I am a distributed systems researcher at IBM Almaden Research Center. My research interests lie at the intersection of distributed systems and machine learning. I joined IBM Research Almaden in June of 2018 as a research staff member in the AI Platforms team. I hold a Ph.D. degree in Computer Science from Virginia Tech (VT). In my earlier years (2009 to 2013), I worked as a tools developer on various open source projects (GNU GDB, Embedded Linux, and BusyBox) at Mentor Graphics.
The overarching goal of my research is to enable efficient and flexible systems for the growing data demands of modern applications, like distributed machine learning, running on existing as well as emerging computing platforms. My ongoing work focuses on (1) distributed machine/federated learning systems, (2) serverless and microservice-based systems, and (3) efficient storage for Docker containers.
My research has appeared in a number of premier conferences and workshops in computer systems, AI/ML, and high-performance computing, including USENIX FAST, ATC, HotStorage, ACM/IEEE SC, ACM HPDC, SoCC, AISec [Best Paper Award], and AAAI. I regularly perform professional community services and have served as a program committee member for top conferences such as SC, HPDC, ICDCS, MSST, CCGrid, and a reviewer for journals like ToS, TPDS, TKDE, TCC and JPDC. At IBM, I have been recognized as a 2019 Outstanding Research Accomplishment winner for Advancing Adversarial Robustness in AI Models. In 2020, I received two Research Accomplishment awards for my research on i) Enterprise-Strength Federated Learning for Hybrid Cloud and Edge, and ii) Container Storage.
You can find the list of my publications and patents in the corresponding sections. I actively collaborate with academic community and serve as a reviewer for conferences, journals, and research proposals.