Servant-leader of teams that develop software products using AI, machine learning, NLP, computer vision and related technologies. Recipient of the 2019 ACM SIGKDD lifetime service award for distinguished contributions to the principles and practice of data mining and for outstanding service to society through the development of machine learning products for the healthcare & life science industries.
Technologies: AI, Machine Learning, Data Mining, Big Data, Data Science, Statistics, Image Processing, Natural Language Processing, Information Extraction.
LinkedIn: AI strategy for the company, Agentic AI platform, Agentic Search, Generative Recommenders, Responsible AI, Supervised Fine Tuning, Reinforcement learning, Large scale LP solvers for Ads marketplace
Pinterest: Content Ingestion (scheduling, crawling, scraping, parsing, distribution, proving causal impact from acquisition on top line metrics like MAU etc), Trust & Safety (detection and removal of unsafe content and users, Pinner Identity and access management, compliance with legal regulations), Content Quality (Signals for pin, page cohesion, landing page and image quality), Spam detection, Knowledge/Product Graph, Content Understanding (Pins, Board, image, text, product web pages etc), Whole Page Optimization, and User Modeling for site-wide personalization.
Health Products: Computer Aided Detection/Diagnosis, Personalized Therapy Selection, Evidence-Based Care, Patient Self-Management, Care-Team Coordination, Team Documentation.
Life Science Products: Real World Evidence, Patient Recruitment for Clinical Trials, Pharmacovigilance, Health Economics Outcomes Research / Comparative Effectiveness research, Drug Discovery.
EMail: FirstName Dot LastName At GMail
Ph. D. in Electrical & Computer Engineering, Duke University, Sept '04
Dissertation: Adaptive classifier design using labeled and unlabeled data
MS in Electrical & Computer Engineering, Duke University, June '01
Thesis: Multi-aspect target detection in SAR imagery
B.Tech. in Electrical Engineering, Indian Institute of Technology (IIT), Kharagpur, India, June '99
Dissertation: Wavelet Vector-Quantization based still grayscale Image Compression
ACM SIGKDD lifetime service award for distinguished contributions to data mining and for outstanding service to society, 2019.
ACM SIGKDD Data Mining Case Studies Award for the Best Deployed Industrial Data Mining Application, 2009
Best Paper Award, International Workshop on Digital Mammography, IWDM, 2008
IMO Math Olympiad Scholarship awarded by the Dept. of Atomic Energy, Govt. of India,'95-'99
National Science Talent Scholarship (NTSE) awarded by Govt. of India'94-'99
VP Engineering, Core AI, LinkedIn, Oct 2025 - Present.
VP Engineering, Head of Content Engineering and Core Data Science, Pinterest, Oct 2019 - Oct 2025.
Director & Distinguished Engineer, IBM Watson Health, Feb 2015 - Sept 2019.
Director & Principal Scientist, Siemens Medical Solutions USA, Nov 2004 - Jan 2015
Research-Intern, Microsoft Research Ltd. (Cambridge, UK), summer '03
Intern, ITC Ltd., the Indian subsidiary of BAT plc. (India), summer '98
Organization of International Research Conferences
Applied Data Science Invited Keynote Chair, ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 26
Sponsorships Chair, The 30th Web Conference, Apr 21
Sponsorships Chair, 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 20
Sponsorships Chair, 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 18
General Chair, 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 16
Sponsorships Chair, 21st ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 15
Sponsorships Chair, 20th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug 14
Workshop chair, SIAM International Conference on Data Mining, Apr 13
Sponsorships Chair, 18th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Beijing, China, Aug 12
Chair, 2nd ACM SIGHIT International Health Informatics Symposium Panel on "Increasing Data Availability & the Changing Focus of Healthcare Analytics Research: Trends, Opportunities and Gaps", Miami, FL, Jan 12
Vice Chair of the Program Commitee, IEEE International Conference on Data Mining, Vancouver, Dec 11
Advisor to the White House, Office of Science & Technology Policy, FNIH & NCI on new competition-based approaches to funding clinical research to accomplish policy goals, Aug 11
Sponsorships Chair, 17th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, Aug 11
