Who will speak at Data Day Texas 2020

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We have 60+ more speakers to announce. Want to join us as a speaker? Check out our proposals page.

Confirmed Speakers

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.

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: How to start your first computer vision project.

Dr. Ying Ding (Austin)

Dr. Ying Ding is the Bill & Lewis Suit Professor of Information Technology at the University of Texas School of Information. 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.

Alon Gavra (Israel) @sangha_deb

Alon Gavra is a platform team lead at AppsFlyer. Originally a backend developer, he’s transitioned to lead the real time infrastructure team and took on the role of bringing some of the most heavily used infrastructure in AppsFlyer to the next level. A strong believer in sleep-driven design, Alon’s main focus is stability and resiliency in building massive data ingestion and storage solutions.
Alon will present the following Data Engineering session: Managing your Kafka in an explosive growth environment.

Anna Lisa Gentile (San Jose ) @anligentile

Dr. Anna Lisa Gentile (LinkedIn) is a Researcher at IBM Research Almaden. Her research is principally focused on studying methods and techniques for semantic annotating unstructured and semi-structured content. Her main Research Areas are Information Extraction (IE), Natural Language Processing (NLP) and Semantic Web. She obtained her PhD with a thesis on Named Entity Disambiguation at the University of Bari, Italy in 2010. She has published more than 60 peer-reviewed scientific publications including papers at major venues such as LREC, EMNLP, ESWC and ISWC. She has been serving as Organizing Committee member for conferences such as ISWC, ESWC, WWW amongst many others and organized workshop series such as LD4IE on Linked Data for Information Extraction and HumBL on Augmenting Intelligence with Bias-Aware Humans- in-the-Loop.
Dr. Gentile will present the following Human in the Loop session session: Information Extraction with Humans in the Loop.

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 hosting the following session: Modeling, Querying, and Seeing Time Series Data within a Self-Organizing Mesh Network.

Amy Hodler (Kettle Falls, Washington) @amyhodler

Amy Hodler is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. She is the co-author of the O’Reilly book, Graph Algorithms: Practical Examples in Apache Spark and Neo4j. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.
Amy will present the following sessions:
Responsible AI Requires Context and Connections.

Graph Feature Engineering for More Accurate Machine Learning (90 minute workshop)

Human in the Loop Keynote
Robert Munro (San Francisco ) @WWRob

Robert Munro (LinkedIn) 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. In addition to publishing more than 50 papers, Robert is the author of the upcoming Manning publication Human in the Loop Machine Learning. He has a PhD from Stanford University.
Rob will present the following Human in the Loop keynote: Human Centered Machine Learning.

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 present the following Human in the Loop session: Cost-Optimized Data Labeling Strategy.

Shioulin Sam (New York, NY)

Shioulin Sam is a research engineer at Cloudera Fast Forward Labs, where she bridges academic research in machine learning with industrial applications. Previously, she managed a portfolio of early stage ventures focusing on women-led startups and public market investments and worked in the investment management industry designing quantitative strategies. She holds a PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.
Shoulin will present the following session: Learning with limited labeled data.

Brent Schneeman (Austin) @schnee

Brent Schneeman swipes right for science and seeks to strengthen the scientific method muscle in whatever group he finds himself. Operating from a “lead by example” mindset, Brent frequently rolls up his sleeves and writes code to help bring predictive models to business problems. Passionate about building great teams and cultures, he’s pretty sure that a “servant leadership” posture is the right posture in his personal and professional lives.
Professionally, he tends to look after teams of data- and machine-learning-oriented contributors (analysts, scientists, and engineers) who collaborate on diverse sets of machine learning projects such as continuous optimization, customer customer churn prediction, fraud detection, and applying diverse techniques to unstructured data. Brent has worked at Vrbo, PayPal, Visa, and other small- and large-companies in individual contributor or management roles, mostly in product development organizations. He currently is attempting to make the world safe for machine learning with Alegion.
A storyteller, Brent has presented at the UT McCombs School, South By Southwest, NLP Day, multiple Data Days, and various meetups. He has one degree in Mathematics and another in Electrical Engineering and lives in Austin Texas with his wife, three kids, two cats and one dog. While he spends most of his free time mowing the lawn, he enjoys making photographs, running around downtown, and occasionally tries to make sense of neural network architectures.
Brent will present the following Human in the Loop session session: Adding a Machine to the Loop: What if the Loop began with Humans?.

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.
Rosaria will present the following session: Practicing data science: A collection of case studies.

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.
Dr. Aasman will present the following session: Creating Explainable AI With Rules.

