Rich Caruana

Senior principal researcher at Microsoft Research

Hideaki Imamura

Researcher at Preferred Networks, Inc. / Optuna

Shubham Agarwal

ML Engineer
at LinkedIn

job title

Hideaki Imamura

Hideaki Imamura is a researcher at Preferred Networks, Inc., and one of the core developers involved with Optuna development since 2020. He earned his Master’s degree in Computer Science from The University of Tokyo. He was the project manager for Optuna V3.0, is one of the authors of the Japanese book on Optuna and book on Bayesian optimization, and has been invited to give lectures and tutorials on Optuna and Bayesian optimization at ICIAM 2023 workshops and multiple domestic workshops in Japan.

Shubham Agarwal

I am Shubham Agarwal, Staff ML Engineer at LinkedIn with a focus on content moderation, I have spent the last five years at the intersection of machine learning and real-world applications. My work is driven by a deep passion for applying AutoML technologies to solve complex business problems at scale, affecting millions of lives. I am eager to share insights and strategies at AutoML 2024, demonstrating the transformative potential of AutoML in creating impactful, scalable solutions

Rich Caruana

Rich Caruana is a senior principal researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty in the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery. Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped create interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, transparent modeling, deep learning, and computational ecology.