Together with Domain Experts in Scientific Collaborations: To Find The Right Answer or to Ask the Right Question? Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, and Aleksandra Mojsilović. ACM International Conference on Supporting Group Work, Sanibel Island, FL, p. 237, January 2020.

C91. Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets. Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, and Kush R. Varshney. NeurIPS Fair Machine Learning for Health Workshop, Vancouver, Canada, December 2019.

C90. Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, and Laura B. Kleiman. NeurIPS Workshop on Machine Learning for Health, Vancouver, Canada, December 2019.

C89. DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning. Michiel Bakker, Duy Patrick Tu, Humberto Riverón Valdés, Krishna Gummadi, Kush R. Varshney, Adrian Weller and Alex 'Sandy' Pentland. NeurIPS Workshop on Human-Centric Machine Learning, Vancouver, Canada, December 2019.

C88. Subgroup Preservation in Financial Data Anonymized by a Variational Autoencoder. Samuel C. Maina, Reginald E. Bryant, William Ogallo, Kush R. Varshney, Skyler Speakman, Celia Cintas, Aisha Walcott-Bryant, and Robert-Florian Samoilescu. NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, Vancouver, Canada, December 2019.

C87. Teaching AI Ethical Values Using Reinforcement Learning and Policy Orchestration. Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, and Francesca Rossi. International Joint Conference on Artificial Intelligence, Macao, p. 6377-6381, August 2019.

C86. Event-Driven Continuous Time Bayesian Networks: An Application in Modeling Progression out of Poverty through Integrated Social Services. Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, and Kush R. Varshney. IJCAI Workshop on AI for Social Good, Macau, August 2019.

C85. On Fairness in Budget-Constrained Decision Making. Michiel Bakker, Alejandro Noriega Campero, Duy Patrick Tu, Prasanna Sattigeri, Kush R. Varshney, and Alex 'Sandy' Pentland. KDD Workshop on Explainable Artificial Intelligence, Anchorage, AK, August 2019.

C84. Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact. Kush R. Varshney and Aleksandra Mojsilović. ICML Workshop on AI for Social Good, Long Beach, CA, June 2019.

C83. 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, and Aleksandra Mojsilović. ICML Workshop on Human in the Loop Learning, Long Beach, CA, June 2019.

C82. Topological Data Analysis of Decision Boundaries with Application to Model Selection. Karthikeyan Natesan Ramamurthy, Kush R. Varshney, and Krishnan Mody. International Conference on Machine Learning, Long Beach, CA, p. 5351-5360, June 2019.

C81. Bias Mitigation Post-Processing for Individual and Group Fairness. Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, and Ruchir Puri. IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, p. 2847-2851, May 2019.

C80. Constructing and Compressing Frames in Blockchain-Based Verifiable Multi-Party Computation. Ravi Kiran Raman, Kush R. Varshney, Roman Vaculin, Nelson Kibichii Bore, Sekou L. Remy, Eleftheria K. Pissadaki, and Michael Hind. IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, p. 7500-7504, May 2019.

C79. 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 K. Pissadaki, Biplav Srivastava, Roman Vaculin, Kush R. Varshney, and Komminist Weldemariam. IEEE International Conference on Blockchain and Cryptocurrency, Seoul, Korea, May 2019.

C78. A Scalabale Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaboration. Ravi Kiran Raman, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, and Kush R. Varshney. IEEE International Conference on Blockchain and Cryptocurrency, Seoul, Korea, May 2019.

C77. Fairness GAN: Generating Datasets with Fairness Properties using a Generative Adversarial Network. Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, and Kush R. Varshney. ICLR Workshop on Safe Machine Learning, New Orleans, LA, May 2019.

C76. Fair Transfer Learning with Missing Protected Attributes. Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, and Supriyo Chakraborty. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, Honolulu, HI, p. 91-98, January 2019.

C75. TED: Teaching AI to Explain Its Decisions. Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, Honolulu, HI, p. 123-129, January 2019.

C74. Financial Forecasting and Analysis for Low-Wage Workers. Wenyu Zhang, Raya Horesh, Karthikeyan Natesan Ramamurthy, Lingfei Wu, Jinfeng Yi, Kryn Anderson, and Kush R. Varshney. Data for Good Exchange Conference, New York, NY, September 2018.

C73. Teaching Machines to Understand Data Science Code by Semantic Enrichment of Dataflow Graphs. Evan Patterson, Ioana Baldini, Aleksandra Mojsilović, and Kush R. Varshney. KDD Workshop on Fragile Earth: Theory Guided Data Science to Enhance Scientific Discovery, London, UK, p. 5, August 2018.

C72. Semantic Representation of Data Science Programs. Evan Patterson, Ioana Baldini, Aleksandra Mojsilović, and Kush R. Varshney. International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence, Stockholm, Sweden, p. 5847-5849, July 2018.

C71. Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory. Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, and Pramod K. Varshney. ICML Workshop on Human Interpretability in Machine Learning, Stockholm, Sweden, p. 15-20, July 2018.

C70. The Effect of Extremist Violence on Hateful Speech Online. Alexandra Olteanu, Carlos Castillo, Jeremy Boy, and Kush R. Varshney. International AAAI Conference on Weblogs and Social Media, Palo Alto, CA, p. 221-230, June 2018.

C69. False Positive Control with Concave Penalties using Stability Selection. Bhanukiran Vinzamuri and Kush R. Varshney. IEEE Data Science Workshop, Lausanne, Switzerland, p. 76-80, June 2018.

C68. Assessing National Development Plans for Alignment with Sustainable Development Goals via Semantic Search. Jonathan Galsurkar, Moninder Singh, Lingfei Wu, Aditya Vempaty, Mikhail Sushkov, Devika Iyer, Serge Kapto, and Kush R. Varshney. Conference on Innovative Applications of Artificial Intelligence, New Orleans, LA, p. 7753-7758, February 2018.

