Prem Melville

Ph.D.

TECH ENTREPRENEUR MACHINE LEARNING SPECIALIST

Publications
Activities
Patents

ABOUT PREM

Prem Melville is a tech entrepreneur and Data Science leader, with a Ph.D. in Machine Learning, and over 50 peer-reviewed publications, 7 patents and 10 international awards for his contributions to the fields of Machine Learning, Data Mining and Natural Language Processing.

He is currently the Head of Machine Learning at one of the largest multi-strategy global hedge funds. He was the founder and CEO of Social Alpha, a pioneer of social analytics solutions for financial markets. Prior to that, Prem led Data Science efforts at IBM Watson Labs, where he drove innovation in Machine Learning applications to analyzing social media, business analytics and e-commerce.

Prem led the development of a text analytics system for UNICEF, which won the INFORMS Innovative Applications in Analytics Award 2014 and was covered by the Wall Street Journal. His work on Amplifying the Voice of Youth in Africa won the Best Application Paper Award at KDD 2013. His team’s work on resource-efficient sequential decision making deployed in the New York State Government also won the Best Application Paper Award at KDD 2010, and was covered by CNN. Prem’s work in Recommender Systems was awarded the Classic Paper Award at AAAI 2019. His team has won several highly-competitive international data mining challenges, including the KDD Cup 2009, KDD Cup 2008 and the INFORMS Data Mining Contest 2008.

In 2014, Prem co-chaired the longest-running and biggest Data Science conference, KDD 2014. He also serves on the Editorial Board of Data Mining and Knowledge Discovery – the leading journal in the field. He has served on the organizing committees of the top conferences in the field – ICML, KDD, CIKM, WSDM and Mining and Learning with Graphs (MLG). Prem also organized the first workshop on Social Media Analytics (SOMA 2010) and the first workshop on Budgeted Learning.

Prem got his Ph.D. from the Department of Computer Sciences at the University of Texas at Austin. At UT, he was a member of the Machine Learning group, led by Raymond Mooney. Prior to that he graduated summa cum laude from Brandeis University, with degrees in Computer Science and in Math. At Brandeis, he worked at the DEMO Lab, Interaction Lab, and the Vision Lab, and was inducted into the Phi Beta Kappa honor society.

Prem’s research interests lie in Machine Learning, Data Mining and Natural Language Processing. He has published work in a broad range of topics, including social media analytics, sentiment analysis, emerging topic detection, assessing influence in networks, active learning, ensemble methods, active feature-value acquisition, dual supervision, recommender systems, class probability estimation, semi-supervised learning, text classification, and applications of data mining to analyzing social media, business analytics and e-commerce.

