Marquette University College of Engineering

Richard J. Povinelli, Ph.D., P.E.

Assistant Professor
Director Of Computer Engineering Laboratories
Electrical and Computer Engineering
Marquette University

Office: Haggerty Hall 224
Phone: (414) 288-7088
Fax: (414) 288-5579

Email: Richard.Povinelli@marquette.edu

Website: http://povinelli.eece.mu.edu

Research Interests

Time Series Data Mining
Dynamical Systems
Chaos
Nonlinear Signal Processing
Machine Learning
Evolutionary Algorithms
Financial Engineering

Professional Preparation

Marquette University	Ph.D.	1999
Rensselaer Polytechnic Institute	M.S.	1989
University of Illinois	B.S.	1987
University of Illinois	B.A.	1987

Honors

2005	Award Winning Paper PhysioNet/Computers in Cardiology Challenge
2003	Young Engineer of the Year Award Engineers and Scientists of Milwaukee

Professional Activities

2001	Senior Member Institute of Electrical and Electronics Engineers (IEEE)
2001 – present	Upsilon Pi Epsilon
2000 – present	American Society for Engineering Education (ASEE)
2000 – present	Organizing Committee Artificial Neural Networks in Engineering (ANNIE) Conference
1999 – present	IEEE Computer Society
1999 – present	Association for Computing Machinery (ACM)
1999 – present	ACM – Special Interest Group Knowledge Discovery in Data (SIGKDD)
1999 – present	Sigma Xi
1997 – present 1985 - 1987	Institute of Electrical and Electronics Engineers (IEEE)
1994	6 Step Problem Solving Trainer
1992	Field Laptop/CD-ROM Project Leader
1992	Successful Service Communication Broadcast
1985 – present	Tau Beta Pi,
1985 – present	Phi Beta Kappa
1985 – present	Golden Key
1985 – present	Eta Kappa Nu

Current Research

Identifying temporal patterns for characterization and forecasting of time series data using embedding theorems, genetic algorithms and wavelets. Applying these algorithms to engineering and financial time series.

Selected Recent Publications

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, Felice M. Roberts, Jinjin Ye. (in press) "Statistical Models of Reconstructed Phase Spaces for Signal Classification," IEEE Transactions on Signal Processing.

Behrooz Mirafzal, Richard J. Povinelli, and Nabeel A. O. Demerdash. (in press) “Inter-Turn Fault Diagnosis in Induction Motors Using the Pendulous Oscillation Phenomenon,” IEEE Transactions on Energy Conversion.

Kevin M. Indrebo, Richard J. Povinelli, Michael T. Johnson. (in press) “Sub-banded Reconstructed Phase Spaces for Speech Recognition,” Speech Communication.

Michael T. Johnson, Richard J. Povinelli, Andrew C. Lindgren, Jinjin Ye, Xiaolin Liu, Kevin M. Indrebo. (2005) “Time-Domain Isolated Phoneme Classification using Reconstructed Phase Spaces,” IEEE Transactions on Speech and Audio Processing, vol. 13, no. 4, July, 458-466.

Michael T. Johnson, Richard J. Povinelli. (2005) "Generalized Phase Space Projection for Nonlinear Noise Reduction," Physca D, vol. 201, no. 3-4, February, pp. 306-317.

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, Jinjin Ye. (2004) “Time Series Classification using Gaussian Mixture Models of Reconstructed Phase Spaces,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, June, 779-783.

John F. Bangura, Richard J. Povinelli, Nabeel A.O. Demerdash, Ronald H. Brown. (2003). “Diagnostics of Eccentricities and Bar/End-Ring Connector Breakages in Polyphase Induction Motors through a Combination of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Techniques,” IEEE Transactions On Industry Applications, vol. 39, no. 4, July/August, 1005-1013.

Richard J. Povinelli, Xin Feng. (2003) “A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events,” IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 2, March/April, 339-352.

David H. Diggs, Richard J. Povinelli. (in press) “A Temporal Pattern Approach for Predicting Weekly Financial Time Series,” Artificial Neural Networks in Engineering, St. Louis, Missouri, November 2003.

Michele R. B. Malinowski, Richard J. Povinelli. (in press). “Searching for Non-Sense: Identification of Pacemaker Non-Sense and Non-Capture Failures using Machine Learning Techniques” Computers in Cardiology, Thessaloniki, Greece, September 2003.

Richard J. Povinelli, John F. Bangura, Nabeel A.O. Demerdash, Ronald H. Brown. (2002). “Diagnostics of Bar and End-Ring Connector Breakage Faults in Polyphase Induction Motors Through a Novel Dual Track of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Modeling,” IEEE Transactions on Energy Conversion, vol. 17, no. 1, March 2002, 39-46.