Datacast

Alba Cervera-Lierta is a postdoctoral researcher at the Alán Aspuru-Guzik group at the University of Toronto. She obtained her Ph.D. at the University of Barcelona in 2019. Her background is in particle physics and quantum information theory. She has focused on quantum computation algorithms in the last years, particularly those suited for noisy-intermediate scale quantum computation.

Jean-Yves (or "J-Y") Stephan is the CEO & Co-Founder of Data Mechanics, a Y-Combinator-backed startup building a data engineering platform that makes Apache Spark more developer-friendly and more cost-effective. Before Data Mechanics, he was a software engineer at Databricks, the unified analytics platform created by Apache Spark's founders. JY did his undergraduate studies in Computer Science & Applied Math at Ecole Polytechnique (Paris, France) before pursuing a Masters at Stanford in Management Science & Engineering.

Chris Mattmann is the Chief Technology and Innovation Officer at NASA JPL. He was also JPL's first Principal Scientist in the area of Data Science. He has over 19 years of experience at JPL and has conceived, realized, and delivered the architecture for the next generation of reusable science data processing systems for NASA's Orbiting Carbon Observatory, NPP Sounder PEATE, and the Soil Moisture Active Passive (SMAP) Earth science missions. His work has been funded by NASA, DARPA, DHS, NSF, NIH, NLM, and private industry and commercial partnerships. He was the first Vice President (VP) of Apache OODT (Object Oriented Data Technology), the first NASA project at the Apache Software Foundation (ASF), and he led the project's transition from JPL to the ASF.

He contributes to open source and was a former Director at the Apache Software Foundation (2013-18). He was one of the initial contributors to Apache Nutch as a member of its project management committee, the predecessor to Apache Hadoop. He is the progenitor of the Apache Tika framework, the digital "babel fish," and the de-facto content analysis and detection framework that exists. Today he contributes to TensorFlow and all things machine learning.

Finally, he is the Director of the Information Retrieval & Data Science (IRDS) group at USC and Adjunct Associate Professor. He teaches graduate courses in Content Detection & Analysis & Search Engines & Information Retrieval. He has materially contributed to understanding the Deep Web and Dark Web through the DARPA MEMEX project. His work helped uncover the Panama Papers scandal.

Marcello La Rocca is a research scientist and a full-stack engineer. He works as a senior backend engineer at Tundra.com, developing large-scale web applications and machine learning infrastructure. He has gained invaluable experience at Twitter, Microsoft, and Apple - working on applied research in academia and industry. His work and interests focus on graphs, optimization algorithms, genetic algorithms, machine learning, and quantum computing.

Dave Bechberger is known for his expertise in distributed data architecture and being a Graph Database Subject Matter Expert.  He is known for his pragmatic approach to data architectures and for implementing large-scale distributed data architectures for big data analysis and data science workflows using various SQL and NoSQL data technologies. He is the author of "Graph Database in Action" by Manning publications and has spoken both nationally and internationally at conferences on subjects related to distributed data and graph databases.

Dave spent 20+ years developing, managing, and consulting on software projects and is currently a member of the Amazon Neptune service team. He works with both customers and engineering teams to simplify and speed the adoption of graph technologies.

Dr. Jason Corso is the new director of the Stevens Institute for AI. He is also the co-founder and CEO of Voxel51, an AI software company creating development tools for improving the performance of computer vision and machine learning systems. Previously, he was a professor of electrical engineering and computer science at the University of Michigan. A veteran in the field of computer vision, Jason has dedicated over 20 years to academic research and has authored nearly 150 academic papers and hundreds of thousands of lines of open-source code on video understanding, robotics, and data science. He received his Ph.D. and MSE degrees from Johns Hopkins University and his bachelor’s degree from Loyola University Maryland, all in computer science.

Barr Moses is the CEO & co-founder of Monte Carlo, a data reliability company committed to accelerating the world’s data adoption by reducing Data Downtime. Monte Carlo is backed by Accel, GGV, and other top Silicon Valley investors, including the former Chief Data Scientist of the U.S., DJ Patil. Previously, Barr was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. Prior to that, Barr was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit and graduated from Stanford University with a B.Sc. in Mathematical and Computational Science.

Carl Gold, the Chief Data Scientist at Zuora, has a Ph.D. from the California Institute of Technology and first-author publications in leading Machine Learning and Neuroscience journals. Before coming to Zuora, he spent most of his post-academic career as a quantitative analyst on Wall Street. Now a data scientist, Carl has written a book about using insights from data to reduce customer churn, to be released in December 2020 entitled "Fighting Churn With Data."

Jessie J. Smith (Jess) is a second-year Ph.D. student in the Department of Information Science at the University of Colorado Boulder. Her Ph.D. research focuses on AI ethics, machine learning fairness and bias, and ethical speculation in the computer science classroom. Since receiving her Bachelor's in Software Engineering, Jess works to engage in public scholarship about her research to encourage transparency and interdisciplinary dialogue about technology's unintended consequences. She is also the co-host and co-creator of The Radical AI Podcast.

Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel to explain machine learning in pedestrian terms.

Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a Ph.D. in mathematics from the University of Michigan, a master's and bachelor's from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.