View More

Publications

  • Amplifying the Voice of Youth in Africa via Text Analytics. [PDF]
    Prem Melville, Vijil Chenthamarakshan, Richard Lawrence, James Powell et al.
    In Proceedings of the 19th Conference on Knowledge Discovery and Data Mining (KDD-13) , Chicago, IL, August, 2013.
    Best Paper Award (Application).
  • Novel Topic Detection on Massive Data Streams using Distributed Dictionary Learning. [PDF]
    Shiva Kasiviswanathan, Guojing Cong, Prem Melville, and Richard Lawrence.
    In IBM Journal of Research & Development, Vol. 57, No. 3/4, 2013.
  • Online L1-Dictionary Learning with Application to Novel Document Detection. [PDF][Supplementary]
    Shiva Kasiviswanathan, Huahua Wang, Arindam Banerjee, and Prem Melville.
    In Neural Information Processing Systems (NIPS-2012), Lake Tahoe, Nevada, December, 2012.
  • Tax Collections Optimization for New York State. [PDF]
    Naoki Abe, Prem Melville, Cezar Pendus, David Jensen et al.
    In Informs Interfaces, Vol. 42, No. 1, 2012.
  • Learning to Rank for Robust Question Answering [PDF]
    Arvind Agarwal, Hema Raghavan, Prem Melville, Karthik Subbian, Richard Lawrence, David Gondek, and James Fan.
    In Proceedings of the International Conference on Information and Knowledge Management (CIKM-2012), Maui, Hawaii, October, 2012.
  • Novel Document Detection using Online L1-Dictionary Learning. [PDF]
    Shiva Kasiviswanathan, Huahua Wang, Arindam Banerjee, and Prem Melville.
    In Proceedings of Social Media Analytics (SOMA-2012), Beijing, China, August, 2012.
  • Emerging Topic Detection using Dictionary Learning. [PDF]
    Shiva Kasiviswanathan, Prem Melville, Arindam Banerjee, and Vikas Sindhwani.
    In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM-2011), Glasgow, Scotland, October, 2011.
  • Supervised Rank Aggregation for Predicting Influence in Networks. [PDF]
    Karthik Subbian and Prem Melville.
    In Proceedings of the IEEE International Conference on Social Computing (SocialCom-2011), Boston, MA, October, 2011.
  • Concept Labeling: Building Text Classifiers with Minimal Supervision. [PDF]
    Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani and Richard D. Lawrence.
    In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona, Spain, July, 2011.
  • Selective Data Acquisition. [PDF]
    Josh Attenberg, Prem Melville, Foster Provost and Maytal Saar-Tsechansky.
    In Cost-Sensitive Machine Learning, B. Krishnapuram, S. Yu, and R.B. Rao (Eds), Chapman and Hall, 2011.
  • Proceedings of the First Workshop on Social Media Analytics. [ACM-DL]
    Prem Melville, Jure Leskovec and Foster Provost (Editors).
    ACM, New York, NY, USA, 2010.
  • A Predictive Perspective on Measures of Influence in Networks [PDF]
    Prem Melville, Karthik Subbian, Claudia Perlich, Richard Lawrence, and Estepen Meliksetian.
    In Proceedings of the Workshop on Information in Networks (WIN-10), New York, September, 2010.
  • A Unified Approach to Active Dual Supervision for Labeling Features and Examples. [PDF]
    Josh Attenberg, Prem Melville and Foster Provost.
    In Proceedings of the European Conference on Machine Learning (ECML-10), Barcelona, Spain, September, 2010.
  • Optimizing Debt Collections Using Constrained Reinforcement Learning. [PDF]
    Naoki Abe, Prem Melville, Cezar Pendus, Chandan Reddy, David Jensen et al.
    In Proceedings of the 16th Conference on Knowledge Discovery and Data Mining (KDD-10), Washington D.C., July, 2010.
    Best Paper Award (Application).
  • Supervised Kemeny Rank Aggregation for Influence Prediction in Networks [PDF]
    Karthik Subbian and Prem Melville.
    In Machine Learning Symposium, New York Academy of Sciences, New York, October, 2010.
  • Guided Feature Labeling for Budget-Sensitive Learning Under Extreme Class Imbalance. [PDF]
    Josh Attenberg, Prem Melville and Foster Provost.
    In ICML 2010 Workshop on Budgeted Learning (BL-ICML10), Haifa, Israel, June, 2010.
  • Social Media Analytics. [PDF]
    Richard Lawrence, Prem Melville, Claudia Perlich, Vikas Sindhwani, Estepan Meliksetian et al.
    In ORMS Today, Volume 37, Number 1, February, 2010.
  • Recommender Systems. [PDF]
    Prem Melville and Vikas Sindhwani.
    In Encyclopedia of Machine Learning, Claude Sammut and Geoffrey Webb (Eds), Springer, 2010.
  • A Machine-Learning Approach to Discovering Company Home Pages. [PDF]
    Wojciech Gryc, Prem Melville, and Richard Lawrence.
    In Proceedings of the IEEE International Conference on Digital Ecosystems and Technologies (DEST 2010), Dubai, UAE, April, 2010.
  • Medical Data Mining: Lessons from Winning Two Competitions. [PDF]
