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


Publications and Patents

Journal and Magazine Articles

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)


Book Chapters

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.


Conference Papers

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, 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, 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, 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, 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

P21. 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.

P20. 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.

P19. Interpretable Two-Level Boolean Rule Learning. Guolong Su, Dennis Wei, Kush R. Varshney, and Dmitry M. Malioutov. INFORMS Annual Meeting, Houston, TX, October 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

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

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

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

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

A11. Accelerating Data-Driven Scientific Discovery. Flavio P. Calmon and Kush R. Varshney. 15/609,586, filed May 31, 2017.

A10. Representation of a Data Analysis Using a Flow Graph. Ioana M. Baldini Soares, Aleksandra Mojsilović, Evan J. Paterson, Kush R. Varshney. 15/399,420, filed January 5, 2017.

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.

A8. Computing Personalized Probabilistic Familiarity Based on Known Artifact Data. Florian Pinel, Nan Shao, Kush R. Varshney, and Lav R. Varshney. 14/587,021, filed December 31, 2014.

A7. Association-Based Product Design. Kush R. Varshney, Lav R. Varshney, and Jun Wang. 14/561,869, filed December 5, 2014.

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.

A5. Generating Work Products Using Work Product Metrics and Predicted Constituent Availability. Debarun Bhattacharjya, Kush R. Varshney, and Lav R. Varshney. 14/459,848, filed August 14, 2014.

A4. 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. 14/266,970, filed May 1, 2014.

J26. Copyright 2017 International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied by any means or distributed royalty free without further permission by computer-based and other information-service systems. Permission to republish any other portion of this paper must be obtained from the Editor.
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J21. Copyright 2017 Kush R. Varshney and Homa Alemzadeh. Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
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J15. Copyright 2015 Kush R. Varshney, George H. Chen, Brian Abelson, Kendall Nowocin, Vivek Sakhrani, Ling Xu, and Brian L. Spatocco. Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
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C53. Copyright 2016 Guolong Su, Dennis Wei, Kush R. Varshney, and Dmitry M. Malioutov. One print or electronic copy may be made for personal use only.
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C1. Copyright 2006 Society of Photo-Optical Instrumentation Engineers. This paper was published in the proceedings of the SPIE Defense and Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XIII and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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