Kunjal Panchal

Kunjal Panchal is a Research Scientist at Adobe Research. Her research lies at the intersection of machine learning and systems, with a focus on efficient training, optimization, and deployment of machine learning models in resource-constrained environments.

She received her Ph.D. in Computer Science from the University of Massachusetts Amherst in 2026, where she was advised by Professor Hui Guan in the MLSys Lab. Her doctoral research explored personalized federated learning, memory-efficient fine-tuning of large language models, and the computational and memory trade-offs of alternatives to backpropagation.

Prior to joining Adobe Research full-time, Kunjal completed four research internships at Adobe, working on projects spanning federated learning, in-context learning, on-device systems optimization, and agentic AI. Her broader research interests center on making advanced AI models more efficient, adaptive, and practical for real-world applications.