CV

📄 Download PDF: SenYang_NYU_CV_Apr26.pdf

(Last refreshed April 2026.)

Education

• Ph.D. in Operations Management — New York University, Stern School of Business (Sept 2018 – Aug 2025)
  • Advisors: Jiawei Zhang, Divya Singhvi
  • Research: machine learning, stochastic optimization, convex optimization, online learning
• B.Sc. in Mathematics — The Chinese University of Hong Kong (Sept 2014 – Jun 2018)
  • First Class Honours; GPA 3.77 / 4.00
  • Admission Scholarship — Mainland: tuition fee waived for 4 years

Experience

• Cubist (Point72) — Quantitative Research Intern, NYC (Sep 2025 – Feb 2026)
  • Built AlphaBot, a multi-LLM agent system for systematic alpha discovery on US equities. Identified 80+ significant meta-alphas across momentum, mean reversion, liquidity, information flow and other risk factors, then expanded into broader downstream alpha families with automatic code implementation; across the three frontier model families in the ensemble, research-output quality ranks Opus > GPT > Gemini.
  • Developed rolling-period statistical validation and robust selection pipeline (odd/even split overfitting controls) to minimize overfit-to-regime.
  • Trained ensemble models combining surviving alphas across regression families, loss functions, and targets — promising Sharpe on both large- and small-cap US equities.
• Optiver US — Quantitative Research Summer Intern, Chicago (Jun 2024 – Aug 2024)
  • Volatility-change-rate (VCR) estimation for 0-dte SPXW.
  • Built dynamic rolling-window algorithm + smoothing, lifting R² from 0.23 to 0.44.
• NYU Stern — Instructor, J-term 2022
  • Taught Operations Management. Course evaluation 4.0 / 5.0.

Personal projects

• AlphaBot — Personal Deployment. Independent deployment of the AlphaBot architecture on mid-frequency crypto trading outside Cubist (joint with Beier Liu) as a cross-market validation of the scaffolding. 18-month live track on a self-funded $10K testing account; public performance dashboard — set the time range to Jan 1, 2025 to view the full track. Same architecture as the Cubist deployment; different market, same scaffolding produces surviving factors — evidence that the agent system is doing real work rather than overfitting to a single market regime.

• IvorySquare (Apr 2026 – present, with Han Yan) — a framework that treats peer-reviewed methodology — across finance, accounting, economics, and operations research — as a first-class tool surface for LLM agents. Skills are paper-derived, citation-grounded, and gated by purpose-built evaluation harnesses; the human-expert layer remains disjoint from engineering through declarative persona contracts. The skill graph is structured in two coupled tiers — a foundational concept layer at textbook-subsection granularity under prerequisite ordering, and a paper-derived methodology layer with formal implementations, worked-example replication, and line-item citation provenance — both exposed as declarative MCP and OpenAI tool surfaces. Motivating research direction: academic citation networks as a structured post-training substrate for tool-using LLMs — each skill supplies both a tool-use trace and a verifiable ground-truth signal, and the citation topology gives a natural curriculum from primitive methods to composite ones.

Working papers

• Adaptive Gradient Descent Algorithms for Online Optimization Problems in Operations — Sen Yang, Jinzhi Bu, Siyi Wang. Working paper, 2024–.
• Online Gradient Descent Algorithm for Multi-Item E-commerce Order Fulfillment — Sen Yang, Divya Singhvi, Jiawei Zhang. Working paper, 2024–.

Awards

• Funding Master Gold Medal for Graduating Students — Wu Yee Sun College, CUHK.

Skills

• Languages: Python, MATLAB.