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David M. Pennock New York, NY USA My email
address is dp E nnock.com, except replace the E with @ I blog at Oddhead Blog Find me
elsewhere on twitter, g+, delicious, flickr, facebook, and linkedin |
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Highlights |
Currently Principal
Researcher and Assistant Managing Director at Microsoft Research in NYC 60+ journal and
conference publications, including PNAS, Science, IEEE
Computer, Theoretical Computer
Science, Electronic Commerce
Research, Electronic Markets,
AAAI, EC, WWW, STOC, KDD, UAI, SIGIR, ICML, NIPS, INFOCOM, SAINT, VLDB 3 patents, 13 patent
applications; over 40 presentations Press interviews
include New York Times, Newsweek, Washington Post, The New Yorker, Investors
Business Daily, LA Business Journal, Dow Jones/Wall Street Journal Reports
on my research have appeared in Discover Magazine, New Scientist, CNN/Money, Named Top 35
Technology Innovator Under Age 35 by MIT Technology Review, 2005 |
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Research
Interests |
– Chance
Tech: technology for prediction, finance, insurance, and gambling – Design and analysis of new online markets, including prediction markets Topics: electronic commerce, artificial
intelligence, prediction markets, probability, decision theory, machine
learning |
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Education |
Ph.D. Computer Science University of Michigan, Ann Arbor, MI, Dec 1999 Concentration in
Artificial Intelligence; Graduate Certificate in Complex Systems M.S. Computer Science Duke University, Durham, NC, Aug 1994 B.S. Physics, 2nd
Major: Computer Science Duke University, Durham,
NC, May 1993 |
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Activities |
Santa Fe Institute
Complex Systems Summer School, June 1996 Duke in Cambridge
Program in England, Summer 1992 Pi Kappa Alpha
Fraternity, 1990–1993; Community Service Chair, 1991–1992 |
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Honors |
Outstanding Paper
Award, ACM Conference on Electronic Commerce, 2008 ACM Senior Member,
2006 MIT Technology
ReviewÕs TR35: Top 35 Technology Innovator Under Age 35, 2005 Fellowship, Michigan
Decision Behavior Consortium, 1998 Best Student Paper
Finalist, Decision Analysis Society,1998 Graduated magna cum
laude, 1993 DeanÕs List,
1989–1993 Golden Key National Honors Society, 1991 Phi Eta Sigma Freshman Honors Society, 1990 |
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Journal
and Conference Publications Journal
and Conference Publications Journal
and Conference Publications Journal
and Conference Publications |
A. Beygelzimer, J.
Langford, and D.M. Pennock (2012). Learning
performance of prediction markets with Kelly bettors. International Conference on Autonomous
Agents and Multiagent Systems S. Goel, M. Mahdian,
D.M. Pennock, and D.M. Reeves (2012). TrustBets:
Operating a prediction market on an IOU network. International Conference on Autonomous Agents and Multiagent Systems L. Xia and D.M.
Pennock (2011). An efficient
Monte-Carlo algorithm for pricing combinatorial prediction markets for
tournaments. International Joint
Conference on Artificial Intelligence: 452-457 D.M. Pennock and L.
Xia (2011). Price updating in
combinatorial prediction markets with Bayesian networks. Conference on Uncertainty in Artificial
Intelligence: 581-588 S. Goel, J. Hofman,
S. Lahaie, D.M. Pennock, and D.J. Watts (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of
Sciences, 107(41): 17486-17490 S. Goel, D.M. Reeves,
D.J. Watts, D.M. Pennock (2010). Prediction
without markets. ACM Conference on
Electronic Commerce: 357-366 A. Othman, T.
Sandholm, D.M. Pennock, and D.M. Reeves (2010). A practical liquidity-sensitive automated market maker. ACM Conference on Electronic Commerce:
377-386 Y. Chen and D.M.
