I am a Theoretical Physicist and Computer Scientist interested in String Theory and its connections with Mathematics and AI. I have applied transformers and reinforcement learning to problems in knot theory and low-dimensional topology, Physics-informed Neural Networks (PINNs) as well as Kernel methods (including the Neural Tangent Kernel) to approximate Calabi-Yau metrics, Decision Trees to Sheaf Cohomology computations, as well as classical Neural Networks in supervised ML to learn topological data of Calabi-Yau manifolds. We also proposed Kolmogorov-Arnold Networks (KANs) as an alternative to NNs (see my Research for a summary).
I have also applied a variety of supervised and unsupervised ML tools (Regression, Decision Trees, Neural Networks, Support Vector Machines, Principal Component Analysis, Clustering Algorithms) to study a multi-omic blood atlas of Covid 19 patients.
Finally, I have written a Physics Report on Data science applications to string theory. It comes with a Github repo.
I am involved in the organization of the following seminars (all times are Eastern time):
| Tue | 11AM-12PM | Bi-weekly String Pheno seminar series (Zoom) |
| Tue | 12:30PM-1:30PM | High Energy Physics Theory Seminar (Dana 114) |
| Fri | 12PM-1PM | Math-Phys Lunch Seminar (Nightingale 544) |
A collection of articles that have been written about my research: