Dr Martin heads the Algal Processing Group that focusses on the development of efficient and scalable processes for the production of microalgal biomass and conversion into fuels and bioproducts (http://chemeng.unimelb.edu.au/algal-processing-group/).

Dr Martin’s primary research motivation is to understand the response of biological systems to stresses imposed by industrial processing to enable development of sustainable technologies and processes. This spans from the molecular level (e.g. proteins and lipids) through to microbial consortia (e.g. bacteria-algae interactions).

She is a Project Leader within the ARC Hub for Digital Bioprocess Development, the Dairy Innovation Research Hub and within the Future Fuels CRC. She was Head of the School of Chemical and Biomedical Engineering (2017-2022), Head of the Department of Chemical Engineering (2012-17) and the Discipline Leader in the CRC for Greenhouse Gas Technologies (CO2CRC) for Membrane Technology from 2003-2015.

In March 2012, he received a Ph.D. degree (Dr. rer. nat.) in the scientific discipline of Computer Science from the University of Potsdam (Germany). His research and teaching interests include Computing Systems, Distributed Systems, Process Mining, Process Querying, Artificial Intelligence, and Algorithms. Artem Polyvyanyy has published over 120 academic manuscripts, including book chapters, peer-reviewed journal articles, and refereed papers at international conferences. He has actively contributed to open-source initiatives that significantly impact research and practice, including Oryx and jBPT.

He has authored over 180 scientific publications in the areas of process mining and business process management, including a textbook used in 300+ universities and translated to six languages. Using this book, Marcello co-developed a series of MOOCs (Massive Open Online Courses) on the subject, which have attracted over 25,000 participants to date. Marcello has also delivered consultancy, professional training and provided strategic advice to hundreds of practitioners in Australia, Europe and the United States. In 2020, he was named Australia’s Field Leader for his research in Information Systems by The Australian.

Ling completed PhD in the area of data mining and machine learning at the University of Sydney, 2017, under the supervision of Dr Irena Koprinska from the School of Computer Science, University of Sydney and Dr Bin Li from Data61 (now at Fudan University). Ling has been awarded the Springer Theses Award in 2019 and Google PhD Fellowship in Machine Learning in 2017. Ling received Bachelor of Engineering (Software) with Honours Class I in 2012 from the University of Sydney.

His main scientific contributions are in three areas:

• The design and analysis of evolutionary algorithms focused on solving complex optimisation problems.
• The development and application of advanced analytics and AI models to help decision makers explore plausible futures and identify solutions for disparate problems.
• Understanding cooperation and coordination in distributed multi-agent systems.

He has established and led a number of research teams working at the interface of academia and commerce. He has recruited and trained top data scientists who have gone on to develop highly successful careers in various industries.

His research interests cover a broad range of intelligent and biologically/nature inspired learning and adaptive systems, as well as machine learning, statistical, and hybrid intelligent techniques encompassing data and information fusion, learning and adaptation methods, multiple classifier and prediction systems, robust optimisation approaches, processing and modelling of uncertainty in pattern recognition, diagnostic analysis and decision support systems.

He obtained his PhD in Computer Science from the University of Tartu in 2015. He was recipient of a best student paper award and co-inventor of two US patent applications. His research areas of interest include business process management, process mining, and social network analysis.