I am a PhD student in Computer Science at the University of New Mexico. I have previously obtained two separate degrees in Electrical Engineering in Belgium (approximately Masters level), as well as an MS in Computer Science at Stanford.
My main research interests lie in that nebulous region between biology and computation, including Genetic Algorithms, Genetic Programming, Neural Networks, Theoretical Biology, Computational Biology, etc... I am a member of the Adaptive Computation group at UNM, a regular visitor at the Santa Fe Institute (which is a Really Cool Place), and ex-member of the EVCA (EVolving Cellular Automata) group there.
My advisor here at UNM is Stephanie Forrest. In the past, I've done some work on her Computer Immune Systems project, especially on the theoretical aspects of it.
My RESUME is now online as well: Postscript or pdf version.
Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering. D'haeseleer, P., Liang, S. , and Somogyi, R., To appear in Bioinformatics
Gene network inference using a linear, additive regulation model. D'haeseleer, P., Fuhrman, S., Submitted to Bioinformatics
Tracing genetic information flow from gene expression to pathways and molecular networks. In DNA microarrays: The new frontier in gene discovery and gene expression analysis. Short course syllabus, Society for Neuroscience Annual Meeting.
Linear Modeling of mRNA expression levels during CNS development and injury. D'haeseleer, P., Wen, X., Fuhrman, S., and Somogyi, R., Pacific Symposium on Biocomputing '99, pp. 41-52, World Scientific Publishing Co., 1999.
Gene Expression Analysis and Genetic Network Modeling. D'haeseleer, P., Liang, S. , and Somogyi, R., Pacific Symposium on Biocomputing '99, Tutorial session on Gene Expression and Genetic Networks.
Data Requirements for Inferring Genetic Networks from Expression Data. D'haeseleer, P., Pacific Symposium on Biocomputing '99, Poster session.
A distributed approach to anomaly detection. D'haeseleer, P., Forrest, S., and Helman, P., in preparation.
Mining the Gene Expression Matrix: Inferring gene relationships from large scale gene expression data. D'haeseleer, P., Wen, X., Fuhrman, S., and Somogyi, R., Information Processing in Cells and Tissues, Paton, R.C., and Holcombe, M. Eds., pp. 203-212, Plenum Publishing, 1998.
An Immunological Approach to Change Detection: Theoretical Results. D'haeseleer, P., Proceedings of the 9th IEEE Computer Security Foundations Workshop, IEEE Computer Society Press, 1996.
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications. D'haeseleer, P., Forrest, S. and Helman, P., Proceedings of the IEEE Symposium on Security and Privacy, IEEE Computer Society Press, 1996.
A change detection method inspired by the immune system: Theory, algorithms and techniques. D'haeseleer, P., Technical Report CS95-06, The University of New Mexico, Albuquerque, NM, 1995.
Further Efficient Algorithms for Generating Antibody Strings. D'haeseleer, P., Technical Report CS95-03, The University of New Mexico, Albuquerque, NM, 1995.
Effects of Locality in Individual and Population Evolution. D'haeseleer, P. and Bluming, J. In Advances in Genetic Programming , K.E. Kinnear Jr. Ed., Cambridge MA, MIT Press, 1994.
Context Preserving Crossover in Genetic Programming. Proceedings of 1st IEEE Conf. on Evolutionary Computation , IEEE Press, 1994.
Optimal pad location method for microelectronic circuit cell placement, Scepanovic, R. and D'haeseleer, P., United States Patent 5,638,293, 1997.
Method and apparatus for computing minimum wirelength position (MWP) for cell in cell placement for integrated circuit chip, D'haeseleer, P. and Scepanovic, R., United States Patent 5,859,781, 1999.