Associate Investigators

Danial Yazdani is a Decision Scientist and Data Analyst with over a decade of combined research and practical experience. He received his Ph.D. degree in computer science from Liverpool John Moores University, Liverpool, U.K., in 2018. He is currently a Research Fellow with the Data Science Institute, University of Technology Sydney, Sydney, Australia. His primary research interests include learning and optimization in dynamic environments, where he has contributed as the first author in over 20 peer-reviewed publications in this field, nine of which were published in prestigious IEEE/ACM Transactions. Dr. Yazdani was a recipient of the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award, the Best Thesis Award from the Faculty of Engineering and Technology at Liverpool John Moores University, and the SUSTech Presidential Outstanding Postdoctoral Award from Southern University of Science and Technology.

Roberto Amadini is an Associate Professor in Computer Science at the University of Bologna since 2022. He is also honorary member of the University of Melbourne since 2019, and member of the Bologna Business School since 2020. He earned his Ph.D. in Computer Science from the University of Bologna in 2015. He has held research positions at the Department of Computer Science and Engineering, University of Bologna, from 2015 to 2016, at the Department of Computing and Information Systems, University of Melbourne, Australia, from 2016 to 2019, and as a Senior Researcher at the Department of Computer Science and Engineering, University of Bologna, from 2019 to 2022. His primary research field is Constraint Programming, where he focused in particular on portfolio approaches and string constraint solving.

Xiaodong Li received his Ph.D. degree in Artificial Intelligence from University of Otago, Dunedin, New Zealand. He is a professor in Artificial Intelligence currently with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, data mining/analytics, multiobjective optimization, multimodal optimization, large-scale optimization, deep learning, math-heuristic methods, and swarm intelligence. He served as an Associate Editor of journals including IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 “ACM SIGEVO Impact Award” and 2017 IEEE CIS “IEEE Transactions on Evolutionary Computation Outstanding Paper Award”. He is an IEEE Fellow, and an IEEE CIS Distinguished Lecturer (2024 – 2026). He is the General Chair for GECCO2024.

Dr. Mansoureh Maadi finished her PhD at the School of Computing and Information Systems, focusing on developing computational methods to capture uncertainty in ensemble learning models toward human-machine collaboration models. She has expertise in different areas of AI, ranging from evolutionary computation to machine learning. Furthermore, Mansoureh is dedicated to formulating and modelling multifaceted problems with an optimization lens to unlock innovative solutions. Before joining CIS, she worked as a lecturer at Damghan University (IRAN) for almost eight years.

Dr João Luiz Junho Pereira is a Brazilian postdoctoral researcher from the Aeronautics Institute of Technology (ITA) visiting OPTIMA. He loves to improve the human way of living and creating resources using Artificial Intelligence. He received his PhD from the Federal University of Itajubá, which was winner of the biggest award  in brazilian science, the 2023 CAPES, after creating the first metaheuristic for multi-objective optimization from Brazil and the first in the world Inspired by Lightning and applied it, other metaheuristics, and machine learning to solve the most diverse and complex problems in mechanical engineering and beyond. His main areas of activities are optimization with metaheuristics, machine learning, design of experiments (DOE), and finite element method. If you have an interesting problem, don’t hesitate to get in touch with him to apply the most modern and current optimization techniques to push the frontiers of knowledge of everything that is known about it.

Dr Terrence Mak is a Lecturer in the Department of Data Science & AI, Faculty of IT at Monash University. His primary research focus is to seek novel methodologies combining both machine learning and optimization to solve grand climate change challenges in the energy sector to prepare for the future era. He is particularly interested in tackling problems on energy sustainability, net-zero emissions,
climate-change resiliency, and disaster management. He works for the ARPA-E Grid Optimization Challenge and is also a member of the Monash Energy Institute and the Monash Data Futures Institute.

Senior Lecturer in Mathematics, School of IT, Deakin University

Long is a Research Fellow in the Department of Data Science and Artificial Intelligence in the Faculty of Information Technology at Monash University. His research investigates optimisation and AI-based algorithms for autonomous multi-agent systems. Long specialises in algorithms for solving planning and scheduling problems in the maritime transport, intelligent transport, and wireless communication domains. His expertise includes mixed integer linear programming, online learning, and high-performance computing. Long works with OPTIMA AI Associate Professor Markus Wagner.

Dr William Umboh is a Lecturer in Computational Theory at the School of Computing and Information Systems at The University of Melbourne. He applies a theoretical computer science approach to discrete optimisation problems. In particular, his primary focus is on designing approximation and online algorithms for network design and joint replenishment problems. Recently, he is also interested in developing principled approaches to using machine-learned predictions to augment approximation and online algorithms.

Previously, he was a Lecturer in Algorithms at the University of Sydney, and a postdoc at the Eindhoven University of Technology with Prof. Nikhil Bansal. Before that, he obtained his PhD in Computer Science from the University of Wisconsin-Madison where he was advised by Profs. Shuchi Chawla and Eric Bach.

Dr Ye Pu is a Lecturer (Assistant Professor) in the Department of Electrical and Electronic Engineering at the University of Melbourne. Prior to joining Melbourne, she was a post-doctoral researcher in the Berkeley Artificial Intelligence Research (BAIR) Lab in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, from 2016 to 2018. She received a BS degree from Shanghai Jiao Tong University, China, in 2008, an MS degree from the Technical University of Berlin, Germany, in 2011, and a Ph.D. degree from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, in 2016. She received the Swiss National Science Foundation – Early Postdoc Mobility Fellowship from 2016 to 2018 and is the recipient of an ARC Discovery Early Career Researcher Award from 2022 to 2025.