Who will speak at Data Day Texas 2022

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We continue to confirm speakers for Data Day Texas 2022. If you are doing something cool with data and want to share it at Data Day Texas, we are still accepting proposals proposals.

Genevera Allen ( Houston ) @genevera_allen

Genevera Allen (LinkedIn / Google Scholar) is an Associate Professor of Electrical and Computer Engineering, Statistics and Computer Science at Rice University and an investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital and Baylor College of Medicine. She is also the Founder and Faculty Director of the Rice Center for Transforming Data to Knowledge, informally called the Rice D2K Lab.
Dr. Allen's research focuses on developing statistical machine learning tools to help scientists make reproducible data-driven discoveries. Her work lies in the areas of interpretable machine learning, optimization, data integration, modern multivariate analysis, and graphical models with applications in neuroscience and bioinformatics. Dr. Allen is the recipient of several honors including a National Science Foundation Career award, the George R. Brown School of Engineering's Research and Teaching Excellence Award at Rice University, and in 2014, she was named to the "Forbes '30 under 30': Science and Healthcare" list. Dr. Allen received her PhD in statistics from Stanford University (2010), under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University (2006).

Paul Azunre (Austin) @pazunre

Paul Azunre (LinkedIn) holds a Ph.D. in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. He has helped develop scientific software in key roles at established organizations such as Oracle and Dun & Bradstreet, as well as a variety of startups. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open-source initiative focused on using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field.
In his spare time, under the alias Dr. Pushkin, Paul is part of the underground hip-hop, R&B and Afrobeats/Afropop/Afrohiphop group - Isolirium (Spotify / Soundcloud).
Paul is also author of the recently published Transfer Learning for Natural Language Processing from Manning.
Paul will present the following session: Business Transformers - Leveraging Transfer Learning for B2B Insights.

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.

Dave Bechberger ( Anchorage ) @bechbd

Dave Bechberger is a Sr. Graph Architect on the AWS Neptune service team. A long time graph and distributed data practitioner, Dave has spent over 20 years in full stack software development and specializes in building data architectures in complex data domains such as bioinformatics, oil and gas, supply chain management, etc. Dave has previously spoken at a variety of national and international technical conferences including NDC Oslo, NDC London, as well as previous GraphDay conferences in Texas, San Francisco and Seattle. He is a co-author of Graph Databases in Action by Manning Publications.
Dave will present the following session: A gentle introduction to using graph neural networks on knowledge graphs.

Michael Berthold (Berlin)

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.

Dr. Matthias Broecheler (Seattle) @mbroecheler

Dr. Matthias Broecheler (LinkedIn) is the inventor of the Titan graph database and co-founder of Aurelius, the original company behind the Apache TinkerPop graph framework, acquired by DataStax in 2015. A sought after speaker, he introduced Titan at the 2012 Cassandra Summit and gave the keynote at the first Graph Day Texas in 2016 (interview). Matthias is co-author of The Practitioner's Guide to Graph Data, published by O'Reilly. Matthias received his PhD in Computer Science at University of Maryland, College Park.

Yue Cathy Chang ( Sunnyvale ) @yuec

Yue Cathy Chang is an executive recognized for thought leadership and execution in digital transformation. She is passionate about addressing business challenges and often finds herself and her team "parachuting" into situations to tackle challenging and meaningful data needs. Cathy has led teams and functions at blue-chip enterprises as well as startups, across financial services and high-tech industries, working with leaders of centralized and distributed data teams, all betting the next product differentiation on data. She is currently an AVP in banking and financial services at an American multinational technology corporation.
Cathy holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University, MBA and MS degrees from MIT, and two granted US patents. She's a co-author, with Jike Chong, of the Manning publication How to Lead in Data Science.
Cathy will co-present the following Data Science session:
For the overwhelmed data professionals: What to do when there is so much to do?

