Who will speak at Data Day Texas 2019?

Our discount hotel block sells out early. For the best selection, Book a room now.

We're now beginning to announce the first wave of speakers for Data Day Texas 2019. We'll be adding speakers every few days. Bookmark this page for updates. If you'd like to see the list of speakers who appeared at Data Day Texas 2018, you can view it here.

Streaming Data Keynote
Gwen Shapira (Cupertino, CA ) @gwenshap

Gwen Shapira (LinkedIn) is a system architect at Confluent, where she helps customers achieve success with their Apache Kafka implementations. She has 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. Gwen currently specializes in building real-time reliable data-processing pipelines using Apache Kafka. Gwen is an Oracle Ace director, the co-author of two O'Reilly books: Kafka: the definitive guide and Hadoop Application Architectures, and a frequent presenter at industry conferences. She is also a committer on Apache Kafka and Apache Sqoop. When Gwen isn’t coding or building data pipelines, you can find her pedaling her bike, exploring the roads and trails of California and beyond.

Graph Keynote
Dr. Denise Koessler Gosnell (Charleston) @DeniseKGosnell

Dr. Denise Gosnell leads a team at DataStax which builds some of the largest, distributed graph applications in the world. Her passion centers on examining, applying, and evangelizing the applications of graph data and complex graph problems. As an NSF Fellow, Dr. Gosnell earned her Ph.D. in Computer Science from the University of Tennessee. Her research coined the concept of "social fingerprinting" by applying graph algorithms to predict user identity from social media interactions.​ ​Since then, Dr. Gosnell has built, published, patented, and spoke on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals.Dr. Gosnell will be presenting the Graph Summit Keynote: From Theory to Production.

NLP Keynote
Robert Munro (San Francisco ) @WWRob

Robert Munro (LinkedIn) most recetly was Chief Technology Officer at Figure-Eight (formerly known as Crowdflower). Previously, he ran Product for AWS's first Natural Language Processing services in the Deep Learning team at Amazon AI. Robert is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. Robert has published more than 50 papers, has a PhD from Stanford University.

Jans Aasman (SF Bay)

Jans Aasman (Wikipedia / LinkedIn) is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of the graph database, AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in- hand with numerous Fortune 500 organizations as well as US and Foreign governments. Jans recently authored an IEEE article on “Enterprise Knowledge Graphs”.
Dr. Aasman spent a large part of his professional life in telecommunications research, specializing in applied Artificial Intelligence projects and intelligent user interfaces. He gathered patents in the areas of speech technology, multimodal user interaction, recommendation engines while developing precursor technology for tablets and personal assistants. He was also a professor in the Industrial Design department of the Technical University of Delft. Dr. Aasman is a noted conference speaker at such events as Smart Data, NoSQL Now, International Semantic Web Conference, GeoWeb, AAAI, Enterprise Data World, Text Analytics, and TTI Vanguard to name a few.

Jesse Anderson (Reno) @jessetanderson

Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers. He is widely regarded as an expert in the field and for his novel teaching practices. Jesse is published on O’Reilly and Pragmatic Programmers. He has been covered in prestigious publications such as The Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired. You can learn more about Jesse at Jesse-Anderson.com.
Jesse will present the following session: Creating a Data Engineering Culture.

Roger Barga (Seattle)

Roger Barga is General Manager for the New Cloud Service Initiative at Amazon. Prior to that, Roger was general manager and director of development at Amazon Web Services, where he was responsible for Kinesis data streaming services. Previously, Roger was in the Cloud Machine Learning Group at Microsoft, where he was responsible for product management of the Azure Machine Learning service. Roger is also an affiliate professor at the University of Washington, where he is a lecturer in the Data Science and Machine Learning programs. Roger holds a PhD in computer science, has been granted over 30 patents, has published over 100 peer-reviewed technical papers and book chapters, and has authored a book on predictive analytics.

Dave Bechberger (Houston) @bechbd

Dave Bechberger is a Sr. Architect at Gene by Gene, a genetic genealogy and bioinformatics company, where he works extensively on developing their next-generation data architecture. Dave has spent his career engaging in full stack software development but specializes in building data architectures in complex data domains such as bioinformatics, oil and gas, supply chain management, etc. He uses his knowledge of graph and other big data technologies to build out highly performant and scalable systems. Dave has previously spoken at a variety of international technical conferences including NDC Oslo, NDC London, and Graph DayTexas.