C67. Neurology-as-a-Service for the Developing World. Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, and Justin Dauwels. NIPS Workshop on Machine Learning for the Developing World, Long Beach, CA, December 2017.

C66. Scalable Demand-Aware Recommendation. Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, and Yao Li. Advances in Neural Information Processing Systems, Long Beach, CA, p. 2412-2421, December 2017.

C65. Optimized Pre-Processing for Discrimination Prevention. Flavio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. Advances in Neural Information Processing Systems, Long Beach, CA, p. 3992-4001, December 2017.

C64. Exploring the Causal Relationships between Initial Opioid Prescriptions and Outcomes. Jinghe Zhang, Vijay S. Iyengar, Dennis Wei, Bhanukiran Vinzamuri, Hamsa Bastani, Alexander R. Macalalad, Anne E. Fischer, Gigi Yuen-Reed, Aleksandra Mojsilović, and Kush R. Varshney. AMIA Workshop on Data Mining for Medical Informatics, Washington, DC, November 2017.

C63. An End-To-End Machine Learning Pipeline That Ensures Fairness Policies. Samiulla Shaikh, Harit Vishwakarma, Sameep Mehta, Kush R. Varshney, Karthikeyan Natesan Ramamurthy, and Dennis Wei. Data for Good Exchange Conference, New York, NY, September 2017.

C62. The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection. Alexandra Olteanu, Kartik Talamadupula, and Kush R. Varshney. ACM Web Science Conference, Troy, NY, p. 405-406, June 2017.

C61. Statistical Analysis of Peer Detailing for Children's Diarrhea Treatments. Yumeng Tao, Debarun Bhattacharjya, Aliza R. Heching, Aditya Vempaty, Moninder Singh, Felix Lam, Kush R. Varshney, and Aleksandra Mojsilović. AAAI Spring Symposium on AI for Social Good, Stanford, CA, p. 101-106, March 2017.

C60. Machine Representation of Data Analyses: Towards a Platform for Collaborative Data Science. Evan J. Patterson, Ioana Baldini, Aleksandra Mojsilović, and Kush R. Varshney. AAAI Spring Symposium on AI for Social Good, Stanford, CA, p. 53-59, March 2017.

C59. Information Retrieval, Fusion, Completion, and Clustering for Employee Expertise Estimation. Raya Horesh, Kush R. Varshney, and Jinfeng Yi. IEEE International Conference on Big Data, Washington, DC, p. 1385-1393, December 2016.

C58. Stable Estimation of Granger-Causal Factors of Country-Level Innovation. Aurélie C. Lozano, Prasanna Sattigeri, Aleksandra Mojsilović, and Kush R. Varshney. IEEE Global Conference on Signal and Information Processing, Washington, DC, p. 1290-1294, December 2016.

C57. Interpretable Machine Learning via Convex Cardinal Shape Composition. Kush R. Varshney. Allerton Conference on Communication, Control, and Computing, Monticello, Illinois, p. 327-330, September 2016.

C56. Learning Sparse Two-Level Boolean Rules. Guolong Su, Dennis Wei, Kush R. Varshney, and Dmitry M. Malioutov. IEEE Workshop on Machine Learning for Signal Processing, Salerno, Italy, September 2016.

C55. Dynamic Matrix Factorization with Social Influence. Aleksandr Y. Aravkin, Kush R. Varshney, and Liu Yang. IEEE Workshop on Machine Learning for Signal Processing, Salerno, Italy, September 2016.

C54. Understanding Innovation to Drive Sustainable Development. Prasanna Sattigeri, Aurélie Lozano, Aleksandra Mojsilović, Kush R. Varshney, and Mahmoud Naghshineh. ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, New York, p. 21–25, June 2016.

C53. Interpretable Two-Level Boolean Rule Learning for Classification. Guolong Su, Dennis Wei, Kush R. Varshney, and Dmitry M. Malioutov. ICML Workshop on Human Interpretability in Machine Learning, New York, New York, p. 66–70, June 2016.

C52. Fidelity Loss in Distribution-Preserving Anonymization and Histogram Equalization. Lav R. Varshney and Kush R. Varshney. Conference on Information Sciences and Systems, Princeton, New Jersey, p. 30-35, March 2016.

C51. Engineering Safety in Machine Learning. Kush R. Varshney. Information Theory and Applications Workshop, La Jolla, California, February 2016.

C50. Predictive Modeling of Customer Repayment for Sustainable Pay-As-You-Go Solar Power in Rural India. Hugo Gerard, Kamalesh Rao, Mark Simithraaratchy, Kush R. Varshney, Kunal Kabra, and G. Paul Needham. Data for Good Exchange, New York, New York, September 2015.

C49. Data Science of the People, for the People, by the People: A Viewpoint on an Emerging Dichotomy. Kush R. Varshney. Data for Good Exchange, New York, New York, September 2015.

C48. From Open Data Ecosystems to Systems of Innovation: A Journey to Realize the Promise of Open Data. Shubir Kapoor, Aleksandra Mojsilović, Jade Nguyen Strattner, and Kush R. Varshney. Data for Good Exchange, New York, New York, September 2015.

C47. A Robust Nonlinear Kalman Smoothing Approach for Dynamic Matrix Factorization. Aleksandr Y. Aravkin, Kush R. Varshney, and Dmitry M. Malioutov. Signal Processing with Adaptive Sparse Structured Representations Workshop, Cambridge, United Kingdom, July 2015.

C46. A Semiquantitative Group Testing Approach for Learning Interpretable Clinical Prediction Rules. Amin Emad, Kush R. Varshney, and Dmitry M. Malioutov. Signal Processing with Adaptive Sparse Structured Representations Workshop, Cambridge, United Kingdom, July 2015.

C45. Optigrow: People Analytics for Job Transfers. Dennis Wei, Kush R. Varshney, and Marcy Wagman. IEEE International Congress on Big Data, New York, New York, p. 535-542, June-July 2015.

