Speaker Bio

Mohammad Adibuzzaman, Purdue


Peter Bailis, Stanford Computer Science

Peter Bailis is an assistant professor of Computer Science at Stanford University. Peter's research in the Stanford DAWN project (http://dawn.cs.stanford.edu) focuses on the design and implementation of post-database data-intensive systems. He received a Ph.D. from UC Berkeley in 2015 and an A.B. from Harvard College in 2011, both in Computer Science.

Chaitanya Baru, National Science Foundation (NSF)

Dr. Chaitanya Baru is Senior Advisor for Data Science in the Computer and Information Science & Engineering Directorate at the National Science Foundation, Alexandria, VA. He co-chairs the NSF working group for the Harnessing the Data Revolution Big Idea and also has responsibility for the cross-Foundation BIGDATA research program. He is advisor to the NSF Big Data Regional Innovation Hubs and Spokes program (BD Hubs/Spokes) and was engaged in the development of the NSF Transdisciplinary Research in Principles of Data Science (TRIPODS) program. He co-chairs the Big Data Interagency Working Group—which is part of the Networking and IT R&D program of the National Coordination Office, White House Office of Science and Technology Policy—and is a primary co-author of the Federal Big Data R&D Strategic Plan (released May 2016). Dr. Baru is on assignment at NSF from the San Diego Supercomputer Center (SDSC), University of California San Diego, where he is a Distinguished Scientist and Director of the Advanced Cyberinfrastructure Development Group (acid.sdsc.edu) and the Center for Large-scale Data Systems Research (CLDS).

Stephen Brobst, Teradata

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.


Surajit Chaudhuri, Microsoft

Surajit Chaudhuri is a Distinguished Scientist at Microsoft Research and leads the Data Management, Exploration and Mining group. He also works closely with Microsoft\u2019s Cloud and Enterprise Division. Surajit\u2019s current areas of interest are Big Data platforms, self-manageability, and cloud database services. Working with his colleagues in Microsoft Research, he helped incorporate the Database Engine Tuning Advisor and Data Cleaning technology in Microsoft SQL Server. Surajit is an ACM Fellow, a recipient of the ACM SIGMOD Edgar F. Codd Innovations Award, ACM SIGMOD Contributions Award, a VLDB 10-year Best Paper Award, and an IEEE Data Engineering Influential Paper Award.

Jason Crane, UCSF

Jason Crane is director of scientific software in the Surbeck Laboratory for Advanced Imaging in the Department of Radiology and Biomedical Imaging at UCSF.  His interests are in the areas of spectroscopic imaging, translational medical imaging research, scientific software and high-performance computing.  He holds a PhD in physical chemistry from UC Berkeley.

Somalee Datta, Stanford School of Medicine

I am a technologist with a passion for making an impact on healthcare. I am currently working at the intersection of Big Data and Biomedicine. I believe that with the explosion of data in healthcare and with new methods to analyze such large amounts of data, we will see massive changes in how human diseases are addressed via novel drugs, large scale genomics, wearable sensors, and software to tie it all together. I want to drive part of this revolution.

I started my career with a PhD in Computational Physics. My early career included development of hardware accelerated ray tracing algorithms (aka realistic 3D rendering) and a video codec. What got me hooked on healthcare was a biotech startup (now public) where I started as the founding scientific team member and helped build the drug development platform and the R&D team. My first discovery project is now the most advanced program in company's portfolio. Fast forwarding to now, at Stanford, I am building a Precision Health platform.

Priya Desai, Stanford Center Genomics and Personalized Medicine

Priya Desai is a Biomedical Engineer at SCGPM responsible for technologies and applications at Genetics Bioinformatics Service Center at Stanford. She has a background in Physics and Astronomy and was part of the software development team for the Chandra X-ray Center at the Harvard Smithsonian Center for Astrophysics, Cambridge, MA. She has co-authored multiple papers in the field of line identifications and spectral diagnosis of Sun-like stars and was the Principal Investigator for NASA funded work.
She has applied her experience with large datasets to challenges in genomics and bioinformatics. She was part of Pritchard Lab at in the Genetics department at the Stanford where she helped develop SciReader , a recommendation engine for biomedical literature which is now maintained by SCGPM. She is also very interested in education and is the project coordinator for Genomics Research Internship program at Stanford (GRIPS).
Priya has a MS in Physics from Indian Institute of Technology, Bombay and completed her graduate coursework in Math at the University of Texas in Austin.


Sujit Dey, UCSD

Sujit Dey is a Professor in the Department of Electrical and Computer Engineering, University of California, San Diego, where he heads the Mobile Systems Design Laboratory, which is developing innovative technologies in mobile cloud computing, adaptive multimedia and networking, green computing and communications, and predictive and prescriptive analytics to enable future applications in connected health, immersive multimedia, smart cities, and smart transportation. He is the Director of the Center for Wireless Communications, and the Director of the Institute for the Global Entrepreneur at UCSD.​ ​In 2017, he was appointed as the Jacobs Family Chair in Engineering Management Leadership.

