Where I express personal thoughts in words
Marcello La Rocca is a research scientist and a full-stack engineer. He works as a consultant, creating 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.
Various properties are commonly considered when choosing the recommendation approach, whether for offline or online scenarios. These properties have trade-offs, so it is critical to understand and evaluate their effects on the overall performance and the user experience. This blog post is my attempt to summarize these properties succinctly.
Dave Bechberger is known for his expertise in distributed data architecture and being a Graph Database SME. 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.
Last month, I had the opportunity to attend the Toronto Machine Learning Summit 2020, organized by the great people at the Toronto Machine Learning Society. I previously attended their MLOps event in the summer, which I also have written an in-depth recap here.
The summit aims to promote and encourage the adoption of successful machine learning initiatives within Canada and abroad. There was a variety of thought-provoking content tailored towards business leaders, practitioners, and researchers. In this long-form post, I would like to dissect content from the talks that I found most useful from attending the conference.
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.
