I'm Nicholas Tavares, a graduate mathematician who builds research code that gets validated and infrastructure that runs itself.
Network topology of markets under stress. A from-scratch autodiff engine. A deliberation protocol for panels of language models. GPU fractals. A deckbuilder about rigging slot machines. It all connects, I promise.
I'm a mathematician and ML engineer who builds systems that find structure in chaos. My academic work sits at the intersection of graph theory, information theory, and network analysis, using tools from pure mathematics to study complex systems and multi-agent architectures.
I recently finished my M.S. in Mathematics. A lot of my research keeps circling one question: whether the network topology of markets genuinely reorganizes under macroeconomic stress, or whether that structure is mostly hindsight. It's an open problem, and it deserves rigor, not vibes.
The rest of my time goes to building. Lately that ranges from an automatic differentiation engine written from scratch to prove I understand the chain rule, to a deliberation protocol for panels of language models, to a raymarcher that renders 4D fractals in real time. I believe the best insights come from asking better questions, not running faster models.
Short version: personal site, academic work, nothing here tells you what to do with money.
This website is maintained in my personal capacity only. It is not affiliated with, endorsed by, or associated with any employer or financial institution. All views and opinions expressed here are my own and do not represent the views of any firm. For professional background, see my LinkedIn profile.
Nothing on this site constitutes investment advice, a solicitation to buy or sell securities, or a recommendation of any investment product, strategy, or course of action. All content is for educational and academic research purposes only and should not be used as the basis for any investment decision.
All research findings, statistical results, and quantitative analysis presented here are theoretical and academic in nature. Historical patterns observed in specific datasets do not predict future performance and have not been validated for real-world application. Past performance, whether simulated or backtested, does not indicate future results.
Publication-track quantitative research with novel mathematical contributions.
A framework for how multi-asset portfolio structures reorganize during geopolitical crises. It constructs multi-layer similarity networks across 91 ETFs in 8 asset classes using shrinkage correlation, distance correlation, and tail-dependence measures, then applies Leiden community detection, Hidden Markov Model regime identification, and information-theoretic tools (transfer entropy via the KSG estimator, Granger causality) to reveal directional information flow between asset clusters.
Research systems, open source libraries, agents, graphics, and one video game.
This list writes itself. A pipeline on my machine scans the local repos twice a day, pulls live git stats, runs a safety filter over every description, and republishes the JSON this page reads. New work shows up here when it's real, not when I remember to edit HTML.
From abstract structures to applied algorithms.
Researching multi-asset topology dynamics during geopolitical crises and developing novel metrics (CMI, TDS) with full statistical robustness validation. Building multi-agent research systems for prediction market data, and releasing open source along the way: a multi-model deliberation engine, a from-scratch autodiff library, and a forecast comparison toolkit.
Coursework in Machine Learning (CPE595), Applied Statistics (MA544), Time Series Analysis (MA641), Optimization & Stochastic Calculus (MA576), and Big Data Technologies (BIA678). Thesis on multi-asset network topology under macroeconomic stress, plus research projects that bridge academic rigor with working software.
Open to academic research collaborations, quantitative mathematics discussions, and software engineering opportunities. If you're working on interesting problems at the intersection of mathematics and computation, I'm happy to connect.