Dongyang Fan

Dongyang Fan is a Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Computer Science. She works in the Machine Learning and Optimization Laboratory (MLO) under EPFL's Doctoral Program in Computer and Communication Sciences, conducting cutting-edge research in artificial intelligence and machine learning.

Her research spans Machine Learning, Deep Learning, Natural Language Processing, Optimization, Federated Learning, Mixture of Experts architectures, On-device Learning, and Ethics in AI. She focuses on developing efficient, ethical, and personalized AI systems with practical applications in edge computing and collaborative environments, addressing challenges in model training, data valuation, and real-world deployment constraints.

Analysis of her recent publications reveals a concentrated effort on large language models (LLMs), with significant contributions to on-device collaborative learning frameworks, Mixture of Experts optimization, and ethical implications of web data usage. Her work bridges theoretical advancements in model architecture with practical implementations for resource-constrained devices, emphasizing privacy-preserving techniques and fairness-aware design principles.

She is an integral member of EPFL's Machine Learning and Optimization Laboratory, contributing to research teams focused on next-generation AI algorithms and their optimization for real-world applications in distributed systems and edge computing environments.