Faculty Details

Photo of Janusz  Szczypula

Janusz Szczypula

Teaching Professor

Teaching Track

Full-time Faculty

Voice: n/a
Email: js1m@andrew.cmu.edu


Janusz Szczypula has been on the Heinz School faculty since 1997. He served as Director of the Master of Information Systems Management (MISM) program from 1998 to 2003. Szczypula holds a M.S. in Computer Science from the Jagiellonian University and a M. Pub. Aff. from the University of Texas at Austin. He also earned a M.Phil. in Policy Analysis as well as a Ph.D. from Carnegie Mellon University.

In 1997 Szczypula received the Carnegie Mellon University's 1997 William W. Cooper Doctoral Dissertation Award for the best dissertation in Management and Management Science. In 2001 he has received an Outstanding Professor award from the National Society of Collegiate Scholars. The same year he received the Staff Excellence Award from the MISM program. He has also received the Heinz School’s top award for teaching excellence in 1995 and 2006.

Selected Publications

"Idea Creation, Constructivism and Evolution: Key Characteristics in the Videogame Artifact Design Process," European Management Journal, 24(4), 2006 (with Ted Tschang).

"New Course Curricula for the 21st Century: Learning and Creating Knowledge in Virtual Settings," in Access to Knowledge: New Information Technologies and the Emergence of the Virtual University, T. Della Senta and T. Tschang (editors), Elsevier Scientific/Pergamon Press, 2001, (with Ted Tschang and Om Vikas)

"Forecasting Analogous Time Series," in Principles of Forecasting: A Handbook for Researchers and Practitioners, Scott Armstrong (editor), Kluwer Academic Publishers, 2001 (with George Duncan and Wilpen Gorr).

"Communication and Information: Alternative Uses of the Internet in Households, " Information Systems Research, 10(4), 1999 (with R. Kraut, T. Mukhopadhyay, S. Kiesler, and W. Scherlis).
"A Brief History of Balance Through Time," Journal of Mathematical Sociology, 21(1-2), 113-131, 1996, (with Patrick Doreian, Roman Kapuscinski, and David Krackhardt).

Research Interest(s)

Forecasting Methods, Social Networks, and Decision Support Systems


PhD, Information and Decision Systems, Carnegie Mellon University

Working Papers

Forecasting Crime

Organizations in the private sector must do strategic planning over long-term horizons to locate new facilities, plan new products, develop competitive advantages, and so forth. Consequently, long-term forecasts of demand, costs of raw materials, etc. are important in the private sector. There is no such strategic counterpart to police work; consequently, long-term forecasts are of little value to police. Police primarily need short-term forecasts; for example, crime levels one week or one month ahead. Currently, police mostly respond to new crime patterns as they occur. Client-server computing for realtime access to police records and computerized crime mapping have made it possible for police to keep abreast with crime. With short-term forecasting police may be able to get one step ahead of criminals by anticipating and preventing crime. The organization of this paper proceeds first with a description of short-term forecasting models, to provide basic terms and concepts. Next is a discussion of unique features of crime space-time series data, and the need for data pooling to handle small-area model estimation problems. Lastly are a discussion of particular forecasting requirements of police and a summary.


Forecasting Analogous Time Series

Organizations that use time series forecasting on a regular basis generally forecast many variables, such as demand for many products or services. Within the population of variables forecasted by an organization, we can expect that there will be groups of analogous time series that follow similar, time-based patterns. The co-variation of analogous time series is a largely untapped source of information that can improve forecast accuracy (and explainability). This paper takes the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. Bayesian pooling uses data from analogous time series as multiple observations per time period in a group-level model. It then combines estimated parameters of the group model with conventional time series model parameters, using "shrinkage" weights estimated empirically from the data. Major benefits of this approach are that it 1) minimizes the number of parameters to be estimated (many other pooling approaches suffer from too many parameters to estimate), 2) builds on conventional time series models already familiar to forecasters, and 3) combines time series and cross-sectional perspectives in flexible and effective ways.


Comparative Study of Cross Sectional Methods for Time Series with Structural Changes

Comparative Study of Cross Sectional Methods for Time Series with Structural Changes