Some of the topics featured at Data Day

Here is a rough guide to the topics covered at Data Day. These not all of the talks. We are still waiting on the flight itinerary and the final abstracts for a few of the speakers.
A description of the talks can be found on the sessions and workshops pages.

Apache Cassandra

Patrick McFadin (DataStax): What have we done!? 10 years of Cassandra
Jonathan Ellis (DataStax): Cassandra and the Cloud
Jon Haddad (The Last Pickle): Cassandra Performance Tuning and Crushing SLAs
Ben Bromhead (Instaclustr): Cassandra and Kubernetes
Aaron Ploetz (Target): Performance Data Modeling at Scale
Dikang Gu (Facebook) / Pengchao Wang (Facebook): Cassandra pluggable storage engine
Jeff Carpenter (DataStax): Cassandra Architecture FTW!

AI / Machine Learning

Lukas Biewald - Crowdflower: KEYNOTE: Deep Learning in the Real World
Kristian Hammond (Narrative Science): Here and now: Bringing AI into the enterprise
Lynn Pausic - Expero / Chris LaCava - Expero: Vital Role of Humans in Machine Learning
Qazaleh Mirsharif - CrowdFlower: Understanding the development of visual focus of attention in infants using computer vision tools
Jonathan Mugan (Deep Grammar): Generating Natural-Language Text with Neural Networks
Arnaud de Moissac - DCbrain / Jean-Reynald Macé - Areva: AI and Graph to optimize steam process in a large process plant
Nicholas Strayer - Vanderbilt University: Making Magic with Keras and Shiny
Gunnar Kleemann - Berkeley Data Science Group / Kiersten Henderson - Austin Capital Data Group: Biorevolutions: Machine Learning and Graph Analysis Illuminate Biotechnology Startup Success

Natural Language Processing

William Lyon - Neo4j: Graph Analysis of Russian Twitter Trolls
Dan Bennett (Thomson Reuters): Building a Knowledge Graph
Jonathan Mugan (Deep Grammar): Generating Natural-Language Text with Neural Networks
David Bechberger (Gene by Gene): Improving Graph Based Entity Resolution using Data Mining and NLP
Jason Kessler (CDK Global): Lexicon Mining for Semiotic Squares: Exploding Binary Classification
Mayank Kejriwal (USC Information Sciences Institute - ISI): Building advanced search and analytics engines over arbitrary domains...without a data scientist
Rob McDaniel (Lingistic / Rakuten): Detecting Bias in News Articles
Julia Silge (Stack Overflow): Text Mining Using Tidy Data Principles
Denis Vrdoljak / Gunnar Kleemann )Berkeley Data Science Group): Silicon Valley vs New York: Who has Better Data Scientists? (a knowledge graph)

Graph Databases, Frameworks, and Processing

See also Knowledge Graphs
William Lyon - Neo4j: Graph Analysis of Russian Twitter Trolls
David Bechberger - Gene by Gene: Improving Graph Based Entity Resolution using Data Mining and NLP
Ryan Boyd - Neo4j: Data Science Tools: Cypher for Data Munging
Arnaud de Moissac - DCbrain / Jean-Reynald Macé - Areva: AI and Graph to optimize steam process in a large process plant
David Gilardi - DataStax: 3 ways to build a near real-time recommendation engine
Dr. Denise Gosnell (DataStax): Everything is not a graph problem (but there are plenty)
Corey Lanum - Cambridge Intelligence: How to Destroy Your Graph Project with Terrible Visualization
Dr. Victor Lee (TigerGraph): Real-time deep link analytics: The next stage of graph analytics
Misty Nodine - Spiceworks: Understanding People Using Three Different Kinds of Graphs
Steve Purves (Expero): Graph Convolutional Networks for Node Classification