Pennock (2010). Designing Markets for
Prediction. AI Magazine, 31(4):
42-52 Y. Chen, S. Dimitrov,
R. Sami, D.M. Reeves, D.M. Pennock, R.D. Hanson, L. Fortnow, and R. Gonen
(2010). Gaming prediction markets:
Equilibrium strategies with a market maker. Algorithmica, 58(4): 930-969 S. Goel, D.M. Reeves,
and D.M. Pennock (2009). Collective
revelation: A mechanism for self-verified, weighted, and truthful predictions.
ACM Conference on Electronic Commerce:
265-274 M. Guo and D.M.
Pennock (2009). Combinatorial
prediction markets for event hierarchies. International Conference on Autonomous Agents and Multiagent Systems,
(1): 201-208 X. Gao, Y. Chen, and
D.M. Pennock (2009). Betting on the
real line. Workshop on Internet and
Network Economics: 553-560 J. Feigenbaum, D.C.
Parkes, and D.M. Pennock (2009). Computational
challenges in e-commerce. Communications
of the ACM, 52(1): 70-74 S. Goel, J. Hofman,
J. Langford, D.M. Pennock, and D.M. Reeves (2009). Centmail: Rate limiting via certified micro-donations. Conference on Email and Anti-Spam N.S. Lambert, J. Langford, J. Wortman, Y. Chen,
D.M. Reeves, Y. Shoham, and D.M. Pennock (2008). Self-financed wagering mechanisms for forecasting. ACM Conference on Electronic Commerce:
170-179. Won Outstanding Paper Award N.S. Lambert, D.M. Pennock,
and Y. Shoham (2008). Eliciting
properties of probability distributions. ACM Conference on Electronic Commerce: 129-138 Y. Chen, L. Fortnow,
N.S. Lambert, D.M. Pennock, and J. Wortman (2008). Complexity of combinatorial market makers. ACM Conference on Electronic Commerce: 190-199 Y. Chen, S. Goel,
D.M. Pennock (2008). Pricing
combinatorial markets for tournaments. ACM Symposium on Theory of Computing: 305-314 S. Lahaie, D.C.
Parkes, and D.M. Pennock (2008). An
expressive auction design for online display advertising. National Conference on Artificial
Intelligence: 108-113 Y. Chen, A. Ghosh,
R.P. McAfee, and D.M. Pennock (2008). Sharing
online advertising revenue with consumers. Workshop on Internet and Network Economics: 556-565 M. Mahdian, R.P. McAfee,
and D.M. Pennock (2008). The secretary
problem with a hazard rate condition. Workshop
on Internet and Network Economics: 708-715 Y. Chen, L. Fortnow,
E. Nikolova, and D.M. Pennock (2007). Betting
on permutations. ACM Conference on
Electronic Commerce: 326-335 S. Lahaie and D.M.
Pennock (2007). Revenue analysis of a
family of ranking rules for keyword auctions. ACM Conference on Electronic Commerce: 50-56 S.-T. Park and D.M.
Pennock (2007). Applying collaborative
filtering techniques to movie search for better ranking and browsing. ACM Conference on Knowledge Discovery and
Data Mining: 550-559 A. Ghosh, M. Mahdian,
D.M. Reeves, D.M. Pennock, and R. Fugger (2007). Mechanism design on trust networks. Workshop on Internet and Network Economics: 257-268 J. Feng, H. Bhargava,
and D.M. Pennock (2007). Implementing
sponsored search in web search engines: Computational evaluation of
alternative mechanisms. Informs Journal on Computing, 19(1):
137-148 V. Dani, O. Madani,
D.M. Pennock, S.K. Sanghai, and B. Galebach (2006). An empirical comparison of algorithms for aggregating expert
predictions. Conference on
Uncertainty in Artificial Intelligence S.-T. Park, D.
Pennock, O. Madani, N. Good, and D. DeCoste (2006). Nave filterbots for robust cold-start recommendations. ACM Conference on Knowledge Discovery and
Data Mining: 699-705 B. Mangold, M.