Assoc General Chair, 16th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Washington, DC, Jul 10
Workshop Organization in International Research Conferences
Co-organizer, NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare. Dec 13
Co-organizer, NIPS 2011 Workshop, From statistical genetics to predictive models in personalized medicine, Dec 11
Co-organizer, The ACM SIGKDD Workshop on Data Mining for Medicine & Healthcare, San Diego, CA, Aug 11
Co-organizer,The ICML 2011 Workshop on Learning from Unstructured Clinical Text, Bellevue, WA, Jun 11
Co-organizer, The NIPS 2010 Workshop on Predictove Modeling for Personalized Medicine, Vancouver, Dec 10
Co-organizer, The NIPS 2008 Workshop on Cost Sensitive Learning, Vancouver, Dec 08
Co-organizer, The Annual International Data Mining Competition, KDD Cup-2008, Las Vegas, NV, Aug 08
Co-organizer, The ACM SIGKDD Workshop on Mining Medical Data, Las Vegas, NV, Aug 08
Editing Journals
Section Editor, Journal of Pattern Recognition Research, Aug 08-present
Member of Editorial Board, Open Electrical and Electronic Engineering Journal, ’06-present
Reviewer for International Research Journals,'02-present
Journal of Machine Learning Research, Machine Learning Journal, IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Knowledge and Data Engineering, Neurocomputing, IEEE Trans. Neural networks, IEEE Signal Processing Letters, IEEE Trans. Signal Processing, EURASIP Journal on Applied Signal Processing, IEEE Trans. Circuits and Systems (B), Bioinformatics, IEEE/ACM Trans. Computational Biology and Bioinformatics, Technometrics, Computer Methods & Programs in Medicine, Journal of Computer Science & Technology (Chinese Academy of Sciences)
Reviewer for International Research Conferences (only selected conferences listed), '04-present
Pacific Symposium on Biocomputing (PSB), International Conference on Machine Learning (ICML), International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), Neural Information Processing Systems (NIPS), International Conference on Artificial Intelligence and Statistics (AISTATS).
R. Bharat Rao, Balaji Krishnapuram, Andrew Tomkins, and Qiang Yang (editors), Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, ACM Press, 2010.
Balaji Krishnapuram, Shipeng Yu, R. Bharat Rao (editors), Cost-Sensitive Machine Learning, CRC Press, Jan 2012.
Balaji Krishnapuram, Mohak Shah, Alex Smola, Charu Aggarwal, Rajeev Rastogi, Dou Shen, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, ACM 2016.
Balaji Krishnapuram, Lawrence Carin, and Alexander Hartemink, “Gene expression analysis: Joint feature selection and classifier design,” in Kernel Methods in Computational Biology, B. Scholkopf, K. Tsuda, and J.-P. Vert (editors), pp. 299-318, MIT press, 2004.
Balaji Krishnapuram, Jefferey Sichina, and Lawrence Carin, “Physics based detection of targets in SAR imagery using support vector machines,” IEEE Sensors Journal, Vol. 3, No. 2, pp. 147-158, April 2003.
Balaji Krishnapuram, Lawrence Carin, and Alexander Hartemink, “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data,” Journal of Computational Biology, Vol. 11, pp 227--242, March 2004.
Balaji Krishnapuram, Alexander Hartemink, Lawrence Carin, and Mario Figueiredo, “A Bayesian approach to joint feature selection and classifier design,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26, No. 9, pp. 1105-1111, September 2004.
Yijun Yu, Balaji Krishnapuram, and Lawrence Carin, “Inverse scattering with sparse Bayesian vector regression,” Inverse Problems, Special Issue on Electromagnetic Characterization of Buried Obstacles, Vol. 20, No. 6, pp. S217-S231, December 2004.
Balaji Krishnapuram, Lawrence Carin, Mario Figueiredo, and Alexander Hartemink, “Sparse multinomial logistic regression: fast algorithms, and generalization bounds,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 27, No. 6, pp. 957-968, June 2005.
Shihao Ji, Balaji Krishnapuram, and Lawrence Carin, “Variational Bayes for continuous hidden Markov models and its application to active learning,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28, No 4, pp. 522-532, April 2006.
Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “Multi-Task learning for classification with Dirichlet process priors,” Journal of Machine Learning Research, Vol 8, pp 33-63, Jan 2007.
David Williams, Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “On incomplete data classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, pp. 427-436, Mar 2007.
