Education
Ph.D. Computer Science, University of Memphis, 2002
M.S. Computer Science, University of Szeged, Hungary, 1995
B.S. Computer Science, University of Szeged, Hungary, 1993
Profile
Dr. Kelemen is an adjunct faculty member of the department
of Biostatistics. He is also a tenure track assistant professor
at the department of Computer and Information Sciences at
Niagara University.
Research Interests
My current research interests include bioinformatics and computational
biology; medical, computational and artificial intelligence;
biometrics; neural networks and machine learning; pattern
recognition, decision making, optimization; intelligent agents
and cognitive modeling.
Selected Publications
- Liang, Y., Kelemen, A., (2006) “Associating phenotypes with molecular events: a review of statistical advances and challenges underpinning microarray analyses” Journal of Functional and Integrative Genomics, Vol. 6, pp. 1-13.
- Kelemen, A., Liang, Y., (2006) “Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches”, (H. Hsu, ed.), in “Advanced Data Mining Technologies in Bioinformatics”, pp.136-154.
- Liang, Y., Kelemen, A., Tayo, B. O., (2006) “Model based or algorithms based? Gene Expression Based Statistical Methods to find Evidence of Diabetes”. Journal of Statistical Methods for Medical Research Vol. 15(3).
- Kelemen, A., Franklin, S., Liang, Y., (2005) “Constraint Satisfaction in Conscious Software Agents - A Practical Application”, Journal of Applied Artificial Intelligence. 19:491-514.
- Kelemen, A., Liang, Y., Franklin, S. (2005) “Learning High Quality Decisions with Neural Networks in “Conscious” Software Agents”, Journal of World Scientific and Engineering Academy and Society, 9(4), pp.1482-1492.
- Liang, Y., Tayo, B., Cai, X., Kelemen, A., (2005) “Differential and Trajectory Methods for Time Course Gene Expression Data”. Bioinformatics, 20(13): 3009-3016.
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Liang, Y., Kelemen, A., (2005) “Temporal Gene Expression Classification with Regularised Neural Network”. International Journal of Bioinformatics Research and Applications, 1(4), pp. 399-413.
- Yulan Liang and Kelemen, A., (2004)
“Hierarchical Bayesian Neural Network for Gene Expression
Temporal Patterns”, Statistical Applications in Genetics
and Molecular Biology: Vol. 3: No. 1, Article 20. (http://www.bepress.com/sagmb/vol3/iss1/art20)
- Kelemen, A., and Liang, Y., “Bayesian
Regularized Neural Network for Multiple Gene Expression
Pattern Classification ”, In the proceedings of the
IEEE International Joint Conference on Neural Networks,
Portland, OR, 2003 pp. 654-659
- Kelemen, A., Liang, Y., Kozma, R., and
Franklin, S., "Optimizing Intelligent Agent's Constraint
Satisfaction with Neural Networks", in: "Innovations
in Intelligent Systems" (A. Abraham, L. Jain, J. Kacprzyk,
Eds.), in the Series "Studies in Fuzziness and
Soft Computing", Springer-Verlag, Heidelberg,
Germany, pp. 255-272, 2002.
- Kelemen, A., Liang, Y., and Franklin,
S., "A Comparative Study of Different Machine Learning
Approaches for Decision Making", in: "Recent Advances
in Simulation, Computational Methods and Soft Computing"
(N. E. Mastorakis, ed.) in the "Electrical and
Computer Engineering Series", WSEAS Press, Piraeus,
Greece, pp. 181-186, 2002.
- Liang,Y., and Kelemen, A., (2002) “Mining Heterogeneous Gene Expression Data with Time Lagged Recurrent Neural Networks”. In the proceedings of Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 415-423. (best student paper award)
- Kelemen, A., Kozma, R., and Liang, Y.,
"Neuro-Fuzzy Classification for the Job Assignment
Problem", in the proceedings of the IEEE International
Joint Conference on Neural Networks, Hawaii, pp. 1831-1837,
2002.
- Franklin, S., Kelemen, A., and McCauley, L., (1998) “IDA: A Cognitive Agent Architecture”, in IEEE Conference on Systems. Man and Cybernetics, IEEE Press, pp. 2646-2651.
Professional Affiliations
Institute of Electrical and Electronics Engineers, American
Mathematical Society, Bolyai Janos Mathematical Society, Hungarian
Operations Research Society, International Mensa, Phi Kappa
Phi Academic Honor Society, Upsilon Pi Epsilon
Faculty Links
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