David Sweet

Publications

Featured publications, research papers, and preprints.

• 

  D. Sweet, Experimentation for Engineers: From A/B Testing to Bayesian Optimization (Manning Publications, 2023)

  A practical handbook for engineers and data scientists covering online controlled experiments, multi-armed bandits, multi-objective optimization, and Bayesian optimization in production software systems.

  • Order print or digital: Manning Books · Amazon
  • Source code: GitHub (dsweet99/e4e)
  • Feature article: Yeshiva University Katz School Blog

• M. Bafna, S. Jadhav, and D. Sweet, Taking the GP Out of the Loop

  International Conference on Machine Learning (ICML 2026); arXiv:2506.12818.

  Scalable Bayesian optimization in O(N) time replacing standard Gaussian process inference bottlenecks with Epistemic Nearest Neighbors (ENN). Eliminates cubic computation scaling while retaining high-quality uncertainty estimates for batched and sequential decision making.

  • Paper: ICML / DOI · arXiv:2506.12818
  • Source code: GitHub (yubo-research/enn)
  • Packages: Rust Crate (ennbo) · PyPI (ennbo)

• 

  D. Sweet, E. Ott, and J. A. Yorke, Topology in chaotic scattering

  Nature 399(6733), 315–316 (1999). Cover article.

  Demonstrated fractal basin boundaries and topological disconnectivity in chaotic light scattering between reflective spheres. The chaotic scattering geometry became the physical basis for the permanent interactive Tetrasphere exhibit at the National Museum of Mathematics (MoMath) in New York.

  • Reprints: Nature Reprint (PDF) · nature.com
  • Exhibit: Tetrasphere Exhibit (MoMath) · Exhibit Details & Photos
  • Article archive: Chaos in Geometrical Optics

• S. Jadhav and D. Sweet, Fast, precise Thompson sampling for Bayesian optimization

  NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU). [Poster presentation].

• J. Ren and D. Sweet, Optimal initialization of batch Bayesian optimization

  arXiv:2404.17997 (2024).

• D. Sweet, B. W. Zeff, E. Ott, and D. P. Lathrop, Three-Dimensional Optical Billiard Scattering

  Physica D 154(3–4), 207–218 (2001).

• D. Sweet, E. Ott, J. M. Finn, and T. M. Antonsen, Blowout Bifurcations and the Onset of Magnetic Activity in Turbulent Dynamos

  Physical Review E 63, 066211 (2001).

• D. Sweet, E. Ott, T. M. Antonsen, and D. P. Lathrop, Blowout Bifurcations and the Onset of Magnetic Dynamo Action

  Physics of Plasmas 8(5), 1944–1952 (2001).

• D. Sweet, H. E. Nusse, and J. A. Yorke, Stagger-and-Step Method: Detecting and Computing Chaotic Saddles in Higher Dimensions

  Physical Review Letters 86(11), 2261–2264 (2001).

• D. Sweet and E. Ott, Fractal Basin Boundaries in Higher-Dimensional Chaotic Scattering

  Physics Letters A 266(2–3), 134–139 (2000).

• D. Sweet and E. Ott, Fractal Dimension of Higher-Dimensional Chaotic Repellors

  Physica D 139(1–2), 1–27 (2000).

• C. R. Hu, S. G. Matinyan, B. Müller, and D. Sweet, Wave Packet Collisions in Yang-Mills-Higgs Theory

  Physical Review D 53(7), 3823–3834 (1996).

Books

Authored and contributed books.

• D. Sweet, Experimentation for Engineers: From A/B Testing to Bayesian Optimization (Manning Publications, 2023)

  • Order print or digital: Manning Books · Amazon
  • Source code: GitHub (dsweet99/e4e)
  • Feature article: Yeshiva University Katz School Blog

• D. Sweet, Foreword to Bayesian Optimization in Action by Quan Nguyen (Manning Publications, 2024)

• D. Sweet, et al., KDE 2.0 Development (Sams Publishing, 2000). Available: Download PDF [4.9 MB]

• Contributing author, Chapters 34–38, N. Wells, Special Edition: Using KDE (MacMillan / QUE, 2000)

Teaching

University courses and guest lectures on quantitative trading and experimental optimization.

• NYU Stern School of Business — Guest Lectures

  Systematic Investing & Trading Strategies with Prof. Vasant Dhar

  • High-Frequency Trading as Applied Epistemology — Systematic Investing, April 2026
  • Experimental Optimization in Quantitative Trading — Systematic Investing, April 2025
  • Experimental Optimization in Quantitative Trading — Robo-Advisors & Systematic Investing, April 2024
  • Experimentation in Trading — Robo-Advisors & Systematic Investing, April 2023
  • Optimization of High-Frequency Trading Systems — Spring 2019
  • Optimization of High-Frequency Trading Systems — Spring 2018
  • Optimization of High-Frequency Trading Systems — Spring 2017
  • High Frequency Trading — Fall & Spring 2014

• Yeshiva University — Katz School Masters Programs

  Graduate courses and research talks

  • Experimental Optimization Course — AI and Data Analytics (Fall 2024 notes; Summer 2023; Spring 2022)
  • Experimental Optimization — DAV masters program, Spring 2022
  • Experimental Optimization — Guest lecture for Data Driven Organizations, Prof. Andrew Catlin, Fall 2021
  • Predictive Models — AI masters program, Summer 2021

Presentations & Podcasts

Invited research talks and podcast interviews.

• Taking the GP Out of the Loop, Meta Adaptive Experimentation Group, Oct 2025

• Bayesian Optimization in O(N): Speeding Automated Design and Discovery, Katz School Faculty Research Initiative, May 2025

• Motivation to Experiment, invited talk for AI for Good

• Recommender systems and high-frequency trading, Practical AI Podcast

• Chaos Theory, High-Frequency Trading, and Experimentations at Scale, Datacast Podcast

• Tuning Up with David Sweet, Test Guild Performance Podcast

• What a Quantitative Trader Can Teach You About A/B Testing and Beyond, DevDiscuss Podcast

Tools

Open-source software for Bayesian optimization, agent testing, and code quality.

Profiles

Writing, research identity, and professional profiles.

• X (@phinance99)
• LinkedIn
• GitHub
• Google Scholar
• ORCID