Andrew Ray (Sam's Club): Writing Distributed Graph Algorithms
Juan Sequeda (Capsenta): G-CORE: A Core for Future Graph Query Languages, designed by the LDBC Graph Query Language Task Force
Claudius Weinberger (ArangoDB): Fishing Graphs in a Hadoop Data Lake

Knowledge Graphs, Ontology, and the Semantic Web

Jans Aasman (Franz, Inc.) : Navigating Time and Probability in Knowledge Graphs
Dan Bennett (Thomson Reuters) : Building a Knowledge Graph
Michael Grove (Stardog Union) : Knowledge Graphs: You're doing them wrong!
Juan Sequeda (Capsenta): Integrating Semantic Web Technologies in the Real World: A journey between two cities
Sebastian Hellmann (DBpedia Association) : DBpedia - A Global Open Knowledge Network
Mayank Kejriwal (USC Information Sciences Institute - ISI) : Building advanced search and analytics engines over arbitrary domains...without a data scientist
Denis Vrdoljak / Gunnar Kleemann (Berkeley Data Science Group): Silicon Valley vs New York: Who has Better Data Scientists? (a knowledge graph)
Atanas Kiryakov (Ontotext) : Cognitive Graph Analytics on Company Data and News: Popularity ranking, importance and similarity
Gunnar Kleemann (Berkeley Data Science Group) / Kiersten Henderson (Austin Capital Data Group): Biorevolutions: Machine Learning and Graph Analysis Illuminate Biotechnology Startup Success

The R Language / R User Day

David Smith - Microsoft: Speeding up R with Parallel Programming in the Cloud
Jane Fine - MongoDB: Using R for Advanced Analytics with MongoDB
Mara Averick - RStudio: Pilgrim’s Progress: a journey from confusion to contribution
Nicholas Strayer - Vanderbilt University: Making Magic with Keras and Shiny
Lucy D'Agostino McGowan - Vanderbilt University Medical Center: Making Causal Claims as a Data Scientist: Tips and Tricks Using R
Gabriela de Queiroz - ‎R-Ladies: Statistics for Data Science: what you should know and why
Jasmine Dumas - Simple Finance: R, What is it good for? Absolutely Everything
Chester Ismay - DataCamp: infer: an R package for tidy statistical inference
Alex Engler (Urban Institute): Introduction to SparkR in AWS EMR (90 minute session)
Albert Y. Kim - Amherst College: Something old, something new, something borrowed, something blue: Ways to teach data science (and learn it too!)
Jessica Minnier - Oregon Health and Science University: Building Shiny Apps: Challenges and Responsibilities
Jonathan Nolis - Lenati: Using R on small teams in industry
Hilary Parker - Stitch Fix: Opinionated Analysis Development
Dave Robinson - Stack Overflow: We R What We Ask: The Landscape of R Users on Stack Overflow
Emily Robinson - Etsy: The Lesser Known Stars of the Tidyverse
Julia Silge (Stack Overflow): Text Mining Using Tidy Data Principles

Data Science

Mara Averick - RStudio: Pilgrim’s Progress: a journey from confusion to contribution
Ryan Boyd - Neo4j: Data Science Tools: Cypher for Data Munging
Lucy D'Agostino McGowan - Vanderbilt University Medical Center: Making Causal Claims as a Data Scientist: Tips and Tricks Using R
Alex Engler (Urban Institute): Introduction to SparkR in AWS EMR (90 minute session)
Gabriela de Queiroz - ‎R-Ladies: Statistics for Data Science: what you should know and why
Mayank Kejriwal (USC Information Sciences Institute - ISI): Building advanced search and analytics engines over arbitrary domains...without a data scientist
Albert Y. Kim - Amherst College: Something old, something new, something borrowed, something blue: Ways to teach data science (and learn it too!)
Dr. Steve Kramer - Paragon Science: Identifying viral bots and cyborgs in social media
Denis Vrdoljak / Gunnar Kleemann )Berkeley Data Science Group): Silicon Valley vs New York: Who has Better Data Scientists? (a knowledge graph)