Reza Bonyadi received his Ph.D. in computer science from the University of Adelaide, where he focused on the theoretical aspects of continuous space optimization algorithms such as stability, convergence, and transformation invariance. He is an expert in theory and applications of machine learning, including image understanding, natural language understanding, digital signal processing, and reinforcement learning, as well as optimization, including linear, non-linear, and constrained optimization. He published over 50 articles in top international journals, conferences, and chapters of books in the field of optimization,
pattern recognition, and machine learning. He has been involved as a committee member in over 40 international journals and conferences in the field of
optimization and machine learning, including ICML and NeuroIPS.
Apart from his academic achievements, Reza has significant experience in designing software solutions for complex analytical/machine learning problems for large organisations, including at Rio Tinto (as the lead of advanced analytics) and Microsoft (as a senior manager of AI-driven products), with large user pools at the scale of billions of users per day. His expertise lies in designing decision support systems, stochastic simulators, pattern recognition, and data clustering algorithms, and trend prediction/learning methods for complex real-world problems.