Find an expert

Dr Mark C Allenby is a Senior Lecturer in Biomedical Engineering (2021-ongoing) and an ARC DECRA Fellow (2022-2025) within UQ's School of Chemical Engineering. Mark is also an Adjunct Senior Lecturer at QUT and previously an Advance Queensland Fellow (2019-2022). Mark has principally supervised 5 PhDs and 2 MPhil/RAs, co-supervised 7 PhDs, and has been awarded over $2.8M of funding as chief investigator across 20 competitive funding rounds in 4 years. Mark received a PhD and MSc in chemical engineering from Imperial College London, UK and bachelors degrees in mathematics and chemistry from Pepperdine University, USA. Mark's leadership is exhibited by the:

• 2024 ASBTE Emerging Leadership Award
• 2024 Friends of CCRM Australia Industry Advisory Network
• 2024, 2023, and 2022 Executive Board Member of ASBTE
• 2023 TERMIS-AP Young Investigator Award
• 2023 RegMedNet Rising Star Finalist
• 2020 QUT ECR Award

Research Interests: Mark leads the BioMimetic Systems Engineering (BMSE) Lab. In the BMSE Lab, we combine Tissue Engineering, Biomedical Image Analysis, and Computational Biology to study and solve biological and medical problems using advanced cell culture and computer models. Initially, we will focus on Systems of Blood, Blood Vessels, and Vascularised Tissue as these are essential building blocks for human and mammalian function. Our work aligns with bioprocess engineering fundamentals, cell therapy or medical device manufacturing, and clinical collaborators in haematology, vascular surgery, neurosurgery, and radiology. Our systems engineering approaches allow us to examine, computationally model, experimentally engineer, optimise, control, scale, and automate dynamic systems of several entities such as multi-cellular tissues or cell-material and cell-fluid systems.

Academic Interests: Mark is the Convener of UQ's Biomedical Engineering (BME) major, ranked #2 in Australia. BME at UQ spans schools of Chemical Engineering (ChE; #1 in Australia), Electrical Engineering, and Mechanical Engineering, and engages with UQ's Faculty of Medicine and associated healthcare services. Mark is part of ChE teaching and scholarship committees, and Mark acts as the academic advisor for ChE-BME undergraduates. Mark is the creator and coordinator of Quantitative Methods in Biomedical Engineering, and is a lecturer of Process Modeling & Dynamics. Mark has previously taught courses in biomaterials, process modelling, and reaction engineering in ChE and BME departments at three universities in the UK and Australia.

Our BMSE Lab is currently looking for excellent computational researchers. These include candidates and collaborators with experience in microscopy and medical image processing, cell population dynamics simulation, and/or biomechanics simulations (Python, MATLAB, R, ANSYS) to analyse high-content experimental data. Postdoc candidates are welcome to contact us to explore fellowship applications. Interested PhD and MPhil candidates should consider applying with us to the UQ Annual HDR Scholarship Round. We are always recruiting masters and undergraduate thesis project students for thesis projects advertised on our lab website.

Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

Dr Torniainen's main research interests are in the fields of biomedical signal and image processing, biophotonics, and applied spectroscopy. He holds BSc/MSc in Electrical Engineering from Aalto University (Finland, 2015) and a PhD in Applied Physics from University of Eastern Finland (Finland, 2020). He has previously worked with developing preprocessing techniques for EEG/MEG, real-time analysis methods for physiological signals (e.g., ECG/EMG/EDA), and near-infrared spectroscopy based analysis of tissue integrity for musculoskeletal tissues. His current research focus is on machine learning in image processing, analysis, and synthesis of biomedical images acquired using a combination of terahertz imaging, nano-FTIR, and Raman spectroscopy. The purpose of this study is to better understand the interaction between light and multi-layered tissues such as articular cartilage and skin.