    Saharon Rosset, Claudia Perlich, Grzegorz Swirszcz, Prem Melville and Yan Liu.
    In Data Mining and Knowledge Discovery Journal (DMKD), 2009.
  • Social Media Analytics: Channeling the Power of the Blogosphere for Marketing Insight. [PDF]
    Prem Melville, Vikas Sindhwani and Richard Lawrence.
    In Proceedings of the Workshop on Information in Networks (WIN-2009), New York, September, 2009.
  • Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification. [PDF]
    Prem Melville, Wojciech Gryc, and Richard Lawrence.
    In Proceedings of the 15th Conference on Knowledge Discovery and Data Mining (KDD-09), Paris, France, June, 2009.
  • Machine Learning for Social Media Analytics [PDF]
    Prem Melville, Vikas Sindhwani, Richard Lawrence, Estepan Meliksetian et al.
    In Machine Learning Symposium, New York Academy of Sciences, New York, November, 2009.
  • Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision. [PDF]
    Vikas Sindhwani, Prem Melville and Richard Lawrence.
    In Proceedings of the 26th International Conference on Machine Learning (ICML-09), Montreal, Canada, June, 2009.
  • Winning the KDD Cup Orange Challenge with Ensemble Selection. [PDF]
    Alexandru Niculescu-Mizil, Claudia Perlich, Grzegorz Swirszcz, Vikas Sindhwani, Yan Liu, Prem Melville et al.
    Journal of Machine Learning Research, 2009.
  • Active Dual Supervision: Reducing the Cost of Annotating Examples and Features. [PDF]
    Prem Melville and Vikas Sindhwani.
    In Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, Boulder, Colorado, June, 2009.
  • Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria. [PDF]
    Pei-Yun Hsueh, Prem Melville and Vikas Sindhawni.
    In Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, Boulder, Colorado, June, 2009.
  • Active Feature-value Acquisition. [PDF]
    Maytal Saar-Tsechansky, Prem Melville, and Foster Provost.
    Management Science, Vol. 55, No. 4, pp. 664–684, April 2009.
  • Prediction-time Active Feature-value Acquisition for Customer Targeting. [PDF]
    Pallika Kanani and Prem Melville.
    In Proceedings of  the NIPS 2008 Workshop on Cost Sensitive Learning, Whistler, Canada, December 12, 2008.
  • Document-Word Co-Regularization for Semi-supervised Sentiment Analysis. [PDF][Extended Version]
    Vikas Sindhwani and Prem Melville.
    In Proceedings of IEEE International Conference on Data Mining (ICDM-08), Pisa, Italy, December 15-19, 2008.
  • Customer Targeting Models Using Actively-Selected Web Content. [PDF]
    Prem Melville, Saharon Rosset, and Richard Lawrence.
    In Proceedings of 14th Conference on Knowledge Discovery and Data Mining(KDD-08), Las Vegas, August 24-27, 2008.
  • Using Predictive Analysis to Improve Invoice-to-Cash Collection. [PDF]
    Sai Zeng, Prem Melville, Christian Lang, Ioana Boier-Martin, and Conrad Murphy.
    In Proceedings of 14th Conference on Knowledge Discovery and Data Mining(KDD-08), Las Vegas, August 24-27, 2008.
  • Breast Cancer Identification: KDD CUP Winner’s Report. [PDF]
    Claudia Perlich, Prem Melville, Yan Liu, Grzegorz Swirszcz, Richard Lawrence, Saharon Rosset.
    SIGKDD Explorations, Vol. 10, Issue 2, 39-42, 2008.
  • Winner’s Report: KDD CUP Breast Cancer Identification. [PDF]
    Claudia Perlich, Prem Melville, Yan Liu, Saharon Rosset, Grzegorz Swirszcz, and Richard Lawrence.
    In Proceedings of the KDD-08 Workshop on Mining Medical Data, Las Vegas, August 24-27, 2008. (Workshop version of the above.)
  • Integrating Data Modeling and Dynamic Optimization using Constrained
    Reinforcement Learning.     [PDF]
    Naoki Abe, Prem Melville, Chandan K. Reddy, Cezar Pendus and David L. Jensen.
    Technical Report, 2008.
  • Learning Blog Sentiment with Reduced Supervision. [PDF]
    Prem Melville, Yan Liu, Wojciech Gryc, Richard Lawrence, Claudia Perlich.
    Technical Report, 2008.
  • Cost-Effective Clustering through Active Feature-value Acquisition. [PDF]
    Duy Vu, Prem Melville, Mikhail Bilenko, and Maytal Saar-Tsechansky.
    Technical Report, 2008.
  • Data Acquisition and Cost-Effective Predictive Modeling: Targeting Offers for Electronic Commerce.[PDF]
    Foster Provost, Prem Melville, and Maytal Saar-Tsechansky.
    In Proceedings of the Ninth International Conference on Electronic Commerce, 2007.
  • Finding New Customers using Unstructured and Structured Data.[PDF]
    Prem Melville, Yan Liu, Richard Lawrence, Ildar Khabibrakhmanov, Cezar Pendus, and Timothy Bowden.
    In Proceedings of the KDD-07 Workshop on Mining Multiple Information Sources, 2007.
  • Intelligent Information Acquisition for Improved Clustering. [PDF]
