I am an assistant professor at the Applied Math Department of UWaterloo. I am doing research at the interface of language models, and quantum/classical many-body physics, with interests in:

• Recurrent Neural Network & Transformer‑based quantum states for quantum many-body simulation.
• Generative-enhanced combinatorial optimization.

I’m eager to connect with prospective students and collaborators interested in the synergy of language models and quantum/many‑body science! You can check out the group section of this website for more details.

📝 Selected Publications

• “Recurrent neural network wave functions”
  Phys. Rev. Research 2, 023358 (2020)
  Introduces a variational ansatz for many-body quantum systems using recurrent neural networks (RNNs). Demonstrates that RNN-based wave functions can accurately capture ground-state properties—including energy, correlations, and entanglement—in both 1D and 2D spin systems, achieving performance on par with traditional methods like DMRG and QMC.
  Read on PRResearch

• “Variational neural annealing”
  Nature Machine Intelligence 3, 952–961 (2021)
  Presents a classical/quantum variational framework that bridges ideas from annealing algorithms and autoregressive generative models (e.g., RNNs). This approach outperforms traditional annealing methods on spin-glass optimization problems, enabling neural networks to effectively explore complex energy landscapes.
  Read on Nature Machine Intelligence

🧑‍🏫 Teaching

• Teaching AMATH449/CS479/CS679: “Neural Networks”.