Dooley, G.W. Flake, H. Hoffman, T. Kasturi, D.M. Pennock, and R. Dornfest
(2005). The Tech Buzz Game. IEEE Computer, 38(7): 94-97 Y. Chen, T. Mullen,
C.-H. Chu and D.M. Pennock (2005). Information
markets vs. opinion pools: An empirical comparison. ACM Conference on Electronic Commerce A.I. Schein, A.
Popescul, L.H. Ungar, and D.M. Pennock (2005). CROC: A new evaluation criterion for recommender systems. Electronic Commerce Research, 5(1):
51-74 D.M. Pennock (2004). A Dynamic pari-mutuel market for hedging,
wagering, and information aggregation. ACM Conference on Electronic Commerce O. Madani, D.M.
Pennock, and G.W. Flake (2004). Co-validation: Using disagreement on
unlabeled data to validate classification algorithms. Neural
Information Processing Systems E. Servan-Schreiber,
J. Wolfers, D.M. Pennock, and B. Galebach (2004). Prediction markets: Does Money Matter? Electronic Markets,14(3) G.W. Flake and D.M. Pennock (2004). Self-organization,
self-regulation, and self-similarity on the fractal web. Chapter in The
Colours of Infinity, Clear
Press, UK J. Feigenbaum, L. Fortnow, D.M. Pennock, and R. Sami (2004). Computation in a distributed information
market. Theoretical Computer Science L. Fortnow, J.
Kilian, D.M. Pennock, and M.P. Wellman (2004). Betting boolean-style: A
framework for trading in securities based on logical formulas. Decision Support Systems, 39(1):87-104 S.-T. Park, D.M.
Pennock, C.L. Giles, and R. Krovetz (2004). Analysis of lexical signatures for improving information persistence
on the World Wide Web. ACM
Transactions on Information Systems, 22(4): 540-572 D. Pavlov, E. Manavoglu, D.M. Pennock and C.L.
Giles (2004). Collaborative filtering with maximum entropy. IEEE Intelligent Systems, 19(6): 40-48 A. De Bruyn, C.L. Giles, and D.M. Pennock (2004). Offering collaborative-like
recommendations when data is sparse: The case of attraction-weighted
information filtering. Conference
on Adaptive Hypermedia S.-T. Park, D.M.
Pennock, and C.L. Giles (2004).
Comparing static and dynamic measurements and models of the Internet's AS
topology. Joint Conference of the IEEE Computer and Communications
Societies G.W. Flake, D.M.
Pennock, and D.C. Fain (2003). The self-organized web: The yin to the
semantic webÕs yang. IEEE Intelligent Systems S. Debnath, D.M.
Pennock, S. Lawrence, and C.L. Giles (2003). Information incorporation in
online in-game sports betting markets. ACM Conference on Electronic
Commerce K. Dave, S. Lawrence
and D.M. Pennock (2003). Mining the peanut gallery: Opinion extraction and
semantic classification of product reviews. World Wide Web Conference D. Pavlov, A.
Popescul, D.M. Pennock and L.H. Ungar (2003). Mixtures of Conditional
Maximum Entropy Models. International Conference on Machine Learning S.K. Lam, D.M. Pennock,
D. Cosley, and S. Lawrence (2003). 1 Billion Pages = 1 Million Dollars?
Mining the Web to Play "Who Wants to be a Millionaire?". Conference on Uncertainty in Artificial
Intelligence S.-T Park, A.
Khrabrov, D.M. Pennock, S. Lawrence, C.L. Giles, and L.H. Ungar (2003). Static
and Dynamic Analysis of the Internet's Susceptibility to Faults and Attacks.
Joint Conference of the IEEE Computer and Communications Societies A. Popescul, L.H. Ungar, S.Lawrence, and D.M. Pennock (2003). Statistical relational learning for
document mining. International Conference on Data Mining, pp.
275-282 D.Y. Pavlov and D.M.