    Duy Vu, Prem Melville, Mikhail Bilenko, and Maytal Saar-Tsechansky.
    In Workshop on Information Technologies and Systems (WITS), 2007.
  • Predictive Modeling for Collections of Accounts Receivable.[PDF]
    Sai Zeng, Prem Melville, Christian Lang, Ioana Boier-Martin, and Conrad Murphy.
    In Proceedings of the KDD-07 Workshop Domain Driven Data Mining, 2007.
  • An Expected Utility Approach to Active Feature-value Acquisition. [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond Mooney.
    In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05), 2005.
  • Economical Active Feature-value Acquisition through Expected Utility Estimation [PDF]
    Melville, P., Saar-Tsechansky, M., Provost, F. and Mooney, R.J.
    Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL, August 2005.
  • Active Learning for Probability Estimation using Jensen-Shannon Divergence [PDF]
    Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, and Raymond J. Mooney
    In Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal, 2005.
  • Combining Bias and Variance Reduction Techniques for Regression [PDF]
    Yuk Lai Suen, Prem Melville and Raymond J. Mooney
    In Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal, 2005
  • Creating Diverse Ensemble Classifiers to Reduce Supervision. [PDF]
    Prem Melville
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, November 2005.
    Also appears as Technical Report TR-05-49, Artificial Intelligence Lab, University of Texas at Austin, December 2005.
  • Active Feature Acquisition for Classifier Induction [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney.
    In Proceedings of the Fourth International Conference on Data Mining (ICDM-2004). Brighton, UK. November 2004.
    Also appears as Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin, Feb 2004.
  • Diverse Ensembles for Active Learning [PDF][Tech Report]
    Prem Melville and Raymond J. Mooney.
    Proceedings of the 21st International Conference on Machine Learning     (ICML-2004), pp. 584-591, Banff, Canada, July 2004.
  • Experiments on Ensembles with Missing and Noisy Data [PDF]
    Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney
    Proceedings of the Fifth International Workshop on Multiple Classifier Systems (MCS-2004), F. Roli, J. Kittler, and T. Windeatt (Eds.), Lecture Notes in Computer Science, Vol. 3077, pp. 293-302, Cagliari, Italy, Springer Verlag, June 2004.
  • Creating Diversity in Ensembles Using Artificial Data [PDF]
    Prem Melville and Raymond J. Mooney
    Information Fusion: Special Issue on Diversity in Multiclassifier Systems, 2004.
  • Relational Data Mining with Inductive Logic Programming for Link Discovery [PDF]
    Raymond J. Moonney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, and David Page
    Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Srivakumar, and Y. Yesha (Eds.), AAAI Press, 2004.
  • Creating Diverse Ensemble Classifiers [PDF]
    Prem Melville
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, Oct 2003.
    Also appears as Technical Report UT-AI-TR-03-306, Artificial Intelligence Lab, University of Texas at Austin, December 2003.
  • Constructing Diverse Classifier Ensembles Using Artificial Training Examples [PDF]
    Prem Melville and Raymond J. Mooney
    Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), pp. 505-510, Acapulco, Mexico, August, 2003.
  • Scaling up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism [PDF]
    Lappoon R. Tang, Raymond J. Mooney and Prem Melville
    Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), Washington DC, August 2003.
  • Relational Data Mining with Inductive Logic Programming for Link Discovery [PDF]
    Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page and Vítor Santos Costa
    Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
    (Workshop version of the book chapter.)
  • Content-Boosted Collaborative Filtering for Improved Recommendations [PDF]
    Prem Melville, Raymond J. Mooney and Ramadass Nagarajan
    Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187-192, Edmonton, Canada, July 2002.
    Winner of Classic Paper Award at AAAI 2019.
  • Content-Boosted Collaborative Filtering [PDF]
    Prem Melville, Raymond J. Mooney and Ramadass Nagarajan
    Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001. (Workshop version of the above.)
  • Natural language assistant: A Dialog System for Online Product Recommendation [PDF]
    J. Chai, V. Horvath, N. Nicolov, M. Stys, N. Kambhatla, W. Zadrozny, and P. Melville
    AI Magazine, 23(2):63–76, 2002.

