About Me
I am a PhD student at Rice University working broadly in machine learning and artificial intelligence, with interests spanning the mathematical foundations of deep learning, generative modeling, and scientific machine learning. Much of my current applied research focuses on problems in the physical sciences, particularly atomistic modeling and materials science.
I am advised by Dr. Anastasios Kyrillidis in Computer Science and Dr. Geoffroy Hautier in Materials Science. I also collaborate with Dr. Christopher Jermaine and Dr. Thomas Reps of UW–Madison. Earlier in my PhD, I worked on machine-learning methods for structural biology and protein crystallography in collaboration with the lab of Dr. George Phillips (ret.).
My research has focused on three main areas:
- Theory and mathematical foundations of neural networks
- Generative modeling, particularly diffusion models
- Scientific machine learning, including machine-learning interatomic potentials (MLIPs) and atomistic modeling
A central theme of my work is understanding how mathematical and problem-specific structure can be incorporated into learning systems while retaining the flexibility and scalability of modern deep learning. My research spans theoretical analysis, model and algorithm development, large-scale experimentation, and the development of research software for practical scientific workflows.
For an up-to-date resume including internships, awards, etc, please feel free to contact me!
Tutorials and Lecture Notes
Diffusion and Related
Transformers and Related
- Lecture Slides on Introduction to Transformers (pdf)
- Reinforcement Learning from Human Feedback Introduction (pdf)
- Transformer Mathematics In Extensive Detail (pdf)
- Deep Dive Into Attention Computations (pdf)