Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Build-Your-Own-LM: A Journey into Language Modeling
Published:
Last updated: 6/1/2026, with the tokenizer optimizations and transformer description.
Hamiltonian Neural Networks
Published:
Code for this blog post can be found at link
portfolio
Pysing
A Python library for simulating lattice models
Portfolio item number 2
Short description of portfolio item number 2 
publications
The Case for Data Centre Hyperloops
Published in 51st ACM/IEEE International Symposium on Computer Architecture (ISCA), 2024
A systems architecture that physically moves SSD-resident datasets through data centres, reducing data-movement time and energy for large-scale workloads.
All-to-all Reconfigurability with Sparse and Higher Order Ising Machines
Published in Nature Communications, 2024
A reconfigurable probabilistic-bit Ising-machine architecture that emulates all-to-all connectivity while retaining highly parallel sampling for hard combinatorial optimization.
CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching
Published in ML4Astro Workshop, Co-located with ICML 2025, 2025
A flow-matching framework that compresses cosmological field simulations into compact, interpretable representations for reconstruction, generation, and parameter inference.
Graph Neural Networks for Interferometer Simulations
Published in NeurIPS 2025 Workshop on AI for Science, 2025
Graph neural network surrogates that reproduce LIGO interferometer simulations up to 815 times faster, supporting more efficient instrumentation design.
talks
Graph Neural Networks for Interferometer Emulation
Published:
Gave a talk on my summer research at LIGO Laboratory on using graph neural networks and Kolmogorov-Arnold Networks for interferometer emulation, as part of the annual undergraduate research symposium.
Statistical Physics and Machine Learning
Published:
Gave an invited talk on the connections between statistical physics and machine learning to a group of PhD students from MIT, Tufts, Northwestern, and Brandeis.
teaching
CMPTG CS 5 - Spring 2025
Seminar, University of California, Santa Barbara, College of Creative Studies, 2025
I had the privilege of teaching a 10 week seminar course on statistical mechanics and its connections to machine learning theory. We covered the following topics: 1] Boltzmann statistics, 2] the Ising model and mean field theories, 3] Energy based models and Boltzmann machines, 4] Diffusion Models, 5] Fokker-Planck equations and the probability flow ODE, 6] Effective field theories of neural networks, and 7] Neural tangent kernels.
PHYS CS 5 - Winter 2026
Undergraduate Colloquium, University of California, Santa Barbara, College of Creative Studies, 2026
I taught this ten-week undergraduate colloquium on non-equilibrium statistical mechanics under the supervision of Ian Banta. The course developed stochastic and microscopic descriptions of systems away from thermal equilibrium, with topics including stochastic processes, transport, Brownian motion, Fokker-Planck dynamics, kinetic theory, hydrodynamics, and projection-operator methods.