Skip to content
View mkzung's full-sized avatar
💭
Rockin
💭
Rockin
11 followers · 3 following

Achievements

Achievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Quickdraw

Achievements

Achievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Quickdraw

Block or report mkzung

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
mkzung/README.md

Max Gorbuk

I work on the gap between the reported number and the real one: fabricated volume, who actually sets a price, what a private valuation headline is worth.

Nothing ships until a checker can fail it. I mutate my own prose to find the claims nothing guards, and each study prints the arguments that did not survive.

Stanford GSB Venture Capital Initiative (Prof. Ilya Strebulaev). Incoming MSc Analytics & Management, London Business School, Aug 2026.

15 pull requests merged upstream across 8 organisations: Apache Arrow, Polars, ccxt x3, freqtrade, DefiLlama, UK AI Security Institute's inspect_evals x3, Nethermind x2, DN Institute x3.

Market forensics

  • xstocks-price-discovery: which venue prices a tokenized stock. The exchange leads 21 of 23 rankable pair-days, and the quieter a pool, the more the exchange prints against it, at a rank correlation of -0.93.
  • dex-volume-integrity: fabricated volume on Base and BNB Chain, counted from transfers rather than from an aggregator.
  • tokenized-equity-wash-trading: the same mints screened on a centralised book and in Solana pools. Three more signatures published in the DN Institute wiki.

Private-market valuation

LLM evaluation

Also: alphaforge (factor backtesting), morpho-vault-counterfactuals (six risk detectors on Morpho vault history), fundarb (funding-rate arbitrage).

Elsewhere: named contributor on the WEF x Stanford GSB report The Future of Venture Capital (2026), and founder of MedAI, Rospatent-registered pediatric clinical decision support, live in 10 clinics.

Off the clock I trace a family line back to a man born around 1770, held to the same evidence rule, which nineteenth-century parish clerks did nothing to make easier.

Python, pandas/Polars, Solana and EVM data, Inspect AI, Rust, C++ where a fix needs it.

gorbuk.com · LinkedIn · Stanford · gorbuk@stanford.edu

Pinned Loading

  1. ethbtc-suspicious-patterns ethbtc-suspicious-patterns Public

    Six-detector forensic analysis of ETH/BTC market data — wash-trading investigation with defensible, reproducible evidence. DN Institute Challenge #492 submission (PR 1712n/market-data-challenge#24).

    Python 1

  2. contract-tracker contract-tracker Public

    Email-driven contract-approval tracker — IMAP poller + RFC-5322 thread reconstruction + OpenAI JSON-mode classifier + Streamlit dashboard. Built for a construction firm.

    Python 1

  3. fundarb fundarb Public

    Cross-venue funding-rate arbitrage CLI for crypto perpetuals — Hyperliquid + Orderly + Backpack. Python, ed25519/EIP-712 signing, testnet/mainnet config, live signal generation + execution.

    Python 1

  4. research-automation-pipeline research-automation-pipeline Public

    Reference architecture for AI research-automation pipelines (n8n + Python + LLM) — generic template, derived from work at Stanford GSB Venture Capital Initiative, published with advisor permission.

    Python 1

  5. awesome-clinical-ai-safety awesome-clinical-ai-safety Public

    A curated list of resources on clinical AI safety: evaluation, regulation, deployment, and post-market surveillance for medical AI.

    1 1

  6. morpho-vault-counterfactuals morpho-vault-counterfactuals Public

    Historical replay + counterfactual stress testing for Morpho MetaMorpho vaults: 6 risk detectors (oracle freeze, collateral cascade, depositor exit, utilization, liquidation latency, LTV stress)

    Python 2