Speaker Bio 2019

Stephen Brobst, Teradata

Eliminating Bias in the Deployment of Machine Learning

Stephen Brobst

Stephen Brobst is the Chief Technology Officer for Teradata Corporation.  Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management.
Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996.  During Barack Obama's first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD).  In 2014 he was ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.


Edward (Denny) Dahl

3.7 Decades of Quantum Computing

Edward Dahl, D-Wave

Dr. Dahl received a PhD in physics from Stanford University in computational simulation of quantum systems. He has worked with neural networks and has extensive experience in high performance and parallel computation. He is now in his eighth year with D-Wave Systems and has taught more than thirty training classes to new users of their platform. He lives near the Los Alamos National Laboratory which is one of the three customer sites for the D-Wave system.


Tathagata Das

Software Engineer at Databricks, Apache Spark Committer, member of PMC

Tathagata Das

Tathagata Das is a Software Engineer at Databricks and an Apache Spark committer + member of its PMC. He was the lead developer behind Spark Streaming and currently develops Structured Streaming and Databricks Delta. Previously, he was a grad student in the UC Berkeley at AMPLab where he conducted research about data-center processing frameworks and networks with Scott Shenker and Ion Stoica.


Bill Howe

Faculty, University of Washington

Bill Howe, University of Washington

Bill Howe is Associate Professor in the Information School and Adjunct Associate Professor in the Allen School of Computer Science & Engineering and the Department of Electrical Engineering. His research interests are in data management, curation, analytics, and visualization in the sciences. As Founding Associate Director of the UW eScience Institute, Howe played a leadership role in the Data Science Environment program at UW through a $32.8 million grant awarded jointly to UW, NYU, and UC Berkeley, and founded UW's Data Science for Social Good Program. With support from the MacArthur Foundation and Microsoft, Howe directs UW's participation in the Cascadia Urban Analytics Cooperative, where he focuses on responsible data science. He founded the UW Data Science Masters Degree, serving as its inaugural Program Chair, and created a first MOOC on data science that attracted over 200,000 students. His research has been featured in the Economist and Nature News, and he co-authored what have remained the most-cited papers from VLDB 2010 and SIGMOD 2012. He has received two Jim Gray Seed Grant awards from Microsoft Research and two ``Best of Conference'' invited papers from VLDB Journal. He has a Ph.D. in Computer Science from Portland State University and a Bachelor's degree in Industrial & Systems Engineering from Georgia Tech. 


Adam Kennedy

Data Engineering Manager, Siri Search, Apple

Adam Kennedy

Adam Kennedy leads a Data Engineering and Compute team within the Siri group at Apple. His team builds and operates special purpose high performance data and compute infrastructure for search and machine learning ecosystems at the upper limits of scalability. As a technical leader fluent in data science, engineering and operations he is highly valued as a translator, and a critical resource to connect disparate teams building planetary-class solutions at Apple scale (most of which he can’t talk about). Prior to Apple he was Engineer #2 at Kaggle, and is the author of over 200 Open Source software packages


Eddie Ma

Engineering Director at Uber

Eddie Ma, Uber

Eddie Ma is the Engineering Director at Uber. Eddie currently leads Uber's Financial Intelligence group, leveraging wealth of Uber's operational, marketing, community and transactional data to predict long term performance in each city and region that Uber operates. The predictions helped leadership to determine global operational and investment strategies. Previously at Uber, Eddie also lead engineering effort on predicting fraudulent activities on Uber's platform, covering payment, promotional and identity fraud. The platform and services Eddie spearheaded provided real-time preventive protection for Uber's user community. Prior to Uber, Eddie was leading Data Infrastructure at Facebook, storage and data center virtualization at Microsoft and VMware.


