I am a final year PhD student in the Machine Learning Department at Carnegie Mellon University, advised by Prof. Virginia Smith. My work spans the intersection of machine learning (ML), optimization, and systems.
I aim to understand, design, and implement ML systems that are both theoretically sound and practically impactful, bridging low-level hardware, algorithms, and high-level applications. My work includes developing algorithms and frameworks to
- Perform on-device ML inference with minimal memory and compute (e.g., EdgeML on microcontrollers),
- Enable efficient federated learning on heterogeneous networks (e.g., k-fed: one-shot federated clustering),
- Support adaptive inference for cloud-based LLM deployments (e.g., B-Distil, ABC),
- Continually solve large-scale optimization problems for resource allocation (e.g., Deep Learning cluster scheduling, Network Traffic Engineering).
In Summer '21, I interned with Kazuhito Koishida at Microsoft Applied Sciences Group, Redmond, where I worked on adaptive ML inference methods. Before graduate school, I spent two enriching years as a Research Fellow at Microsoft Research India, collaborating with Prateek Jain and Harsha Simhadri. I completed my undergraduate studies at the Indian Institute of Technology, Patna, where I worked with Prof. Arijit Mondal on designing a RISC-V microprocessor and accompanying toolchain, applied in FPGA-based hardware simulations and educational tools.