TruEra Research
Making practical ML-driven systems trustworthy with foundational research.
The heart of AI Quality efforts
We should be able to trust the models we build. We work on AI Quality analytics: enabling the deep evaluation and introspection of models that is necessary to build high-quality, trustworthy ML systems. This involves properly explaining, debugging, and monitoring models and their data.
Get a Sneak Peek of the Next Gen of TruEra Diagnostics
TruEra is offering three months of free access to a new TruEra Diagnostics experience, where you can build better models, faster. If you are accepted for this exclusive program, you will have access to the TruEra Diagnostics product for free for up to 3 months, including new cutting-edge features including: an automated test harness for ML, the model leaderboard, and enhanced performance debugging. To apply, just click the button.
Get a Sneak Peek of the Next Gen of TruEra Diagnostics
Get free early access to the new TruEra Diagnostics experience, where you can build better models, faster. Includes new, cutting-edge features: an automated test harness for ML, the model leaderboard, enhanced performance debugging, and more.
Research Directions
Highlights
Stanford Trustworthy ML Course
Chief Scientist Anupam Datta and advisor John Mitchell are co-teaching a course at Stanford focused on research methods to achieve trustworthy ML models. The course will eventually be open to everyone as a MOOC.
TruEra at AAAI
TruEa recently participated in the 35th annual AAAI conference and presented a tutorial talking about how model explainability can act as a backbone to improving model quality and understanding.
TruLens OSS
Neural networks are a hugely important class of models, and many techniques to explain them can be synthesized in a single, generalizable framework. We released the TruLens library, an open-source, framework-independent package, to make this easy for everyone.
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Research Team
Anupam Datta
Co-Founder, President and Chief Scientist
Anupam is passionate about enabling responsible adoption of artificial intelligence. As a Professor of Electrical & Computer Engineering and Computer Science at Carnegie Mellon University for over a decade, he has led groundbreaking research in the areas of AI explainability and governance as well as privacy and data protection. Anupam obtained PhD and MS degrees from Stanford University and a BTech from the Indian Institute of Technology, Kharagpur, all in Computer Science.
Divya Gopinath
Research and Engineering
Divya is a research engineer with expertise in machine learning and full-stack development. She holds Bachelor’s and Master’s degrees in Computer Science and Engineering from the Massachusetts Institute of Technology, where her research focused on building interpretable machine learning algorithms currently deployed to help doctors manage emergency room patients.
David Kurokawa
Research and Engineering
David is a research engineer with expertise in machine learning, algorithmic game theory, fair division, and auction theory. He holds a Bachelor's degree with honors in Mathematics and Computer Science from the University of British Columbia and a PhD in Computer Science from Carnegie Mellon University. Prior to TruEra, David was an engineer at Google and Amazon.
Piotr Mardziel
Research and Engineering
Piotr Mardziel works on transparency and accountability in machine learning with applications to security, privacy, and fairness. He holds Bachelor’s and Master’s degrees from the Worcester Polytechnic Institute and a PhD in computer science from University of Maryland, College Park. He has conducted post-doctoral research at Carnegie Mellon University, as well as taught classes in trustworthy machine learning at Stanford University and machine learning privacy and security at Carnegie Mellon University.
Shayak Sen
Co-Founder and Chief Technology Officer
When Shayak started building production grade machine learning models for algorithmic trading 10 years ago, he realized the need for putting the ‘science’ back in ‘data science’. Since then, he has been building systems and leading research to make machine learning and big data systems more explainable, privacy compliant, and fair. Shayak’s research at Carnegie Mellon University introduced a number of pioneering breakthroughs to the field of explainable AI. Shayak obtained his PhD in Computer Science from Carnegie Mellon University and BTech in Computer Science from the Indian Institute of Technology, Delhi.
Rick Shih
Research and Engineering
Rick Shih is a founding engineer at TruEra, where he focuses on creating explanation technology for deep neural networks and gradient boosted machines. He holds a Bachelor's degree in computer science from University of Maryland, College Park and a Master’s degree in machine learning from the University of California, San Diego. He was a staff machine learning and full stack engineer at Bloomreach, where he implemented and deployed the first production ML projects at the company. Rick led multiple efforts in search ranking, search recall, personalization, content management, merchandising tools, and diagnostic tools.
