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Daniel Wigdor | CEO, AXL & Professor, University of Toronto — 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
katieszilagyi.com ↗	68 match 1 shared topics	Katie Szilagyi – Assistant Professor, Faculty of Law, University of Manitoba	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
danielgrzenda.com ↗	67 match 1 shared topics	About – Daniel Grzenda – PhD Student - University of Chicago	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
darlingtree.com ↗	67 match 1 shared topics	Si Chen - Professor of Computer Science \| West Chester University	United States~ en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	partial · 8
bryonaragam.com ↗	67 match 1 shared topics	Bryon Aragam // University of Chicago	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
danielgamo.com ↗	66 match 1 shared topics	Daniel Gamo \| Mind Universe	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
kavinduravishan.com ↗	66 match 1 shared topics	Kavindu Ravishan Perera - AI Researcher & Project Researcher at University of Oulu	Finland~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
kayarvizhy.com ↗	65 match 1 shared topics	Associate Professor, CSE, BMSCE	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
bruno-ribeiro.com ↗	65 match 1 shared topics	Bruno Ribeiro - Purdue University	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
omguniversity.com ↗	65 match 1 shared topics	OMG University – omg university	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
danielboeppler.com ↗	65 match 1 shared topics	Daniel Boeppler — Senior UX/UI & Product Designer · Toronto, ON	Canada~ en	artificial-intelligence	robots llmsaihumans	emailphone	none
humanintheloopuniversity.com ↗	65 match 1 shared topics	HIL University · Universal AI Literacy	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bugejamark.com ↗	65 match 1 shared topics	Mark Bugeja \| AI, Computer Vision & Digital Tourism Researcher \| University of Malta	Malta~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
omri-porat.com ↗	65 match 1 shared topics	Omri Porat \| Tel Aviv University	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bcn-aim.org ↗	65 match 1 shared topics	BCN-AIM – Artificial Intelligence in Medicine Lab @ University of Barcelona	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
vivekvijay.com ↗	64 match 1 shared topics	Vivek Vijay - Associate Professor, IIT Jodhpur	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
factslab.io ↗	64 match 1 shared topics	FACTS.lab \| Formal And CompuTational Semantics lab at the University of Rochester	United States~ en	artificial-intelligence Spree Commerce	robotsllmsaihumans	emailphone	none
human-centred.ai ↗	64 match 1 shared topics	HCAI-Essex Lab \| Website of the Human Centred Artificial Intelligence Lab at University of Essex	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
anjanidhrangadhariya.com ↗	64 match 1 shared topics	Anjani Dhrangadhariya - Switzerland, F. Hoffmann-La Roche AG, University of Geneva, PhD \| about.me	en	artificial-intelligence	robotsllmsaihumans	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.