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2033 K Street NW, Room 340C
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  • By Appointment Only
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Bock is credited with pioneering the development of Collective Learning Systems Theory in the mid 1970s, and the pre-supervised instantiation of this theory, the well-known software engine ALISA (Adaptive Learning Image and Signal Analysis) in the early 1990s. ALISA is a general-purpose classification system which now includes a variety of useful image and signal processing tools, including the Texture, Segmentation, Geometry, Vector, Shape, and Structure modules. Over the last 25 years, Professor Bock's research in Statistical Learning has been funded by a variety of organizations, including the Research Institute for Applied Knowledge Processing (FAW) in Ulm, Germany; Robert Bosch in Germany; the Hubble Space Telescope Project (NASA); the National Institute of Standards and Technology (NIST), the United States Navy, and currently the Defense Threat Reduction Agency (DTRA) in the US Department of Defense. The current academic research focus of Professor Bock and his Project ALISA team of doctoral students is on the fusion of the ALISA modules and several innovative unsupervised clustering algorithms into an adaptive hierarchical Statistical Learning Engine for high-level cognitive processing of natural language. Professor Bock's long-term research objective is the design and implementation of an artificially intelligent being whose cognitive capabilities are on a par with those of human beings.

MS, 1964, Purdue University
BA, 1962, Ripon College

Artificial Cognition; Adaptive Statistical Learning; Cognitive Neuroscience; Image and Signal Processing; R&D Methodology for Science and Engineering

Selected Publications: 

1. Peter Bock, Project ALISA (Adaptive Learning Image and Signal Analysis) ,, a perpetual online exhibition for Universities, Robotics, and Industry Research,, July 2009.

2. Alice Armstrong and Peter Bock, Using Tactics-Based Learning to Accelerate Recovery of an Adaptive Learning System in a Changing Environment , Proceedings of the 36th Applied Imagery Recognition Workshop, October 2007.

3. Carsten Oertel and Peter Bock, General shape generation by contouring fractals and applying linear boundary regression , Proceedings of the 36th Applied Imagery Recognition Workshop, October 2007.

4. D. Portnoy and Peter Bock, Unsupervised Discovery of Homograph Senses Using Lexical Context Deconvolution , 9th World Multi-Conference on Systems, Cybernetics and Informatics, July 2005.

5. T. Ko and Peter Bock, Classifying Material Surfaces from Images Using Color, Orientation and Scale Invariant Texture Features , AIPR 2004: Emerging Technologies and Applications for Imagery Pattern Recognition, October 2004.

6. D. Portnoy, Peter Bock, P. Heimberg, and E. Moore, Using ALISA for High-Speed Classification of the Components and their Concentrations in Mixtures of Radioisotopes , SPIE: Design and Microfabrication of Novel X-Ray Optics (Snigirev, Anatoly A.; Mancini, Derrick C. (Editors), August 2004.

7. Peter Bock, Getting it Right: R&D Methods for Science and Engineering , Book, Academic Press, San Diego CA, 2001.

8. Peter Bock, The Emergence of Artificial Cognition: An Introduction to Collective Learning , Book, World Scientific Publishing Company, River Edge NJ, 1993.