Nina Mishra's Home Page
Email
nmishra@gmail.com, nmishra@cs.stanford.edu
Synopsis
My research interests are
in data science, data mining, web search, machine learning and privacy. I have many
years of experience leading projects in industry at Amazon, Microsoft Research
and HP Labs, as well as 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 14 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.
Biography
o
Principal Scientist,
Amazon, 2015-present.
o
Visiting Scholar,
Stanford University, 2014-present.
o
Senior
Researcher, Microsoft
Research Silicon Valley. 2007-2014.
o
Associate
Professor, Computer Science Department, University of Virginia,
2005-2008.
o
Acting
Faculty, Computer Science Department, Stanford University,
2002-2005.
o
Senior
Research Scientist, Hewlett-Packard Labs, 1997-2005.
o
PhD
Computer Science, University of Illinois at Urbana-Champaign, 1997
Publications
o
Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers.
Robust
Random Cut Forest Based Anomaly Detection on Streams.
In
International Conference on Machine Learning (ICML), 2016
o
Katherine Ellis, Moises Goldszmidt, Gert Lanckriet, Nina Mishra and
Omer Reingold.
Equality
and Social Mobility in Twitter Discussion Groups, in International
Conference on Web Search and Data Mining (WSDM), ACM, 2016
o
James Cook, Abhimanyu Das, Krishnaram Kenthapadi, and Nina
Mishra, Ranking Twitter Discussion
Groups, in ACM Conference on Online Social Networks (COSN),
2014
o Nina Mishra,
Ryen White, Samuel Ieong, and Eric Horvitz, Time-Critical Search,
in SIGIR (Information retrieval), ACM, 2014
o External
Coverage:
o How Search can Help in a
Medical Crisis, Microsoft Research
o Isabelle
Stanton, Samuel Ieong, and Nina Mishra, Circumlocution
in Diagnostic Medical Queries, in SIGIR (Information Retrieval),
ACM, 2014
o
Ideas from this paper formed the basis for a CLEF eHealth
challenge task.
o Krishnaram Kenthapadi, Nina Mishra,
and Kobbi Nissim, Denials
Leak Information: Simulatable Auditing, in Journal
of Computer and System Sciences, vol. 79 (8), pp. 1322-1340, Elsevier,
December 2013
o
Nina Mishra, Daniel Romero, and Panayiotis Tsaparas, Estimating
the Relative Utility of Networks for Predicting User Activities, in CIKM,
ACM International Conference on Information and Knowledge Management (CIKM),
2013
o
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and
Nina Mishra, Privacy
via the Johnson-Lindenstrauss Transform, in Journal
of Privacy and Confidentiality, vol. 5 (1), July 2013
o
James Cook, Krishnaram Kenthapadi, and Nina Mishra, Group
Chats on Twitter, in International World Wide Web Conference (WWW),
ACM, 2013
o Samuel Ieong,
Nina Mishra, and Or Sheffet, Predicting Preference Flips in
Commerce Search, in International Conference on Machine Learning
(ICML), 2012
o
Samuel Ieong, Nina Mishra, Eldar Sadikov, and Li Zhang, Domain bias in web search,
in International Conference on Web Search and Data Mining (WSDM), ACM,
2012
o
External Coverage:
o Turn Domain Bias In Search
Results To Your Advantage, Search Engine Land
o Domain Name Matters: Searchers
Pick Brand Over Quality, Study Finds, Search Engine Land
o Srikanth
Jagabathula, Nina Mishra, and Sreenivas Gollapudi, Shopping for Products You
Don't Know You Need, in WSDM (Web Search and Data Mining),
2011
o Shubha Nabar
and Nina Mishra, Releasing Private Contingency
Tables, in Journal of Privacy and Confidentiality, 2010
o Gagan
Aggarwal, Nina Mishra, and Benny Pinkas, Secure Computation of the
Median (and Other Elements of Specified Ranks), in Journal of
Cryptology, 2010
o Umar Syed,
Alex Slivkins, and Nina Mishra, Adapting to the Shifting
Intent of Search Queries, in NIPS (Neural Information Processing
Systems Conference), 2009
o Aleksandra
Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alex Ntoulas, Releasing Search Queries and
Clicks Privately, in International World Wide Web Conference
(WWW), ACM, 2009.