Dr. Dey served as the Faculty Director of the von Liebig Entrepreneurism Center from 2013-2015, and as the Chief Scientist, Mobile Networks, at Allot Communications from 2012-2013. He founded Ortiva Wireless in 2004, where he served as its founding CEO and later as CTO and Chief Technologist till its acquisition by Allot Communications in 2012. Prior to Ortiva, he served as the Chair of the Advisory Board of Zyray Wireless till its acquisition by Broadcom in 2004, and as an advisor to multiple companies including ST Microelectronics and NEC. Prior to joining UCSD in 1997, he was a Senior Research Staff Member at NEC C&C Research Laboratories in Princeton, NJ. He received his Ph.D. in Computer Science from Duke University in 1991.

Dr. Dey has co-authored more than 250 publications, and a book on low-power design. He holds 18 U.S. and 2 international patents, resulting in multiple technology licensing and commercialization. He has been a recipient of six IEEE/ACM Best Paper Awards, and has chaired multiple IEEE conferences and workshops. Dr. Dey is a Fellow of the IEEE.

R. Adams Dudley, UCSF

R. Adams Dudley is professor of medicine and health policy at the University of California, San Francisco (UCSF) and associate director for research at the Philip R. Lee Institute for Health Policy Studies at UCSF. His major research interests include developing measures of quality of care and resource utilization and assessing the impact of value-based purchasing by employers and health plans. Dr. Dudley leads several ongoing projects to measure and reward higher quality of care, including randomized trials of pay-for-performance incentives and programs to assess the appropriateness of high cost procedures. In addition, the California Hospital Assessment and Reporting Task Force (CHART), which he founded and leads, has convened representatives of consumers, purchasers, health plans, and hospitals in California to establish a consensus, universal, robust hospital performance reporting system. The award-winning CHART web site, was designated in 2009 as the best hospital public reporting program in the country by the Center for Studying Health Change. He also authored the Agency for Healthcare Research and Quality’s recent Decision Guide on Pay-for-Performance and its Decision Guide on Consumer Incentives. He has been elected to the American Society for Clinical Investigation and, in 2005, received the Robert Wood Johnson Foundation Investigator Award in Health Policy Research.

Jessilyn Dunn, Stanford

Dr. Jessilyn Dunn is a Mobilize Postdoctoral Fellow at Stanford in the NIH Big Data to Knowledge Mobilize Center of Excellence, where she works jointly with Drs. Michael Snyder and Scott Delp in the Departments of Genetics and Bioengineering. Dr. Dunn will be joining Duke University in January 2019 as an Assistant Professor in the Departments of Biomedical Engineering and Biostatistics/Bioinformatics. Her primary areas of research are focused on biomedical data science and mobile health; her work includes multi-omics, wearable sensor, and electronic health records integration and digital biomarker discovery. She completed her PhD at Georgia Tech and Emory University and her BS at Johns Hopkins University, both in Biomedical Engineering. Dr. Dunn has worked as a visiting scholar at the US Centers for Disease Control and Prevention and at the National Cardiovascular Research Institute in Madrid, Spain. Her work has been internationally recognized with coverage by media sources including the NIH Director’s Blog, the American Heart Association Science News, Wired, Time, and US News and World Report.

Ozgun Erdogan, Citus Data


David Glazer, MIT, Verily Life Sciences


Brian Granger CalPloy, co-founder Jupyter Project

Brian Granger is an associate professor of physics and data science at Cal Poly State University in San Luis Obispo, CA. His research focuses on building open-source tools for interactive computing, data science, and data visualization. Brian is a leader of the IPython project, co-founder of Project Jupyter, co-founder of the Altair project for statistical visualization, and an active contributor to a number of other open-source projects focused on data science in Python. He is an advisory board member of NumFOCUS and a faculty fellow of the Cal Poly Center for Innovation and Entrepreneurship.

Jim Green, Cisco, CTO IoT Software group

Jim Green serves as CTO for Cisco’s IoT Software Group guiding Cisco’s Jasper and Kinetic product lines’ technical vision and strategy. Jim’s education and long experience in networking, software, and industrial engineering provides him with a strong perspective regarding the future of IoT.  He created Cisco’s leading Edge and Fog product, and contributes to Cisco’s overall hardware and software IoT development efforts.
Jim joined Cisco in July 2013 through the acquisition of Composite Software. He brings more than 30 years of experience in developing products and technology for enterprise computing. In previous companies, he achieved several industry firsts by developing unique networking and distributed computing products.
Jim was CEO of Composite for ten years where he established Composite's data virtualization market leadership. At webMethods he served as CTO and executive vice president of product development.  Prior to joining webMethods, Jim was CEO of Active Software, where he grew the company from a start-up to an industry leader in Enterprise Application Integration (EAI) software.

