Yang Li

I obtained my Ph.D. from Carnegie Mellon University (CMU). My advisor is Prof. José M. F. Moura, member of the National Academy of Engineering. My research areas are computer systems/architecture and deep learning. I have worked as a research intern at IBM Research and Microsoft Research. I received my M.S. from Carnegie Mellon University, M.S.E. from the University of Texas at Austin (UT Austin), and B.E. from Tsinghua University. E-mail:  yangli1@andrew.cmu.edu

Research Interests

• computer systems and architecture: resource management for cloud infrastructures

• deep learning: deep learning models for graph-structured data

Selected Publications

• Y. Li and J. M. F. Moura.
  "Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data".
  European Conference on Artificial Intelligence (ECAI), 2020 (acceptance rate: 25%).

 

• Y. Li, C. R. Lefurgy, K. Rajamani, M. S. Allen-Ware, G. J. Silva, D. D. Heimsoth, S. Ghose, and O. Mutlu.
  "A Scalable Priority-Aware Approach to Managing Data Center Server Power".
  IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2019 (acceptance rate: 22%).

   

• C. Xu, K. Rajamani, A. Ferreira, W. Felter, J. Rubio, and Y. Li.
  "dCat: Dynamic Cache Management for Efficient, Performance-Sensitive Infrastructure-as-a-Service".
  ACM European Conference on Computer Systems (EuroSys), 2018 (acceptance rate: 16%).

 

• Y. Li, S. Ghose, J. Choi, J. Sun, H. Wang, and O. Mutlu.
  "Utility-based Hybrid Memory Management".
  IEEE International Conference on Cluster Computing (IEEE Cluster), 2017 (acceptance rate: 22%).

 

• Y. Li, D. Wang, S. Ghose, J. Liu, S. Govindan, S. James, E. Peterson, J. Siegler, R. Ausavarungnirun, and O. Mutlu.
  "SizeCap: Efficiently Handling Power Surges for Fuel Cell Powered Data Centers".
  IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2016 (acceptance rate: 22%).

 

• Y. Li and D. Z. Pan.
  "An Accurate Semi-Analytical Framework for Full-Chip TSV-induced Stress modeling".
  IEEE/ACM Design Automation Conference (DAC), 2013 (acceptance rate: 23%).