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My research operates at the intersection of high-dimensional financial time series, modern applied mathematics, and computational physics. I’ve always gravitated toward non-parametric, structure-seeking techniques—drawing on geometry, spectral methods, and ideas inspired by quantum field theory and statistical mechanics. I view financial markets as constrained dynamical systems with symmetries, coupling structures, and propagation dynamics, not as noisy regression exercises. I spent over a decade at the University of Chicago, completing Ph.D.-level coursework in economics, mathematics, and physics, including quantum field theory... more

Bio

David Sargent Wood is the Chief Quantitative Strategist and Co-Chief Technology Officer at Brooklyn Investment Group. Following Brooklyn’s 2025 acquisition, he also serves as Senior Managing Director at Nuveen. He leads the development of cutting-edge financial technology systems while managing the portfolio management team and overseeing day-to-day systematic investment management… more

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Education

Research

Blog

  1. Deep learning at scale: lesson from experiments from Perlmutter
  2. Scalable GPU programming for exascale: multi-node and multi-GPU strategies
  3. From bricolage to engineering: regularizing complexity
  4. Bayesian deep learning: uncertainty, inductive bias, and modern theory
  5. Generative AI and diffusion models
  6. Selling modern financial products: comparing SMAs, SaaS, and asset management
  7. From physical toil to cognitive burnout: modern labor and the ethics of software design
  8. Internal states, external worlds: concurrency, consensus, and sociotechnical challenges in computing
  9. Engineering speak: prolegomena to ideal technical discourses
  10. Self-healing computation: building resilient financial computational services
  11. Cognitive mirror: how LLMs compress language and challenge our sense of self
  12. Basis trade and treasury deleveraging
  13. The Triffin dilemma and the exorbitant privilege
  14. Client-side connection pool management
  15. Zoo keeping: dynamic assembling based on subclass attributes
  16. Go getters: a monadic way
  17. Take a walk on the functional side: yes, we need monads
  18. Working with distributed workers
  19. Cooperative concurrency with FastAPI: it's the generators, stupid
  20. Wrapping around wrappers: a primer on Python decorators
  21. Simple SSL-based encryption