R. Seigneuric, M.H.W. Starmans, G. Fung, Balaji Krishnapuram, D.S.A. Nuyten, A. van Erk, M.G. Magagnin, K.M. Rouschop, S. Krishnan, R. Bharat Rao, C.T.A. Evelo, A.C. Begg, B.G. Wouters, P. Lambin, “Impact of a supervised gene signature of early hypoxia on patient survival”, Radiotherapy & Oncology, Vol. 83 , No. 3 , pp. 374-382, 2007.
Glenn Fung, Murat Dundar, Balaji Krishnapuram, and R. Bharat Rao, “Multiple instance learning via alternate optimization,” IEEE Trans. Biomedical Engineering, Vol. 55, No. 3, pp. 1015-1021, Mar 2008.
Vikas Raykar, Ramani Duraiswami, and Balaji Krishnapuram, “Efficient algorithms for learning preference relations,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 30, No. 7, pp. 1158-1170, Jul 2008.
R. Bharat Rao, Oksana Yakhnenko, Balaji Krishnapuram, “KDD cup 2008 and the workshop on mining medical data,” ACM SIGKDD Explorations, Vol. 10, No. 2, pp. 34-38, Dec 2008.
M.H.W. Starmans, B. Krishnapuram, H. Steck, D.S.A. Nuyten, R. Seigneuric, F.M. Buffa, A.L. Harris, B.G. Wouters, P. Lambin, “Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types”, the British Journal of Cancer, Vol. 99, pp. 1884-1890, Dec 2008.
Volkan Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer Dy, “Using local dependencies within batches to improve large margin classifiers,“ Journal of Machine Learning Research, Vol. 10, pp. 183-206, Feb 2009.
Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao,"Bayesian Co-Training,“ Journal of Machine Learning Research, Vol. 12, pp. 2649−2680, Sep 2011.
S. Yu, F. Farooq, A. van Esbroeck, G. Fung, V. Anand, and B. Krishnapuram, “Predicting Readmission risk with Institutions Specific Prediction Models,“ Journal of Artificial Intelligence in Medicine, Vol 65(2) pp. 89-96, 2015.
Eric Jones, Jiangqi He, Balaji Krishnapuram, John Pormann, John A. Board, and Lawrence Carin, “An electromagnetic simulation and SAR processing environment,” 2001 SPIE AeroSense Conference, Proceedings of SPIE, Vol. 4367, Orlando, FA, April 2001.
Balaji Krishnapuram and Lawrence Carin, “Support vector machines for improved multi-aspect target recognition using the fisher kernel scores of hidden markov models,” 2002 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 3, pp 2989-2992, IEEE Press, Orlando, FA, May 2002.
Balaji Krishnapuram, Lawrence Carin and Alexander J. Hartemink, “Applying logistic regression and RVM to achieve accurate probabilistic cancer diagnosis from gene expression profiles,” 2002 IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS) , IEEE Press, Raleigh, NC, October 2002.
Balaji Krishnapuram, Lawrence Carin and Alexander J. Hartemink, “Joint classifier and feature optimization for cancer diagnosis using gene expression data,” The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB) 2003, ACM press, Berlin, Germany, April 2003.
Qiuhua Liu, Balaji Krishnapuram, Pallavi Pratapa, Xuejun Liao, Alexander Hartemink and Lawrence Carin, “Identification of differentially expressed proteins using MALDI-TOF mass spectra,” 2003 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2003.
Xuejun Liao, Hui Li and Balaji Krishnapuram, “An M-ary KMP classifier for multi-aspect target classification,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004.
Balaji Krishnapuram, Christopher Bishop and Martin Szummer, “Generative Bayesian models for shape recognition,” The Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR-9), Tokyo, Japan, October 2004.
Balaji Krishnapuram, David Williams, Ya Xue, Lawrence Carin, Alexander Hartemink, and Mario Figueiredo, “On semi-supervised classification,” Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2004.
Balaji Krishnapuram, David Williams, Ya Xue, Lawrence Carin, Mario Figueiredo, and Alexander Hartemink, “Active learning of features and labels,” Workshop on learning with multiple views at the 22nd International Conference on Machine Learning (ICML), Bonn, Germany, August 2005
Glenn Fung, Romer Rosales, and Balaji Krishnapuram, “Learning rankings via convex hull separation,” Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2005.
Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram, “Learning multiple classifiers with Dirichlet process mixture priors,” Workshop on Nonparametric Bayesian methods, Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2005.
