Gary M. Weiss, Ph.D. Associate Professor & Director of the WISDM Lab 308A John Mulcahy Hall Department of Computer & Information Sciences Fordham University 441 East Fordham Road, Bronx NY 10458 Email: gweiss(at)cis.fordham.edu Phone: 718-817-0785 Google Scholar Profile

I am an associate professor of Computer and Information Science at Fordham University. Prior to coming to Fordham, I worked for many years at AT&T Bell Labs and (after the Lucent split-off) AT&T Labs. There I worked for several years as a software engineer designing telephone switching software, before moving on to expert system development, and, finally, data mining. I spent my final five years at AT&T in a marketing analysis group applying data mining methods to solve complex business problems. I received a B.S. degree in Computer Science from Cornell University, an M.S. in Computer Science from Stanford University and a Ph.D. degree in Computer Science from Rutgers University. I have published over forty papers in the areas of machine learning and data mining as well as several in the area of expert systems and object-oriented programming.

My primary research area is machine learning/data mining. Machine learning strives to automatically improve the performance of a system over time, as experience is accumulated, whereas the related area of data mining concerns the automatic extraction of knowledge from large amounts of data via intelligent algorithms. My current research focus involves WIreless Sensor Data Mining (WISDM). My WISDM research group is currently developing an app for Android-based phones so that the sensor data from these phones can be "mined" for useful information, resulting in new and useful applications. We have already demonstrated that we can effectively determine a user's activity (walking, jogging, climbing stairs, etc.), their identify, and sometimes even their characteristics (height, sex, etc.) just based on the accelerometer data they generate by carrying their phones. For more information, please visit the WISDM Lab page. This work is supported by the NSF and Google.

Prior to the WISDM project, by research generally involved studying how we can deal with many of the real-world issues that make learning, and data mining, more difficult. My recent work has focused on how class distribution affects data mining and how one might be able to choose data intelligently when data is costly, to improve the effectiveness of data mining. I have also studied the problem of why it is so difficult to deal with rare cases and rare classes in data mining. Recently, I have actively promoted work in the area on Utility-Based Data Mining, by organizing KDD workshops on this topic in 2005 and 2006 and guest editing a special issue of the Data Mining and Knowledge Discovery journal on this topic in 2008. While in industry I also conducted research in expert systems and in object technology. I helped develop an rule-based object-oriented expert system for maintaining telephone switching systems which in 1998 received a AAAI Innovative Application for Artificial Intelligence award. For more on my research, please visit my research page.

My Erdös-Bacon number, on the other hand, is infinite.

Favorite data mining related quote: "In God we trust. All others must have data." Rick Peterson, former New York Mets pitching coach (quoted in New York Times, Jun 13, 2004).

Member Association for Computing Machinery (ACM)
Member ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD)
Member Council on Undergraduate Research