Andreas  S. Weigend, PhD

+1 (917) 697-3800

303 East Pike Street #401
Seattle, WA 98122, USA

aweigend@stanford.edu
www.stanford.edu/~aweigend

 

 

Summary

 

 

Current Employment

Amazon.com, Chief Scientist                                                                                                                         Seattle, WA

  • Responsible for research in machine learning and computational marketing
  • In charge of development and strategic application of analytic approaches throughout the firm
  • Applications include real-time predictions of customer intent and satisfaction, actionable models of shopping behavior, personalization and long-term optimization of pricing and promotions
  • Point person for relations with academic and other external researchers

 

Stanford University, Department of Statistics, Visiting Professor                                                                      Stanford, CA

  • Teaching “Data Mining and E-Business” (Statistics252, 3 units)

 

 

Expertise

Strategic

  • Leverage information technology and the internet to create value from data
  • Transfer relevant scientific research from academia to business

Scientific and Technical

  • Machine learning, data mining and knowledge discovery
  • Time series analysis and prediction
  • Risk management, quantitative and behavioral finance
  • Information retrieval and visualization

 

 

Experience and Environments

   Academic

  • Business School (New York University, Associate Professor; 3 years full-time; 
    Executive MBA faculty at China Europe International Business School – CEIBS; 1 year)
  • Computer Science (University of Colorado, Assistant Professor; 4 years full-time)

   Research

  • Xerox PARC (Palo Alto Research Center, 2 years full-time)
  • Published six books and more than 100 scientific papers, some cited more than 300 times

   Entrepreneurship

  • Startups (Shockmarket, Moodlogic; 2 years full-time)
  • Consulting (data mining, behavioral analytics, trading and risk models; 10 years part-time)

 

 

Education

 

Stanford University

PhD Thesis on Neural Networks for Time Series Analysis and Prediction

Advisors: D. E. Rumelhart, J. H. Friedman,
W. B. Arthur, B. A. Huberman, W. A. Little

Stanford, CA (86-91)
PhD in Physics
MS in Physics

Bonn University

Masters Thesis on Comparing Computer Simulations of Elementary Particle Physics with Experimental Data at CERN

Bonn, Germany (83-86) Diplom-Physiker

Trinity College, Cambridge

Graduate coursework in Physics and Philosophy

Cambridge, UK (82-83)

Karlsruhe University

Undergraduate degrees in Electrical Engineering and Physics

Karlsruhe, Germany (79-82) Vordiplom

 

 

 

 

Previous Full-Time Positions

 

 

ShockMarket Corporation  (Chief Scientist)

Palo Alto, CA (00-01)

A financial information services startup providing information products and market sentiment
Funded by Deutsche Bank, D. E. Shaw, Odeon Capital and others, currently “mothballed”

Research and Development

  • Directed interdisciplinary team to:
    • Develop analytics based on real-time transactions from online brokerages
    • Design core products based on quantitative data analysis and behavioral finance concepts
    • Verify validity of data collection and results using advanced statistical techniques
  • Assembled and managed board of scientific advisors (D. Kahneman, T. Odean, N. Schwarz,
    R    . H. Thaler)

Corporate Activities

  • Worked closely with CEO to define strategy and vision
  • Developed partnerships with hedge funds, distribution channels, and financial institutions
  • Presented to major brokerages, financial and information services companies, and venture firms

 

 

MoodLogic, Inc. (Chief Scientist) (formerly Emotioneering, Inc.)

San Francisco, CA (99-00)

A music technology startup providing software systems and data services for audio consumer electronics, music desktop applications,
and music subscription services

Research and Development

  • Designed music navigation system based on music perception and digital fingerprinting
  • Led initial research and developed successful prototype

Corporate Activities

  • Created long-term vision for managing, programming, and discovering music
  • Built interdisciplinary team (hired first eight employees)

 

 

 

New York University, Stern School of Business

New York, NY (97-00)

Associate Professor of Information Systems

(Sabbatical at Stanford University, Department of Statistics, 99-01)

  • Directed research group on data mining and knowledge discovery
  • Received New York University Teaching Award for course on Data Mining in Finance

 

 

University of Colorado at Boulder

Boulder, CO (93-96)

Assistant Professor of Computer Science and Cognitive Science

  • Founded research group on time series analysis and prediction
  • Taught courses on artificial intelligence, neural networks, and music cognition

 

 

Xerox Palo Alto Research Center (PARC)

Palo Alto, CA (91-93)

Member of Research Staff, Machine Perception Group

  • Invented architecture for hierarchical classification of text documents
  • Applied neural networks to optical character recognition

 

 

Other Positions

 

 

Consulting: Financial

 

Yodlee, Inc.

Redwood Shores, CA (02)

Led team to develop strategy for data and information products based on account aggregation

 

Lava Trading, Inc

New York, NY (02)

Presented value-added products and new order types based on real-time analytics on direct trading data

 

Grantham, Mayo, Van Otterloo & Co. LLC

Boston, MA (98-99)

Implemented and evaluated Hidden Markov Models for trading strategies, now used in production

 

J.P. Morgan

New York, NY (95-97)

Developed and delivered model for volatility prediction

 

Nikko Securities

Tokyo, Japan (96)

Built trading model for Nikkei 225 index

 

Prediction Company

Santa Fe, NM (96)

Assessed novel machine learning technologies for statistical arbitrage

 

Union Bank of Switzerland

Zurich, Switzerland (95-96)

Invented information radar to alert investors of relevant trends and events

 

Morgan Stanley

New York, NY (95-96)

Developed neural network to optimize Sharpe ratio in trading model

 

Goldman Sachs

New York, NY (93-95)

Evaluated use of neural networks for financial time series prediction

 

Other engagements included the Swiss Stock Exchange (Zurich), Shanghai Credit Information Systems, Stockstore (Breda, Netherlands), Bank of China (Beijing), and Bank of Thailand (Bangkok).