Jike Chong (Sunnyvale) @jikechong

Jike Chong is an executive who nurtures teams and crafts cultures to produce billion-dollar business impacts. He built and grew multiple high-performing data functions in public and private companies and nurtured dozens of ambitious individual contributor data scientists into leaders; some have gone on to lead teams of more than 70 data scientists. Jike was part of the executive team that took Yiren Digital Ltd public on NYSE. He also expanded and led the data team as the chief data scientist at Acorns, designed and executed a project predicting venture investment risks at Silver Lake, and led the Hiring Marketplace Data Science team at LinkedIn, serving a business line with $4B a year in revenue.
Jike received his bachelor’s and master’s degrees in electrical and computer engineering from Carnegie Mellon University and a PhD in electrical engineering and computer science from the University of California, Berkeley. He's a co-author, with Yue Cathy Chang of the Manning publication How to Lead in Data Science.
Jike will co-present the following Data Science session:
For the overwhelmed data professionals: What to do when there is so much to do?

Shirshanka Das (Santa Clara) @shirshanka

Shirshanka Das (LinkedIn) is co-founder and CEO of Acryl Data, the company which is commercializing the open source DataHub project, a real-time metadata platform used by LinkedIn, Expedia, Saxo Bank, Klarna, Viasat, and many others. Prior to founding Acryl, he was the overall architect for Big Data at LinkedIn from 2010 to 2020, and responsible for creating the metadata and data management strategy at the company. As part of this, he founded the DataHub project and shaped its evolution to a metadata platform that powers DataOps, MLOps, productivity, and governance use cases at LinkedIn. He is also a PMC and committer on the Apache Gobblin project which manages 100PB+ of data assets at rest at LinkedIn, and is deployed in production at other large companies like Verizon, PayPal etc. Prior to LinkedIn, Shirshanka worked on high-performance serving systems at Yahoo and PayPal. Shirshanka has a Ph.D. in Computer Science from UCLA.

Max De Marzi (Chicago) @maxdemarzi

Marx De Marzi (Linkedin) is addicted to graphs. You may consider him a graph database enthusiast. He spent 8 years at Neo4j and recently made the swith to AWS Neptune. He is a blogger and an open source contributor, both activities which stem from passion: teaching people about graphs. He is always open to talk graphs, always learning, and nothing thrills him more than finding easy graph solutions to hard relational problems. He has been helping people get to the "graph epiphany" for over a decade. He is an avid graph database modeler, leveraging his knowledge of mechanical sympathy and experience to deliver dozens of graph uses cases over the years.
Max will present the following Graph Day session: Outrageous ideas for Graph Databases

Dr. Ying Ding (Austin)

Dr. Ying Ding is the Bill & Lewis Suit Professor of Information Technology at the University of Texas School of Information. Before that, she was a professor and director of graduate studies for data science program at School of Informatics, Computing, and Engineering at Indiana University. She has led the effort to develop the online data science graduate program for Indiana University. She also worked as a senior researcher at Department of Computer Science, University of Innsburck (Austria) and Free University of Amsterdam (the Netherlands). She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences, and serves as the editorial board member for several top journals in Information Science and Semantic Web. She is the co-founder of Data2Discovery company advancing cutting edge AI technologies in drug discovery and healthcare. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies.
Ying will present the following session: Fighting COVID-19 using Knowledge Graphs

Heather Hedden (Boston) @hhedden

Heather Hedden (LinkedIn) has been a taxonomist for over 26 years in various organizations and as an independent consultant. She is currently a data and knowledge engineer on the professional services team of Semantic Web Company, vendor of PoolParty software. Previously worked as a taxonomist at Cengage Learning, Gale, Viziant, First Wind, and Project Performance Corporation. Heather has designed and developed, taxonomies, ontologies, and metadata schema for internal and externally published content. She gives workshops on taxonomy creation at conferences, as corporate training, and through an independently offered online course. Heather is author of The Accidental Taxonomist.
Heather will host the following two sessions:
Introduction to Taxonomies for Data Scientists (workshop)
The Future of Taxonomies - Linking data to knowledge (presentation).

