Associate Investigators

Markus Wagner from Monash University works on topics that range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to software engineering and renewable energy production. While his research is fundamental at times, he is no stranger to industry-funded research; some of his translational projects are funded by Google, Facebook, and by several companies in defence and mining. Markus has been deeply passionate about sustainability since his high-school days… and still is. He serves as the first ever Sustainability Officer (since 2019) at the ACM Special Interest Group on Genetic and Evolutionary Computation, he founded the IEEE CIS Task Force on Computational Intelligence in the Energy Domain, and he is Associate Director for Smart Energy Systems at the Monash Energy Institute.

OPTIMA Associate Investigator Maria Vrakopoulou is a Lecturer in the Power and Energy System group at the Department of Electrical and Electronic Engineering at the University of Melbourne. She received her diploma in Electrical and Computer Engineering from the University of Patras, Greece in May 2008, and her PhD degree from the Department of Electrical Engineering and Information Technology, at ETH Zurich, Switzerland in June 2014. Dr. Vrakopoulou pursued her research as a post-doc in the University of Michigan, Ann Arbor, MI, USA for a year, and then she earned a 3-year Marie Curie fellowship award to continue her post-doctoral research in the University of California, Berkeley, CA, USA, and ETH Zurich, Switzerland. Her research interests concentrate on the optimization and analysis of planning and operation problems for power systems under renewable generation uncertainty.

Xiaoqian is a research fellow based at Monash University’s Faculty of Business and Economics, in the Department of Econometrics and Business Statistics. Her research interests include time series forecasting, distributed computing and statistical modeling. In particular, she is now working on a nonlinear optimization problem both with nonlinear and integer constraints in a hierarchical framework to deal with high-dimensional hierarchies.

Damjan is an Associate Professor and the Director of Engagement at the Department of Econometrics and Business Statistics, Monash University.

He completed his DPhil at the University of Oxford, which included contributing to the landmark multi-disease genome-wide association study by the Wellcome Trust Case Control Consortium (Nature, 2007).  Since then he has worked both in academia and industry, and maintains a broad portfolio of research projects.  These include collaborations with actuaries and other industry partners.  His current projects combine optimisation and statistical modelling to create methods for efficient sampling and inference, with a particular focus on auditing election results, and developing computational tools for Bayesian inference.

Damjan has contributed substantially to the statistics profession through the Statistical Society of Australia, and is currently serving in the role of Treasurer.

Dr Buser Say is a Lecturer (i.e., Assistant Professor) in the Department of Data Science and Artificial Intelligence (DSAI) at Monash University, the Artificial Intelligence and Machine Learning theme coordinator for Building 4.0 Cooperative Research Centre (CRC), and an academic supervisor for Monash DeepNeuron. Buser has earned his Ph.D. (2020) under the supervision of Professor Scott Sanner on the topic of optimal planning with deep neural networks, his MASc. (2016) on the topic of least-commitment partial-order planning under the supervision of Professor J. Christopher Beck and Professor Andre Augusto Cire and his BASc. in Industrial Engineering (2014) from University of Toronto. He was also a Postgraduate Affiliate with the Vector Institute and a visiting researcher at the Australian National University. Buser is working with OPTIMA Industry Partner ENGIE.

Fotios received his Ph.D. from Monash Business School, Department of Econometrics and Business Statistics, Monash University, Australia (2023). His Ph.D. thesis, Optimal Premium Pricing in a Competitive Stochastic Non-Life Insurance Market: Game Theoretic Approaches, is in Actuarial Science and Risk Management. His primary research interests focus on the general area of mathematical modelling under risk and uncertainty with an emphasis on actuarial science, decision making, risk theory, optimal insurance and game theoretical applications. He has been appointed to the position of Research Assistant in the Department of Econometrics and Business Statistics at Monash University since July 2023. He has been a Teaching Associate in the areas of Actuarial Science and Quantitative Finance in the Department of Econometrics and Business Statistics at Monash University since 2019.

Hao Wang is a Senior Lecturer  in the Department of Data Science and AI, Faculty of Information Technology, Monash University. His research interests are in the optimisation and applied AI techniques for sustainable power and energy systems, with a recent focus on integrating distributed energy resources, such as solar photovoltaics, electric vehicles, and consumer energy resources, towards net zero. He is also interested in economic mechanism design and business models in smart power grids. He’s a recipient of the 2023 ARC DECRA Fellowship (with the project “Reliable Integration of Distributed Low-Carbon Energy Resources”) and the Washington Research Foundation (WRF) Innovation Fellowship.

Yuan Sun is a Lecturer at La Trobe Business School. He has worked primarily in artificial intelligence and optimisation, and his recent research focuses on developing machine learning techniques to enhance traditional optimisation methods such as column generation, Lagrangian relaxation and (meta)heuristics. His research has been published in top-tier journals and conferences such as IEEE TPMAI, EJOR, ICLR and AAAI.

Dr Ellie Hajizadeh is a Lecturer and head of Soft Matter Informatics Research Group at the Department of Mechanical Engineering, University of Melbourne. Her research lies at the confluent of several research disciplines, including computational materials science, multiscale simulations (from atomistic to mesoscopic to continuum), machine learning and optimisation with the aim of obtaining an integrated multidisciplinary framework for accelerated materials discovery and design. Prior to her current role, Ellie was a Senior Research Scientist at the Australian Deference Science and Technology Group (DSTG) and a Postdoctoral Research Fellow at the University of Michigan. At DSTG, Ellie established several national and international programs, including a $1.8 million Next Generation Technologies Fund (NGTF) research program in the field of Multiscale Simulation of Soft Matter, where she built teams including Defence Industry and Australian Universities as well as a technological collaborative project with allied nations. In her Postdoctoral Research Fellow at the University of Michigan, Ellie co-invented and recently delivered an accelerated multiscale materials simulation technology to Dow Chemical company in US, which is an integral part of their sustainable paint portfolio and provides them with a competitive advantage in their global market. Ellie is current Secretary of the Australian Society of Rheology.

Frank Neumann is a professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals “Evolutionary Computation” (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of cybersecurity, renewable energy, logistics, and mining.