About Me
I am a senior quantitative researcher at PathPoint Labs, a firm focused on extracting logistical arbitrage opportunities in the physical natural gas market. Prior to joining PathPoint, I was the lead quantitative researcher at Rotella Capital Management, one of the earliest fully systematic trading firms. There, I served as the portfolio manager for Polaris, the firm's flagship global macro program with a 34-year track record, as well as for the firm's machine learning-based commodities program. I also facilitated the transition of these assets to Eckhardt Trading Company. While at Rotella Capital, I also worked for Qdeck, a fintech spinoff of Rotella, where I developed predictive signals for equities, futures, and cryptocurrencies. These signals were utilized by RIAs, allocators, and hedge funds. I also built tools that provided a rigorous, quantitative approach for RIAs to evaluate the risk and reward of various portfolio allocations.
I completed my Ph.D. at the University of Groningen advised by Tobias Müller. My thesis Hyperbolic Voronoi Percolation gives some insight into the influence curvature has on the structure of random surfaces.
Before studying in the Netherlands, I completed my master's in mathematics at Western Washington University (WWU) where I worked on time series analysis and information theory with Kimihiro Noguchi and Amites Sarkar, respectively. See the research section for more information. I also worked with numerous organizations on statistical/economic problems including Haggen, Inc., Office of Survey Research at WWU, and Washington State Democrats Coordinated Campaign.
Research
We place points as "randomly as possible" in the hyperbolic plane. Let crystal cells grow from each point until they run into another cell, we color the cells with probability $p$ and ask the question, is it possible to walk forever using only the colored cells?
We keep edges of an infinite piece of gridpaper with probability $p$, is it possible to walk forever using only those edges?
Singular Spectrum Analysis: Seasonal Volatility
Applying the ideas of singular value decomposition to time series to account for seasonal volatility.