Pennock (2002). A maximum entropy approach to collaborative filtering in
dynamic, sparse, high dimensional domains. Neural Information
Processing Systems E.J. Glover, D.M.
Pennock, S.Lawrence, and B.Krovetz (2002). Inferring hierarchical
descriptions. Conference on Information and Knowledge Management D.M. Pennock, G.W.
Flake, S.Lawrence, E.J. Glover, C.L. Giles (2002). Winners don't take all:
Characterizing the competition for links on the web, Proceedings of
the National Academy of Sciences, 99 (8): 5207-5211 D.M. Pennock, S.
Debnath, E.J. Glover, C.L. Giles (2002). Modeling information
incorporation in markets with application to detecting and explaining events,
Conference on Uncertainty in Artificial
Intelligence E. Glover, K.
Tsioutsiouliklis, S. Lawrence, D. Pennock, G. Flake (2002). Using web
structure for classifying and describing web pages, World Wide Web
Conference S. Chakrabarti, M.
Joshi, K. Punera, D.M. Pennock (2002). The structure of broad topics on
the Web, World Wide Web Conference A.I. Schein, A.
Popescul, L.H. Ungar, D.M. Pennock (2002). Methods and metrics for
cold-start recommendations, ACM Conference on Information Retrieval D. Cosley, S.
Lawrence, D.M. Pennock (2002). REFEREE: An open framework for practical
testing of recommender systems using ResearchIndex, Conference on Very
Large Data Bases D.M. Pennock, S.
Lawrence, F.. Nielsen, C. Lee Giles (2001). Extracting collective probabilistic forecasts from web games, ACM Conference on Knowledge Discovery and
Data Mining, pp. 174–183 A. Popescul, L.H.
Ungar, D.M. Pennock, S. Lawrence (2001). Probabilistic
models for unified collaborative and content-based recommendation in
sparse-data environments, Conference
on Uncertainty in Artificial Intelligence, pp. 437–444 D.M. Pennock, S.
Lawrence, C.L. Giles, F.. Nielsen (2001). The real power of artificial markets. Science, 291(5506): 987–988 S. Lawrence, D.M.
Pennock, G.W. Flake, R. Krovetz, F.M. Coetzee, E. Glover, F. Nielsen, A.
Kruger, C.L. Giles (2001). Persistence
of web references in scientific research. Computer, 34(2), pp. 26–31 E.J. Glover, G.W.
Flake, S. Lawrence, W.P. Birmingham, A. Kruger, C.L Giles, D.M. Pennock
(2001). Improving category specific
web search by learning query modifications. Symposium on Applications and the Internet, pp. 23–31 D.M. Pennock and M.P.
Wellman (2000). Compact securities
markets for Pareto optimal reallocation of risk. Conference on Uncertainty in Artificial Intelligence, pp.
481–488 D.M. Pennock, E.
Horvitz, S. Lawrence, C.L. Giles (2000). Collaborative
filtering by personality diagnosis: A hybrid memory- and model-based approach.
Conference on Uncertainty in Artificial
Intelligence, pp. 473–480 D.M. Pennock, P.
Maynard-Reid, C.L. Giles (2000). A
normative examination of ensemble learning algorithms. International Conference on Machine Learning, pp.
735–742 D.M. Pennock, E.
Horvitz, C.L. Giles (2000). Social
choice theory and recommender systems: Analysis of the axiomatic foundations
of collaborative filtering. National
Conference on Artificial Intelligence, pp. 729–734 D.M. Pennock and M.P.
Wellman (1999). Graphical
representations of consensus belief. Conference
on Uncertainty in Artificial Intelligence, pp. 531–540 D.M. Pennock (1998). Logarithmic time parallel Bayesian
inference. Conference on
Uncertainty in Artificial Intelligence, pp. 431–438 D.M. Pennock and M.P.
Wellman (1997). Representing aggregate
belief through the competitive equilibrium of a securities market. Conference on Uncertainty in Artificial
Intelligence, pp. 392–400 D.M. Pennock and Q.F.
