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Conferences / Data Day Texas

Data Day Texas 2022

Jun 13, 2022 · AT&T Hotel and Conference Center (University of Texas at Austin), Austin, TX, United States

Conference
Data Day Texas Vendor & industry
Dates
Jun 13, 2022
Venue
AT&T Hotel and Conference Center (University of Texas at Austin)
Place
Austin, TX, United States
Format
In person
Status
Past
Attendance
—
Website
datadaytexas.com
Recorded
19 sponsors · 50 speaker appearances
Notes
Speakers: all sessions on the year's sessions page (includes the Graph Day / track sessions held within Data Day); affiliations as shown on the sessions page, or from the speaker bio on the speakers page when the sessions page gives none. Originally scheduled for Sat Jan 22, 2022; Eventbrite later says "Data Day Texas 2022 has rescheduled for Monday, June 13" (9am-6pm). Sponsor and vendor event pages (Neo4j, PoolParty, AllegroGraph, Graphable) also list June 13, 2022; AllegroGraph brands it "Global Graph Summit - Data Day Texas". The sessions page appears to retain some entries from the January plan (duplicates removed). Sponsor list from the venue page, which may reflect the January plan.

Ticket prices

TicketPriceValid untilNotesSource
Early registration (through 12/20)$285Dec 20, 2021Face value before Eventbrite fees. Listed when the event was scheduled for Jan 22, 2022.web.archive.org
Registration (through 5/17)$295May 17, 2022Face value before Eventbrite fees. Listed after the move to June 13, 2022.web.archive.org
Team discount (3 or more)$236—Face value before Eventbrite fees.web.archive.org
Registration$475—Face value before Eventbrite fees.web.archive.org

Sponsors 19

Gold 1

Silver 16

Other 2

Speakers and talks 50

SpeakerCompanyRoleTalkLinks
Alexander MorriseGraphistrySpeakerA 90 minute hands-on GNN Primer—
Amy HodlerRelationalAISpeakerRelational Graphs Part II: UN Demo of Composable Knowledge—
Amy HodlerRelationalAISpeakerRelational Graphs Part I: Business Models Become the Program—
Andy PetrellaKensuSpeakerWhat is Data Observability, and why should data teams consider it?—
Arthur KeenArangoDBSpeakerMachine Learning, Semantics, and Knowledge Graphs—
Bivin SadlerSouthern Methodist UniversitySpeakerA Comparison of Deep Learning Versus Parametric Time Series Models—
Bob Van LuijtSeMI TechnologiesSpeakerIntroduction to Vector Search Engines—
Boroslav IordanovSemantic ArtsSpeakerWhat's up with upper ontologies?—
Brandon BaylorChevronPanelistPanel: How do you scale a knowledge Graph when there is no consensus—
Chris RossbachKatana GraphSpeakerKatana Graph Technical Foundations—
Corey LanumCambridge IntelligenceSpeakerVisual timeline analytics: applying concepts from graph theory to timeline and time series data—
Dave BechbergerAmazon Web ServicesSpeakerA gentle introduction to using graph neural networks on knowledge graphs—
David HughesGraphableSpeakerClinical trials exploration: surfacing a clinical application from a larger Bio-Pharma KnowledgeGraph—
Elliott CordoCapsuleSpeakerBuilding a Modern Data Platform Using Advanced Redshift Topologies—
Heather HeddenSemantic Web CompanySpeakerThe Future of Taxonomies - Linking data to knowledge—
Heather HeddenSemantic Web CompanySpeakerIntroduction to Taxonomies for Data Scientists—
James HansenChevronPanelistPanel: How do you scale a knowledge Graph when there is no consensus—
Jans AasmanFranz IncSpeakerWhat Happens Next? Event Predictions with Machine Learning and Graph Neural Networks—
Jike Chong—SpeakerData Professional's Career: Techniques to Practice Rigor and Avoid Ten Mistakes—
Jike Chong—SpeakerFor the overwhelmed data professionals: What to do when there is so much to do?—
Joey JablonskiPythianSpeakerApproaches to Modern Data Governance—
Jonathan MuganDeUmbraSpeakerRLlib for Deep Hierarchical Multiagent Reinforcement Learning—
Jonathan MuganDeUmbraSpeakerA Path to Strong AI—
Jörg SchadArangoDBSpeakerIt was the best of Graph, it was the worst of Graph - Choosing between Graph ML and Graph Analytics—
Josh ShinavierLinkedInSpeakerTranspilers Gone Wild - announcing Hydra—
Josh ShinavierLinkedInPanelistPanel: How do you scale a knowledge Graph when there is no consensus—
Kirk MarpleUnstrukSpeakerUnstructured Data Management: It's not just for your documents—
Leo MeyerovichGraphistrySpeakerWhat is Graph Intelligence—
Max De MarziAmazon Web ServicesSpeakerOutrageous ideas for Graph Databases—
Michael HungerNeo4jSpeaker——
Michael UscholdSemantic ArtsWorkshopOntology for Data Scientists—
Michael UscholdSemantic ArtsPanelistPanel: How do you scale a knowledge Graph when there is no consensus—
Michael ZelenetzPEAK6SpeakerThis Dashboard Should Have Been a Meeting—
Michelle YiRelationalAISpeakerRelational Graphs Part II: UN Demo of Composable Knowledge—
Michelle YiRelationalAISpeakerRelational Graphs Part I: Business Models Become the Program—
Milecia McGregorIterativeSpeakerUsing Reproducible Experiments To Create Better Models—
Paige RobertsVerticaSpeakerShortcut MLOps with In-Database Machine Learning—
Paul AzunreDun & BradstreetSpeakerBusiness Transformers - Leveraging Transfer Learning for B2B Insights—
Ryan MichaelKaskadaSpeakerUnlocking time-based Machine Learning with a new paradigm for engineering features from event-based data—
Ryan MitchellGerson Lehrman GroupSpeakerWhat is Truth? - Strategies for managing semantic triples in large complex systems—
Ryan WisneskyConexusPanelistPanel: How do you scale a knowledge Graph when there is no consensus—
Sachin SharmaArangoDBSpeakerGraph Neural Networks with PyTorch Geometric and ArangoDB—
Satoru HayasakaKNIMESpeakerIntroduction to Codeless Deep Learning—
Sean RobinsonGraphableSpeakerHistory of Network Science - A Look at How Networks Have Connected Us—
Shirshanka DasDataHubSpeakerThe Data Practitioner's Guide to Metadata—
Shirshanka DasDataHubSpeakerWhat is a metadata platform and why do you need it?—
Thorsten LiebigderivoSpeakerConnecting the Dots in a Million Node Graph—
Ying DingThe University of Texas at AustinSpeakerFighting COVID-19 using Knowledge Graphs—
Yue Cathy Chang—SpeakerData Professional's Career: Techniques to Practice Rigor and Avoid Ten Mistakes—
Yue Cathy Chang—SpeakerFor the overwhelmed data professionals: What to do when there is so much to do?—

Sources