CV
Education
- Carnegie Mellon University
- PhD Candidate in Electrical & Computer Engineering
- Thesis: Optimizing Decentralized Learning - System and Device-Level Strategies
- University of North Carolina at Chapel Hill, 2019
- B.S in Physics & B.S in Computer Science with Highest Honors
- Minor in Mathematics
- Honors Thesis: Unsupervised Learning of the Phase Transitions for 2D Ising Model
- Honors Thesis: A Frameless Image Sensor with Application in Astronomy
Current Research
- Ph.D. Research, Carnegie Mellon University
- Department of Electrical & Computer Engineering
- Advisors: Carlee Joe-Wong, Bob Iannucci
- Employ techniques like reinforcement learning, graph neural networks, and multi-armed bandits to reduce computation and communication overhead.
- Develop solutions to optimize energy usage at both the device and network levels, improving overall system performance and scalability in IoT/edge applications.
- Investigate collaborative learning frameworks such as federated learning, addressing challenges of distributed learning tasks across networks of devices with heterogeneous data distribution and varying computational capabilities.
Publications
Y. Hu, B. Iannucci, and C. Joe-Wong. Towards Adaptive Optimal Placement in Heterogeneous Computing. (under review).
Y. Hu, I. Lin, J. Zuo, B. Iannucci, and C. Joe-Wong. QuBA-FL: Bitwidth-Adaptive Quantized Training for Efficient Federated Learning at the Edge. (submitted)
Y. Hu, J. Zuo, E. Zhang, B. Iannucci, and C. Joe-Wong. Tin-Tin: Towards Tiny Learning on Tiny Devices with Integer-based Neural Network Training. (submitted)
Y. Hu, J. Zuo, A. Zhao, B. Iannucci, and C. Joe-Wong (2024). CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT, 2024 International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys), Hong Kong, 2024 pp. 1-6. doi: 10.1109/FMSys62467.2024.00005.
Y. Hu, J. Zuo, B. Iannucci, and C. Joe-Wong (2023). Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning, 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Madrid, Spain, 2023, pp. 357-365, doi: 10.1109/SECON58729.2023.10287493.
Y. Hu, C. Zhang, E. Andert, H. Singh, A. Shrivastava, J. Laudon, Y. Zhou, B. Iannucci, and C. Joe-Wong (2023). GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing, Proceedings of Machine Learning and Systems (MLSys), 5.
M. Khayatian, M. Mehrabian, E. Andert, R. Grimsley, K. Liang, Y. Hu, I. Mccormack, C. Joe-Wong, J. Aldrich, B. Iannucci and A. Shrivastava (2022). Plan B - Design Methodology for Cyber-Physical Systems Robust to Timing Failures, Transactions on Cyber-Physical Systems (TCPS). doi: 10.1145/3516449
N. Rodríguez and Y. Hu (2020). On the steady-states of a two-species non-local cross-diffusion model, Journal of Applied Analysis, 26(1), 1-19. doi: 10.1515/jaa-2020-2003.
Awards & Fellowships
- Wei Shen and Xuehong Zhang Presidential Fellowship, CMU ECE Department, 2024
- Machine Learning and Systems Rising Stars Award, 2023
- Qualcomm Innovation Fellowship, Finalist, 2021
- CPS-IoT Student Design Competition - Network Computing on the Edge, 2nd Place, 2021
- Paul E. Shearin Outstanding Senior Award in Physics, UNC Department of Physics, 2019
- Daniel C. Johnson Outstanding Junior Award in Physics, UNC Department of Physics, 2018
Past Experience
- Data Science Intern, Ericsson Inc.
- Ericsson Global AI Accelerator Group, 2021
- Safe Reinforcement Learning for Network Optimization
- Investigated the tradeoff between maximizing total network throughput and maintaining user quality of service (QoS) during remote electrical tilt adjustment in cellular networks.
- Evaluated and implemented multiple safe RL algorithms in simulation environments to optimize antenna tilt dynamically while adhering to QoS requirements.
- Student Researcher, University of North Carolina at Chapel Hill
- Department of Physics, 2019. Advisor: Joaquín E Drut
- Unsupervised Learning of Phase Transitions for Quantum Models (Thesis)
- Applied unsupervised learning to identify phase transitions in lattice spin systems.
- Used principal component analysis (PCA) and Monte Carlo simulations to clarify phase boundaries via dimensionality reduction.
- Department of Computer Science, 2018. Advisor: Montek Singh
- Asynchronous Frameless Imaging for Astronomy (Thesis)
- Tackled the design challenge of creating a frameless image sensor for astronomy, aiming for high-resolution imaging in ultra-low-light environments, while minimizing sensitivity to noise.
- Developed methods to lower noise floor and enabled single-photon detection in ultra-low-light environments.
- Department of Mathematics, 2018. Advisor: Nancy Rodríguez
- Cross-Diffusion Models of Species Behavior
- Investigated species behavior in response to environmental constraints to model spatial dynamics and interactions affecting population distribution.
- Simulated interspecies interactions in MATLAB using the finite element method (FEM), contributing to a journal publication.
Teaching Experience
- Invited Lecture on Federated Learning, Duke University ECE Department, 2024
- Course: Edge Computing
- Teaching Assistant, CMU ECE Department, 2021
- Course: Mathematical Foundations of Electrical Engineering
- Teaching Assistant, CMU ECE Department, 2020
- Course: How to Write Low-Power Code for the IoT?