o Nominated for a Best Paper Award
o External
Coverage:
o Noise could Mask Web
Searchers' IDs, New Scientist, ACM TechNews,
TechFlash
o Making the Web
More User-Friendly, Microsoft Research
o Rakesh
Agrawal, Alan Halverson, Krishnaram Kenthapadi, Nina Mishra, and Panayiotis
Tsaparas, Generating Labels from Clicks,
in International Conference on Web Search and Data Mining (WSDM), ACM,
2009
o Nina Mishra,
Robert Schreiber, Isabelle Stanton, and Robert E. Tarjan, Finding Strongly-Knit
Clusters in Social Networks, in Internet Mathematics, 2009.
o
Invited to special issue of WAW'07.
o Westley
Weimer and Nina Mishra, Privately Finding
Specifications, in IEEE Trans. Software Eng, vol. 34, no. 1,
pp. 21-32, 2008
o Shubha Nabar,
Krishnaram Kenthapadi, Nina Mishra, and Rajeev Motwani, A Survey of Query Auditing
Techniques for Data Privacy, in Privacy-Preserving Data Mining:
Models and Algorithms, Kluwer Academic Publishers, 2008
o Nina Mishra,
Robert Schreiber, Isabelle Stanton, and Robert E. Tarjan, Clustering Social Networks,
in WAW, 2007
o Kamalika
Chaudhuri and Nina Mishra, When Random Sampling Preserves
Privacy, in CRYPTO, 2006
o Nina Mishra
and Mark Sandler, Privacy via pseudorandom
sketches, in Proceedings of the Twenty-Fifth ACM
SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Chicago, IL,
USA June 26-28, 2006, 2006
o Nina Mishra,
Robert Schreiber, and Robert E. Tarjan, Finding Closely-Related Groups of
Objects in Very Large Datasets, in Hewlett-Packard Technical Conference (HP
TechCon), 2006
o Shubha U.
Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, and Rajeev Motwani,
Towards Robustness in Query
Auditing, in VLDB, Very Large Data Bases Endowment Inc., 2006
o
Nina Mishra, Rajeev Motwani, and Sergei Vassilvitskii, Sublinear
Projective Clustering with Outliers, in 15th Annual Fall Workshop on
Computational Geometry and Visualization, 2005
o Krishnaram
Kenthapadi, Nina Mishra, and Kobbi Nissim, Simulatable Auditing,
in PODS, Association for Computing Machinery, Inc., 2005
o Gagan
Aggarwal, Nina Mishra, and Benny Pinkas, Secure Computation of the k
th-Ranked Element, in EUROCRYPT, 2004
o Nina Mishra,
Dana Ron, and Ram Swaminathan, A New Conceptual Clustering
Framework, in Machine Learning, vol. 56, no. 1-3, pp.
115-151, 2004
o Haym Hirsh,
Nina Mishra, and Leonard Pitt, Version Spaces and the
Consistency Problem, in Artificial Intelligence, vol. 156,
no. 2, pp. 115-138, 2004
o Gagan Aggarwal,
Mayank Bawa, Prasanna Ganesan, Hector Garcia-Molina, Krishnaram Kenthapadi,
Nina Mishra, Rajeev Motwani, Utkarsh Srivastava, Dilys Thomas, Jennifer Widom,
and Ying Xu, Vision Paper: Enabling Privacy
for the Paranoids, in VLDB, Very Large Data Bases Endowment
Inc., 2004
o Nina Mishra
and Rajeev Motwani, Introduction: Special Issue on
Theoretical Advances in Data Clustering, in Machine Learning,
vol. 56, no. 1-3, pp. 5-7, 2004
o Gagan
Aggarwal, Mayur Datar, Nina Mishra, and Rajeev Motwani, On Identifying Stable Ways to
Configure Systems, in ICAC, 2004
o Nina Mishra,
Dana Ron, and Ram Swaminathan, On Finding Large Conjunctive
Clusters, in COLT, Springer, 2003
o Sudipto Guha, Adam Meyerson,
Nina Mishra, Rajeev Motwani, Liadan
O'Callaghan.
Clustering
Data Streams: Theory and Practice. IEEE TKDE, 2003.
o Nina Mishra,
Dana Ron, Ram Swaminathan, Large Clusters of Web
Pages, in WAW, 2002
o Liadan O'Callaghan, Nina Mishra, Adam Meyerson,
Sudipto Guha, Nina
Mishra.
Streaming-Data
Algorithms for High-Quality Clustering.
