Machine Learning for High-Risk Applications: Techniques for Responsible AI
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Patrick Hall is principal scientist at bnh.ai, a D.C.-based law firm focused on AI and data analytics. Patrick also serves as visiting faculty at the George Washington University School of Business (GWSB), where his teaching and research focus on data mining, machine learning, and the responsible use of these technologies. Before co-founding bnh.ai, Patrick led responsible AI efforts at H2O.ai, a leading machine learning software firm. His work at H2O.ai resulted in one of the world's first commercial solutions for explainable and fair machine learning. Among other academic and technology media writing, Patrick is the primary author of popular e-books on explainable and responsible machine learning.
Before joining H2O.ai, Patrick held global customer-facing and R&D roles at SAS, where he authored multiple patents in automated market segmentation using novel clustering methods and deep learning. He was also the 11th person worldwide to become a Cloudera certified data scientist during these years. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
James is a quantitative researcher focused on US power markets and renewable resource asset management. He previously served as a consultant for financial services organizations, insurers, regulators, and health care providers to help build more equitable AI/ML models. He currently serves on the board of the Foundation for Best Practices in Machine Learning, a European-based nonprofit that helps spread ethical AI best practices through open-source guidelines. James holds an MS in Mathematics from the Colorado School of Mines.
Parul Pandey has a background in Electrical Engineering and currently works as a Machine Learning Engineer at Weights & Biases, a developers first MLOps platform. Prior to this, she worked as a Data Scientist at H2O.ai, where she combined data science and developer advocacy in her work. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin's Top Voice in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
Before joining H2O.ai, Patrick held global customer-facing and R&D roles at SAS, where he authored multiple patents in automated market segmentation using novel clustering methods and deep learning. He was also the 11th person worldwide to become a Cloudera certified data scientist during these years. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
James is a quantitative researcher focused on US power markets and renewable resource asset management. He previously served as a consultant for financial services organizations, insurers, regulators, and health care providers to help build more equitable AI/ML models. He currently serves on the board of the Foundation for Best Practices in Machine Learning, a European-based nonprofit that helps spread ethical AI best practices through open-source guidelines. James holds an MS in Mathematics from the Colorado School of Mines.
Parul Pandey has a background in Electrical Engineering and currently works as a Machine Learning Engineer at Weights & Biases, a developers first MLOps platform. Prior to this, she worked as a Data Scientist at H2O.ai, where she combined data science and developer advocacy in her work. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin's Top Voice in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
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