Building frontier systems in AI and quantum computing
I'm an Applied Math, Engineering, and Physics student at the University of Wisconsin–Madison building across AI systems and quantum computing. I previously co-founded a fintech startup, where I helped ship a production quantitative paper trading competition platform. Outside of work, I'm usually reading papers, making coffee, or watching F1.
Led company strategy across technology, product, partnerships, fundraising, hiring, and operations at an early-stage fintech startup in Madison, WI. Architected and deployed Agori's production platform — an end-to-end system powering real-time quantitative paper trading competitions. Managed an 18-system tech stack including AWS, GitHub CI/CD, and a $12k vendor contract. Hired 4 and managed 3, including an engineer.
Active member participating in seminars on quantum computing and quantum information. Competed in the 2024 IBM Fall Fest hackathon.
Selected through ASM Shared Governance to represent students. Collaborated monthly with the Vice Chancellor for Finance and Administration and university stakeholders on finance and administration topics.
Architected a system solution and proposal to modernize the website, communication, and storage for a nonprofit supporting 70+ schools with kids in unstable food situations. After board approval, managed budget and infrastructure improvements. Reported to the board of directors as a special advisor.
Competed in a national cybersecurity competition run by the Air Force Association. Led Cisco networking for my school team and trained 2+ members each year. Hardened Ubuntu 22, Fedora 36, Windows 10, and Windows Server 2019 systems for competition.
BS in Applied Math, Engineering, and Physics (AMEP). Coursework spanning Modern Physics, Linear Algebra, Calculus, Chemistry, and Programming.
Semester abroad at UNSW Kensington in Sydney, Australia. Coursework in Numerical Linear Algebra, Philosophy of AI, and Mathematical Computing.
Designed and deployed a system that aggregates and evaluates purchasing opportunities in auction markets. Uses a Python analytics pipeline to ingest listings, then performs GPU-accelerated OCR/computer vision and ML matching with an embedding engine to get market comps from a range of marketplaces. Persists results in AWS (DynamoDB, S3) for back testing and future neural network training.
Developed a novel method of detecting a wide range of local and non-local errors in a quantum computer. Generated and labeled 1 million+ simulation runs for neural network training. Trained a neural network to consistently detect at least 95% of errors in a 12-qubit system.
Built a covered-call optimization engine that streams live options/price data from Interactive Brokers (IBKR) and recommends strike/expiration and position sizing under capital constraints. Implemented a Monte Carlo risk model to evaluate thousands of scenarios and compute expected P&L, win rate, and Value-at-Risk.
Simulated neutral-atom qubits in QuTiP: built time-dependent Hamiltonians for ground-to-Rydberg drives with detuning, modeled open-system dynamics via Lindblad operators. Designed and optimized Gaussian control pulses with SciPy to realize single- and two-qubit gates. Produced reproducible Jupyter analyses with parameter sweeps and clear plots of Rabi oscillations, coherence, and state populations.