Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
John-Mark Agosta

John-Mark Agosta
Principal Data Scientist, Microsoft

Website | @quantifiedcar

John Mark Agosta is a principal data scientist at Microsoft, where he leads a team that is expanding the machine learning and artificial intelligence capabilities of Azure. Previously, John worked with startups and labs in the Bay Area, including “The Connected Car 2025” at Toyota ITC, peer-to-peer malware detection at Intel, and automated planning at SRI. His dedication to probability and AI led him to found an annual applications workshop for the Uncertainty in AI conference. When feeling low, he recharges his spirits by singing Russian music with Slavyanka, the Bay Area’s Slavic music chorus.

Sessions

9:00am12:30pm Tuesday, March 6, 2018
Data science and machine learning
Location: LL21 C/D Level: Intermediate
Mario Inchiosa (Microsoft), Vanja Paunic (Microsoft), Robert Horton (Microsoft), Debraj GuhaThakurta (Microsoft), Ali Zaidi (Microsoft), Tomas Singliar (Microsoft), John-Mark Agosta (Microsoft)
R and Python top the list of languages used in data science and machine learning, and data scientists and engineers fluent in one of these languages are increasingly marketable. Come learn how to build and operationalize machine learning models using distributed functions and do scalable, end-to-end data science in R and Python on single machines, Spark clusters, and cloud-based infrastructure. Read more.
5:10pm5:50pm Wednesday, March 7, 2018
Balasubramanian Narasimhan (Stanford University), John-Mark Agosta (Microsoft), Philip Lavori (Stanford University)
Clinical collaboration benefits from pooling data to train models from large datasets, but it's hampered by concerns about sharing data. Balasubramanian Narasimhan, John-Mark Agosta, and Philip Lavori outline a privacy-preserving alternative that creates statistical models equivalent to one from the entire dataset. Read more.