Justin Fine (Los Angeles)

Justin Fine is based in Los Angeles, CA and is a Sales Engineer working mainly in the SoCal region. His academic background is applied mathematics and has worked with graphs for over 12 years in many different verticals while consulting (federal, telecoms, financial, etc). During this time as a consultant his focus was mainly advanced analytics utilizing NoSQL technologies. He recently comes from Microsoft's Azure team where he was a Data Solution architect and is very excited to be part of the Neo4j family! When Justin isn't nerding he enjoys scotch, cigars, and reading with his cat Penny.
Justin will co-present the following session: Graph Feature Engineering for More Accurate Machine Learning (90 minute workshop)

Rick Houlihan (Austin)

Rick Houlihan is a principal technologist and leads the NoSQL blackbelt team at AWS and has designed hundreds of NoSQL database schemas for some of the largest and most highly scaled applications in the world. Many of Rick’s designs are deployed at the foundation of core Amazon and AWS services such as CloudTrail, IAM, CloudWatch, EC2, Alexa, and a variety of retail internet and fulfillment-center services. Rick brings over 25 years of technology expertise and has authored nine patents across a diverse set of technologies including complex event processing, neural network analysis, microprocessor design, cloud virtualization, and NoSQL technologies. As an innovator in the NoSQL space, Rick has developed a repeatable process for building real-world applications that deliver highly efficient denormalized data models for workloads of any scale, and he regularly delivers highly rated sessions at re:Invent and other AWS conferences on this specific topic.
Michael will present the following Data Engineering and Architecture session: Where’s my lookup table? Modeling relational data in a denormalized world.

Shawn Rutledge (Seattle)

Shawn Rutledge is an accomplished machine learning practitioner with over a decade of experience building analytics solutions across verticals as diverse as financial services, travel, and social media. He is currently Chief Scientist at Sigma IQ, an early-stage Fintech startup. Before that, he was Principal Scientist at kFold Enterprises, a machine learning products and services firm he founded in 2009. While he has served as a technology executive for IBM, First Data, and Expedia, he is most at home in the startup environment and has been a principal contributor, leader, advisor, and angel investor to more than a dozen Seattle area startups. Shawn holds a bachelor's degree in Computer Science and has completed graduate coursework in Statistics at Stanford.
Shawn will present the following Texas AI Summit session: Machine Learning Counterclockwise.

Dave Bechberger (Anchorage) @bechbd

Dave Bechberger is a Solution Architect in the Graph Practice at DataStax where he helps customers build large distributed graph backed applications. Prior to that he was the Chief Architect at Gene by Gene, a genetic genealogy and bioinformatics company, where he worked to migrate their legacy technology stack to modern technologies including heavy use of graph databases and Cassandra. 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 national and international technical conferences including NDC Oslo, NDC London, as well as previous GraphDay conferences in Texas, San Francisco and Seattle. Dave is co-author of the upcoming Manning publication: Graph Databases in Action.
Dave will co-present the following session: Improving Real-Time Predictive Algorithms with Asynchronous Graph Augmentation.

Chris Davis (Dallas) @phoo

Dr. Chris Irwin Davis is a professor of computer science at the University of Texas at Dallas who teaches database theory and design. He also has 15 years of experience working for Fortune 500 companies in data management and software development lifecycle.
Chris will present the following session: Automated Encoding of Knowledge from Unstructured Natural Language Text into a Graph Database


Jonathan Ellis (Austin) @spyced

Jonathan Ellis is CTO and co-founder at DataStax. Prior to DataStax, Jonathan worked extensively with Apache Cassandra while employed at Rackspace. Prior to Rackspace, Jonathan built a multi-petabyte, scalable storage system based on Reed-Solomon encoding for backup provider Mozy.
Jonathan will be presenting Cassandra keynote for the Distributed Data track.

Graham Ganssle (Austin) @grahamganssle

Graham Ganssle (LinkedIn / GitHub) loves data. As Head of Data Science at Expero, his favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.
Graham Ganssle’s physics Ph.D. focused on digital signal processing, specifically on a (then) new optimization method which used naturally coupled wavefields to stabilize convergence. He also holds a masters degree in applied physics and a professional geoscientist license. Graham worked in the oil and gas vertical for ten years, performing data science and quantitative geophysics for clients around the world. He has numerous publications on a variety of scientific topics and has been awarded both scientific and business achievement awards.
Off the clock, Graham’s an inept aspiring rock climber and a triathlete. He’s constantly imploring his bride (and, more successfully, his puppy) to get muddy with him on the trail. Most Saturday mornings you can find Graham clacking away at his keyboard on his newest experiment or craziest inspiration.

Michael Grove (Washington, DC) @mikegrovesoft

Michael Grove is VP of Engineering and co-founder of Stardog where he oversees the development of the Stardog Knowledge Graph Platform. Michael studied Computer Science at the University of Maryland and is an alumnus of its well-regarded MIND Lab which specialized in semantic technologies. Before Stardog, he worked at Fujitsu Resarch on the use of graphs and semantic technologies in pervasive computing environments. Michael is an expert in large scale database and reasoning systems and has worked with graphs and graph databases for nearly fifteen years.

