Greater Chicago Area
I’m a Molecular Engineering and Computer Science student at the University of Chicago specializing in quantum engineering. My work sits at the intersection of quantum technologies, artificial intelligence, and software engineering, where I enjoy building systems that bridge cutting-edge research with real-world impact. Currently, I work at the European Defense Tech Hub in Berlin, researching dual-use quantum sensing, secure communications, and emerging defense technologies. I also develop software as a SolvingFun intern, helping transform educational technology into scalable web applications. Beyond my internships, I build machine learning systems for brain-computer interfaces, develop generative AI models for computational chemistry, and explore applications of quantum computing, signal processing, and intelligent systems. I’m particularly interested in technologies that advance national security, scientific discovery, and human-computer interaction. I’m always excited to connect with researchers, engineers, founders, and students working on quantum technology, AI, defense innovation, and frontier engineering.
- Analyzed Quantum Foundations & Operators: Mastered the mathematical foundations of quantum information science, including Hilbert spaces, tensor products, and unitary time evolution using Pauli operator matrices - Evaluated Error Correction & Algorithms: Investigated topological quantum error correction via the Toric Code, alongside analyzing the algorithmic speedups of Grover’s search algorithm compared to classical computational limits - Characterized Mixed State Dynamics: Studied open quantum systems by analyzing probability density matrices and mixed states, bridging the gap between classical probability distributions and quantum Von Neumann entropy
- Trained geometric deep learning models by integrating E(n) Equivariant Graph Neural Networks (EGNNs) to strictly enforce rigid Euclidean parity and physical symmetry constraints on molecular structures - Architected a multi-stage geometric refinement pipeline coupling EGNNs with Flow Matching models to predict transition states, achieving a 0.355 RMSD via iterative coordinate denoising - Simulated stochastic molecular reactivity in digital environments by building 3D spatial computation pipelines to map geometric synthesis barriers