C44. Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity. Dennis Wei, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. 2015 SIAM International Conference on Data Mining, Vancouver, Canada, p. 226-234, April-May 2015. (Best Research Paper Honorable Mention)

C43. Learning Interpretable Classification Rules Using Sequential Row Sampling. Sanjeeb Dash, Dmitry M. Malioutov, and Kush R. Varshney. IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, p. 3337-3341, April 2015.

C42. Robust Binary Hypothesis Testing Under Contaminated Likelihoods. Dennis Wei and Kush R. Varshney. IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, p. 3407-3411, April 2015.

C41. Persistent Topology of Decision Boundaries. Kush R. Varshney and Karthikeyan Natesan Ramamurthy. IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, p. 3931-3935, April 2015.

C40. Targeting Direct Cash Transfers to the Extremely Poor. Brian Abelson, Kush R. Varshney, and Joy Sun. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, New York, p. 1563-1572, August 2014. (Best Social Good Paper Award)

C39. Predicting Employee Expertise for Talent Management in the Enterprise. Kush R. Varshney, Vijil Chenthamarakshan, Scott W. Fancher, Jun Wang, Dongping Fang, and Aleksandra Mojsilović. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, New York, p. 1729-1738, August 2014.

C38. An Analysis of Losing Unimportant Points in Tennis. Kush R. Varshney. KDD Workshop on Large-Scale Sports Analytics, New York, New York, August 2014.

C37. Computing Persistent Homology Under Random Projection. Karthikeyan Natesan Ramamurthy, Kush R. Varshney, and Jayaraman J. Thiagarajan. IEEE International Workshop on Statistical Signal Processing, Gold Coast, Australia, p. 105-108, June-July 2014.

C36. Food Steganography with Olfactory White. Kush R. Varshney and Lav R. Varshney. IEEE International Workshop on Statistical Signal Processing, Gold Coast, Australia, p. 21-24, June-July 2014.

C35. Active Odor Cancellation. Kush R. Varshney and Lav R. Varshney. IEEE International Workshop on Statistical Signal Processing, Gold Coast, Australia, p. 25-28, June-July 2014.

C34. Screening for Learning Classification Rules via Boolean Compressed Sensing. Sanjeeb Dash, Dmitry M. Malioutov, and Kush R. Varshney. IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, p. 3360-3364, May 2014.

C33. Prescriptive Analytics for Allocating Sales Teams to Opportunities. Ban Kawas, Mark S. Squillante, Dharmashankar Subramanian, and Kush R. Varshney. IEEE International Conference on Data Mining Workshops, Dallas, Texas, p. 211-218, December 2013.

C32. Quantifying and Recommending Expertise When New Skills Emerge. Dongping Fang, Kush R. Varshney, Jun Wang, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilović, and John H. Bauer. IEEE International Conference on Data Mining Workshops, Dallas, Texas, p. 672-679, December 2013.

C31. Quantile Regression for Workforce Analytics. Karthikeyan Natesan Ramamurthy, Kush R. Varshney, and Moninder Singh. IEEE Global Conference on Signal and Information Processing, Austin, Texas, p. 1134, December 2013.

C30. Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data. Kush R. Varshney, Lav R. Varshney, Jun Wang, and Daniel Meyers. International Joint Conference on Artificial Intelligence Workshop on Cooking with Computers, Beijing, China, p. 3-12, August 2013.

C29. Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks. Kush R. Varshney and Peter M. van de Ven. ACM International Symposium on Mobile Ad Hoc Networking and Computing, Bengaluru, India, p. 31-38, July-August 2013.

C28. Expertise Assessment with Multi-Cue Semantic Information. Jun Wang, Kush R. Varshney, Aleksandra Mojsilović, Dongping Fang, and John H. Bauer. IEEE International Conference on Service Operations and Logistics, and Informatics, Dongguan, China, p. 534-539, July 2013. (Best Paper Award)

C27. Dose-Response Signal Estimation and Optimization for Salesforce Management. Kush R. Varshney and Moninder Singh. IEEE International Conference on Service Operations and Logistics, and Informatics, Dongguan, China, p. 328-333, July 2013.

C26. Cognition as a Part of Computational Creativity. Lav R. Varshney, Florian Pinel, Kush R. Varshney, Angela Schörgendorfer, and Yi-Min Chee. IEEE International Conference on Cognitive Informatics and Cognitive Computing, New York, New York, p. 36-43, July 2013.

C25. A Salesforce Control Theory Analysis of Enterprise Microblog Posts. Kush R. Varshney and N. Sadat Shami. International AAAI Conference on Weblogs and Social Media Workshop on Social Computing for Workforce 2.0, Cambridge, Massachusetts, p. 16-19, July 2013.

C24. Predicting and Recommending Skills in the Social Enterprise. Kush R. Varshney, Jun Wang, Aleksandra Mojsilović, Dongping Fang, and John H. Bauer. International AAAI Conference on Weblogs and Social Media Workshop on Social Computing for Workforce 2.0, Cambridge, Massachusetts, p. 20-23, July 2013.

C23. Exact Rule Learning via Boolean Compressed Sensing. Dmitry M. Malioutov and Kush R. Varshney. International Conference on Machine Learning, Atlanta, Georgia, p. 765-773, June 2013.

C22. Opinion Dynamics with Bounded Confidence in the Bayes Risk Error Divergence Sense. Kush R. Varshney. IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, p. 6600-6604, May 2013.

C21. Interactive Visual Salesforce Analytics. Kush R. Varshney, Jamie C. Rasmussen, Aleksandra Mojsilović, Moninder Singh, and Joan M. DiMicco. International Conference on Information Systems, Orlando, Florida, December 2012.

