Pavel Kuksa is an alumnus of SEQAM. He completed his Ph.D in 2011. Before joining Rutgers, he received his B.Sc. in Computer Science, Bauman Moscow University of Technology and M.Sc. in Information and Computer sciences, Bauman Moscow University of Technology, Russia, 2002 and 2004 respectively. His research interest includes machine learning, bioinformatics, data mining, information retrieval, pattern recognition. He is currently working at NEC Laboratories America, NJ.
- DNA Barcoding: kernel-based methods for classification of organisms based on their DNA sequence information
- Human motion recognition: clustering of motion sequences and classification of human activities based on video sequences
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P. Kuksa and V. Pavlovic. "Efficient evaluation of large sequence kernels". Proc. ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD). 2012. [More]
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P. Kuksa, I. Khan and V. Pavlovic. "Generalized Similarity Kernels for Efficient Sequence Classification". Proc. SIAM International Conference on Data Mining (SDM). 2012. [More]
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P. Kuksa et al.. "Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction". Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010. 2010. pp. 128-144. [More]
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P. Kuksa and V. Pavlovic. "Spatial Representation for Efficient Sequence Classification". 20th International Conference on Pattern Recognition, ICPR 2010. 2010. pp. 3320-3323. [More]
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P. Kuksa and V. Pavlovic. "Efficient alignment-free DNA barcode analytics", BMC Bioinformatics 2009,, Vol. 10. 2009. [More]
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Alumni
