CV

Last Update: 12/7/2023

Contact

• quzhao@ucsd.edu

Education

• B.S. in Computer Science, UC San Diego, 2023
  • GPA - 3.857

Research Interest

• Machine learning theory, Kernel method, neural tangent kernel.
• Efficient neuromorphic computing methods: Vector Symbolic Architecture/Hyperdimensional Computing.
• Novel learning paradigms: Online, Few-shot, Federated, Continual, Unsupervised, Multimodal Learning.

Work experience

• Fall 2021 - Present
  • System Energy Efficiency Lab
  • Undergraduatre Researcher
  • Supervisor: Prof. Tajana Rosing

Honors & Rewards

• Computer Science & Engineering annual Awards (Excellence in Research) June 2023
  • One among two recipients in graduating class.

Skills

• Language: English (Full professional proficiency), Chinese (Native)
• Java, C/C++, Python, Matlab, System Verilog.
• LaTeX, Git, Markdown, Kubernetes.
• Build deep learning architectures using Pytorch, TensorFlow.
• Math: Statistics/probability, calculus, differential eq, discrete, graph theory, linear optimization, linear algebra.
• Computer Science: Networks, programming language, cryptography, computing theory, data structure, circuits/computer architecture ML/AI/DL/Recommander System.

Publications

MultimodalHD: Federated Learning Over Heterogeneous Sensor Modalities using Hyperdimensional Computin

Quanling Zhao, Xiaofan Yu, Shengfan Hu, Tajana Rosing, "MultimodalHD: Federated Learning Over Heterogeneous Sensor Modalities using Hyperdimensional Computing" - Design, Automation, and Test in Europe (DATE), 2024

Unleashing Hyperdimensional Computing with Nyström Method based Encoding

Quanling Zhao, Anthony Thomas, Ari Brin, Xiaofan Yu, Tajana Rosing, "Unleashing Hyperdimensional Computing with Nyström Method based Encoding" - MLNCP@NeurIPS, 2023

Poster Abstract: Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing

Quanling Zhao, Xiaofan Yu, Tajana Rosing, "“Poster Abstract: Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing" - ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2023

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan, Quanling Zhao, Emily Ekaireb, Xiyuan Zhang, Arya Mazumdar, Tajana Rosing, "Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks" - ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI), 2023

FedHD: federated learning with hyperdimensional computing

Quanling Zhao, Kai Lee, Jeffrey Liu, Muhammad Huzaifa, Xiaofan Yu, Tajana Rosing, "FedHD: Federated Learning with Hyperdimensional Computing" - ACM Annual International Conference on Mobile Computing And Networking (MobiCom) Demo, 2022

ns3-fl: Simulating Federated Learning with ns-3

Emily Ekaireb, Xiaofan Yu, Kazim Ergun, Quanling Zhao, Kai Lee, Muhammad Huzaifa, Tajana Rosing, "ns3-fl: Simulating Federated Learning with ns-3" - Workshop on ns-3 (WNS3), 2022