C20. An Analytics Approach for Proactively Combating Voluntary Attrition of Employees. Moninder Singh, Kush R. Varshney, Jun Wang, Aleksandra Mojsilović, Alisia R. Gill, Patricia I. Faur, and Raphael Ezry. IEEE International Conference on Data Mining Workshops, p. 317-323, Brussels, Belgium, December 2012.

C19. Deconvolving the Productivity of Salespeople via Constrained Quadratic Programming. Gautam K. Bhat and Kush R. Varshney. 36th National Systems Conference, Annamalainagar, India, December 2012.

C18. Decision Trees for Heterogeneous Dose-Response Signal Analysis. Kush R. Varshney, Moninder Singh, and Jun Wang. IEEE International Workshop on Statistical Signal Processing, p. 916-919, Ann Arbor, Michigan, August 2012.

C17. Does Selection Bias Blind Performance Diagnostics of Business Decision Models? A Case Study in Salesforce Optimization. Jun Wang, Moninder Singh, and Kush R. Varshney. 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, p. 416-421, Suzhou, China, July 2012.

C16. Legislative Prediction via Random Walks over a Heterogeneous Graph. Jun Wang, Kush R. Varshney, and Aleksandra Mojsilović. 2012 SIAM International Conference on Data Mining, p. 1095-1106, Anaheim, California, April 2012.

C15. Dynamic Matrix Factorization: A State Space Approach. John Z. Sun, Kush R. Varshney, and Karthik Subbian. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, p. 1897-1900, Kyoto, Japan, March 2012. (software)

C14. Estimating Post-Event Seller Productivity Profiles in Dynamic Sales Organizations. Kush R. Varshney, Moninder Singh, Mayank Sharma, and Aleksandra Mojsilović. IEEE International Conference on Data Mining Workshops, p. 1191-1198, Vancouver, Canada, December 2011.

C13. A Risk Bound for Ensemble Classification with a Reject Option. Kush R. Varshney. IEEE International Workshop on Statistical Signal Processing, p. 773-776, Nice, France, June 2011.

C12. Multilevel Minimax Hypothesis Testing. Kush R. Varshney and Lav R. Varshney. IEEE International Workshop on Statistical Signal Processing, p. 109-112, Nice, France, June 2011.

C11. MCMC Inference of the Shape and Variability of Time-Response Signals. Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Aleksandra Mojsilović, and Moninder Singh. 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, p. 3956-3959, Prague, Czech Republic, May 2011.

C10. Spatially-Correlated Sensor Discriminant Analysis. Kush R. Varshney. 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, p. 3680-3683, Prague, Czech Republic, May 2011.

C9. Categorical Decision Making by People, Committees, and Crowds. Lav R. Varshney, Joong Bum Rhim, Kush R. Varshney, and Vivek K Goyal. 2011 Information Theory and Applications Workshop, La Jolla, California, February 2011.

C8. Class-Specific Error Bounds for Ensemble Classifiers. Ryan J. Prenger, Tracy D. Lemmond, Kush R. Varshney, Barry Y. Chen, and William G. Hanley. The Sixteenth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, p. 843-852, Arlington, Virginia, July 2010.

C7. Learning Dimensionality-Reduced Classifiers for Information Fusion. Kush R. Varshney and Alan S. Willsky. The Twelfth International Conference on Information Fusion, p. 1881-1888, Seattle, Washington, July 2009. (Best Student Paper Travel Award)

C6. Supervised Learning of Classifiers via Level Set Segmentation. Kush R. Varshney and Alan S. Willsky. 2008 IEEE Workshop on Machine Learning for Signal Processing, p. 115-120, Cancún, Mexico, October 2008.

C5. Minimum Mean Bayes Risk Error Quantization of Prior Probabilities. Kush R. Varshney and Lav R. Varshney. 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, p. 3445-3448, Las Vegas, Nevada, April 2008. (poster)

C4. Genis Açili Radarda Görüntü Olusturma ve Yönbagimlilik Tespiti için Seyrek Sinyal Temsiline Dayali bir Yaklasim (A Sparse Signal Representation-based Approach to Image Formation and Anisotropy Determination in Wide-Angle Radar). Kush R. Varshney, Müjdat Çetin, John W. Fisher III, and Alan S. Willsky. IEEE 15th Signal Processing and Communication Applications Conference, Eskisehir, Turkey, June 2007.

C3. Multi-View Stereo Reconstruction of Total Knee Replacement from X-Rays. Kush R. Varshney, Nikos Paragios, Alain Kulski, Remy Raymond, Phillipe Hernigou, and Alain Rahmouni. The Fourth IEEE International Symposium on Biomedical Imaging: From Nano to Macro, p. 1148-1151, Arlington, Virginia, April 2007. (poster)

C2. Wide-Angle SAR Image Formation with Migratory Scattering Centers and Regularization in Hough Space. Kush R. Varshney, Müjdat Çetin, John W. Fisher III, and Alan S. Willsky. The Fourteenth Annual Workshop on Adaptive Sensor Array Processing, Lexington, Massachusetts, June 2006.

C1. Joint Image Formation and Anisotropy Characterization in Wide-Angle SAR. Kush R. Varshney, Müjdat Çetin, John W. Fisher III, and Alan S. Willsky. SPIE Defense and Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XIII, Orlando (Kissimmee), Florida, April 2006. (poster)

Conference Presentations

P39. A Retrospective Study of Contraceptive Discontinuation Across Multiple Sub-Saharan African Countries. Victor Akinwande, Celia Cintas, Ehud Karavani, Megan MacGregor, Dennis Wei, Kush R. Varshney, and Pablo Nepomnaschy. Annual Meetings of the Human Biology Association, Reno, NV, April 2023.

P38. On the Duality of Transparency and Value Alignment and Their Mismatch. Kush R. Varshney. ACM Conference on Fairness, Accountability, and Transparency CRAFT Session on Emerging Problems: New Challenges in FAccT from Research, to Practice, to Policy, Seoul, Korea, June 2022.