Amy Hodler (Kettle Falls, Washington) @amyhodler

Amy Hodler is the AI evangelist for Fidder Labs, educating data scientists on the use of continuous monitoring for accuracy and bias as well as creating more explainable ML and ultimately more trustworthy AI. Previously, she was AI and Graph Analytics Program Manager at Neo4j, where she promoted the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy is the co-author of the O’Reilly book, Graph Algorithms: Practical Examples in Apache Spark and Neo4j, co-author of Knowledge Graphs: Data in Context for Responsive Businesses, and a contributor to the upcoming book, AI on Trial.
Amy will host the following two MLOps sessions:
- Continuous ML Improvement: Automated Monitoring with Built-In Explainability
- 4 Types of ML Drift and How to Catch Them (Or “Why your AI is wrong, eventually”)

Joey Jablonski ( Austin ) @jrjablo

Joey Jablonski (LinkedIn) is VP of Product at Manifold, where he builds trusted relationships with our customers, allowing us to more fully understand their needs and how we can help. Joey partners with customers to ensure that a product mind-set is part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. He has extensive experience delivering innovative solutions to customers.
Prior to Manifold, Joey was VP of Core Data at Northwestern Mutual, where he delivered high quality and compliant data products to facilitate decision, automation, and new product launches. While there, he built the first data product management team to enable better engineering prioritization, more effective product value definition, and technology simplification. Prior to Northwestern Mutual, Joey was VP of Data Engineering and Analytics at iHeartMedia. There, he led a team delivering data science, data engineering, broadcast engineering, and attribution capabilities to the largest audio media company in the world.

Brad Klingenberg (San Francisco)

Brad Klingenberg is the Chief Algorithms Officer at Daily Harvest, the direct-to-consumer brand that helps you stock your home with clean, delicious food built on real fruit + vegetables and ready in minutes. Brad leads the data team and is charged with using data and algorithms to tailor the Daily Harvest experience to individual food values and taste preferences through the co-creation of food and digital personalization. Prior to Daily Harvest, Brad was the Chief Algorithm Officer at Stitch Fix, where he oversaw a team of more than 140 data scientists and engineers. As the leader of the Algorithms team, Brad was responsible for developing and improving the core algorithmic capabilities that leverage data to power Stitch Fix. Brad has also served as an advisor/consultant for Candidate Labs, Udacity, Opendoor.com, and Netflix. He currently lives in Boulder, CO with his family and holds a PhD from Stanford University, as well as a Bachelor of Science - Master of Science dual degree from University of Colorado at Boulder.

Corey Lanum (Boston) @corey_lanum

Corey Lanum (LinkedIn), has a distinguished background in graph visualization. Over the last 15 years he has managed technical and business relationships with dozens of the largest defense and intelligence agencies in North America, in addition to working with many security and anti-fraud organizations in private industry. Prior to joining Cambridge Intelligence as their US Manager, Corey was helping the customers of i2 (now IBM) and SS8 to solve their most complex graph data challenges.
Corey is the author of Visualizing Graph Data from Manning Publications.
Cory will present the following session: Visual timeline analytics: applying concepts from graph theory to timeline and time series data

William Lyon (SFBay) @lyonwj

William Lyon (LinkedIn / blog) is a software developer at Neo4j. 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. William is author of the Manning publication Full Stack GraphQL Applications With React, Node.js, and Neo4j and co-host of the GraphStuff.FM podcast.
William will host a 90 minute hands-on session: Intro to GraphQL for Developers and Data Scientists.

Milecia McGregor (Tulsa) @flippedcoding

Milecia McGregor is a senior software engineer that's worked with JavaScript, Angular, React, Node, PHP, Python, .NET, SQL, AWS, Heroku, Azure, and many other tools to build web apps. She also has a master's degree in mechanical and aerospace engineering and has published research in machine learning and robotics. She started Flipped Coding in 2017 to help people learn web development with real-world projects and she publishes articles covering all aspects of software on several publications, including freeCodeCamp. She also travels around the world speaking at tech conferences about various software engineering topics ranging from machine learning, PWAs, and managing a career in tech.
Milecia will host the following MLOps session: Using Reproducible Experiments To Create Better Models.

