Zitong (Cynthia) Huang

Zitong (Cynthia) Huang

Senior Undergraduate Student @ the University of Southern California

Welcome! I’m Zitong (Cynthia) Huang, a 4th-year undergraduate student at the University of Southern California, majoring in Computer Engineering & Computer Science. I have been fortunate to work closely with Prof. Ajitesh Srivastava and Prof. Robin Jia . I am also glad to be a member of USC CAIS++ , where I explore how AI can be applied for social good and collaborate on projects at the intersection of technology and impact.

Broadly, I aim to develop methods that make foundation models more efficient, reliable, and generalizable across resource-constrained settings. My research interests include:

  • Automated Machine Learning: How can we automatically make principled decisions about model architectures, discovering the most sparse, compact, and high-performing structures within a vast design space?
    Specific topics include: Neural Architecture Search (NAS), model compression, sparse & compact networks.

  • Efficient Adaptation: How can we enable fine-grained, low-overhead behavioral steering for foundation models after pre-training, allowing them to adapt to new tasks, users, or constraints with minimal compute?
    Specific topics include: model steering, lightweight post-training, LoRA-based adaptation.

If you’re interested in my work, feel free to reach out!

📰 Recent News

  • Oct 2025 — Selected as an Ming Hsieh Institute Undergraduate Scholar at USC!
  • Oct 2025 — Our work “SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space” was accepted to IEEE BigData 2025!
  • Oct 2024 — Our work “Simultaneous Weight and Architecture Optimization for Neural Networks" was accepted to NeurIPS 2024 FITML Workshop!
  • April 2024 — Selected as a USC Women in Science and Engineering(WiSE) Undergraduate Research Scholar!

📚 Publications

🎓 Teaching & Service

  • Course Producer — EE 155 (Introduction to Computer Programming for Electrical Engineers) USC Viterbi School of Engineering Aug 2023 – Dec 2023
    Supported undergraduate students through weekly labs, office hours, and debugging guidance.
    Helped redesign instructional materials to improve conceptual clarity and assignment structure.

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