Dan E Huang
About LBAI Lab Publications Teaching
Language-Based Artificial Intelligence Lab
We study the intersection of programming languages and AI. Languages bridge software and hardware, and so constrain how we express, reason about, and efficiently implement AI systems. Please contact danehuang [at] sfsu [dot] edu if interested in doing research in the areas listed below.
Research Areas
  • Languages for AI (e.g., Probabilistic Programming). Projects involve developing probabilistic programming systems (e.g., for Bayesian Deep Learning), which enable modeling and reasoning about uncertainty, and applying them to science application (i.e., AI for science).
  • AI for Formal Languages. Projects involve constructing deep learning systems for reasoning about mathematics expressed in formal languages.
  • Emerging Hardware (e.g., Quantum Computing). Projects involve working on programming models to take advantage of emerging quantum hardware.
Recent Publications
  • Exploring Torsional Conformer Space with Physical Prior Mean Function-Driven Meta-Gaussian Processes (Journal of Chemical Physics, 2023) [paper]
    Chong Teng, Daniel Huang, Elizabeth Donahue, and Junwei Lucas Bao.
  • Quantum Computing and Visualization: A Disruptive Technological Change Ahead. (IEEE Computer Graphics and Applications, 43(6), Nov/Dec, 2023) [paper]
    E. Wes Bethel, Mercy G. Amankwah, Jan Balewski, Roel Van Beeumen, Daan Camps, Daniel Huang, and Talita Perciano.
  • A Spur to Molecular Geometry Optimization: Gradient-Enhanced Universal Kriging with On-the-Fly Adaptive Ab Initio Prior Mean Functions in Curvilinear Coordinates. (Journal of Chemical Physics, Emerging Investigators Special Collection, 2023) [paper]
    Chong Teng, Daniel Huang, and Junwei Lucas Bao.
Recent Preprints
  • Push: Concurrent Probabilistic Programming for Bayesian Deep Learning. (Arxiv, 2023) [preprint] [Push]
    Daniel Huang, Chris Camano, Jonathan Tsegaye, and Jonathan Austin Gale.
  • On Training Derivative-Constrained Neural Networks. (Arxiv, 2023) [preprint] [code]
    Kai Chieh Lo and Daniel Huang.
Members
Graduate
  • Pranjal Prafull Newalkar (Computer Science)
  • Sanket Shah (Computer Science)
  • Jonathan Austin Gale (Computer Science)
  • Sunny Tan (Computer Science)
  • Jonathan Tsegaye (Computer Science)
  • Qin Geng (Computer Science)
  • Darshil Dhameliya (Computer Science)
  • Kai Chieh Lo (Computer Science)
  • Kai Yee (Mathematics)
Undergraduate
  • Raschid Al-Kurdi (Computer Science)
  • Chris Camano (Computer Science and Mathematics)