Qiyao (Catherine) Liang

Qiyao (Catherine) Liang

PhD student at MIT EECS

MIT

Biography

I’m a third-year PhD student in the Electrical Engineering and Computer Science department at MIT. My primary interest is in the intersection of physics, AI, and neuroscience. I’m advised by Ila Fiete from the MIT Brain and Cognitive Science department. Some of my recent interests are understanding the mechanisms of compositional generalization in generative models, how structural and/or functional modularity emerge within artificial and biological systems, and beyond. I’m interested in a broad range of topics regarding studying the principles of artificial/biological intelligence and consciousness as emergent phenomena, via quantitative tools from physics as well as empirical studies. I completed my undergraduate studies at Duke University in physics and math, where I worked on controlling and denoising quantum computers.

Interests
  • Physics of (Artificial) Intelligence
  • Mechanistic Interpretability of AI
  • Neuroevolution
  • Artificial life
  • AI for Science
  • Quantum Computing
Education
  • PhD in EECS, 2022-2027 (Expected)

    Massachusetts Institute of Technology

  • SM in EECS, 2022-2024

    Massachusetts Institute of Technology

  • BS in Physics, minor in Math, 2018-2022

    Duke University

Publications

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For an updated list of publications, please see my Google Scholar page!
(2024). Pulse optimization for high-precision motional-mode characterization in trapped-ion quantum computers. Quantum Sci. Technol. 9 035007.

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(2024). Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?. ICLR 2024 Workshop ME-FoMo.

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(2023). Modeling the Performance of Early Fault-Tolerant Quantum Algorithms. Phys. Rev. Res. 6 023118.

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(2023). Efficient motional-mode characterization for high-fidelity trapped-ion quantum computing. Quantum Sci. Technol. 8 024002.

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(2021). Batch Optimization of Frequency-Modulated Pulses for Robust Two-Qubit Gates in Ion Chains. Phys. Rev. Applied 16, 024039.

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Talks

Modeling Quantum Algorithm Performance on Early Fault-Tolerant Quantum Computers
Will quantum computing become useful in the early fault-tolerant era (EFTQC)?