Dr. Jun Li (SF Bay)

Dr. Jun Li is currently a Principal Architect at eBay. He is leading the GraphDB development. From 2017 to 2018, he led the development on distributed database monitoring to ensure high performance, high scalability and high availability of NuData, the eBay’s distributed document store. Before joining eBay in 2017, Jun spent 16 years in Hewlett Packard Labs at Palo Alto. From 2013 to 2016, his work was focused on high-performance in-memory analytics systems. Jun has 21 patents granted and 32 pending for the work done in the last 18 years. Jun received his Ph.D. in Computer Engineering from Carnegie Mellon University in 2000.
Dr. Li will co-present the following session: NuGraphStore: a Transactional Graph Store Backend for JanusGraph

Rob McDaniel (Seattle)

Rob McDaniel is the CTO at Sigma IQ, a company that provides artificial intelligence for corporate finance. . Most recently, Rob was Manager of Applied Science at Rakuten, where he managed the AI that expands the depth and quality of Rakuten's global product catalog.

Rob has a diverse background in engineering and machine learning, both with major corporations and startups. He has worked on problems related to machine translation, taxonomy classification and information extraction, and has a passion for unsupervised methods and graph theory.

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 TensorFlow session: Moving Your Machine Learning Models to Production with TensorFlow Extended

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. Josh is co-author of the upcoming Manning publication: Graph Databases in Action.

Database Keynote
Dr. Marko A. Rodriguez (Santa Fe) @twarko

Dr. Marko A. Rodriguez (LinkedIn) is a graph and stream computing specialist currently focused on designing stream-based virtual machines for processing graph-based structures within distributed computing environments. Marko is the co-founder of Apache TinkerPop where he is developing the next generation TinkerPop4 virtual machine and bytecode specification that will enable the natural integration of any data processor and query language. Marko is also the founder of RReduX which, along with developing TinkerPop4, is designing a universal distributed computer called GMachine. Dr. Rodriguez received his Ph.D. in computer science from the University of California at Santa Cruz and was a Director's Fellow at the Center for Nonlinear Studies at the Los Alamos National Laboratory.
Dr. Rodriguez will present the following session: mm-ADT : A Multi-Model Abstract Data Type

Dr. Mohammad Roohitavaf (SF Bay)

Dr. Jun Li is currently a research scientist at eBay Inc., developing transaction support for NuGraphStore. As an intern at eBay in 2018, he developed efficient session guarantee consistency for distributed KV stores. He received his PhD degree from Michigan State University in 2019. His research interests are in the broad area of distributed systems especially data consistency, fault-tolerance, and distributed transactions.
Dr. Roohitavaf will co-present the following session: NuGraphStore: a Transactional Graph Store Backend for JanusGraph

Knowledge Graph Keynote
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).
Juan will present the following session: A Brief History of Knowledge Graph's Main Ideas

TinkerPop Keynote
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.

Michael Uschold (Seattle, WA ) @UscholdM

Michael Uschold, Senior Ontology Consultant at Semantic Arts, has over twenty-five years’ experience in developing and transitioning semantic technology from academia to industry. He pioneered the field of ontology engineering, co-authoring the first paper and giving the first tutorial on the topic in 1995 in the UK.
As a senior ontology consultant at Semantic Arts since October 2010, Michael trains and guides clients to better understand and leverage semantic technology using knowledge graphs. He has built commercial enterprise ontologies in digital asset management, finance, healthcare, legal research, consumer products, electrical devices, manufacturing and corporation registration. More recently he has focused on semantic application development using SPARQL for application code and R2RML for converting relational data into a knowledge graph.
During 2008-2009, Uschold worked at Reinvent on a team that developed a semantic advertising platform that substantially increased revenue. As a research scientist at Boeing from 1997-2008 he defined, led and participated in numerous projects applying semantic technology to enterprise challenges. He is a frequent invited speaker and panelist at national and international events, and serves on the editorial board of the Applied Ontology Journal. He received his Ph.D. in AI from Edinburgh University in 1991 and an MSc. from Rutgers University in Computer Science in 1982.
Michael will present the following 90 minute workshop: Ontology for Data Scientists.

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 co-present the following session: JGTSDB: A JanusGraph/TimescaleDB Mashup

Dr. Gene Zhang (SF Bay)

Dr. Gene Zhang is currently a Distinguished Architect at eBay. He has been leading the cloud-native storage backend development for NuData, including KV store, document store, global secondary indexes, graph store, with transaction support, since 2016. Gene has over 20 years industry experiences in R&D of database products and distributed systems. The products and features under his development leadership include Db2 for z/OS complex SQL, materialize views, pureXML, IDAA, Huawei’s GaussDB MPP database, etc. He attained his PhD from UCLA in 1998.
Dr. Zhang will co-present the following session: NuGraphStore: a Transactional Graph Store Backend for JanusGraph

 


Martin Fowler of Thoughtworks holding a "fireside chat" for the Data Day 2019 audience.

 


Perennial Data Day favorite, Holden Karau, presenting the latest on Spark at DDTX19.

 


Jonathon Morgan, CEO of New Knowledge, discussing how to build a data science team, at DDTX19.