As ML becomes increasingly important for businesses and data science teams alike, managing its risks is quickly becoming one of the biggest challenges to the technology’s widespread adoption. Join Andrew Bur, Steven Touw, Richard Geering, Joseph Regensburger, and Alfred Rossi for a hands-on overview of how to train, validate, and audit machine learning models (ML) in practice. You’ll learn practical tools and best practices to help safely deploy ML while fully harnessing the technology.
Get in-depth overviews of how ML works in practice and learn best practices for risk management in ML across teams. You’ll focus on understanding both opaque algorithms and large datasets, and how they may adapt and change over time.
In teams, you’ll tackle concrete data science problems to learn how and when ML solves specific problems within the enterprise, how to apply ML to maximize the value of corporate data while minimizing its downsides, and how to manage teams of data scientists engaged in deploying ML within their companies.
You’ll determine the lessons your team learned from their particular use case, then combine these shared insights to illustrate the benefits of a robust risk management framework for ML.
Andrew Burt, an internationally recognized expert on the intersection between data privacy, security and AI, leads Immuta’s Legal Engineering team. The team, comprised of lawyers with deep expertise in data science, focuses on automating compliance and oversight activities within the Immuta Automated Data Governance software platform.
Before joining Immuta, Andrew was Special Advisor for Policy to the head of the FBI Cyber Division, where he was the lead author on the FBI’s after action report on the 2014 Sony data breach. Andrew also served as Chief Compliance and Privacy Officer for the Cyber Division, overseeing privacy and compliance policies for sensitive data across the FBI’s 56 field offices.
Andrew is a term member of the Council on Foreign Relations and is a visiting fellow at Yale Law School’s Information Society Project. A published author and former journalist, Andrew holds a J.D. from Yale Law School and a B.A. with first class honors from McGill University.
Steve Touw is the cofounder and CTO of Immuta. Steve has a long history of designing large-scale geotemporal analytics across the US intelligence community, including some of the very first Hadoop analytics, as well as frameworks to manage complex multitenant data policy controls. He and his cofounders at Immuta drew on this real-world experience to build a software product to make data security and privacy controls easier. Previously, Steve was the CTO of 42six (acquired by Computer Sciences Corporation), where he led a large big data services engineering team. Steve holds a BS in geography from the University of Maryland.
Richard Geering is vice president of governance, risk, and compliance at Immuta. He has over 20 years’ experience in the financial services industry, in global leadership roles in risk, sales, and trading in London, New York, and Barbados. Most recently, he was the chief operational risk officer for a global custodian bank. Richard holds a BSc (with honors) in physics from the University of Nottingham.
Joseph Regensburger leads the Research Group at Immuta, where he focuses on model risk management and privacy-preserving machine learning. Previously, he was chief scientist at Illumination Works, LLC and principal research scientist at the Battelle Memorial Institute. Joseph has led research efforts characterizing airport security screening devices, engineering image analysis software, and developing machine learning algorithms for biological detection. He received both Battelle’s Technical Achievement Award and Illumination Works’s Innovation Award. He holds a PhD in physics from the Ohio State University, where his research focused on experimental high-energy physics—specifically the detection of rare decays of D0 mesons.
Alfred Rossi is a theoretical computer scientist and research scientist at Immuta, where his efforts are currently focused on differential privacy and model risk management. His research interests include clustering (especially in alternative settings) and privacy. Alfred holds a PhD in computer science and an MS in physics, both from the Ohio State University.
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