Glenn Fung, Balaji Krishnapuram, Nicolas Merlet, Eli Ratner, Phlippe Bamberger, Jonathan Stoeckel and R. Bharat Rao, “Addressing image variability while learning classifiers for detecting clusters of micro-calcifications,“ International Workshop on Digital Mammography (IWDM), Manchester, UK, June 2006
Volkan Vural, Glenn Fung, Balaji Krishnapuram, and Jennifer Dy, “Batch Classification with applications to Computer Aided Diagnosis,” European Conference on Machine Learning (ECML), Berlin, Germany, August 2006.
Vivian van den Boogaart, Annemarie Dingemans, Victor Thijssen, Robert-Jan van Suylen, Balaji Krishnapuram, Arjan Griffioen, “Angiogenesis gene expression profiling as prognostic marker in non-small cell lung cancer”, October 2006.
Glenn Fung, Murat Dundar, Balaji Krishnapuram, and R. Bharat Rao, “Multiple Instance Algorithms for Computer Aided Diagnosis,” Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2006.
Murat Dundar, Balaji Krishnapuram, Jinbo Bi, and R. Bharat Rao, “Learning classifiers when the training data is not IID,” International Joint Conference on Artificial Intelligence (IJCAI) January 2007.
Vikas Raykar, Ramani Duraiswami, and Balaji Krishnapuram, “A fast algorithm for learning large scale preference relations,” International Conference on Artificial Intelligence and Statistics (AISTATS), Puerto Rico, March 2007.
Murat Dundar, Balaji Krishnapuram, Matthias Wolf, Sarang Lakare, Luca bogoni, Jinbo Bi, R. Bharat Rao, “Training a CAD classifier with correlated data,” Proceedings of SPIE, March 2007
Balaji Krishnapuram, C. Dehing, H. Steck, H. van der Weide, D. De Ruysscher, B. Nijsten, S. Wanders, L. Boersma, R.B. Rao, and Ph. Lambin, “A knowledge-model for predicting radiation-induced Esophagitis,“ 49th Annual meeting of the American Society for Therapeutic Radiology and Oncology (ASTRO), Nov 2007.
Shipeng Yu, Balaji Krishnapuram, Romer Rosales, Harald Steck, and R. Bharat Rao, “Bayesian Co-training,” Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2007.
Vikas Raykar, Harald Steck, Balaji Krishnpuram, Cary Dehing-Oberije, and Philippe Lambin, “On ranking in survival analysis: bounds on the concordance index,” Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2007.
Balaji Krishnapuram, Jonathan Stoeckel, Vikas Raykar, R. Bharat Rao, Philippe Bamberger, Eli Ratner, Nicolas Merlet, Inna stainvas, Menahem Abramov, and Alexandra Manevitch, “Multiple instance learning improves CAD detection of masses in digital mammography,“ International Workshop on Digital Mammography (IWDM), Tucson, AZ, June 2008.
Isaac Leichter, Richard Lederman, Eli Ratner, Nicolas Merlet, Glenn Fung, Balaji Krishnapuram, and Philippe Bamberger, “Does a mammography CAD algorithm with varying filtering levels of detection marks, used to reduce the false mark rate, adversely affect the detection of small masses?,” International Workshop on Digital Mammography (IWDM), Tucson, AZ, June 2008.
Vikas Raykar, Balaji Krishnapuram, Murat Dundar, Jinbo Bi, and R. Bharat Rao “Bayesian multiple instance learning: automatic feature selection and inductive transfer,” 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Shipeng Yu, Balaji Krishnapuram, Romer Rosales, and R. Bharat Rao, “Active sensing,” International Conference on Artificial Intelligence and Statistics (AISTATS), Apr 2009.
Balaji Krishnapuram, R. Bharat Rao, Glenn Fung, Jinbo Bi, Murat Dundar, Vikas Raykar, Shipeng Yu, Sriram Krishnan, Xiang Zhou, Arun Krishnan, Marcos Salganicoff, Luca Bogoni, Matthias Wolf, Anna Jerebko, and Jonathan Stoeckel, “Mining Medical Images,” Data Mining Case Studies, 15th ACM SIGKDD Conference on Knowledge Discovery & Datamining (KDD), Paris, France, Aug 2009.
Faisal Farooq, Balaji Krishnapuram, Janine Yeater, and Neeraj Chopra, “Automatic Abstraction for CMS Quality Measures,” The Insitute of Healthcare Improvement’s 21st annual National Forum for Quality Improvement in Healthcare (IHI NQF 2009), Florida, Dec 2009
Vikas Raykar, Balaji Krishnapuram, and Shipeng Yu, “Designing efficient cascaded classifiers: Tradeoff between accuracy and cost,” 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), July 2010.
