Data Mining,
    Machine Learning,
    and Privacy-Preserving Algorithms

    Data Mining Algorithms.
    My research interests are in data science, data mining, web search, machine learning and privacy. I have over 16 years of experience leading projects in industry at Microsoft Research and HP Labs and over 6 years of experience in academia as Associate Professor at the University of Virginia and Acting Faculty at Stanford University.

    The projects that I pursue encompass the design and evaluation of new data mining algorithms on real, colossal-sized datasets. I authored ~50 publications in top venues including: Web Search: WWW, WSDM, SIGIR; Machine Learning: ICML, NIPS, AAAI, COLT; Databases: VLDB, PODS; Cryptography: CRYPTO, EUROCRYPT; Theory: FOCS and SODA. My research publications received external recognition: best paper award nomination, algorithm in Wikipedia and taught in graduate courses around the world. Also, my research has product implications at Microsoft, specifically in the Bing search engine, and was featured in external press coverage including New Scientist, ACM TechNews, IEEE Computing Now, Search Engine Land and Microsoft Research. I've been granted 13 patent applications with a dozen more still in the application stage. I've had the distinct privilege of helping others advance in their careers, including 15 summer interns and many full-time researchers.

    My service to the community includes: serving on journal editorial boards Machine Learning, Journal of Privacy and Confidentiality, IEEE Transactions on Knowledge and Data Engineering and IEEE Intelligent Systems; chairing the premier machine learning conference ICML in 2003, as well as numerous program committees for web search, data mining and machine learning conferences. I was awarded an NSF Grant as a Principal Investigator and served on 8 PhD dissertation committees. I taught several courses at Stanford University and the University of Virginia.