Books Published

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Deep Learning for NLP and Speech Recognition

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big d

Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural

Java机器学习

精通Java机器学习

Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural

精通Java机器学习

精通Java机器学习

精通Java机器学习

Selected Patents

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1. Communication Security, Uday Kamath, Kevin O'Leary, Kilian Colleran, US Patent Grant. 10587650, Mar 2020.
2. Systems and methods for rapidly building, managing, and sharing machine learning models, C Hughes, T Estes, J Liu, C Brandon, U Kamath US Patent App. 16/613,301
3. Low-resource Multilingual Learning Framework for Text Classification.

Selected Papers published in Journals, Conferences and Work

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1. Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff. Mitch Naylor, Christi French, Samantha Terker and U. Kamath, IMLH 2021. 
2. Deep learning improves antimicrobial peptide recognition., D Veltri, U Kamath, A Shehu,Bioinformatics 34 (16), 2740-2747
3. Effective automated feature construction and selection for classification of biological sequences, U Kamath, K De Jong, A Shehu, PloS one 9 (7), e99982
4. An evolutionary algorithm approach for feature generation from sequence data and its application to DNA splice site prediction, U Kamath, J Compton, R Islamaj-Doğan, KA De Jong, A Shehu, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
5. Improving recognition of antimicrobial peptides and target selectivity through machine learning and genetic programming, D Veltri, U Kamath, A Shehu, IEEE/ACM transactions on computational biology and bioinformatics 
6. Boosted mean shift clustering, Y. Ren, U Kamath, C Domeniconi, G Zhang,Joint European conference on machine learning and knowledge discovery in databases.
7. Using evolutionary computation to improve svm classification, U Kamath, A Shehu, K De Jong, IEEE Congress on Evolutionary Computation
8. SAX-EFG: An evolutionary feature generation framework for time series classification, U Kamath, J Lin, K De Jong, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
9. Selecting predictive features for recognition of hypersensitive sites of regulatory genomic sequences with an evolutionary algorithm, U Kamath, KA De Jong, A Shehu, Proceedings of the 12th annual conference on Genetic and evolutionary computation.
10. A spatial EA framework for parallelizing machine learning methods, U Kamath, J Kaers, A Shehu, KA De Jong, International Conference on Parallel Problem Solving from Nature
11. A two-stage evolutionary approach for effective classification of hypersensitive dna sequences, U Kamath, A Shehu, KA De Jong, Journal of bioinformatics and computational biology.
12. An analysis of a spatial ea parallel boosting algorithm, U Kamath, C Domeniconi, KA De Jong, Proceedings of the 15th annual conference on Genetic and evolutionary computation.
13. An evolutionary-based approach for feature generation: Eukaryotic promoter recognition, U Kamath, KA De Jong, A Shehu, IEEE Congress of Evolutionary Computation.
14. Feature and kernel evolution for recognition of hypersensitive sites in DNA sequences, U Kamath, A Shehu, KA De Jong, International Conference on Bio-Inspired Models of Network, Information, and Computing Systems.
15. A novel method to improve recognition of antimicrobial peptides through distal sequence-based features, D Veltri, U Kamath, A Shehu, IEEE International Conference on Bioinformatics and Biomedicine.
16. Parallel boosted clustering, Y Ren, U Kamath, C Domeniconi, Z Xu, Neurocomputing.
17. A new methodology for the GP theory toolbox, J. Bassett, U Kamath, K De Jong Proceedings of the 14th annual conference on Genetic and evolutionary computation.
18. EML: a scalable, transparent meta-learning paradigm for big data applications, U Kamath, C Domeniconi, A Shehu, K De Jong, Innovations in Big Data Mining and Embedded Knowledge.
19. Theoretical and empirical analysis of a spatial ea parallel boosting algorithm, U Kamath, C Domeniconi, K De Jong, Evolutionary computation.
20. Using Quantitative Genetics and Phenotypic Traits in Genetic Programming, U Kamath, JK Bassett, KA De Jong,INTECH Open Access Publisher