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.


Sila Kiliccote, Stanford

Sila Kiliccote is the Managing Director of Grid Innovations at Stanford and the leader of the Grid Integration, Systems and Mobility research at SLAC National Accelerator Laboratory. She has worked as a demand response expert at Google and spent over 10 years at Lawrence Berkeley National Laboratory (LBNL) as a deputy of the Demand Response Research Center leading the grid integration initiatives. At LBNL, she worked with a team to develop OpenADR, Virtual Grid Integration Laboratory (VirGIL) and worked on the deployment of micro-PMUs for distribution systems.  Kiliccote holds an Electrical Engineering degree from University of New Hampshire and a Master of Building Science degree from Carnegie Mellon University.


Christine Kirkpatrick, UC San Diego



Tim Kraska, MIT

Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. Currently, his research focuses on building systems for machine learning, and using machine learning for systems. Before joining MIT, Tim was an Assistant Professor at Brown, spent time at Google Research, and was a PostDoc in the AMPLab at UC Berkeley after he got his PhD from ETH Zurich. Tim is a 2017 Alfred P. Sloan Research Fellow in computer science, received the 2017 VMware Systems Research Award, an NSF CAREER Award, an Air Force Young Investigator award, two Very Large Data Bases (VLDB) conference best-demo awards, and a best-paper award from the IEEE International Conference on Data Engineering (ICDE)


Ian Mathews, Redivis


Michael Mahoney, UC Berkely and RISELab

Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received him PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council's Committee on the Analysis of Massive Data, he co-organized the Simons Institute's fall 2013 program on the Theoretical Foundations of Big Data Analysis, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He is currently the lead PI for the NSF/TRIPODS-funded FODA
(Foundations of Data Analysis) Institute at UC Berkeley.


Ben Nachman, LBNL/CERN

Ph.D. in Physics at Stanford University, currently Owen Chamberlain postdoctoral fellow at Lawrence Berkeley National Laboratory and Simons-Berkeley research fellow at the Simons Institute for the Theory of Computing at the University of California, Berkeley.


Frank Nothaft, Databricks



Rajat Monga, Google

Rajat Monga leads TensorFlow at the Google Brain team. As a founding member of the team he has been involved in co-designing and co-implementing DistBelief and more recently TensorFlow, an open source machine learning system. Prior to this role, he led teams in AdWords, built out the engineering teams and co-designed web scale crawling and content-matching infrastructure at Attributor, co-implemented and scaled eBay’s search engine and designed and implemented complex server systems across a number of startups. Rajat received a B.Tech. in Electrical Engineering from Indian Institute of Technology, Delhi.

Jerry Pan, Facebook

Jerry Pan is a Software Engineer at Facebook, focusing on Big Data systems leveraged by Machine Learning. Before Facebook, he spent many years at Amazon Web Services building distributed systems and Big Data applications.


Ioannis Papapanagiotou, Netflix

Ioannis Papapanagiotou is a senior architect at Netflix. He holds a dual Ph.D. degree in Computer Engineering and Operations Research. His main focus is on distributed systems, cloud computing, and the Internet of Things. In the past, Ioannis has served in the faculty ranks of Purdue University (tenure-track), NC State University and University of New Mexico, and as an engineer at IBM. He has been awarded the NetApp faculty fellowship and established the Nvidia CUDA Research Center at Purdue University. Ioannis has also received the IBM Ph.D. Fellowship and Academy of Athens Ph.D. Fellowship for his Ph.D. research, and best paper awards in several IEEE conferences for his academic contributions. Ioannis has authored a number of research articles and patents. Ioannis is a senior member of ACM and IEEE.


Neoklis Polyzotis, Google

 Neoklis (Alkis) Polyzotis is a researcher at Google Research, where he currently leads the data-management projects in Google\u2019s TensorFlow Extended (TFX) platform for production-grade machine learning. His interests include data management for machine learning, enterprise data search, and interactive data exploration. Before joining Google, he was a professor at UC Santa Cruz. He has received a PhD in Computer Sciences from the University of Wisconsin at Madison and a diploma in engineering from the National Tech. University of Athens, Greece.


Manuel Rivas, Stanford Medicine


Nigam Shah, Stanford, Bioinformatics



Ion Stoica, UC Berkeley



Ian willson, Boeing


Duan Xu, UCSF

Duan Xu is a Professor in the Department of Radiology and Biomedical at UCSF and a member of the Joint UCSF/UC Berkeley Graduate Group in Bioengineering.  His research focuses on the development of novel imaging techniques with applications to study early brain development.  He is also the Director of Scientific Computing Services, which supports all research computing needs within the Department of Radiology as well as collaborators throughout UCSF