Stout (1996). Exploiting a theory of
phase transitions in three- satisfiability problems. National Conference on Artificial Intelligence, pp. 253–258 D.M. Pennock and M.P.
Wellman (1996). Toward a market model
for Bayesian inference. Conference
on Uncertainty in Artificial Intelligence, pp. 405–413 C. Connelly, A.W.
Biermann, D. Pennock, P. Wu (1996). Homestudy
software: Flexible, interactive and distributed software for independent
study. ACM SIGCSE Symposium on
Computer Science Education, 28(1), pp. 63–67 C. Connelly, A.W.
Biermann, D. Pennock, P. Wu (1996). Homestudy
software: Complementary systems for computer science courses. Computer Science Education, 7, pp.
53–71 A.W. Biermann, A.F.
Fahmy, C. Guinn, D. Pennock, D. Ramm, P. Wu (1995). A Computer animated system for demonstrating hardware and software
principles. Journal of Computing in
Small Colleges, 10(3), p. 34. A.W. Biermann, D. Ramm, D. Pennock, A.F. Fahmy, P.
Wu (1994). Visualizing computation:
Full color and motion demonstration of computer mechanisms. National Conference on College Teaching
and Learning A.W. Biermann, A.F.
Fahmy, C. Guinn, D. Pennock, D. Ramm, P. Wu (1994). Teaching a hierarchical model of computation with animation software
in the first course. ACM SIGCSE
Symposium on Computer Science Education, 26(1), pp. 295–299 |
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Other
Publications |
D.M. Pennock and R.
Sami (2007). Computational aspects of
prediction markets. In Algorithmic
Game Theory (N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani, editors),
Cambridge University Press. Y. Chen, L. Fortnow, E. Nikolova, and D.M. Pennock
(2007). Combinatorial betting. ACM SIGecom Exchanges, 7(1): 61-64 D.M. Pennock (2004). The Eudaemonic Pie: A review. AI
Magazine, 25(2): 125-128 D.M. Pennock (2004). Conference report: The fifth ACM conference on electronic commerce.
SIGecom Exchanges, 5(1):
48–56 D.M. Pennock (2001). NP markets, or How to get everyone else to solve your intractable
problems. Workshop on Economic
Agents, Models, and Mechanisms at the International Joint Conference on
Artificial Intelligence, pp. 89–98 D.M. Pennock (2001). Conference report: The second ACM conference on electronic commerce.
SIGecom Exchanges, 2(1):
33–38 P. Rusmevichientong, D.M.Pennock, S. Lawrence, C.L.
Giles (2001). Methods for sampling
pages uniformly from the World Wide Web, AAAI Fall Symposium on Using Uncertainty Within Computation D.M. Pennock and M.P. Wellman (1999). The observability of independence under
monetary-based elicitation. Workshop
on Conditional Independence Structures and Graphical Models, pp.
