Dukka KC

Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications.

• Ph.D., Informatics, Kyoto University, 2006
• M.Inf., Informatics, Kyoto University, 2003
• B.Eng., Computer Science, Kyoto University, 2001

Research interests include:

• Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER)
• Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite)
• Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction
• High-throughput analysis of next-generation sequencing data
• Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction

Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows.

Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research.

The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.