Prof. Muggleton's career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming. Stephen Muggleton's intellectual contributions within Machine Learning include the introduction of definitions for Inductive Logic Programming (ILP), Predicate Invention, Inverse Resolution, Closed World Specialisation, Predicate Utility, Layered Learning, U-learnability, Self-saturation and Stochastic logic programs. Over the last decade he has collaborated increasingly with biological colleagues on applications of Machine Learning to Biological prediction tasks. These tasks have included the determination of protein structure, the activity of drugs and toxins and the assignment of gene function.

Willem Visser is a professor in the Division of Computer Science at Stellenbosch University. His research is mostly focussed around finding bugs in software. More specifically he works on testing, program analysis, symbolic execution, probabilistic symbolic execution and model checking. He is probably most well known for his work on Java PathFinder (JPF) and Symbolic PathFinder (SPF). He previously worked at NASA Ames Research Center, and SEVEN Networks.

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Rupak Majumdar is a Scientific Director at the Max Planck Institute for Software Systems. His research interests are in the verification and control of reactive, real-time, hybrid, and probabilistic systems, software verification and programming languages, logic, and automata theory. Dr. Majumdar received the President's Gold Medal from IIT, Kanpur, the Leon O. Chua award from UC Berkeley, an NSF CAREER award, a Sloan Foundation Fellowship, an ERC Synergy award, "Most Influential Paper" awards from PLDI and POPL, and several best paper awards (including from SIGBED, EAPLS, and SIGDA). He received the B.Tech. degree in Computer Science from the Indian Institute of Technology at Kanpur and the Ph.D. degree in Computer Science from the University of California at Berkeley.