P37. Racial Representation Analysis in Dermatology Academic Materials. Girmaw Abebe Tadesse, Celia Cintas, Roxana Daneshjou, Kush R. Varshney, Peter Staar, Skyler Speakman, Kenya Andrews, Chinyere Agunwa, Justin Jia, Elizabeth Bailey, Ademide Adelekun, Jules B. Lipoff, Ginikanwa Onyekaba, Veronica Rottemberg, and James Zou. AMIA Annual Symposium, San Diego, CA, October–November 2021.

P36. 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 Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. INFORMS Annual Meeting, Anaheim, CA, October 2021.

P35. Addressing the Design Needs of Implementing Fairness in AI via Influence Functions. Brianna Richardson and Kush R. Varshney. INFORMS Annual Meeting, Anaheim, CA, October 2021.

P34. Quantifying Education-Occupation Alignment Through Natural Language Processing. Renzhe Yu, Subhro Das, Sairam Gurajada, Kush R. Varshney, Hari Raghavan and Carlos X. Lastra-Anadon. International Conference on Computational Social Science, July 2021.

P33. Building Trustworthy AI Systems. Kush R. Varshney. Department of Defense Artificial Intelligence Symposium, June 2021.

P32. Human Cognitive Biases in Interpreting Machine Learning. Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, and Richard Tomsett. INFORMS Annual Meeting, National Harbor, MD, November 2020.

P31. Empirical and Theoretical Relationships Among Pillars of Trustworthy Machine Learning. Kush R. Varshney and Moninder Singh. INFORMS Annual Meeting, National Harbor, MD, November 2020.

P30. Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, and Laura B. Kleiman. New York Academy of Sciences Natural Language, Dialog and Speech Symposium, New York, NY, November 2019.

P29. Rigorous Analysis of Racial Bias in Gender Classification. Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilović, and Kush R. Varshney. Women in Machine Learning Workshop, Montreal, Canada, December 2018.

P28. SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy. Minh N. B. Nguyen, Samuel Thomas, Kush R. Varshney, Sujatha Kashyap, and Anne E. Gattiker. Data for Good Exchange, New York, NY, September 2018.

P27. Decision Support for Policymaking: Causal Inference Algorithm and Case Study. Bhanukiran Vinzamuri, Aleksandra Mojsilović, and Kush R. Varshney. Workshop on Mechanism Design for Social Good, Ithaca, NY, June 2018.

P26. How to Foster Innovation: A Data-Driven Approach to Measuring Economic Competitiveness. Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao, Chandra Reddy, Aleksandra Mojsilović, and Kush R. Varshney. Workshop on Mechanism Design for Social Good, Ithaca, NY, June 2018.

P25. Decision Making With Quantized Priors Leads to Discrimination. Lav R. Varshney and Kush R. Varshney. Illinois Summit on Diversity in Psychological Science, Champaign, IL, March, 2018.

P24. A Social Good Program at an Industrial Research Laboratory. Kush R. Varshney. American Association for the Advancement of Science Annual Meeting, Austin, TX, February, 2018.

P23. SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy. Minh N. B. Nguyen, Samuel Thomas, Anne E. Gattiker, Sujatha Kashyap, and Kush R. Varshney. Women in Machine Learning Workshop, Long Beach, CA, December 2017.

P22. Machine Learning from Health Insurance Administrative Data: Opioids, Obamacare, and Other Applications. Kush R. Varshney. Administrative Data Research Facilities Network Conference, Washington, DC, November 2017.

P21. Interpretable Two-Level Boolean Rule Learning. Guolong Su, Dennis Wei, Kush R. Varshney, and Dmitry M. Malioutov. INFORMS Annual Meeting, Houston, TX, October 2017.

P20. Semantic Searching for Efficient Assessment of Sustainable Development in National Plans. Jonathan Galsurkar, Aditya Vempaty, Kush R. Varshney, Lingfei Wu, Mikhail Sushkov, Moninder Singh, Devika Iyer, and Serge Kapto. Data Science for Social Good Conference, Chicago, IL, September 2017.

P19. Cognitive Disease Hunter: Developing Automated Pathogen Feature Extraction from Scientific Literature. Timothy NeCamp, Prasanna Sattigeri, Dennis Wei, Emily Ray, Youssef Drissi, Ananya Poddar, Diwakar Mahajan, Sarah Bowden, Barbara A. Han, Aleksandra Mojsilović, and Kush R. Varshney. Data Science for Social Good Conference, Chicago, IL, September 2017.

P18. Demystifying Social Entrepreneurship: An NLP Based Approach to Finding a Social Good Fellow. Aditya Garg, Alexandra Olteanu, Richard B. Segal, Dmitriy A. Katz-Rogozhnikov, Keerthana Kumar, Joana Maria, Liza Mueller, Ben Beers, and Kush R. Varshney. Data Science for Social Good Conference, Chicago, IL, September 2017.

P17. Decision Support for Policymaking: Causal Inference Algorithm and Case Study. Bhanukiran Vinzamuri, Aleksandra Mojsilović, and Kush R. Varshney. Data Science for Social Good Conference, Chicago, IL, September 2017.

P16. A Bayesian Approach for Predicting Neotropical Primate Reservoirs of Zika Virus. Subhabrata Majumdar, Dennis Wei, Adam Perer, Benjamin Glicksberg, Aleksandra Mojsilović, Kush R. Varshney, and Barabara A. Han. International Symposium on Business and Industrial Statistics, Yorktown Heights, New York, June 2017.

P15. Persistent Homology of Classifier Decision Boundaries. Kush R. Varshney and Karthikeyan Natesan Ramamurthy. STOC/SoCG Workshop on Geometry and Machine Learning, Cambridge, Massachusetts, June 2016.

