Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system.
Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.
Data Mastering at Scale(Track Keynote)
In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.
In-Database Machine Learning in Jupyter(Tutorial)
James Lamb is a software engineer at Saturn Cloud, where he works on a managed data science platform built on Dask and Kubernetes. Before Saturn Cloud, James worked on industrial internet of things (IIoT) problems as a data scientist at AWS and Chicago-based Uptake. He is a core maintainer on LightGBM, and has contributed on other open source data science and data engineering projects such as XGBoost and prefect. James holds Masters degrees in Applied Economics (Marquette University) and Data Science (University of California, Berkeley).
Scaling Machine Learning with Dask(Tutorial)
Sarah Aerni is a Senior Manager of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.
Sam Bail is a data professional with a passion for turning high quality data into valuable insights. Sam holds a PhD in Computer Science and has worked for several data-focused startups. In her current role as Engineering Director at Superconductive, she works on “Great Expectations”, an open source Python library for data validation and documentation.
A recovering data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.
Irene is a Technical Program Manager on the Research & Machine Intelligence team at Google, where she works to bring machine learning into production pipelines through TensorFlow (TFX). Previously, Irene helped Google’s Counter-Abuse Technology team train machine learning models to fight spam and abuse. Prior to joining Google, she spent over a decade at Microsoft working in several engineering and program management roles across the Enterprise and Operating Systems divisions.
David Linthicum was named one of the top 9 Cloud Pioneers in Information Week 7 years ago, but started his cloud journey back in 1999 when he envisioned leveraging IT services over the open internet. Dave was named the #1 cloud influencer via a major report by Apollo Research, and is typically listed as a top 10 cloud influencer, podcaster, and blogger.
David is a cloud computing thought leader, executive, consultant, author, and speaker. David has been a CTO five times for both public and private companies, and a CEO two times in his 35 year career. He is credited with creating 4 billion dollars in shareholder return in those roles. Beyond cloud computing Dave has created, or assisted in creating foundational technical concepts, including Enterprise Application Integration (EAI), Service Oriented Architecture (SOA), and advanced distributed computing architectures. All still in use today. With more than 13 books on computing, more than 7,000 published articles, more than 700 conference presentations, and numerous appearances on radio and TV programs, David has spent the last 30 years leading, showing, and teaching businesses how to use resources more productively and innovate constantly. He has expanded the vision of both startups and established corporations as to what is possible and achievable.
David is a Gigaom research analyst and writes prolifically for InfoWorld as a cloud computing blogger. David also is a contributor to “IEEE Cloud Computing,” Tech Target’s SearchCloud and SearchAWS, as well as is quoted in major business publications including Forbes, Business Week, The Wall Street Journal, and the LA Times. David has appeared on NPR several times as a computing industry commentator, and does a weekly podcast on cloud computing.
Pranjal Singh has been a Data Scientist at Vertica since May 2020. Prior to joining Vertica, Pranjal received his Bachelor’s degree in Data Science from Northeastern University in Boston, MA. He has experience with Software Engineering, Data Analytics, and Machine Learning. Pranjal has a passion for ML and Predictive Analytics, and helping organizations make better decisions with data. He’s an avid sports fan, with a special interest in sports analytics and data.
In-Database Machine Learning in Jupyter(Tutorial)
Rohan Khade is a machine learning and data mining researcher with 10 years of experience working in academia and in collaboration with the industry. Spearheaded research into novel real world machine learning algorithms for large streaming data which improved the current state of the art by 20% in the semiconductor manufacturing industry and reduced feedback time to users from days to hours. Skilled in machine learning, data mining, problem solving, and programming.
Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.
End to End Modeling and Machine Learning (Workshop)
Simon Asplen-Taylor is a former CDO at Rank Group, Tesco, Cushman & Wakefield He is a specialist in transforming business through the use of data, analytics and artificial intelligence. Data IQ list him as one of the 100 most influential people in data 2020. Simon is an Executive Speaker, Fellow of the Royal Statistical Society (FRSS), Fellow of the British Computer Society (FBCS).
Data scientists moving beyond model experimentation looking to understand production workflow
Data scientists seeking to improve the overall practice of management and development
Anyone interested in understanding better collaborative and agile management techniques as applied to data science
Business professionals and industry experts looking to understand data science in practice
Software engineers and technologists who need to work with data science workflows and understand the unique requirements of these systems
CTO, CDS, and other managerial roles that require a bigger picture view of data science
Technologists in the field of DevOps, databases, project management and others looking to break into data science
Students and academics looking for more practical applied training in data science tools and techniques
Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics
With numerous introductory level workshops, you get hands-on experience to quickly build your skills
Post-conference, get access to recorded talks online and learn from over 100+ high quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science practice and management
Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today
Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels
Network at our numerous lunches and events to meet with data scientists, enthusiasts, and business professionals
Get access to other focus area content, including machine learning & deep learning, data visualization, and much more