CV

Education

• Ph.D in Electrical and Computer Engineering, The University of Texas at Austin, 2018 - present
• M.S. in Electrical and Computer Engineering, The University of Texas at Austin, 2018 - 2020
• B.S. in Applied Mathematics, Wuhan University, 2014 - 2018

Work experience

• Summer 2022: Interim Engineering Intern at Qualcomm, CA, USA
  • Studied Qualcomm Research’s project on multi-vendor training for cross-node ML based CSI feedback
  • Proposed the training methodologies to prove that heterogeneous neural network models (including Transformer and CNN) from multiple UE vendors can train together with a gNB vendor
  • Implemented the algorithms in PyTorch to show they achieve near optimal performance and interoperability during inference
• Spring 2021: Machine Learning Research Intern at Toyota InfoTechnology Lab, CA, USA
  • Designed the federated learning framework for the object detection task on vehicle cameras
  • Proposed the algorithm that addresses the client heterogeneity in local data and computation resource and achieved communication and computation efficiency
  • Implemented the algorithm in Tensorflow and justified that the algorithm reaches 88 accuracy while reducing 35% communication cost and 66% training time

Skills

• Python, SQL, R, MATLAB, C
• Google Cloud Platform(GCP), Amazon Web Services(AWS), MinIO, PyTorch, Tensorflow

Professional services

• Journal reviewer:
  • IEEE Transactions on Automatic Control, IEEE Transactions on Signal Processing
• Conference reviewer:
  • ICML (2024), AISTATS (2023, 2024), ICASSP (2023, 2024), MLSP (2023), CDC (2022)

Publications

• Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks Y. Chen, A. Hashemi and H. Vikalo, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094584.

• Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization Over Time-Varying Directed Graphs Y. Chen, A. Hashemi and H. Vikalo, in IEEE Transactions on Automatic Control, vol. 67, no. 12, pp. 6583-6594, Dec. 2022, doi: 10.1109/TAC.2021.3133372.

• Decentralized optimization on time-varying directed graphs under communication constraints Y. Chen, A. Hashemi and H. Vikalo, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 3670-3674, doi: 10.1109/ICASSP39728.2021.9415052.

• Federated Learning with Infrastructure Resource Limitations in Vehicular Object Detection Y. Chen, C. Wang and B. Kim, 2021 IEEE/ACM Symposium on Edge Computing (SEC), San Jose, CA, USA, 2021, pp. 366-370, doi: 10.1145/3453142.3491412.