P14. Learning Interpretable Clinical Prediction Rules using Threshold Group Testing. Amin Emad, Kush R. Varshney, and Dmitry M. Malioutov. NIPS Workshop on Machine Learning in Healthcare, Montréal, Canada, December 2015.

P13. Assessing Expertise in the Enterprise: The Recommender Point of View. Aleksandra Mojsilović and Kush R. Varshney. ACM Recommender Systems Conference, p. 231, Vienna, Austria, September 2015.

P12. Learning Interpretable Classification Rules via Boolean Compressed Sensing. Dmitry M. Malioutov, Kush R. Varshney, and Sanjeeb Dash. Joint Statistical Meetings, Seattle, Washington, August 2015.

P11. Personalization of Product Novelty Assessment via Bayesian Surprise. Nan Shao, Kush R. Varshney, Lav R. Varshney, Florian Pinel, Anshul Sheopuri, and Pavankumar Murali. Joint Statistical Meetings, Boston, Massachusetts, August 2014.

P10. Talent Analytics to Predict Employee Job Roles and Skill Sets Using Diverse Data Sources. Kush R. Varshney, Vijil Chenthamarakshan, Jun Wang, Dongping Fang, and Aleksandra Mojsilović. International Symposium on Business and Industrial Statistics and Conference of the ASA Section on Statistical Learning and Data Mining, Durham, North Carolina, June 2014.

P9. Sales Team Effort Allocation Analytics. Ban Kawas, Mark S. Squillante, Dharmashankar Subramanian, and Kush R. Varshney. INFORMS Annual Meeting, Minneapolis, Minnesota, October 2013.

P8. Dynamic Factor Modeling via Robust Subspace Tracking. Aleksandr Y. Aravkin, Kush R. Varshney, and Dmitry M. Malioutov. Industrial-Academic Workshop on Optimization in Finance and Risk Management, Toronto, Canada, September 2013.

P7. More Contentious Issues Lead to More Factions: Bounded Confidence Opinion Dynamics of Bayesian Decision Makers. Kush R. Varshney. Interdisciplinary Workshop on Information and Decision in Social Networks, Cambridge, Massachusetts, November 2012.

P6. Proactive Retention. Yingdong Lu, Aleksandra Mojsilović, Moninder Singh, Mark S. Squillante, Kush R. Varshney, and Jun Wang. INFORMS Annual Meeting, Phoenix, Arizona, October 2012.

P5. Heterogeneous Graphs in Social Business. Kush R. Varshney, Jun Wang, and Aleksandra Mojsilović. Graph Exploitation Symposium, Dedham, Massachusetts, April 2012.

P4. Map of the Marketplace: Visualizing the Relationships in the IT Services Marketplace. Aleksandra Mojsilović, Kush R. Varshney, and Jun Wang. INFORMS Annual Meeting, Charlotte, North Carolina, November 2011.

P3. Identifying Optimal Sales Team Composition for Business Opportunities. Aleksandra Mojsilović, Moninder Singh, Kush R. Varshney, and Jun Wang. INFORMS Annual Meeting, Charlotte, North Carolina, November 2011.

P2. Classification of IT Service Tickets for Defect Prevention. Kush R. Varshney, Dongping Fang, Aliza R. Heching, Aleksandra Mojsilović, and Moninder Singh. INFORMS Annual Meeting, Charlotte, North Carolina, November 2011.

P1. A Framework for Sales Force Productivity Profile Estimation. Mayank Sharma, Aleksandra Mojsilović, Moninder Singh, and Kush R. Varshney. INFORMS Annual Meeting, Austin, Texas, November 2010.

Theses and Technical Reports

T6. Trustworthiness and Responsibility in AI – Causality, Learning, and Verification. Vaishak Belle, Hana Chockler, Shannon Vallor, Kush R. Varshney, Joost Vennekens, and Sander Beckers. Report from Dagstuhl Seminar 24121, March 2024.

T5. Trustworthy AI Practitioner Requirements. Phaedra Boinodiris, Mayan Murray, Martin Wiertz, Bernard Freund, Graham White, Kim Holmes, Heather Hagerty, Michael Vössing, Heather Domin, Ulrike Zeilberger, Sheri Feinzig, James Winters, Michael Flores, Boz Handy Bosma, Georg Olowsen, Herman Colquhoun, Jr., Jill Villany, Zuzana Leova, Kush Varshney, Bill Dusch, Dai Lins, and Jurriaan Parie. Technical Report ARB 211320a. IBM Academy of Technology, September 2022.

T4. Frugal Hypothesis Testing and Classification. Kush R. Varshney. Ph.D. Thesis. Massachusetts Institute of Technology, Cambridge, Massachusetts, 2010.

T3. The Feature Analysis of Flow-Based Statistics for Network Traffic Classification. Kelley B. Herndon Ford, Kush R. Varshney, Tracy D. Lemmond, William G. Hanley, Barry Y. Chen, and William C. Kallander. Technical Report. Lawrence Livermore National Laboratory, Livermore, California, September 2009.

T2. Surface Evolution for 3-D Shape Reconstruction of Knee Joint Prosthetics and Bones. Kush R. Varshney and Nikos Paragios. Technical Report 0703. Laboratoire MAS, École Centrale Paris, Châtenay-Malabry, France, February, 2007.

T1. Joint Anisotropy Characterization and Image Formation in Wide-Angle Synthetic Aperture Radar. Kush R. Varshney. Master's Thesis. Massachusetts Institute of Technology, Cambridge, Massachusetts, 2006.

Prefaces

F3. AI Ethics. Sameep Mehta, Francesca Rossi, and Kush R. Varshney. IBM Journal of Research and Development, vol. 63, issue 4/5, July/September 2019.

F2. Advances in Computational Creativity Technology. Richard Goodwin, Kush R. Varshney, and Jinjun Xiong. IBM Journal of Research and Development, vol. 63, issue 1, January/February 2019.