Romer Rosales, Faisal Farooq, Balaji Krishnapuram, Shipeng Yu, and Glenn Fung, “Automated Identification of Medical Concepts and Assertions in Medical Text,” 2010 Annual Symposium of the American Medical Informatics Association (AMIA), Nov 2010.
Faisal Farooq, Manavendar Malgireddy, Shipeng Yu, Balaji Krishnapuram, "Extractring Medication Information from Clinical Text", Workshop on Learning from Unstructured Clinical Text at the 28th International Conference on Machine Learning (ICML), Bellevue, WA, Jun 2011.
Faisal Farooq, Shipeng Yu, Balaji Krishnapuram, and R. Bharat Rao, "Leveraging Rich Annotations to Improve Learning of Medical Concepts from Clinical Free Text", 2011 Annual Symposium of the American Medical Informatics Association (AMIA), Nov 2011.
Faisal Farooq, Shipeng Yu, Balaji Krishnapuram, and R. Bharat Rao, "Knowledge Discovery System for Automated Quality Measure Abstraction," 2nd ACM SIGHIT International Health Informatics Symposium (IHI), Jan 2012.
Shipeng Yu, Faisal Farooq, Glenn Fung, Balaji Krishnapuram, Alexander Van Esbroeck, Vikram Anand, “Building Hospital-Specific Readmission Risk Prediction Models for Heart Failure, Acute Myocardial Infarction and Pneumonia patients”, 2012 Annual Symposium of the American Medical Informatics Association (AMIA), Nov 2012.
Faisal Farooq, Shipeng Yu, Balaji Krishnapuram, Vikram Anand, “Categorizing Medications from Unstructured Clinical Notes”, 2013 AMIA summit on Translational Bioinformatics, Mar 2013.
W. Wiessler, A. Dekker, G. Nalbantov, C. Oberije, M. Eble, W. Dries, L. Janvary, P. B ulens, B. Krishnapuram, P. Lambin, “Privacy-preserving, multi-centric machine learning across hospitals and countries: does it work?”, 2013 Conference of the European Society for Therapeutic Radiology and Oncology (ESTRO), Apr 2013.
A. Dekker, G. Nalbantov, C. Oberije, W. Wiessler, M. Eble, W. Dries, L. Janvary, P., B. Krishnapuram, P. Lambin, “Multi-centric learning with a federated IT infrastructure: application to 2-year lung-cancer survival prediction” 2013 Conference of the European Society for Therapeutic Radiology and Oncology (ESTRO), Apr 2013.
Shipeng Yu, Alexander van Esbroeck, Faisal Farooq, Glenn Fung, Vikram Anand and Balaji Krishnapuram, “Predicting Readmission Risk with Institution Specific Prediction Models”, ICHI 2013, Sept 2013.
Balaji Krishnapuram, "Rapid Learning Health Systems: Challenges and Technical Solutions", ACM Bioinformatics, Computational Biology and Health Informatics (BCB) conference, Sept 2014. Invited Industry Keynote Talk
Balaji Krishnapuram, "Leveraging Data & Knowledge to Transform Healthcare," HIMSS CIO Summit, Valencia, Spain, Oct 2015
Faisal Farooq and Balaji Krishnapuram, "Data Science 3.0: A paradigm change that empowers end users," Strata, San Jose, Mar 2016
Shenghua Bao and Balaji Krishnapuram, "Building AI products for Healthcare & Lifesciences," Invited Industry Talk at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Aug 2018
Jinfeng Zhuang, Jennifer Zhao, Anant Subramanian, Yun Lin, Balaji Krishnapuram and Roelof van Zwol, "PinText 2: Attentive Bag of Annotations Embedding," Workshop on Deep Learning Practice for High Dimensional Sparse Data in the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Aug 2020.
R. Bharat Rao, Romer Rosales, Stefan Niculescu, Sriram Krishnan, Luca Bogoni, Xiang S. Zhou, and Balaji Krishnapuram, “Mining medical records for Computer Aided Diagnosis,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA, August 2006
Murat Dundar, Balaji Krishnapuram, Glenn Fung, R. Bharat Rao, “Early Stage Cancer Diagnosis,” Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec 2006.