Ryan Mitchell (Boston) @Kludgist

An expert in web scraping, web security, and data science, Ryan Mitchell has hosted workshops and spoken at many events, including Data Day and DEF CON. She teaches web programming and data science and has taught and designed courses at Northeastern University and Olin College of Engineering. Ryan holds a master’s degree in software engineering from Harvard University Extension School and is currently a senior software engineer at the Gerson Lehrman Group where she creates data science tools. Ryan is the author of Web Scraping with Python (O’Reilly) and Instant Web Scraping with Java (Packt Publishing), as well as two Linkedin courses: Python Data Structures with Trees and Web Scraping with Python.
Ryan will be presenting the following session: What is Truth? - Strategies for managing semantic triples in large complex systems

Jonathan Mugan (Austin) @jmugan

Jonathan Mugan (Linkedin) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day Texas conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.
Jonathan will be presenting the following session: A Path to Strong AI

Jacqueline Nolis (Seattle) @skyetetra

Dr. Jacqueline Nolis (LinkedIn / GitHub) is a data science leader with over 15 years of experience in managing data science teams and projects at companies ranging from DSW to Airbnb. She currently is the Head of Data Science at Saturn Cloud where she helps design products for data scientists. Jacqueline has a PhD in Industrial Engineering and is co-author, with Emily Robison, of the Manning publication Build a Career in Data Science.

Josh Perryman (Bryan / College Station) @joshperryman

Josh Perryman likes to play with data. Oftentimes this is implementing proprietary algorithms closer to the data for performance or scale. Sometimes it is ad-hoc investigation and analysis, a sort of exploratory querying. A few times he’s been able to leverage his experience with data engines for dramatic performance improvements. But the real joy is designing a schema for both functionality and performance, one which increases the productivity of other developers and enables a technology to solve new problems or deliver new value to the business.
Technology isn't just data, and Josh does more than just play with data. He’s worked with high performance computing (HPC) environments, taking computations from hours to minutes or seconds. He has built visualizations which deliver new insights into complex data domains. He’s managed technology personnel, both directly and indirectly, to deliver technology solutions. Josh has put together more types of technology components, software and hardware, than can be counted, because one of his fortes is solving problems by building sustainable systems. Josh is currently Director of Product Development, Graph Solutions at VeracityID.

Jennifer Prendki (San Francisco) @jlprendki

Jennifer Prendki is the founder and CEO of Alectio and has spent a large part of her career promoting the importance of creating a better approach to Machine Learning Lifecycle Management. Her current focus is on helping ML teams build better models with less data. Prior to founding Alectio, she was the VP of Machine Learning at Figure Eight, one of the industry leader in data labeling (recently acquired by Appen); she also headed Machine Learning at Atlassian and various Data Science initiatives on the Search team at Walmart Labs. She is also known for her active support of women in STEM and Technology.
Jennifer will be speaking as part of the MLOps track.

Paige Roberts (Austin) @RobertsPaige

With two decades in the data management industry, Paige Roberts (Linkedin), has worked as an engineer, a trainer, a marketer, a product manager, and a consultant. Now, as Open Source Relations Manager at Vertica, she promotes understanding MPP data processing, open source, and how the analytics revolution is changing the world. Paige is contributor to the upcoming O'Reilly publication 97 Things Every Engineer Should know.
Paige is a total geek who is into role-playing games, LARP’ing in the SCA, Doctor Who, superheroes, space exploration, comics, Tolkien, etc. Paige writes and publishes fantasy and science fiction stories under her maiden name Paige E. Ewing. She won the Kennedy Space Center’s global Space Apps Challenge three years ago for coming up with an idea for growing food on Mars, And she's a pretty mean shot with a recurve, crossbow, or long bow.
Paige will host the following MLOps session: Shortcut MLOps with In-Database Machine Learning.

Sean Robinson (Charlotte)

Sean Robinson is a versatile data scientist with several years of experience optimizing data processes and building intelligent data systems. Specifically, he specializes in the use of graph data science and Neo4j to abstract complex systems within a domain into a highly dimensional, interconnected knowledge graphs to uncover novel insights which would otherwise remain dormant in other data structures. Sean currently serves both as Lead Data Scientist at Graphable as well as creating and instructing new network science courses at the University of North Carolina at Charlotte’s Data Science graduate program where he instructs the next generation of data scientists on how to integrate graph data science into their toolkit.
Sean will be presenting the following session: History of Network Science - A Look at How Networks Have Connected Us

Dr. Bivin Sadler (Dallas)