ICDE, 2002.
o Nina Mishra,
Daniel Oblinger, and Leonard Pitt, Sublinear time approximate
clustering, in SODA, 2001
o Sudipto Guha,
Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan, Clustering
Data Streams, in FOCS, 2000
o
Data
Stream Clustering in Wikipedia
o Carlos
Domingo, Nina Mishra, and Leonard Pitt, Efficient Read-Restricted
Monotone CNF/DNF Dualization by Learning with Membership Queries, in
Machine Learning, vol. 37, pp. 89, 1999
o
Originally invited as one of the best papers from COLT'97.
o Haym Hirsh, Nina Mishra, and Leonard Pitt, Version Spaces Without
Boundary Sets, in Proceedings of the 14th National Conference on
Artificial Intelligence and 9th Innovative Applications of Artificial
Intelligence Conference (AAAI-97/IAAI-97), 1997
o Nina Mishra, Learning from a monotonous,
ignorant teacher, no. 2023, 1997
o Nina Mishra
and Leonard Pitt, Generating all Maximal
Independent Sets of Bounded-degree Hypergraphs, in Proceedings of
the Tenth Annual Conference on Computational Learning Theory, 1997
o Michael
Frazier, Sally Goldman, Nina Mishra, and Leonard Pitt, Learning
from a consistently ignorant teacher, in J. of Comput. Syst. Sci.,
vol. 52, no. 3, pp. 471-492, 1996
o
Invited as one of the best papers from COLT'94.
Professional Activities
Editorial
Boards
o
Machine Learning journal, 2002-present
o
IEEE TKDE (Transactions on Knowledge and Data Engineering),
2005-2007.
o
IEEE Intelligent Systems, 2005-2008.
o
Journal of Privacy and Confidentiality, 2006-present
Program Chair
o
ICML'03, with Tom Fawcett
Program Committees
o ICML'16: International Conference on Machine
Learning
o IJCAI'16: International Joint
Conference on Artificial Intelligence, AI and the Web Track
o KDD'16: Applied Data Science Track
o WSDM'15: Web Search and Data
Mining
o PODS'13: Principles of Database
Systems
o WSDM'12: Web Search and Data Mining
o WSDM'11: Web Search and Data Mining
o WSDM'10:
Web Search and Data Mining
o PODS'09: Principles of Database Systems
o AAAI'08: Conference on Artificial
Intelligence
o KDD'08: Knowledge Discovery and Data Mining
o KDD'07: Knowledge Discovery and Data Mining
o KDD'06: Knowledge Discovery and Data Mining
o ICML'06: International Conference on Machine Learning
o ICML'05:
International Conference on Machine Learning
o KDD'05: Knowledge Discovery and Data Mining
o SDM'05: SIAM International Conference on Data
Mining
o KDD'04: Knowledge Discovery and Data Mining
o SDM'03: SIAM International Conference on Data
Mining
o ICDM'02: IEEE International Conference on Data
Mining
o SDM'02: SIAM International Conference on Data
Mining
o KDD'01: Knowledge Discovery and Data Mining
o IJCAI'01: International Joint Conference on Artificial
Intelligence
o ICML'00: International Conference on Machine
Learning
NSF Panelist: 2002, 2004,
2007.
Advisory
Board, ICML'04
Teaching
o
CS302: Theory of
Computation. University of Virginia. Spring 2007.
o
CS651: Internet Algorithms.
University of Virginia. Fall, 2006.
o
CS851: Data Mining Algorithms.
University of Virginia. Spring, 2006.
o
CS369C: Clustering Algorithms.
Stanford University. Spring, 2005.
o
CS369: A Study of Perturbation
Techniques for Data Privacy.With Cynthia Dwork and Kobbi
Nissim. Stanford University. Spring, 2004.
o
CS361A: Advanced Algorithms for
Internet Applications. With Rajeev Motwani. Stanford University. Autumn,
2002.
Patents Granted (from uspto.gov)
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Patent Number |
Title |
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14 |
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Recommending queries
according to mapping of query communities |
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13 |
System and method for determining an element value in
private datasets |
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12 |
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11 |
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10 |
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9 |
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8 |
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7 |
Predicting parts needed for an onsite repair using
expected waste derived from repair history |
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6 |
Method and system for comparing individual computers to
cluster representations of their peers |
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5 |
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4 |
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3 |
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2 |
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1 |
Computer implemented scalable, incremental and parallel
clustering based on divide and conquer |