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David S. Hayden - Massachusetts Institute of Technology - Machine Learning and Computer Vision — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
hsg.ai ↗	67 match 1 shared topics	HSG-AIML \| Research activities at the chair Artificial Intelligence & Machine Learning at the Institute of Computer Science of the University of St. Gallen.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
skimai.com ↗	67 match 1 shared topics	Skim AI - Machine Learning and Artificial Intelligence Solutions	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
donnysoh.com ↗	67 match 1 shared topics	Donny Soh — Associate Professor, Singapore Institute of Technology	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
vontobel.dev ↗	66 match 1 shared topics	Tobias Vontobel - Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nemati.ai ↗	66 match 1 shared topics	Nemati AI - Next-Level Empowerment with Machine Learning, AI Technologies and innovation	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
csadrian.org ↗	66 match 1 shared topics	Adrián Csiszárik - Machine Learning Scientist	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
cbarakat.com ↗	66 match 1 shared topics	Portfolio of Charbel Barakat - Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
livehooah-ai.com ↗	66 match 1 shared topics	Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
horwitz.ai ↗	66 match 1 shared topics	Eliahu Horwitz - PhD Student in Weight Space Learning \| Computer Vision & AI Research	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
qengineering.eu ↗	66 match 1 shared topics	All the machine vision and deep learning solutions. - Q-engineering	European Union en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
craigmyles.co.uk ↗	66 match 1 shared topics	Craig Myles - AI and Machine Learning in Healthcare	United Kingdom en	artificial-intelligence	robotsllmsaihumans	emailphone	none
danieljmannion.co.uk ↗	66 match 1 shared topics	Material & Machine Learning	United Kingdom en	artificial-intelligence	robotsllmsaihumans	emailphone	none
intellektai.com ↗	66 match 1 shared topics	Machine Learning Development Company - Intellekt AI	en	artificial-intelligence Sanity	robotsllmsaihumans	emailphone	none
neoocr.com ↗	66 match 1 shared topics	Mathematics for AI and Machine Learning \| math4ai.org	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bifold.berlin ↗	66 match 1 shared topics	BIFOLD Berlin - Big Data Management and Machine Learning	en	artificial-intelligence TYPO3	robotsllmsaihumans	emailphone	none
neeuro.com ↗	66 match 1 shared topics	The New Technology of Brain Computer Interface \| Neeuro	en	artificial-intelligence HubSpot	robotsllmsaihumans	emailphone	none
3d-medical.ch ↗	66 match 1 shared topics	3D-Medical — Volumetric machine learning for radiology	Switzerland en	artificial-intelligence	robotsllmsaihumans	emailphone	none
eranfeit.net ↗	66 match 1 shared topics	Eran Feit - Home Of The Computer Vision Tutorials	en	artificial-intelligence WordPress WooCommerce	robots llmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.