Originally from Dallas Texas, Dr. Bivin Sadler finished a BS in mathematics magna cum laude from Texas Tech University before beginning his professional career in Scottsdale, Arizona, at Motorola. He worked as a statistician and software engineer for 2.5 years, working primarily on a companywide tool to predict when software projects could be released with optimal statistical properties (Six Sigma). Upon completion of the project, he moved to San Diego, and while playing professional beach volleyball for two years, finished a master’s degree in applied math at San Diego State University. He then moved back to Dallas to earn a PhD in statistics from SMU and finished his degree in 2014 after winning the Walsh Award for the top score on the qualifying exam taken after the third year of coursework.
Dr. Sadler was hired as part of the faculty at SMU after graduation and began a dual appointment teaching both undergraduate and graduate classes in the statistics department and online with the recently formed Master of Science in Data Science (MSDS) program. Academically, he has presented his work in item response theory at various conferences and is currently working on several domestic and international consulting projects. He became a full-time member of the MSDS faculty in August 2018 and, in addition to consulting projects and teaching, actively contributes towards developing new courses and enhancing existing ones at the SMU MSDS program.

Jörg Schad (Berlin / San Francisco) @joerg_schad

Jörg Schad (Linkedin / GitHub) is CTO at ArangoDB where he splits his time between Berlin and San Francisco. Prior to ArangoDB, he was Technical Community Lead and Distributed Systems Engineer at Mesosphere, and Big Data Engineer at SAP. Jörg received his Ph.D. at Universität des Saarlandes for research around distributed databases and data analytics.
Jörg will host a 90 minute hands-on workshop: Graph Powered Machine Learning for Python Developers

Joshua Shinavier (San Francisco) @joshsh

As a co-founder of what is now Apache TinkerPop, Joshua Shinavier 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. As a Research Scientist at Uber, he led development of the Dragon data integration platform. Joshua is host of The Graph Show, and co-organizer of the Bay Area Category Theory meetup. Joshua holds a PhD in computer science from RPI's Tetherless World Constellation, where he focused on combining knowledge graphs with augmented reality.

Rosaria Silipo (Zürich ) @DMR_Rosaria

Rosaria Silipo (LinkedIn), Principal Data Scientist at KNIME, is the author of 50+ technical publications, including her most recent book “Practicing Data Science: A Collection of Case Studies”. She holds a doctorate degree in bio-engineering and has spent 25+ years working on data science projects for companies in a broad range of fields, including IoT, customer intelligence, the financial industry, and cybersecurity.

Dr. Clair Sullivan (Breckenridge) @CJLovesData1

Dr. Clair Sullivan (LinkedIn / GitHub) is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society's Mary J. Oestmann Professional Women's Achievement Award in 2015.
Clair will host a 90 minute hands-on workshops: Intro to Graph Data Science for Python Developers.

NLP Keynote
David Talby (Seattle ) @davidtalby

David Talby (LinkedIn), is the chief technology officer at John Snow Labs, helping healthcare & life science companies put AI to good use. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.
David will be giving the NLP Keynote: The State of Natural Language Processing at Scale.

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.

Ryan Wisnesky (Cambridge, Massachusetts )

Ryan Wisnesky (LinkedIn) obtained B.S. and M.S. degrees in mathematics and computer science from Stanford University and a Ph.D. in computer science from Harvard University, where he studied the design and implementation of provably correct software systems. Previously, he was a postdoctoral associate in the MIT department of mathematics, where he developed the categorical query language CQL. He currently leads open-source and commercial development of CQL as CTO of Conexus AI. He maintains an active collaboration with the information-integration department of IBM Research, where he contributed to the Clio, Orchid, and HIL projects.

Data Visualization Keynote
Weidong Yang (San Francisco) @wdyang

Weidong Yang is the founder and CEO of Kineviz. He holds a doctorate in Physics and a Masters in Computer and Information Science. After conducting theoretical and experimental research on quantum dots, he worked for 10 years as a product manager and R&D scientist in the Semiconductor industry. He has been awarded 11 US patents and contributed to 20+ peer review publications.
Weidong is also co-founder of Kinetech Arts, a non-profit organization that brings dancers and engineers together to explore the creative potential of new technologies in making art.
Weidong will host the Data Visualization Keynote: What Data Visualization can learn from Dance.

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