Michael Berthold (Konstanz)

Michael Berthold is currently president of KNIME.com AG and co-creator of KNIME (wikipedia entry), the open analytics platform used by thousands of data experts around the world. Since August 2003, Michael has been the Nycomed-Chair for Bioinformatics and Information Mining at Konstanz University, Germany where his research focuses on using machine learning methods for the interactive analysis of large information repositories in the Life Sciences. Previously he held positions in both academia (Carnegie Mellon, UC Berkeley) and industry (Intel, Tripos).
Michael is Past President of the North American Fuzzy Information Processing Society, Associate Editor of several journals and the President of the IEEE System, Man, and Cybernetics Society. He has been involved in the organization of various conferences, most notably the IDA-series of symposia on Intelligent Data Analysis and the conference series on Computational Life Science. Together with David Hand he co-edited the textbook Intelligent Data Analysis: An Introduction which has recently appeared in a completely revised, second edition. He is also co-author of Guide to Intelligent Data Analysis (Springer Verlag) which appeared in summer 2010. When time permits Michael still writes code.

Ryan Boyd (SF Bay) @ryguyrg

Ryan Boyd (Linkedin) is a SF-based software engineer at Neo4j focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Ryan has been consistently one of the highest rated speakers at our conference. We're happy that he has agreed to return to Austin

Michelle Casbon (San Francisco) @texasmichelle

Michelle Casbon is a senior engineer on the Google Cloud Platform developer relations team, where she focuses on open source contributions and community engagement for machine learning and big data tools. Michelle’s development experience spans more than a decade and has primarily focused on multilingual natural language processing, system architecture and integration, and continuous delivery pipelines for machine learning applications. Previously, she was a senior engineer and director of data science at several San Francisco-based startups, building and shipping machine learning products on distributed platforms using both AWS and GCP. She especially loves working with open source projects and has contributed to Apache Spark and Apache Flume. Her writing has been featured in the AI section of O’Reilly Radar. Michelle holds a master’s degree from the University of Cambridge.

Sanghamitra Deb (SF Bay) @sangha_deb

Sanghamitra Deb is a Data Scientist at Chegg, where she works on problems related school and college education to sustain and improve the learning process. Her work involves recommendation systems, graph modeling, deep NLP analysis , data pipelines and machine learning. Previously, Sanghamitra was a data scientist at a Accenture where she worked on a wide variety of problems related data modeling, architecture and visual story telling. Sanghamitra is active in Data Science outreach and believes in applying analytics to a range of domains such as pharma, HR, customer support, market research, etc. Prior to being data scientist she was an astrophysicist who studied the structure of the universe by modeling galaxy clusters.
Sanghamitra will present the following session: Using weak supervision and transfer learning techniques to build knowledge graph to improve student experiences at Chegg.

Nick Gaylord (San Francisco) @texastacos

Nick Gaylord is a senior member of the data science team in Johnson & Johnson's Health Technology group, where he works on a wide range of digital health and wellness applications as well as helping to evangelize data-driven innovation across the enterprise. His previous roles include work on CRM solutions for small business owners at Womply and helping to build human-in-the-loop machine learning platforms at Figure Eight and Idibon. He has a PhD from the University of Texas at Austin, and in his spare time he fixes bikes and collaborates on work applying cognitive science to the public health domain.

Holden Karau (San Francisco) @holdenkarau

Holden Karau is a transgender Canadian, Apache Spark committer, an active open source contributor, and co-author of Learning Spark & High Performance Spark. When not in San Francisco working as a software development engineer at IBM’s Spark Technology Center, Holden talks internationally on Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science. Outside of computers she enjoys dancing & playing with fire.

William Lyon (SFBay) @lyonwj

William Lyon is a software developer at Neo4j, the open source graph database. As an engineer on the Developer Relations team, he works primarily on integrating Neo4j with other technologies, building demo apps, helping other developers build applications with Neo4j, and writing documentation. Prior to joining Neo, William worked as a software developer for several startups in the real estate software, quantitative finance, and predictive API fields. William holds a Masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com.

