Summary
Rahul Krishna is a research scientist at IBM Research specializing in the intersection of machine learning, programming languages, and hybrid cloud systems to modernize and maintain complex applications. With 12 years of experience and a PhD from NC State, he builds actionable analytics and transfer-learning methods that deliver insights beyond prediction for software engineering problems. His postdoctoral work at Columbia produced practical tools—CADET for causal diagnosis of performance faults, ConEX for MCMC-driven configuration tuning, and MTFuzz for transferable gray-box fuzzing—that substantially outperformed prior approaches. Comfortable across systems and ML stacks (Python, C++, TensorFlow/PyTorch, Spark, LLVM) he blends compiler/static-analysis skills with cloud and DevOps experience to move research into production. He has a knack for making ML usable under data scarcity, and his work has exposed real vulnerabilities and performance fixes in large codebases and big-data systems. Based in New York, he combines rigorous empirical methods with engineering pragmatism to tackle security, testing, and optimization challenges in hybrid-cloud environments.
12 years of coding experience
6 years of employment as a software developer
Master's degree, Electrical and Computer Engineering, Master's degree, Electrical and Computer Engineering at North Carolina State University
Bachelor's degree, Electronics and Communication Engineering, 9.06, Bachelor's degree, Electronics and Communication Engineering, 9.06 at M.S. Ramaiah Institute Of Technology
Postdoctoral Research, Computer Science, Postdoctoral Research, Computer Science at Columbia University
English, Tamil, Kannada, German, Hindi