Research Scientist, IBM Research, NY.
imralk AT gmail DOT com
I work in the intersection of Machine Learning and Programming Languages for modernizing and mainitaining cloud-native applications. Prior to this, I was Post doctoral fellow at Columbia University working with Dr. Baishakhi Ray where my research explored the ways in which Machine Learning can be used to trigger, detect, and repair various problem across the software lifecycle.
I obtained my PhD at NC State University, guided by Dr. Tim Menzies. I worked on actionable analytics for software engineering. I created algorithms that go beyond prediction to provide insights that might help developers make better decisions. I focused on data mining approaches (e.g., transfer learning) that can create insights even when there isn’t enough data.
|Oct 12, 2022||CARGO has been awarded 🏆ACM SIGSOFT Distinguished Paper Award🏆|
|Jul 20, 2022||CARGO has been acepted at ASE’22.|
|Feb 1, 2022||STABILIZER has been acepted at MSR’22.|
|Jan 24, 2022||Unicorn has been acepted at EuroSys’22.|
|Nov 15, 2021||A tutorial on refactoring application to microservices with mono2micro was presented in ASE.|
ASECARGO: AI-Guided Dependency Analysis for Migrating Monolithic Applications to Microservices Architecture🏆 ACM SIGSOFT Distinguished PaperIn Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE ’22), October 10–14, 2022, Rochester, MI, USA. 2022
EuroSysUnicorn: Reasoning about Configurable System Performance through the lens of CausalityIn The European Conference on Computer Systems (EuroSys). 2022
TSEDeep learning based vulnerability detection: Are we there yet?IEEE Transactions on Software Engineering. 2021
FSEMTFuzz: Fuzzing with a Multi-Task Neural NetworkIn Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ECSE/FSE). 2020
TSEConEx: Efficient exploration of big-data system configurations for better performanceIEEE Transactions on Software Engineering 2020
TSEWhence to learn? transferring knowledge in configurable systems using beetleIEEE Transactions on Software Engineering 2020