Keith Muller

Industrial Fellow at the Halıcıoğlu Data Science Institute at the University of California, San Diego. He is also a fellow in the Technology and Innovation Office at Teradata Corporation

Portrait: Keith Muller

Keith Muller is currently an Industrial Fellow at the Halıcıoğlu Data Science Institute at the University of California, San Diego. He is also a fellow in the Technology and Innovation Office at Teradata Corporation. He was the Chief Platform Architect at Teradata for the more than 20 years. He was with the Computer Science and Electrical Engineering Department at the University of California, San Diego for 14 years. His research interests include In-Edge Data Analytic Platforms, Filesystems, Storage devices and systems, Large Database Platforms, Operating Systems, and Computer networks. He has a PhD in Computer Science from the University of California.


Paolo Narvaez

Sr. Principal Engineer and Engineering Director for Analytics and AI Solutions, Intel Corproation

Paolo Narvaez is Engineering Director for Analytics and AI Solutions. He is responsible for leading technology development for fully integrated
and optimized solutions for Enterprise and HPC customers. The solutions include hardware references and software optimizations that provide easy to
deploy configurations for multiple industry segments. Prior to his current role, he was a Principal Engineer in Intel’s datacenter pathfinding group,
working on novel heterogeneous computer architectures. Prior to Intel, Paolo held lead architecture positions at Alcatel-Lucent, RMI (acquired by
Broadcom), and Sycamore Networks. Paolo has an S.B., M.Eng., and Ph.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology.


Krzysztof Nienartowicz

Data architect and manager of Gaia Data Processing Centre, Observatory of Geneva

Krzysztof is the data architect and manager of Gaia Data Processing Centre in Geneva located at Observatory of Geneva, Switzerland. He started to work on the biggest econometric databases at the beginning of his career, then moved to CERN in Geneva lured by its biggest (object-oriented) database hosted there. Where he started to appreciate more extended-relational model and co-authored the biggest data-migration at the time while working in CERN database group. For a decade he has been involved in pushing boundaries of astronomy and open-source DBMS in the ESA Gaia mission http://sci.esa.int/gaia/ .


Chris Ré

Associate Professor, Stanford University

Chris Re

Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University who is affiliated with the Statistical Machine Learning Group, Pervasive Parallelism Lab, and Stanford AI Lab. His work's goal is to enable users and developers to build applications that more deeply understand and exploit data. His contributions span database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016. In addition, work from his group has been incorporated into major scientific and humanitarian efforts, including the IceCube neutrino detector, PaleoDeepDive and MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies. He cofounded a company, based on his research, that was acquired by Apple in 2017. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016.


Kathryn Rough

Research Scientist, Google

Kathryn Rough, Google

Kathryn Rough is a Research Scientist at Google, where she investigates the application of machine learning and deep learning to help solve problems in healthcare and medicine. She is particularly interested in researching drug safety and patient safety issues using data from electronic health records and insurance claims. Previously, she received her SD in Epidemiology from the Harvard T.H. Chan School of Public Health, was a postdoctoral fellow at the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital & the Harvard Medical School, and was a Google AI Resident.


Umar Syed

Research Scientist, Google

Umar  Syed, Google

Umar Syed has been a research scientist at Google since 2011, and has been working on BigQuery ML since 2017. He has a Ph.D. in Computer Science from Princeton University, and spent two years as a postdoctoral researcher at the University of Pennsylvania. His expertise is in machine learning.


Matei Zaharia

Cofounder and Chief Technologist at Databricks

Matei Zaharia is a Cofounder and Chief Technologist at Databricks, as well as an Assistant Professor of Computer Science at Stanford. He currently leads the MLflow development effort at Databricks. Previously, Matei started the Apache Spark project during his PhD at UC Berkeley, and co-started the Apache Mesos project. Matei’s current research in the Stanford DAWN lab focuses on systems for machine learning, covering issues ranging from programming tools to performance and security. Matei’s work was recognized through the 2014 ACM Doctoral Dissertation Award, the VMware Systems Research Award, an NSF CAREER Award, and multiple best paper awards at research conferences.