Tomasz G. Smolinski

Associate Professor
Room: SCN344
Office Phone: 302-857-7951
Laboratory: CIBiLI
tsmolinski@desu.edu
Institution: University of Louisville
Location: Louisville, KY
Degree: Ph.D. in Computer Science and Engineering (2004)

Throughout my entire academic career, I have pursued interdisciplinary research linking information technology and computer science with other sciences, as well as business and industry. The areas in which I have worked range from experimental neurobiology and bioinformatics, through audio/video engineering, e-Commerce and supply chain management, to university fiscal and facilities management. These experiences have afforded me the invaluable opportunity to work in collaboration with many scientists, as well as practitioners in the administrative and business fields, and to apply recent advances in computer science and information technology to real-life problems in those areas. However, my greatest passion has always lain in the practical applications of computational methods in the "bio field."

In my lab--the Computational Intelligence, Biological, and Legal Informatics Lab, or CIBiLI--we apply various Computational Intelligence methods (e.g., evolutionary algorithms, rough sets, fuzzy logic, artificial neural networks, etc.) to solve problems in the (very broadly defined) fields of biological sciences and the law. Our projects range from exploration and analysis of large parameter spaces of neuronal models to annotation of amino-acid sequences for protein function prediction. The results of our studies provide insights into how biology responds to changing environments and perturbations, such as diseases, as well as into the molecular mechanisms underlying those diseases. Our newest area of interest lies in the applications of computational intelligence, most notably fuzzy logic, in litigation support systems.

Field(s) of Interest:

Computational Intelligence, Computational Neuroscience, Bio(logical)informatics, Legal Informatics, Machine Learning, Data Mining, Science Education

Journals

  1. Smolinski T.G. and Prinz A.A.  2010 , Rough Sets for Solving Classification Problems in Computational Neuroscience  ,Lecture Notes in Artificial Intelligence  6086 , Springer , 620--629 .
  2. Smolinski T.G.  2010 , Computer Literacy for Life Sciences: Helping the Digital-Era Biology Undergraduates Face Today's Research  ,CBE Life Science Education  9 , The American Society for Cell Biology , 357--363 .
  3. Smolinski T.G., Soto-Treviño C., Rabbah P., Nadim F., and Prinz A.A.  2006 , Analysis of biological neurons via modeling and rule mining  ,International Journal of Information Technology and Intelligent Computing  1 , Academy of Humanities and Economics , 293--302 .

Conference Proceedings

  1. Shim K., Prinz A.A., and Smolinski T.G.  2012 , Analyzing conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models  ,in BMC Neuroscience  13 , BioMed Central , P37 .
  2. Forren E., Johnson-Gray M., Patel P., and Smolinski T.G.  2012 , NeRvolver: a computational intelligence-based system for automated construction, tuning, and analysis of neuronal models  ,in BMC Neuroscience  13 , BioMed Central , P36 .
  3. McKee L., Prinz A.A., and Smolinski T.G.  2011 , Improving visualization and analysis of relationships between neuronal model parameters in discrete parameter spaces  ,in BMC Neuroscience  12 , BioMed Central , P309 .
  4. Smolinski T.G. and Prinz A.A.  2010 , Classifying functional and non-functional model neurons using the theory of rough sets  ,in BMC Neuroscience  11 , BioMed Central , P157 .
  5. Smolinski T.G. and Prinz A.A.  2010 , Multi-objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios  ,in BMC Neuroscience  10 , BioMed Central , P260 .
  6. Smolinski T.G. and Prinz A.A.  2009 , Computational intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron  ,in International Joint Conference on Neural Networks (IJCNN 2009) , IEEE Press , 2964--2970 .
  7. Smolinski T.G., Soto-Treviño C., Rabbah P., Nadim F., and Prinz A.A.  2008 , Systematic selection of model parameter values matching biological behavior under different simulation scenarios  ,in BMC Neuroscience  9 , Springer , P53 .
  8. Smolinski T.G., Soto-Treviño C., Rabbah P., Nadim F., and Prinz A.A.  2007 , Systematic computational exploration of the parameter space of the multi-compartment model of the lobster pyloric pacemaker kernel suggests that the kernel can achieve functional activity under various parameter configurations  ,in BMC Neuroscience  8 , P164 .
  9. Smolinski T.G., Buchanan R., Boratyn G.M., Milanova M.G., and Prinz A.A.  2006 , Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials  ,in BMC Bioinformatics  7 , BioMed Central , S8 .

Book Articles

  1. Smolinski T.G. and Prinz A.A.  2012 , Rough Sets and Neuroscience  ,Intelligent Systems Reference Library  43 Skowron A. and Suraj Z., Eds. , Springer , 493--514 .
  2. Prinz A.A., Smolinski T.G., and Hudson A.E.  2011 , Understanding Animal-to-Animal Variability in Neuronal and Network Properties  ,The Dynamic Brain: Neuronal Variability and its Functional Significance Ding M. and Glanzman D., Eds. , Oxford University Press , 119--138 .
  3. Hassanien A.-E., Milanova M.G., Smolinski T.G., and Abraham A.  2008 , Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges  ,Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications  151 Smolinski T.G., Milanova M.G., and Hassanien A.-E., Eds. , Springer .
  4. Günay C., Smolinski T.G., Lytton W.W., et al.  2008 , Computational Intelligence in Electrophysiology: Trends and Open Problems  ,Applications of Computational Intelligence in Biology: Current Trends and Open Problems  122 Smolinski T.G., Milanova M.G., and Hassanien A.-E., Eds. , Springer .
  5. Smolinski T.G., Prinz A.A., Zurada J.M.  2007 , Hybridization of Rough Sets and Multi-Objective Evolutionary Algorithms for Classificatory Signal Decomposition  ,Rough Computing: Theories, Technologies, and Applications Hassanien A.-E., Suraj Z., Slezak D., Lingras P., Eds. , Information Science Reference , 204--227 .

Books

  1. Smolinski T.G., Milanova M.G., Hassanien A.-E., Eds.  2008 , Applications of Computational Intelligence in Biology: Current Trends and Open Problems  ,Studies in Computational Intelligence  121 , Springer .
  2. Smolinski T.G., Milanova M.G., Hassanien A.-E., Eds.  2008 , Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications  ,Studies in Computational Intelligence  151 , Springer .