Scholar
Evan Chen (Po-Yu Chen)
Google Scholar ID: sL27C_sAAAAJ
PhD Student, Electrical and Computer Engineering, Purdue University
Citations & Impact
Citations
278
H-index
4
i10-index
2
Publications
12
Co-authors
8
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Paper 'Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach' accepted for NeurIPS 2025; Paper 'Differentially-Private Multi-Tier Federated Learning' accepted for ICC 2025; Paper 'Hierarchical Federated Learning with Multi-Timescale Gradient Correction' accepted for NeurIPS 2024.
Research Experience
- Mainly focuses on designing efficient and scalable distributed AI systems, tackling challenges in resource-constrained environments, multi-tier communication networks, and communication-efficient model training. Particularly interested in the intersection of federated learning and large-scale AI, with applications in edge computing, large-scale distributed intelligence, and privacy-preserving AI systems.
Education
- PhD candidate, Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, advised by Professor Christopher Brinton.
Background
- Currently an ECE PhD candidate at Purdue University, advised by Professor Christopher Brinton, specializing in machine learning, deep learning, federated learning, network systems, fog learning, and large language models (LLMs). With six years of experience in neural networks, my research aims to bridge the gap between theory and real-world applications, driving both academic advancements and industrial impact.
Miscellany
- Interests include RL-based post-training strategies, on-device AI/device-cloud collaborative AI, federated learning, distributed optimization, differential privacy.
