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VinumExMachina Atlas of AI in Wine
Select language: Русский

AtlasAbout the author

About the author

Section § 6
Status published
Updated
Languages EN · RU
Measure 72 ch · 2 min
Plate 01 Nikita Khudov
Portrait of Nikita Khudov holding a glass of red wine

Portrait of the author.

Nikita Khudov, DipWSET

Wine expert · AI practitioner · Founder of the W4U wine club

Vinum ex Machina is deliberately a one-person publication: editorial voice, benchmark methodology and technical infrastructure all sit under a single hand, which keeps the perspective coherent as the corpus grows. The work sits at the intersection of wine and applied artificial intelligence, and it is written from both sides of it.

WSET
Level 4 Diploma in Wines
Wine club
W4U — founder and wine researcher
AI
Managing Partner at StrategAI (consulting & integration)
Teaching
HSE ICEF — professor of AI in Business
Tasted
3,000+ wines · 500+ events organized

Wine

Credentials

  • WSET — Level 4 Diploma in Wines (DipWSET)
  • WSET — Level 2 Award in Beer, Level 1 Award in Sake
  • Italian Wine Scholar
  • Chianti Classico Expert

Competitions

  • Semi-finalist, top 30 — Moscow Sommelier Cup
  • Participant — Russian Sommelier Competition

Practice & Research

  • 500+ wine events hosted
  • 3,000+ wines tasted
  • Articles and research notes on wine

AI and work

Previous roles

  • AI Alliance Russia — Deputy CEO
  • Sber — Executive Director, AI Transformation Centre
  • Bain & Company — Senior Associate Consultant

Expert contribution

  • Agency for Strategic Initiatives — Expert Council member
  • Skolkovo — startup mentor
  • HSE ICEF Summer School — visiting lecturer

Education

  • IE HST — MSc in Business Analytics and Big Data, 2018
  • IE Business School — MSc in Management, 2018
  • HSE ICEF — BSc in Economics, 2016
  • UoL LSE — BSc in Banking & Finance, 2016

Collaborators are welcome, and the atlas needs them. Its two datasets were compiled by one person reading published sources, which is a real limit: the Casebook records what a source will confirm, not what a cellar knows, and OenoBench measures what a benchmark can ask. The work that would improve both comes from people who hold the operational record — estates, cooperatives, laboratories, vendors, importers and research groups.

Specifically: a case the Casebook has missed or has got wrong; operational data behind a deployment, in whatever form it exists; a question set or a review of one for OenoBench; a joint study; a translation check; or a correction to anything published here, which is always the most useful message to receive. Russian and English both work.

nikitahudov@gmail.com — or, for a case, the submission form at the foot of the register, which puts it in the same review queue.