Dr Jessica Leung

Education and Training Sub-committee

Jessica is a lecturer in Business Analytics at the Department of Econometrics and Business Statistics at Monash University. Her research interests are convex and combinatorial optimization and machine learning algorithms in graph theory, FinTech and InsurTech applications. Jessica is also the Lead Editor of ORMS Tomorrow, a student membership magazine for the Institute for Operations Research and the Management Sciences (INFORMS). 

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Dr Mario Andrés Muñoz

Education and Training Sub-committee

Dr Mario Andrés Muñoz’s research focuses primarily on understanding what makes a problem easy or hard for an optimization or machine learning method, through scientific experimentation, visualization, predictive modelling, and statistical inference techniques. With a keen interest in interdisciplinary work, he has published in fields as diverse as Biomechanics, Power Networks, Resources Engineering, Corporate Social Responsibility, and Computational Biology. He received his PhD from The University of Melbourne in 2014. Prior to joining OPTIMA, he was a Research Fellow at the School of Mathematics and Statistics, The University of Melbourne. He has published over 50 papers, including 20 articles in leading journals, and currently co-supervises five PhD students. He is the main developer of the MATILDA computational engine for Instance Space Analysis.

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Zahra Namazian

Education and Training Sub-committee

Zahra’s project title is “Optimising multi-item retail inventories using Artificial Intelligence”.

She is based at our Monash University node, working with our industry partner MECCA Industries. OPTIMA Deputy Director Prof. Peter Stuckey supervises her, alongside Monash supervisor Prof. John Betts.

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Wathsala Karunarathne

Education and Training Sub-committee

Wathsala’s research interests are Stochastic modelling and Optimisation. Her research “Scheduling Arrivals into a System that Accepts Random arrivals” focuses on scheduling a fixed number of customers to a system that accepts random customers in a finite time horizon. She is also interested in data-driven queueing problems. OPTIMA CI prof. Peter Taylor and Dr Mark Fackrell supervise her.

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Shengjie Wang

Education and Training Sub-committee

Shengjie’s project is titled “Last mile delivery allocation using deep Q-learning” OPTIMA CI Dr Joyce Zhang supervises her.

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Achini Erandi

Education and Training Sub-committee

Achini is a PhD student at the University of Melbourne. Her project is titled “Simulation and Optimisation approach for staff rostering of a blood donor centre”. To project scope is to determine a method for finding the optimal configuration of staff shifts based on the predicted staffing demand using a simulation model that captures the uncertainty in the donation process. She is co-supervised by OPTIMA CIs Dr Joyce Zhang and Prof. Mark Fackrell.

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Dr Hadi Akbarzadeh Khorshidi

Education and Training Sub-committee

Hadi is a Research Fellow in the School of Computing and Information Systems at the University of Melbourne. He has extensive research experiences in optimisation, machine learning, and uncertainty. He completed his PhD at Monash University in Applied and Computational Mathematics where he developed mathematical models for optimisation and simulation under uncertainty. Before joining Melbourne, he worked as a Senior Data Analyst in the Institute of Safety, Compensation and Recovery Research where he conducted several health-related data-mining projects for Victorian governmental organisations, Transport Accident Commission (TAC) and WorkSafe Victoria (WSV). He is part of a joint research grant between the Universities of Melbourne and Manchester called “Technology for Access to Law”. Also, he is a recipient of Innovation Grant Scheme for proposal on “Machine learning to predict the risk of infertility after breast cancer treatment using FoRECAsT data”. Hadi works with OPTIMA CI Professor Uwe Aickelin.

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Professor Mark Fackrell

Education and Training Sub-committee

Mark is a senior lecturer in the School of Mathematics and Statistics at The University of Melbourne. His research interests include stochastic modelling, queueing theory, matrix-analytic methods, stochastic optimisation, game theory, operations research, machine learning, and healthcare modelling. He also has research links with a number of industries including Red Cross Lifeblood, DST, and Northern Health.

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Professor Andreas Ernst

Education and Training Sub-committee

Andreas has worked in real-world applications of optimisation for over 25 years, including in supply chains, logistics, mining, services (rostering) and energy.  Recent work includes rail planning and scheduling for Pacific National through a Linkage grant and methods using machine learning to improve the performance of optimisation algorithms as part of a ARC Discovery Project with RMIT. Andreas is a Senior Fellow with the Australia Indonesia Centre leading the research on Transport and Logistics in the PAIR program. His research is focused on methods for solving large-scale integer programming problems, including decomposition methods and matheuristics that combine mathematical programming approaches with metaheuristic search.

Andreas is the joint lead in the research theme ADVANCE

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Professor Peter Stuckey

Education and Training Sub-committee

Peter Stuckey from Monash University has a world-leading research program in constraint programming. He led the G12 project, one of the largest projects at NICTA, peaking at 25 researchers, culminating in the Opturion spinout company, delivering optimisation solutions to commercial customers. He has been involved in 3 ARC Linkage Projects and other industry contract research projects, working in Energy, Security, Resources and Transport. His research expertise in optimisation broadly covers modelling languages and model transformation, solving using Artificial Intelligence (AI) and Operations Research (OR) technology, and using machine learning methods in concert with optimisation. For the global influence and uptake of his research, he was awarded the 2010 Google Australia Eureka Prize for Innovation in Computer Science and honoured in 2019 with a Fellowship of the prestigious Association for AI Advancement.

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