Kamal Nigam

Profile

Work Experience

• Engineering Manager, January 2006 - current
  Google, Pittsburgh, PA
  Charter member of Google Pittsburgh, responsible in part for selecting and initiating high-impact projects, recruiting and growing the office from two to eighty, and establishing and building out local facilities. Directly manage a team of twenty leading projects in ads selection, product search and improving the user experience on other Google search properties using applied data mining, machine learning and information extraction techniques.
• Director of Applied Research, July 2003 - January 2006
  Research Scientist, July 2002 - July 2003
  Intelliseek, Inc., Pittsburgh, PA
  Head of Intelliseek's Applied Research Center. Led a group of scientists and engineers in developing a large-scale system that analyzes internet text content for market research analysis. Emphasis on creating and using leading-edge data mining, machine learning, and linguistic algorithms. Responsible for directing the software development, product development, and research agenda of the company. Performed targeted research in the areas of text classification and sentiment analysis, resulting in patent applications and publications.
• Research Scientist, June 2001 - May 2002
  WhizBang! Labs, Pittsburgh, PA
  Applied state-of-the-art machine learning algorithms to business applications on the web. Created a job title extractor that reduced site loss by 50% for Flipdog.com using Maximum Entropy Markov Models. Created a web-based market research application for generating sales leads from corporate data extracted from the web. Invented and implemented text classifiers for automatically placing job postings into a hierarchy. Consistently met deliverable deadlines at or beyond specified performance criteria.
• Graduate Research Assistant, August 1996 - May 2001
  Carnegie Mellon University, Pittsburgh, PA
  Created algorithms and systems for text and hypertext classification and extraction. Coauthored eighteen refereed publications. Performed research in statistical text learning, learning with unlabeled data, and relational text learning. Built a web-crawling agent that extracts information from the web into a propositional knowledge base. Teaching assistant for an introductory data structures and algorithms course and an artificial intelligence course. Summer internships at Justsystem Pittsburgh Research Center and WhizBang! Labs.
• Software Engineer, December 1995 - August 1996
  Virtual Office, New York, NY
  Designed and developed software in a start-up setting. Led projects on server-side web-tracking software, forms-based web page generation, and third-party, sharable certificate management.
• Database Applications Engineer, July 1994 - October 1995
  Oracle Corporation, Redwood Shores, CA
  Designed and developed database applications for Oracle Financial Applications. Heavy emphasis on database design, graphical user interfaces, performance tuning, and quality assurance. Project leader for a new accounting module that tracks foreign currency transfers between corporate subsidiaries.

Teaching Experience

• Visiting Lecturer, Carnegie Mellon University, Fall 2007
  Organized and co-taught Internet Search Technologies, 15-505, a special topics seminar class at CMU targeted to advanced undergraduate and early graduate students. The class teaches large-scale distributed computation, information retrieval, machine learning for text and hypertext, and UI design.
• Teaching Assistant, Carnegie Mellon University, Fall 1999
  Teaching assistant for introductory computer science class, Fundamental Structures of Computer Science I, 15-211.
• Teaching Assistant, Carnegie Mellon University, Fall 1997
  Teaching assistant for undergraduate artificial intelligence class, Fundamentals of Artificial Intelligence, 15-381.
• Laboratory Teaching Assistant, Massachusetts Institute of Technology, Spring 1992
  Laboratory teaching assistant for introductory computer science class, Structure and Interpretation of Computer Programs, 6.001.

Education

• Ph.D. Computer Science, Carnegie Mellon University, 2001
  M.S. Computer Science, Carnegie Mellon University, 1999
  Ph.D. Thesis: Using Unlabeled Data for Text Classification, Advisor: Tom Mitchell
  Thesis research on methods for integrating unlabeled data into supervised learning in text and hypertext domains. Other research in text classification, information extraction, data mining, and clustering.
• S.B. Mathematics with Computer Science
  S.B. Cognitive Science
  Massachusetts Institute of Technology, 1994
  GPA: 4.9/5.0; Phi Beta Kappa; Sigma Xi (science research); National Merit Scholar.
  Research with Susan Carey on human mechanisms for face recognition.

Professional Activities

• KDD-2008 and KDD-2007 Student Travel Awards chair
• Co-coordinator CMU Machine Learning Department/Google Seminar Series
• Area chair for NIPS 2006
• Member, University of Pittsburgh Department of Computer Science Industry Board
• Program Committee member for ICML, KDD, SIGIR, WWWC, AAAI, COLING, ACL, PAKDD, ICWSM, etc., and a number of workshops
• Journal Reviewing for Machine Learning, Journal of Machine Learning Research, Computational Linguistics, ACM TOIS, Applied Intelligence, etc.
• Invited AI Seminar talk at ISI/USC, March 2007
• Lecturer at 2006 Machine Learning Autumn School at CMU's Machine Learning department
• Co-organizer for IJCAI '01 Workshop on Text Learning: Beyond Supervision
• Member of AAAI, ACL, SIGKDD, Sigma Xi

Selected Publications and Works

• Kamal Nigam, Andrew McCallum and Tom Mitchell. Semi-supervised Text Classification Using EM. In Chapelle, O., Scholkopf, B. and Zien, A. (Eds.) Semi-Supervised Learning. MIT Press: Boston. 2006.

• Golan Levin, Kamal Nigam and Jonathan Feinberg. The Dumpster. Commissioned electronic artwork installed at The Whitney Museum Artport and Tate Online. 2006.

• Natalie Glance, Matthew Hurst, Kamal Nigam, Matthew Siegler, Robert Stockton and Takashi Tomokiyo. Deriving Marketing Intelligence from Online Discussion. Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005). 2005.

• Kamal Nigam and Matthew Hurst. Towards a Robust Metric of Opinion. In AAAI Spring Symposium on Exploring Attitude and Affect in Text. 2004.

• Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, Sean Slattery. Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence, 118(1-2). pp 69-114. 2000.

• Andrew McCallum, Kamal Nigam and Lyle Ungar. Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching. In Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000). 2000.

• Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. A Machine Learning Approach to Building Domain-Specific Search Engines. In The Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99). 1999.

• Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Learning to Classify Text from Labeled and Unlabeled Documents. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 792-799. 1998.

Patents

• Niels Provos, Yunkai Zhou, Clayton W. Bavor, Jr., Eric Davis, Mark Palatucci, Kamal P. Nigam, Christopher K. Monson, Panayiotis Mavrommatis, Rachel Nakauchi. Intrusive Software Management. Filed October 5, 2007. Pending.

• Kamal Nigam and Matthew Hurst. Topical sentiments in electronically stored communications. Filed September 30, 2005. Pending.

• Kamal Nigam and Robert Stockton. Method for developing a classifier for classifying communications. Filed March 16, 2004. Pending.