F1. Data Science for Social Good. Aleksandra Mojsilović and Kush R. Varshney. IBM Journal of Research and Development, vol. 61, issue 6, November-December 2017.

Online Contributions

O22. Guest. Kush R. Varshney. MLOps Community Podcast, September 21, 2022.

O21. Guest. Kush R. Varshney. Computer Science Chats, April 27, 2022.

O20. Guest, Trustworthy Machine Learning. Kush R. Varshney. Pipeline Conversations Podcast, ZenML, April 14, 2022.

O19. The Toolkit Approach to Trustworthy AI. Kush R. Varshney. Open Data Science Blog, April 13, 2022.

O18. The Watsons Meet Watson: A Call for Carative AI. Kush R. Varshney. Montreal AI Ethics Institute Blog, March 23, 2022.

O17. Guest, Episode 82. Kush R. Varshney. Artificial Intelligence and You, January 10, 2022.

O16. The Lifecycle View of Trustworthy AI. Olivia Buzek and Kush R. Varshney. IBM Data Science in Practice Blog, October 22, 2021.

O15. AIX360 Meets CodeFlare: How to Scale Explainability Using CodeFlare Pipelines. Carlos Costa, Amit Dhurandhar, Raghu Ganti, Mudhakar Srivatsa, and Kush R. Varshney. CodeFlare Blog, September 29, 2021.

O14. AI Governance: Gain Control Over the AI Lifecycle. Kush R. Varshney. Linux Foundation AI & Data Blog, September 22, 2021.

O13. Foundations of Trustworthy AI: How to Conduct Trustworthy AI Assessment and Mitigation. Kush R. Varshney. Watson Blog, June 3, 2021.

O12. What You Cannot Miss in Any AI Implementation: Fairness. Kush R. Varshney. Linux Foundation AI & Data Blog, May 19, 2021.

O11. IBM Research LIVE: Responsible AI. Kush R. Varshney. May 13, 2021.

O10. Guest, Can We Trust AI. Kush R. Varshney. In AI We Trust?, April 30, 2021.

O9. Guest, Machine Learning and Trustworthy AI. Kush R. Varshney. Making Data Simple, December 9, 2020.

O8. Guest, AI for Good and the Real World. Kush R. Varshney. Talking Machines, Season 4, Episode 19, September 18, 2018.

O7. Introducing AI Fairness 360. Kush R. Varshney. IBM Research Blog, September 19, 2018.

O6. AI for Code Encourages Collaborative, Open Scientific Discovery. Kush R. Varshney. IBM Research Blog, August 15, 2018.

O5. New AI Algorithm Recommends Right Products at the Right Time. Kush R. Varshney. IBM Research Blog, December 8, 2017.

O4. Reducing Discrimination in AI with New Methodology. Kush R. Varshney. IBM Research Blog, December 6, 2017.

O3. Social Good, Meet Data Science. Kush R. Varshney. IBM Big Data & Analytics Hub Blog, February 16, 2016.

O2. For Analytics to Have an Impact, Keep it Simple. Tyler H. McCormick, Cynthia Rudin, Dmitry Malioutov, and Kush Varshney. Data Informed, August 3, 2015.

O1. Statistical Analysis of Serving and Breaking First in Tennis. Kush Varshney. Jon Wertheim's Tennis Mailbag, October 30, 2013.

Patents

I18. Distributed Platform for Computation and Trusted Validation. Nelson Kibichii Bore, Michael Hind, Eleftheria K. Pissadaki, Ravi Kiran Raman, Sekou Lionel Remy, Roman Vaculin, and Kush R. Varshney. US 11,940,978, March 26, 2024.

I28. Initializing Optimization Solvers. Kartik Ahuja, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney. US 11,915,131, February 27, 2024.

I27. Input Encoding for Classifier Generalization. Brian E. D. Kingsbury, Kush R. Varshney, and Hazar Yueksel. US 11,914,678, February 27, 2024.

I20. System and Method for Post Hoc Improvement of Instance-Level and Group-Level Prediction Metrics. Manish Bhide, Pranay Lohia, Karthikeyan Natesan Ramamurthy, Ruchir Puri, Diptikalyan Saha, and Kush R. Varshney. JP 7,289,086, May 31, 2023. US 11,734,585, August 22, 2023.

I24. Mitigating Statistical Bias in Artificial Intelligence Models. Rachel K. E. Bellamy, Kush R. Varshney, and Yunfeng Zhang. US 11,586,849, February 21, 2023.

I21. Semantic Queries Based on Semantic Representation of Programs and Data Source Ontologies. Ioana M. Baldini Soares, Aleksandra Mojsilović, Evan J. Patterson, and Kush R. Varshney. US 11,520,830, December 6, 2022.

I25. Artificial Intelligence Certification of Factsheets Using Blockchain. Samuel C. Hoffman, Kalapriya Kannan, Pranay K. Lohia, Sameep Mehta, and Kush R. Varshney. US 11,483,154, October 25, 2022.

I23. Enhancing Fairness in Transfer Learning for Machine Learning Models with Missing Protected Attributes in Source or Target Domains. Supriyo Chakraborty, Amanda Coston, Ziarah Mustahsan, Karthikeyan Natesan Ramamurthy, Skyler Speakman, Kush R. Varshney, and Dennis Wei. US 11,443,236, September 13, 2022.

I22. Generation and Management of an Artificial Intelligence (AI) Model Documentation Throughout Its Life Cycle. Matthew R. Arnold, Rachel K. E. Bellamy, Kaoutar El Maghraoui, Michael Hind, Stephanie Houde, Kalapriya Kannan, Manish Kesarwani, Sameep Mehta, Aleksandra Mojsilović, Ramya Raghavendra, Darrell C. Reimer, John T. Richards, David J. Piorkowski, Jason Tsay, and Kush R. Varshney. US 11,263,188, March 1, 2022.