Activities

  • Invited Industrial Talk Chair, IEEE International Conference on Data Science and Advanced Analytics, Turin, Italy, October 1-4, 2018 (DSAA 2018).
  • Program Chair, 20th ACM Conference on Knowledge Discovery and Data Mining, Industry Track (KDD 2014).
  • Editorial Board, Data Mining and Knowledge Discovery.
  • Senior Program Committee, KDD 2015.
  • Program Committee, KDD 2013.
  • Program Committee, ICML 2013.
  • Program Committee, AAAI 2013.
  • Program Committee, MLG 2013.
  • Publicity Chair, International Conference on Information and Knowledge Management, Maui, Hawaii, USA, October 29-Nov 2, 2012 (CIKM 2012).
  • Workshop Selection Committee, 18th Conference on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 2012 (KDD 2012).
  • Organizer, Mining and Learning with Graphs, San Diego, CA, Aug 20-21, 2011 (MLG 2011).
  • Publicity Chair, International Conference on Machine Learning, Bellevue, Washington, USA, June 28-July 2, 2011 (ICML 2011).
  • Organizer, KDD 2010 Workshop on Social Media Analytics (SOMA 2010), Washington, DC, July 25, 2010.
  • Organizer, ICML 2010 Workshop on Budgeted Learning, Haifa, Israel, June 25, 2010.
  • Publicity Chair, 16th Conference on Knowledge Discovery and Data Mining, Washington, DC, July 25-28, 2010 (KDD 2010).
  • Proceedings Chair, Third ACM International Conference on Web Search and Data Mining, New York, NY, February 4-6, 2010 (WSDM-2010).
  • Program Committee, International Conference on Knowledge Discovery and Data Mining, (KDD 2012).
  • Program Committee, European Conference on Machine Learning (ECML 2012).
  • Program Committee, International Conference on Machine Learning (ICML 2012).
  • Program Committee, Social Media Analytics (SOMA 2012).
  • Program Committee, International Conference on Machine Learning (ICML 2011).
  • Program Committee, International Joint Conference on Artificial Intelligence (IJCAI-11).
  • Program Committee, International Conference on Knowledge Discovery and Data Mining (KDD-11).
  • Program Committee, International Conference on Machine Learning, Haifa, Israel, June 21-24, 2010 (ICML 2010).
  • Program Committee, Conference on Uncertainty in Artificial Intelligence, Catalina Island, California, July 8-11, 2010 (UAI 2010).
  • Program Committee, IEEE International Conference on Data Mining, Sydney, Australia, December 13-17, 2010 (ICDM 2010).
  • Program Committee, Barcelona, Spain, September 20-24, 2010 (ECML 2010).
  • Program Committee, NAACL 2010 Workshop on Active Learning for NLP, Los Angeles, CA, June 5-6, 2010.
  • Program Committee, 15th Conference on Knowledge Discovery and Data Mining, Paris, France, June 28- July 1, 2009 (KDD 2009).
  • Program Committee, 21st International Joint Conference on Artificial Intelligence, Pasadena, California, July 11-17, 2009 (IJCAI 2009).
  • Program Committee, 10th ACM Conference on Electronic Commerce, Stanford, California, July 6-10, 2009 (EC 2009).
  • Program Committee, European Conference on Machine Learning, Bled, Slovenia, September 7-11, 2009 (ECML 2009).
  • Program Committee, 18th ACM Conference on Information and Knowledge Management, Hong Kong, China, November 2-6, 2009 (CIKM 2009).
  • Program Committee, NAACL 2009 Workshop on Active Learning for NLP, Boulder, Colorado, June 5, 2009.
  • Program Committee, 23rd Conference on Artificial Intelligence, Chicago, IL, July 13-17, 2008 (AAAI 2008).
  • Program Committee, IEEE International Conference on Data Mining, Pisa, Italy, December 15-19, 2008 (ICDM 2008).
  • Program Committee, European Conference on Machine Learning, Antwerp, Belgium, September 15-19, 2008 (ECML 2008). 
  • Program Committee, 17th Conference on Information and Knowledge Management, Napa Valley, California
    October 26-30, 2008 (CIKM 2008).
  • Program Committee, NIPS Workshop on Cost-Sensitive Learning,  Vancouver, Canada, December 13th, 2008.
  • Program Committee, KDD Workshop on Mining Multiple Information Sources, Las Vegas, Nevada, August 24, 2008 (MMIS 2008).
  • Program Committee, Thirteenth Conference on Knowledge Discovery and Data Mining, San Jose, August 12-15, 2007 (KDD 2007).
  • Program Committee, 22nd Conference on Artificial Intelligence, Vancouver, Canada, July 22-26, 2007 (AAAI 2007).
  • Program Committee, Eighteenth European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 (ECML 2007).
  • Program Committee, Twelfth Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, August 20-23, 2006 (KDD 2006).
  • Program Committee, 23rd International Conference on Machine Learning, Pittsburgh, PA, June 25-29, 2006 (ICML 2006).
  • Program Committee, 21st National Conference on Artificial Intelligence, Boston, MA, July 16-20, 2006, (AAAI 2006).
  • Program Committee, Seventeenth European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006 (ECML 2006).
  • Program Committee, Second KDD Workshop on Utility-Based Data Mining, Philadelphia, PA, August 20, 2006 (UBDM 2006).
  • Program Committee, Sixteenth European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, (ECML 2005).
  • Program Committee, First KDD Workshop on Utility-Based Data Mining, Chicago, IL, August 21, 2005 (UBDM 2005).