56–57 |
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Patents |
Granted Patent no. 7,788,158:
Dynamic pari-mutuel market Patent no. 7,457,768:
Methods and apparatus for predicting and selectively collecting preferences
based on personality diagnosis Patent no. 7,165,024:
Inferring hierarchical descriptions of a set of documents Applications 20090254475
Prediction market making method and apparatus 20100070322 Method
and apparatus for administering a bidding language for online advertising 20100058249 System
and method for providing a graphical user interface for prediction markets 20090198613 Method
and apparatus for group decision making 20090070873 Safe web
based interactions 20090024510 System and
method of making markets for a finite subset of orders placed across
continuous and countably infinite outcome spaces 20080306819 System
and method for shaping relevance scores for position auctions 20080220855 System
and method for permutation betting 20080133347 System
and method for providing semantic captchas for online advertising 20080133321 System and method for
measuring awareness of online advertising using captchas 20050021461
Term-based concept market 20050021442
Term-based concept instruments 20050021441 Concept
valuation in a term-based concept market |
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Selected
Presenta-tions |
Designing Markets for Prediction Market Madness: Implementing a 9.2 quintillion
outcome prediction market Crowdsourcing predictions The automated
economy The evolution of
online advertising. NBER Working Group on Market
Design, Boston, MA, May 2009 |
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Selected
Presenta-tions (contÕd) |
Combinatorial
betting Prediction markets
and the wisdom of crowds. Symposium on Statistical
Challenges in Electronic Commerce Research, July 2007 (Keynote Speaker) Markets in uncertainty: Risk, gambling, and
information aggregation A Dynamic pari-mutuel market for hedging, wagering,
and information aggregation Sports betting markets: Characteristics and
information aggregation. International Conference on Gambling and Risk Taking,
Vancouver, Canada, May 2003 Information and forecast accuracy in markets and
market games. Modeling information incorporation in markets and
market games. Markets and Decisions Workshop, Arlington, VA, June 2002 Semantic explanations of market forecasts. Controlled Market
Economies Symposium, Cambridge, MA, May 2002 The power of play: Efficiency, information
aggregation, and forecast accuracy in market games. Institute
for Operations Research and the Management Sciences National Meeting,
Miami, FL, November 2001 Maximizing information, optimizing risk, and
leveraging forecasts in securities markets. NEC Research Symposium,
Bonn, Germany, May 2001 Recommender
systems. Penn State eBusiness Research Center Academic Workshop on
Personalization Issues in e-Business, Arlington, VA, April 2001 Winners
donÕt take all: A model of web link accumulation. Workshop on
Data Mining and Learning on the Web at the 14th Conference on Neural
Information Processing Systems, Breckenridge, CO, December 2000 E-markets
and uncertainty, or What Bayesians can learn by watching market prices. Microsoft
Research, Redmond, WA, June 2000 Introduction
to auctions. University of Pennsylvania, Philadelphia, PA, April, 2000 Group Coordination: A History of
Paradox and Impossibility. Efficient
representations for aggregate belief and compact securities markets. Institute
for Operations Research and the Management Sciences National Meeting,
Philadelphia, PA, November 1999 |
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Professional
Experience |
Principal
Researcher and Assistant Managing Director Principal
Research Scientist Manage a research group focused on algorithmic
economics, or the intersection of computer science and economics. Conduct
basic and applied research on prediction markets, auctions, advertising, and
artificial intelligence. Research
Scientist Research
Scientist Conducted research on information markets, Web
games, recommender systems, Web hyperlinks, social networks, consensus
Bayesian networks, and ensemble learning algorithms Adjunct
Assistant Professor of Computer Science and Engineering Taught CSE 597B, Computational aspects of
e-commerce Research
Intern
Microsoft Research, Redmond, WA, Jun 1998 to Aug 1998 Conducted research on impossibility theorems in
group coordination, recommender systems, and Bayesian networks Research
Assistant University of Michigan, Ann
Arbor, MI, Sep 1995 to
May 1999 Conducted research on computationally efficient and
decentralized mechanisms for aggregating information using markets Teaching
Assistant University of Michigan, Ann
Arbor, MI, Sep 1994 to Dec
