About

Education

Yale University logo

Yale University - BS in Physics and CS

Expected May 2027

also part of aepi, quantum computing club, and played rugby freshmen year

Y Combinator logo

Y Combinator

YC X25

group partner garry tan

Experience

Caucus logo
Caucus (YC X25) - Cofounder & CTO

Feb 2025 – Aug 2025

ai automation for us congress, built cool stuff and met cool people

Orchard logo
Orchard - Software Engineer Intern

Aug 2024 – Oct 2024

voice agents for real estate + software qa

Yale logo
Yale University - Computational Physics Research Assistant

May 2024 – Aug 2024

intersection of AI and physics with Prof. Logan Wright

Summer Science Program logo
Summer Science Program - Astrophysics Research Intern

Jun 2022 – Jul 2022

tracked near earth asteroid 1999 GJ2 with the method of gauss

Projects

Kolmogorov-Arnold Neural Network (KAN) Transformer

Built a transformer-model replacing traditional feed-forward lawyers by layers inspired by the Kolmogor-Arnold Respresentation Theorem, which states that any multivariate continuous function:

f(x1,x2,...,xn):Rn→Rf(x_1, x_2, ..., x_n): \mathbb{R}^n \to \mathbb{R}

can be expressed as:

f(x1,x2,…,xn)=∑q=02nϕq ⁣(∑p=1nψp,q(xp))f(x_1, x_2, \ldots, x_n) = \sum_{q=0}^{2n} \phi_q\!\left(\sum_{p=1}^n \psi_{p,q}(x_p)\right)

where:

  • ϕq:R→R\phi_q : \mathbb{R} \to \mathbb{R} and ψp,q:R→R\psi_{p,q} : \mathbb{R} \to \mathbb{R} are continuous functions
  • x1,x2,…,xnx_1, x_2, \ldots, x_n are the input variables
  • nn is the dimensionality of the input space

Used both rational and taylor polynomials as base functions, and compared results with LSTMs and standard transformers. Results weren't amazing, but was an amazing learning experience.

Quantum Generative Adversarial Networks

Built a generative adversarial network to generate ququart states from a maximally entangled ququart; in particular:

12(∣00⟩+∣11⟩+∣22⟩+∣33⟩)↦12(∣01⟩+∣11⟩+∣23⟩+∣30⟩)\frac{1}{2}(|00\rangle + |11\rangle + |22\rangle + |33\rangle) \mapsto \frac{1}{2}(|01\rangle + |11\rangle + |23\rangle + |30\rangle)

Designed a gradient estimation procedure to train the generator and achieve high fidelity with the target state. Won 1st place at MIT iQuHack 2024 with my roommates.

Quantum Variational Algorithms for Max-Cut

Designed a quantum circuit to solve Max-Cut problems with high accuracy. Created Loss Hamiltonians to enforce the constraints of the problem:

H=12∣E∣−12∑(v,w)∈E(ZvZw)+α(∑v∈V∑w∈WZvZw)2+β∑v∈V(P^v(−)+P^v(+))H = \frac{1}{2}|E| - \frac{1}{2}\sum_{(v, w) \in E}(Z_v Z_w) + \alpha (\sum_{v \in V}\sum_{w \in W} Z_v Z_w)^2 + \beta \sum_{v \in V} (\hat{P}_v^{(-)} + \hat{P}_v^{(+)})

where

P^v(±)=I±Zv2∏i∈N(v)I∓Zi2\hat{P}_v^{(\pm)} = \frac{I \pm Z_v}{2}\prod_{i \in N(v)} \frac{I \mp Z_i}{2}

I also did this with my roommates. Won 2nd place at MIT iQuHack 2025.