Patents

  • “Inferring emerging and evolving topics in streaming text”, V. Sindhwani, P. Melville, S. Kasiviswanathan, A. Banerjee, A. Saha, R. Lawrence, E. Ting, US Patent 8909643, Dec 9, 2014.
  • “A system and method for automated labeling of text documents using ontologies”, Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani and Rick Lawrence, filed 2011.
  • “Predicting influence in social networks”, Prem Melville, Karthik Subbian, Claudia Perlich, Richard Lawrence, and Estepen Meliksetian, US Patent 9031888, May 12, 2015.
  • “Method and system for debt collection optimization”, Naoki Abe, Prem Melville, Richard Lawrence, Cezar Pendus, David Jensen, Vince Thomas, Jim Bennett, and Tim Gardinier, US Patent 7519553, April 14, 2009.
  • “A system and method for integrated graph-based learning using alternative sources of information”, Vikas Sindhwani, Prem Melville, Yan Liu, and Richard Lawrence, filed 2008.
  • “A method and system for identifying companies with specific business objectives”, Prem Melville, Richard Lawrence, Yan Liu, Ildar Khabibrakhmanov, Upendra Chitnis, Timothy Bowden, US Patent 8145619, Mar 27, 2012.
  • “Method and system using machine learning to automatically discover company homes pages on the Internet”, Prem Melville, Wojciech Gryc, Richard Lawrence, Upendra D. Chitnis, Ildar Khabibrakhmanov, and Cezar Pendus, US Patent 8583639, Nov 12, 2013.