I've always been interested in the gap between understanding something in theory and making it work in practice. I'm interested in quantitative research and trading systems, particularly where statistical methods, data infrastructure, and software engineering intersect. Recent projects have involved real-time trading environments, post-trade analytics, and experimentation infrastructure, using technologies such as Python, Kafka data streaming, Grafana, and time-series databases to support data-intensive and latency-sensitive applications. I enjoy problems that sit between theory and implementation and appreciate opportunities to combine analytical thinking with practical engineering. I think one perspective I bring is an appreciation for both the mathematical and systems sides of quantitative work. Many interesting problems in finance depend not only on models, but also on the infrastructure, data pipelines, and tooling that make those models useful in practice. Outside of finance and technology, I enjoy art and illustration. Both art and quantitative work reward a balance between theory and practice: studying fundamentals provides direction, but meaningful progress ultimately comes from applying them through hands-on experience and iteration. If you'd like to discuss quantitative finance, trading systems, data infrastructure, or art, I'd be happy to connect. 听
