Data Conference Databasea Query.Farm index
Companies

Queen Mary University of London

Product category: Not a vendor 99% confident
Org type: University / research 100% confident
Also listed as 1 other name

Queen Mary University London

Conferences sponsored0
Events sponsored0
Speaker slots44 distinct people
Conferences present at4sponsor or speaker
Years active2019–2022

Circuit mix

Where Queen Mary University of London shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.

Presence by conference and year

Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.

  • Title
  • Platinum
  • Gold
  • Silver
  • Bronze
  • Other / unlabeled tier
  • Had speakers

Activity by year

Three measures on one year axis; each panel has its own scale.

Conferences present at

Distinct conferences each year, split by whether the company sponsored or only had speakers there.

  • Sponsored
  • Speakers only

Distinct speakers

People from this company on a program that year, each counted once.

Where they've grown or pulled back

Queen Mary University of London has never sponsored a tracked conference, so this follows where its speakers appear instead.

DownNothing recorded in 2026; had speakers at 1 conference in 2022.

Counted per conference. 2026 is the latest year whose conferences have mostly happened. A conference counts as no longer seen only when later events published speaker lists.

No longer seen 4

No speakers since 2024 or earlier, though the conference published speaker lists after that.

  • FOSDEMlast seen 2020; the conference ran again with speaker lists in 2021–26
  • LDBC Technical User Community (TUC) Meetinglast seen 2022; the conference ran again with speaker lists in 2023–26
  • PyDatalast seen 2019; the conference ran again with speaker lists in 2020–26
  • State of the Maplast seen 2019; the conference ran again with speaker lists in 2022–26

Lookalikes

Companies with the most similar conference footprint: they sponsor and speak at the same conferences. Bars are cosine similarity, from 0 (nothing shared) to 1 (identical).

  1. KIT0.94
  2. The Alan Turing Institute0.81
  3. Planet0.80
  4. TomTom0.79
  5. European Commission0.77
  6. Deltares0.74
  7. GetYourGuide0.69
  8. WhereGroup0.68

16 Cluster: FOSS4G · State of the Map circuit →

Often sponsors alongside

Companies that sponsored the same events, by number of shared events (out of 0 this company sponsored).

No co-sponsors: this company has no recorded sponsorships, or sponsored alone.

Neighborhood: companies with nearby conference footprints

Queen Mary University of London (ringed) and the 30 companies closest to it on the company map. Close together means they sponsor and speak at many of the same conferences. Distances are only meaningful locally; the axes have no meaning. Dot size: conferences present at. Color: cluster.

Queen Mary University of LondonKITDeltaresThe Alan Turing InstituteOslandiaPlanetEuropean Space AgencyNASAOSGeoRivianUniversity of BonnEsriHexagonFusedDevelopment SeedTomTomUniversity of Colorado BoulderArizona State UniversityHumanitarian OpenStreetMap TeamRadiant EarthCNESYouthMappersOmniSciPolitecnico di Milano
Queen Mary University of LondonKITDeltaresThe Alan Turing InstituteOslandiaPlanetEuropean Space AgencyNASAOSGeoRivianUniversity of BonnEsriHexagonDevelopment SeedTomTomArizona State UniversityRadiant EarthCNESYouthMappersPolitecnico di Milano

Sponsorships 0

No sponsorships recorded for Queen Mary University of London.

Speakers 4

SpeakerEventRoleTalkLinks
Naomi ArnoldFifteenth LDBC TUC MeetingspeakerTemporal graph analysis of the far-right social network GabSlides · Video
Ben SteerFOSDEM 2020speakerRaphtory: Streaming analysis of distributed temporal graphsSlides · Video
Dan StowellState of the Map 2019speakerMapping solar panels can save megatons of CO2—
Imran RashidPyData London 2019tutorialTraining intelligent game agents using deep reinforcement learning—