Dr. Alon Orlitsky serves as the Qualcomm Professor for Information Theory and its Applications at the University of California, San Diego, where he holds joint appointments in the Electrical and Computer Engineering and Computer Science departments. After earning his B.Sc. in Mathematics and Electrical Engineering from Ben Gurion University in 1981 and Ph.D. from Stanford University in 1986, he spent a decade at Bell Labs before joining UCSD in 1997. His groundbreaking research spans information theory, machine learning, probability estimation, and speech recognition, earning him numerous prestigious honors including the IEEE W.R.G. Baker Award (1992), IEEE Information Theory Society Paper Award (2006), NeurIPS Best Paper Award (2015), and the Claude E. Shannon Award (2021). He is particularly recognized for founding the Information Theory and Applications Workshop in 2006, which has become a crucial bridge between information theory and emerging fields like machine learning. His leadership roles include serving as IEEE Information Theory Society President in 2016 and contributing to significant outreach projects including the "Bit Player" movie.

Dr. Yoav Freund, born in 1961, serves as Professor of Computer Science and Engineering at the University of California San Diego, where he has revolutionized machine learning through his groundbreaking work on boosting algorithms. An alumnus of Israel's elite Talpiot program, he is internationally renowned for co-inventing the AdaBoost algorithm with Robert Schapire, a breakthrough that earned them both the 2003 Gödel Prize in Theoretical Computer Science and the 2004 ACM Paris Kanellakis Theory and Practice Award. His research spans computational learning theory, probability theory, information theory, and pattern recognition, with particular focus on developing machine learning applications in bioinformatics, computer vision, finance, and high-performance computing. His innovative "black-box" approach to evaluating decision algorithms has transformed how machine learning systems are developed and compared. Elected as an AAAI Fellow in 2008, his work continues to influence various scientific communities and industry applications, from spam filtering to optical character recognition, making him one of the most influential figures in modern machine learning theory and practice.