New York · Financial Engineering

I study markets,
build models, and
turn data into decisions.

I’m Siddhant Yadav, an NYU financial engineering graduate student working across investment research, quantitative finance, and intelligent systems.

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VALUATION QUANTITATIVE FINANCE INVESTMENT RESEARCH APPLIED AI
01 / SELECTED WORK

Research made
operational.

Projects built to answer real financial questions—grounded in transparent assumptions, reproducible analysis, and useful outputs.

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02 / HOW I WORK
“The model matters.
The assumptions matter more.”
01

Financial intuition

Start with the economics, incentives, and decision—not the technique.

02

Transparent systems

Keep data lineage, assumptions, limitations, and model health visible.

03

Useful outputs

Translate analysis into a memo, dashboard, model, or recommendation.

03 / WRITING

Notes from the
workbench.

Working ideas on valuation, markets, risk, and the process of building better financial models.

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04 / PROFILE

Engineer by training.
Investor in progress.

My path runs from electronics and computer engineering, through applied AI and published machine-learning research, to financial engineering at NYU.

That combination shapes how I work: understand the economics, build the system carefully, and communicate the result clearly.

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