 

 

Consulting: Non-Financial

 

Bertelsmann Venture Capital

San Francisco, CA, and Hamburg, Germany (01-02)

Performed due diligence on technology start-ups

 

Opion, Inc.

Herndon, VA (00-01)

Designed algorithms for identifying opinion leaders and trends from message boards

 

Acxiom Corp.                                

Little Rock, AR (00)

Developed strategy for direct marketing on the Web enhancing demographics with online behavioral data

                                                                                                                

Interval Research Corp.                                

Palo Alto, CA (00)

Wrote research proposal for targeted advertising on cable television

 

TextWise LLC

Syracuse, NY (95)

Unified diverse sources of relevance assessment for text mining

 

Siemens Corporate Technology

Munich, Germany (91-98)

Innovated time series analysis software now used in-house and in consulting

 

Other engagements included BHP Research (Melbourne), Deutsche Bahn AG (German Railway, Frankfurt), Electricité de France (Paris), Yahoo (Mountain View), Zone Reactor (Los Angeles), and the CIA.

 

 

Advisory Boards
  • Startups
  • Hedge funds
  • Stanford University’s Asia Technology Initiative

 

 

Awards and Publications (Selection)

 

 

Awards
  • IBM Partnership Award for work on Discovering Trading Styles in Financial Transactions
  • National Science Foundation (NSF) and the Air Force Office of Scientific Research (AFOSR)
    awards and grants for work on Time Series Prediction
  • German National Scholarship Foundation (Studienstiftung des Deutschen Volkes), and German
    Academic Exchange Service (Deutscher Akademischer Autauschdienst, DAAD) scholarships for
    entire undergraduate and graduate education
  • Baden-Württemberg State Award for best undergraduate degree

 

Books
  • Computational Finance. (1999) Abu-Mostafa, Y. S., B. LeBaron, A. W. Lo, and A. S. Weigend (Eds.) Proceedings of the Sixth International Conference on Computational Finance (CF99, New York, January 1999).  Cambridge, MA: MIT Press.
  • Decision Technologies for Financial Engineering. (1997) Weigend, A. S., Y. S. Abu-Mostafa, and A.-P. N. Refenes (Eds.) Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM'96, Pasadena, November 1996).  Singapore: World Scientific.
  • Time Series Prediction: Forecasting the Future and Understanding the Past. (1994) Weigend, A. S., and N. A. Gershenfeld (Eds.) Santa Fe Institute Studies in the Sciences of Complexity XV; Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis (Santa Fe, May 1992). Reading, MA: Addison-Wesley.

 

Journal Articles
  • Predicting Daily Probability Distributions of S&P500 Returns (2000) Weigend, A. S., and S. Shi. Journal of Forecasting 19, p. 375-392.
  • Data Mining for Features Using Scale-Sensitive Gated Experts (1999) Srivastava, A. N., R. Su, and A. S. Weigend.  IEEE Transactions on Pattern Analysis and Machine Intelligence 21, p. 1268-1279.
  • Exploiting Hierarchy in Text Categorization (1999) Weigend, A. S., E. D. Wiener, and J. O. Pedersen. Information Retrieval 1, p. 193-216.
  • A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series (1998) LeBaron, B., and A. S. Weigend.  IEEE Transactions on Neural Networks 9, p. 213-220.
  • Exploiting Local Relations as Soft Constraints to Improve Forecasting (1998) Weigend, A. S., and H. G. Zimmermann.  Journal of Computational Intelligence in Finance 6, p. 14-23.
  • A First Application of Independent Component Analysis to Extracting Structure from Stock Returns (1997) Back, A. D., and A. S. Weigend.  International Journal of Neural Systems 8, p. 473-484.
  • Nonlinear Trading Models Through Sharpe Ratio Maximization (1997) Choey, M., and A. S. Weigend.  International Journal of Neural Systems 8, p. 417-431.
  • Modeling Volatility Using State Space Models (1997) Timmer, J., and A. S. Weigend.  International Journal of Neural Systems 8, p. 385-398.
  • Time Series Analysis and Prediction using Gated Experts with Application to Energy Demand Forecasts (1996) Weigend, A. S.  Applied Artificial Intelligence 10, p. 583-624.
  • Predicting Conditional Probability Distributions: A Connectionist Approach (1995) Weigend, A. S., and A. N. Srivastava.  International Journal of Neural Systems 6, p. 109-118.
  • Paradigm Change in Prediction (1994) Weigend, A. S.  Philosophical Transactions of the Royal Society, Series A (Physical Sciences) 348, p. 405-420 (with discussion).
  • Bayesian Back-Propagation (1991) Buntine, W. L., and A. S. Weigend.  Complex Systems 5, p. 603-643.
  • Predicting the Future: A Connectionist Approach (1990) Weigend, A. S., B. A. Huberman, and D. E. Rumelhart.  International Journal of Neural Systems 1, p. 193-209.

 

The full list of publications is available at www.weigend.com/publications