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David Sargent Wood, Ph.D., is Co-Chief Investment Officer and Chief Quantitative Strategist, Senior Managing Director at Nuveen Brooklyn, where he leads portfolio technology development and runs the portfolio management team, including the firm's day-to-day systematic investment processes. Before Nuveen's 2025... more

Positions

Co-CIO and Chief Quant Strategist, Senior Managing Director, Nuveen Brooklyn (2025–)
Chief Quant Strategist and Co-CTO, Brooklyn Investment Group (2022–2025, acquired by Nuveen)
Head of Quant Strategies, Hum Capital (2020–22)
Quant Researcher, Credit Suisse (2018–20)

Education

Ph.D. in Statistics & Econometrics, The University of Chicago (2018)
M.B.A. in Finance, The University of Chicago (2017)
A.B. in Economics, The University of Chicago (2010)

The shape of thought 10/26/26
The price of understanding: what is engineering for? 10/20/26
Sold twice: a life kept in working order 10/10/26
Fatal strategies: the half-life of fortune and the gradient of complexity 9/27/26
zzHELP: a dispatch from inside the swarm 9/14/26
Should you buy a home with a box spread? 9/13/26
Bull herding and bullshitting: why judgment matters more than intelligence, models, or machines 8/23/26
The financial clinic: interaction engineering in financial services 8/2/26
LLM internals specimen sheet 6/30/26
The Bauhütte Standard: reality before loyalty 6/21/26
Co-Brain: l’approche de la mer, and why AGI may never emerge from LLMs 6/20/26
Deep persuasion: grokking, storehouse, and weights 6/5/26
Hiring against the room 5/25/26
Bend the chimney; move the firewood 5/23/26
Worse is better and the right thing 5/16/26
Good companies and good-looking companies: lost dimensions of work 5/9/26
Cognitive resignation: LLM and the hollowing of expertise 5/2/26
How much customization should a financial platform offer? 4/10/26
Markov processes and Kolmogorov equations 1/1/26
Reflections on financial advisory and portfolio management 12/21/25
Understanding long and short margin mechanics 12/7/25
Deep learning at scale: lesson from experiments from Perlmutter 11/17/25
Scalable GPU programming for exascale: multi-node and multi-GPU strategies 11/16/25
From bricolage to engineering: regularizing complexity 11/7/25
Bayesian deep learning: uncertainty, inductive bias, and modern theory 10/10/25
Selling modern financial products: comparing SMAs, SaaS, and asset management 6/7/25
From physical toil to cognitive burnout: modern labor and the ethics of software design 5/30/25
Internal states, external worlds: concurrency, consensus, and sociotechnical challenges in computing 5/24/25
Engineering speak: prolegomena to ideal technical discourses 5/17/25
Self-healing computation: building resilient financial computational services 5/10/25
Cognitive mirror: how LLMs compress language and challenge our sense of self 5/2/25
Basis trade and treasury deleveraging 4/12/25
The Triffin dilemma and the exorbitant privilege 4/1/25
Client-side connection pool management 3/1/25
Zoo keeping: dynamic assembling based on subclass attributes 4/14/24
Go getters: a monadic way 4/9/24
Take a walk on the functional side: yes, we need monads 4/6/24
Working with distributed workers 4/1/24
Cooperative concurrency with FastAPI: it's the generators, stupid 3/27/24
Wrapping around wrappers: primer on decorators 3/19/24
Simple SSL-based encryption 10/21/21

Research Statement

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… more

Projects

Integers and curves: a gentle introduction to MINLP (2026)
MTS for R—all-purpose toolkit for multivariate time series (with Ruey Tsay and contributions from Jon Lachmann and Renze Dijkhuizen, 2022)
Typeface ultracode, BPM