- Nonparametric and Bayesian structural time series
- Agent-based modeling
- Mechanistic models of non-equilibrium systems
- Probabilistic programming
- Machine learning and probabilistic modeling scientist at Charles River Analytics and research fellow at the Vermont Complex Systems Center
- Former lead data scientist at MassMutual and trombonist
From time to time I will write about things. I will list the things here.
- Keep using plate notation Someone was wrong on the internet. In particular, someone expressed their (questionable) beliefs about plate notation. These are my feelings about that.
- Theoretical data science background I: functional optimization First post in a (maybe) multipost series of theoretical background for data scince. Here lie Euler-Lagrange equations, the general supervised learning problem, and time series smoothing.
- Perturbing correlation matrices: on what we can do when we are presented with a correlation matrix and not with the data that generated it
- Continuum rich-get-richer dynamics and survival theory: on the non-obvious connection between the kernel function of a rich-get-richer process and the hazard function of a random variable.
stsb2Structural time series, round 2. Implements a grammar over structural time series models and an associated modeling + inference library.
verdantcurve: agent-based market model created in collaboration with Colin Van Oort. This package contains
- A heterogeneous and extensible set of algorithmic trading agents
- A matching engine that implements a frequent batch auction, a new type of auction mechanism that greatly reduces the effectiveness of high-frequency trading strategies
- A orderbook and multiple types of orders
- A market construct to facilitate interaction between agents and the matching engine
discrete-shocklet-transform: qualitative, shape-based, timescale-independent time series similarity search algorithm