Petra Selmer (London) @Aethelraed

Dr. Petra Selmer is a member of the Query Languages Standards and Research group at Neo4j, undertaking research into graph query languages and language standards, with the aim of evolving and standardizing property graph querying. She also supports the openCypher project at www.opencypher.org, and was previously part of the team designing and optimizing Neo4j’s Cypher query engine. For many years, she worked as a consultant and developer in a variety of different domains and roles and has a PhD in Computer Science from Birkbeck, University of London, where she researched flexible querying of graph-structured data.

Juan Sequeda (Austin) @juansequeda

Dr. Juan Sequeda is the co-founder of Capsenta, a spin-off from his research, and the Senior Director of Capsenta Labs. He holds a PhD in Computer Science from the University of Texas at Austin. His research interests are on the intersection of Logic and Data and in particular between the Semantic Web and Relational Databases for data integration, ontology based data access and semantic/graph data management. Juan is the recipient of the NSF Graduate Research Fellowship, received 2nd Place in the 2013 Semantic Web Challenge for his work on ConstituteProject.org, Best Student Research Paper at the 2014 International Semantic Web Conference and the 2015 Best Transfer and Innovation Project awarded by Institute for Applied Informatics. Juan is the General Chair of AMW 2018, was the PC chair of the ISWC 2017 In-Use track, is on the Editorial Board of the Journal of Web Semantics, member of multiple program committees (ISWC, ESWC, WWW, AAAI, IJCAI) and co-creator of the Consuming Linked Data Workshop series. Juan is a member of the Graph Query Languages task force of the Linked Data Benchmark Council (LDBC) and has also been an invited expert member and standards editor at the World Wide Web Consortium (W3C).

Joshua Shinavier (San Francisco) @joshsh

Joshua Shinavier is a primordial being of the graph database domain, and holds a PhD in Web science from RPI’s Tetherless World Constellation. He contributed to the first common APIs for graph databases, the original TinkerPop query language which influenced Gremlin, and the first tools which aligned the property graph and RDF data models, starting with neo4j-rdf-sail in 2008. Other graphy adventures have include Lisp hacking at Franz Inc. and Java hacking at Aurelius. As of 2017, he is part of the knowledge graph team at Uber, where he also leads a company-wide effort to unify schemas across RPC, streaming, and storage. He feels, now as ever, that the research, business, and open source communities have a lot to learn from each other with respect to graphs and knowledge representation.

Ted Wilmes (Oklahoma City) @trwilmes

Ted Wilmes, Data Architect at Expero, is a graduate of Trinity University where he studied computer science and art history. He started his professional career at a not-for-profit research and development institution where he performed contract software development work for a variety of government and commercial clients. During this time he worked on everything from large enterprise systems to smaller, cutting edge research and development projects. One of the most rewarding parts of each of these projects was the time spent collaborating with the customer.
As Ted’s career continued, he moved on to an oil and gas startup and continued to dig deeper into the data side of software development, gaining an even deeper interest in how databases work and how to eek as much performance out of them as possible. During this time he became interested in the application of graph databases to certain problem sets. Today, at Expero, Ted enjoys putting his deep knowledge of transactional graph computing to work as he helps customers of all types navigate the burgeoning property graph database landscape.
Outside of work, Ted enjoys spending time with his family out-of-doors, listening to and playing loud music, and contributing to the Apache TinkerPop project as a committer and PMC member.
Ted will present the following Graph Summit session: High Performance JanusGraph Batch & Stream Loading.

Dr. Mingxi Wu (Redwood City)

Dr. Mingxi Wu is the VP of Engineering at TigerGraph responsible for product development, quality assurance, and release. Mingxi excels at engineering team building and tech leadership. Previously, he worked at Ad-Tech startup Turn (acquired by Amobee), Oracle Relational Database Optimizer Group, and Microsoft SQL Server Manageability Group. He won research awards from SIGMOD, VLDB and KDD and holds patents on big data and pending patents on graph management. Mingxi received his PhD from the University of Florida, where he specialized in database and data mining.
Mingxi will present the following Graph Summit session: Eight Prerequisites of a Graph Query Language.