About Me
I am a third year PhD student at Rice University, working at the intersection of artificial intelligence, scientific machine learning, and the physical sciences. I am advised by Dr. Anastasios Kyrillidis in Computer Science, and overseen by Dr. Geoffroy Hautier in Materials Science. I am fortunate to collaborate as well with Dr. Christopher Jermaine, and Dr. Thomas Reps of UW-Madison. Earlier in my PhD, I also worked on AI methods for structural biology and protein crystallography in collaboration with the lab of Dr. George Phillips (ret.).
My research focuses on developing novel machine learning methods and AI systems for scientific discovery. In particular, I work on:
1) novel Neural Network (NN) architectures and attention variants,
2) machine learning interatomic potentials (MLIPs) and atomistic modeling,
3) AI methods in materials science and chemistry.
A central theme of my work is designing architectures that incorporate structure from the underlying scientific problem while retaining the flexibility and scalability of modern deep learning systems. My research combines theoretical analysis, large-scale model development, and interdisciplinary collaboration across computer science, materials science, chemistry, and biophysics.
For an up-to-date resume including internships, awards, etc, please feel free to contact me!
I started writing some explanatory articles covering the mathematics of machine learning in detail. Check them out!
Explanatory Posts
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