See company description.

I came, I tweaked, I ticked, I validated and then a GraphNodeException blew me away.

As a member of a computational lab with diverse software and research projects, my main independent con- tribution was a novel HLA-typing algorithm that computed the entire posterior distribution over possible alleles; Bayesian genetic inference in the most heterogeneous region of the human genome. Created a custom Hidden Markov Model, that operated over a compressed space, in order for this approach to be feasible. Reviewed projects utilizing deep learning and Bayesian inference. Collaborated on a project for bioinformatics workflow organization. OCaml, Python, Bash, R, Stan.

Marketed to and interviewed customers for a computational approach to negotiations, led idea creation and presentation. Created a debate platform on Twitter. At an Ag-tech startup, developed a website for presenting chemical information and built seed recommendation engine based upon trial results. In all cases, owned the technical stack and analytical decisions. Erlang, Haskell, Rails, Javascript, PostgreSQL, and R.

For the risk and economic policy research group, developed and maintained a research and graphing framework. (Matlab, C#) Participated in Agile life-cycle planning.

Independently, extracted the positions, sourced and organized price inputs, priced (Monte Carlo and model based, forward-curve decompositions), and performed the statistical analysis (empirical distribution estimate, extreme value theory) to create a risk system, that reported VaR (custom, nonparametric) for over a thousand portfolios composing the metals business. Based upon analysis, coordinated improvements with world-wide colleagues. F#, Matlab and C++. Analytically, learned about the power of a well calibrated statistic to driveaction, from a software perspective, so many of our tasks are analogous to a compiler stage.

Supported the quantitative aspects of volatility strategies; designed and implemented efficient econometric parameter estimation methods, built models for option pricing, created real-time user interfaces and reporting systems. F#, Excel-VBA and Python. Learned how to communicate and summarize complex ideas to decision makers.

As an early member of the research team, investigated statistical arbitrage strategies using tick data and a custom distributed computing environment. After identifying signals (regression techniques), shifted towards im- plementing trading platforms, and focused on expressing execution logic clearly and efficiently in code. Helped deploy several strategies, monitored their execution for correctness and implemented improvements. OCaml, Excel/VBA. Learned about the pragmatic efficiency of functional programming, that there is an incredible value in thinking about markets from the perspective of arbitrage, and the importance of probabilities and distributional effects in decision making.