I15. Tracking the Evolution of Topic Rankings from Contextual Data. Mary E. Helander, Hemank Lamba, Nizar Lethif, Joana S. B. T. Maria, Emily A. Ray, and Kush R. Varshney, US 11,244,013, February 8, 2022.

I19. Distributed Platform for Computation and Trusted Validation. Nelson Kibichii Bore, Michael Hind, Eleftheria K. Pissadaki, Ravi Kiran Raman, Sekou Lionel Remy, Roman Vaculin, and Kush R. Varshney. US 11,212,076, December 28, 2021.

I8a. Computing Personalized Probabilistic Familiarity Based on Known Artifact Data. Florian Pinel, Nan Shao, Kush R. Varshney, and Lav R. Varshney. US 11,107,008, August 31, 2021.

I17. Distributed Platform for Computation and Trusted Validation. Nelson K. Bore, Michael Hind, Eleftheria K. Pissadaki, Ravi Kiran Raman, Sekou L. Remy, Roman Vaculin, and Kush R. Varshney. US 11,032,063, June 8, 2021.

I10. Representation of a Data Analysis Using a Flow Graph. Ioana M. Baldini Soares, Aleksandra Mojsilović, Evan J. Paterson, and Kush R. Varshney. US 10,891,326, January 12, 2021.

I14. Node Relevance Determination in an Evolving Network. Mary E. Helander, Hemank Lamba, Nizar Lethif, Joana S. B. T. Maria, Emily A. Ray, and Kush R. Varshney. US 10,756,977, August 25, 2020.

I11. Humanitarian Crisis Analysis Using Secondary Information Gathered by a Focused Web Crawler. Ioana M. Baldini Soares, Amit Dhurandhar, Abhishek Kumar, Aleksandra Mojsilović, Kien T. Pham, Kush R. Varshney, and Maja Vukovic. US 10,740,860, August 11, 2020.

I13. Recommendation Prediction Based on Preference Elicitation. Aleksandra Mojsilović, Kush R. Varshney, Jun Wang, and Jinfeng Yi. US 10,678,800, June 9, 2020.

I4. Method, System and Computer Program Product for Automating Expertise Management Using Social and Enterprise Data. John H. Bauer, Dongping Fang, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, and Jun Wang. US 10,643,140, May 5, 2020.

I16. Generating Semantic Flow Graphs Representing Computer Programs. Ioana M. Baldini Soares, Aleksandra Mojsilović, Evan J. Paterson, and Kush R. Varshney. US 10,628,282, April 21, 2020.

I5. Generating Work Products Using Work Product Metrics and Predicted Constituent Availability. Debarun Bhattacharjya, Kush R. Varshney, and Lav R. Varshney. US 10,467,638, November 5, 2019.

I12. Accelerating Data-Driven Scientific Discovery. Flavio P. Calmon and Kush R. Varshney. US 10,388,039, August 20, 2019.

I7. Association-Based Product Design. Kush R. Varshney, Lav R. Varshney, and Jun Wang. US 10,019,689, July 10, 2018.

I8. Computing Personalized Probabilistic Familiarity Based on Known Artifact Data. Florian Pinel, Nan Shao, Kush R. Varshney, and Lav R. Varshney. US 9,852,380, December 26, 2017.

I3. Active Odor Cancellation. Kush R. Varshney and Lav R. Varshney. US 9,600,793, March 21, 2017.

I2. Food Steganography. Kush R. Varshney and Lav R. Varshney. US 9,417,221, August 16, 2016.

I1. Predictive and Descriptive Analysis on Relations Graphs with Heterogeneous Entities. Aleksandra Mojsilović, Kush R. Varshney, and Jun Wang. US 9,195,941, November 24, 2015.

Patent Applications

A34. System Development Incorporating Ethical Context. Venkata N. Pavuluri, John Rofrano, Kush R. Varshney, and Maja Vukovic. 18/392,405, filed December 21, 2023.

A33. Mitigating the Influence of Biased Training Instances Without Refitting. Pierre L. Dognin, Soumya Ghosh, Inkit Padhi, Prasanna Sattigeri, and Kush R. Varshney. 18/045,253, filed October 10, 2022.

A32. Sufficiency Assessment of Machine Learning Models Through Maximum Deviation. Elizabeth Daly, Amit Dhurandhar, Michael Hind, Rahul Nair, Moninder Singh, Kush R. Varshney, and Dennis Wei. 17/931,803, filed September 13, 2022.

A31. Interpretable Neural Architecture Using Continued Fractions. Amit Dhurandhar, Tejaswini Pedapati, Isha Puri, Karthikeyan Shanmugam, Kush R. Varshney, and Dennis Wei. 17/806,188, filed June 9, 2022.

A30. Input-Encoding with Federated Learning. Pradip Bose, Supriyo Chakraborty, Brian E. D. Kingsbury, Kush R. Varshney, Augusto Vega, Dinesh C. Verma, Ashish Verma, Shiqiang Wang, and Hazar Yueksel. 17/239,812, filed April 26, 2021.

A29. Learning Robust Predictors Using Game Theory. Kartik Ahuja, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney. 17/115,489, filed December 8, 2020.

A26. Modeling External Event Effects Upon System Variables. Debarun Bhattacharjya, Tian Gao, Nicholas S. Mattei, Karthikeyan Shanmugam, Dharmashankar Subramanian, and Kush R. Varshney. 16/942,842, filed July 30, 2020.

A9. Method for Market Risk Assessment for Healthcare Applications. Shilpa Mahatma, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Dennis Wei, and Gigi Yuen-Reed. 14/699,482, filed April 29, 2015.

A6. Nonparametric Tracking and Forecasting of Multivariate Data. Aleksandr Y. Aravkin, Dmitry M. Malioutov, and Kush R. Varshney. 14/480,704, filed September 9, 2014, abandoned, July 24, 2018.