AI Explained: Unraveling Unstructured Text and Image Models

Joshua Rubin is Head of AI Science at Fiddler AI, an enterprise AI Observability company. He’s built and led a data science team that developed novel explainability tools for computer vision and multimodal deep-learning models, and techniques for measuring model robustness and drift in unstructured data, key components of Fiddler's LLM observability product. Most recently he's been developing small BERT-scale models to close the feedback loop on measuring large language model performance, serving customers including cloud-native travel platforms, large financial services firms, ad-tech companies, and cryptocurrency exchanges.

Prior to Fiddler, he was a Principal Scientist at Amazon AWS AI and LinkedIn AI, where he led the fairness, explainability, privacy, and model understanding initiatives. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted).