Krishnaram has advanced the state-of-the-art in areas such as fairness, explainability, privacy, and robustness in ML, and helped start new research areas (e.g., co-authoring the second paper on Differential Privacy). Leveraging his expertise in research and industry, he has steered company-wide initiatives on responsible AI and AI observability, led the technical roadmap/design/launch of new AI products, and improved business metrics for existing products via technology transfers. He serves regularly on the senior program committees of conferences such as KDD, FAccT, and WWW, and co-chaired the 2014 ACM Symposium on Computing for Development. 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 60+ papers, with 7500+ citations and filed 150+ patents (71 granted). He has given several invited industry talks, presented tutorials on trustworthy generative AI, privacy, fairness, explainable AI, ML model monitoring, and responsible AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, and instructed a course on responsible AI at Stanford, thereby influencing industry thinking, thought leadership, and practice in his areas of work.