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Kyle Wisniewski

Kyle Wisniewski Data, analytics & quantitative systems

Governed data systems. Quantitative finance. BI & AI decision systems.

Manager, Data and Analytics at University of Denver — Daniels College of Business; graduate student in Applied Quantitative Finance, expected November 2027; builder of the Quantitative Markets & Institutions Lab.

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Kyle Wisniewski
Manager, Data and AnalyticsUniversity of Denver — Daniels College of Business

01 / Selected work

Evidence, in working form.

Professional systems, reproducible research, independent product work, and graduate study use different standards of proof. The distinctions stay visible.

01 / Professional Work / active

The Operating Layer Behind Elevate 2035

Building the governed data and workflow system that connects Daniels' Elevate 2035 strategy to measures, faculty evidence, department action, and Dean's Office review.

Approved sources

  • Airtable
  • Watermark
  • Banner
  • Slate
  • Salesforce

Governed layer

Supabase

Decision interfaces

  • Faculty
  • Department heads
  • Dean's Office

Boundary: This case study describes a confidentiality-safe operating pattern, not the college's internal architecture.

Open case study

02 / Independent quantitative research

Quantitative Markets & Institutions Lab

Clear questions. Testable evidence.

The Quantitative Markets & Institutions Lab examines how pricing, portfolio, derivatives, factor, and risk models behave across real data, estimation error, and changing market regimes.

Quantitative tests
204
Publishing checks
16
Total checks
220

Real market data · Reproducible notebooks · Methods documented · Limitations stated

Boundary: Independent educational research; not investment advice, a solicitation, an employer, or a professional investment track record.

Enter the Lab

03 / Independent Project / exploratory

Rudiment Intelligence

Founded by Kyle Wisniewski, Rudiment Intelligence is an independent prototype for governed business data, traceable answers, role-aware access, and AI-enabled decision workflows.

  1. Governed source
  2. Traceable answer
  3. Abstain when unsupported

Fictional demonstration data

Boundary: Independent prototype; no paid-client, revenue, enterprise-deployment, or validated-market-demand claims are made.

Open prototype record

04 / Education / In progress

Graduate Student, Applied Quantitative Finance

MS study at the University of Denver — Daniels College of Business, with expected completion in November 2027.

Course of study

  • Pricing
  • Financial risk
  • Portfolio decisions

Financial econometrics · Computational finance

Boundary: In-progress education; distinct from professional work and independent research.

View education record
View the complete work record Inspect the open-source repository

02 / The Sovereign Affair

Writing on governed data, AI systems, quantitative finance, and decisions under uncertainty.

The Sovereign Affair is Kyle Wisniewski’s forthcoming newsletter and public writing desk.

Featured essay

EssayAugust 9, 2026

Quantitative Finance, Risk & Decision Science

The Hidden Assumption Inside a 60/40 Portfolio

Stocks and bonds can fall together when their shared exposure to inflation, interest rates, or term premia overwhelms the diversification observed in another regime.

Method
Synthesis of the lab's frozen SPY–TLT correlation study and separate static portfolio stress, supported by BIS, ECB, and Federal Reserve institutional research.
Boundary
Independent educational research; not investment advice, an allocation recommendation, or a professional investment track record.
Read the paper

03 / Professional through-line

Governance is the through-line.

Kyle’s current work makes institutional information more reliable and reviewable. Quantitative-finance research extends the same discipline to models, portfolio risk, and decisions made with incomplete information.

Read the executive biography

  1. Governed data

    Sources, definitions, access, and review.

  2. Quantitative methods

    Assumptions, tests, and limitations.

  3. Decision systems

    Risk, judgment, ownership, and escalation.

Provenance before polish.

Definitions before dashboards.

Models expose assumptions.

Automation retains review.

04 / ContactBring a problem with a decision behind it.Data governance · analytics transformation · financial risk · reliable decision infrastructure