I am an algorithmic trader turned mad scientist. After spending a decade in the trenches of Wall St, building highly optimized trading systems, designing novel portfolio algorithms, and managing some intense years of successful trading, I have moved into the world of AI research. My interest has broadened from Computer Science, Finance, and Mathematics, into the exciting and active areas of Computational Neuroscience, Machine Learning, and Artificial General Intelligence (AGI).
I studied mathematics and finance at the University of Rochester and NYU's Courant Institute. My time on Wall St included roles at several banks and hedge funds (including one which I founded), where I built the systems and strategies to run high-frequency market-making portfolios.
In the last year, I've had the awesome opportunity to engage in self-directed research and development, and to contribute to the open source community. Check out:
When you design and build software for 25 years, you get to use a lot of tools. I love developing in Julia, as I can finally bypass the curse of two languages for scientific computing, though I have extensive experience in C++/Python (and too many others to list).
I focus on elegant code to drive highly optimized systems, understanding the importance of intuitive design for power users.
Trader, researcher, quant, portfolio manager, and trading system designer. I have specialized in structural and statistical arbitrage in equities, ETFs, and futures.
I thrive in high intensity environments. Quick thinking and calm under pressure help me find success in turbulent times (though smart modelling and systems design doesn't hurt).
Neuroscience, machine learning, reinforcement learning, artificial general intelligence... you name it, I'm studying it.
The big problems of tomorrow require intelligent integration of more data than you can imagine. With brain-inspired learning networks we can tackle the problems which are too big and complex for today's machine learning algorithms. Check out my blog on this topic.