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
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.
Evaluate and Track LLM Applications with TruLens
TruLens is an open source library for LLM Observability. Evaluate, iterate faster, and select your best LLM app with TruLens. Starting with just a few lines of code, you can drive rapid iteration with scalable, programmatic feedback.
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.
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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.
Corey Hu
Engineering
NVIDIA, UC Berkeley
Daniel Huang
Engineering
Amazon, CMU
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.