1994 Taught CS 380: Data Structures and Algorithms Research
Assistant
Duke University, Durham, NC,
Sep 1993 to Aug 1994 Co-developed an educational software program titled
ŌThis is How a Computer WorksĶ, currently still in use at Duke University and
elsewhere. |
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Professional
Services |
Chair, ACM Special Interest Group on
Electronic Commerce, 2007-2011 General Chair, Auctions, Market Mechanisms and their Applications,
2009 (inaugural year) Co-Editor, Economic Inquiry, 2008-11; ACM Transactions on Internet
Technology, 2006-8 Editorial Board, ACM Transactions of Economics and Computation, 2011-present Program Co-Chair, ACM Conference on Electronic Commerce, 2006 Track Co-Chair, International World Wide Web
Conference, 2002, 2008 Organizing
Committee Ad Auctions Workshop (a workshop series
I co-founded) 2005,2007,2009; Prediction Markets Workshop (a workshop series
I co-founded) 2007,2008; DIMACS Workshop on Information Markets, 2005;
Session on Markets in Uncertainty at the Institute for Operations Research
and the Management Sciences National Meeting, 2001; Workshop on Data Mining
and Learning on the Web, 2000 Senior Program Committee ACM
Conference on Electronic Commerce, 2009
Program Committee International Joint Conference on Artificial
Intelligence 2003,2005,2009,2011; Auctions, Market Mechanisms and their
Applications, 2011; CrowdConf, 2010; Recommenders 2008; International World
Wide Web Conference, 2005; ACM Conference on Electronic Commerce,
2004,2007-2011; Conference
on Uncertainty in Artificial Intelligence, 2000-2009,2011; Conference on
Email and Anti-Spam, 2004,2005; Conference on Very Large Databases, 2005; ACM Conference on Knowledge Discovery and Data
Mining 2003,2008; National Conference on Artificial Intelligence, 2002,2005,2008;
International Joint Conference on Autonomous Agents and Multi-Agent Systems,
2002,2003,2012; Web Dynamics Workshop at the International World Wide Web
Conference, 2002; Workshop on Agent Mediated Electronic Commerce at the
International Joint Conference on Autonomous Agents and Multi-Agent Systems,
2002,2003; Workshop on Economic Agents, Models, and Mechanisms at the 17th
International Joint Conference on Artificial Intelligence, 2001; Workshop on
Internet Bots: Systems and Applications at the 12th International Conference
on Database and Expert Systems Applications, 2001; International Symposium on
Imprecise Probabilities and Their Applications, 2001 Other Peer Reviewing Services Artificial Intelligence; Journal of Artificial
Intelligence Research; Proceedings of the National Academy of Sciences; Algorithmic
Finance; Journal of Machine Learning Research; ACM Transactions on
Information Systems; IEEE Internet Computing; IEEE Intelligent Systems; IEEE
Transactions on Knowledge and Data Engineering; Decision Analysis; Journal of
Parallel and Distributed Computing; Management Science; Organizational
Behavior and Human Decision Processes; ACM Conference on Electronic Commerce,
2005; National Conference on Artificial Intelligence, 2002; International Joint Conference on
Autonomous Agents and Multi-Agent Systems, 2002; International Joint Conference on Artificial
Intelligence, 1999, 2001; Conference on Uncertainty in Artificial
Intelligence, 1998, 1999; International Conference on Distributed Computing
Systems, 1998 |
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Professional
Services (contÕd) |
Professional Society Memberships Association for Computing Machinery (ACM) Senior
Member Post-Doctoral Scientists Managed Rugierro Cavallo Students Managed or Co-Managed Abe Othman,
Carnegie Mellon (dissertation committee); Mingyu Guo & Lirong Xia, Duke;
Dan
Cosley & Shyong
Lam, U.
Minnesota; Varsha Dani, U. Chicago; Kushal Dav & Rahul Sami, Yale;
Sandip Debnath, Jane Feng, Seung-Taek Park, & Secil Ugurel, Pennsylvania
State; Rodrigo Lopes Canado Fonseca, U. Federal de Minas Gerais; Eric Glover &
Quang Duong, U. Michigan; A. Khrabrov, Alexandrin Popescul, & Jenn Wortman Vaughn, U. Pennsylvania; Finn
rup Nielsen, Technical U. Denmark; Nicolas Lambert & Paat Rusmevichientong, Stanford;
Sumit Sanghai, U. Washington |
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Technical
Background |
Computer Science Artificial
Intelligence Theory Languages Physics Chaos, non-linear dynamics, quantum mechanics, relativity, optics, holography, percolation theory, thermal (statistical) physics, electronics, classical dynamics, and electricity and magnetism Math Probability, non-linear time series analysis,
differential equations, financial engineering, fractals, topology, linear
algebra, and calculus |
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References |
Available
on request |