AI Research Scientist
PhD Visiting Researcher
Hi you! Welcome to my academic website! This is where I write about my academic and professional life.
I like pasta and art, and follow a versatile daily workout routine 😎. My friends and family know me by my contagious smile. I think ... read more
More work-related 🥱, I am currently a Research Scientist at Meta AI. I develop technologies for privacy-preserving machine learning and federated learning. I contribute to Opacus, an open-source library that enables training deep learning models with differential privacy, and FLSim, an open-source library for simulating federated learning systems. I publish papers too.
I was a PhD Visiting Researcher at University of California, Berkeley and member of the Berkeley Artificial Intelligence Research (BAIR). When I was at UC Berkeley, I contributed to FLOW, an open-source deep reinforcement learning-enabled framework for simulation of autonomous and manned cars.
I earned my graduate degree in Computer Science at the University of Texas at Dallas. My work was on the intersection of Computer Systems, Edge Computing, and Machine Learning, specifically, on improving quality of service in IoT and deep learning Applications through Fog Computing. I won the UT Dallas Best Dissertation Award. While at UT Dallas, I created Fog Computing Conference Hub.
I was TA and unoffical lecturer at UT Dallas, and two-time recipient of Best Teaching Assistant Award. I like to contribute the open-source community, so I served as technical program committee for FL-ICML, FL-NeurIPS, AIChallengeIoT, and reviewer of several international journals and conferences, including ICML, NeurIPS, IEEE/ACM TON, IEEE TNSM, TMC, NETWORK, TSC, IoTJ, INFOCOM, and ICDCS.
Prior to all these, I was a web-design freelancer for several years ... read more