Ken Foo is a Visiting Professor at the Medical School of The University of Western Australia, affiliated with the UWA Centre for Medical Research (Harry Perkins Institute of Medical Research). His work bridges biomedical engineering and oncology through advanced imaging technologies for breast cancer applications. His research expertise spans: Optical Coherence Tomography Elastography Breast Cancer Surgery Optimization Tumor Margin Assessment Deep Learning Integration Medical Image Analysis Foo's recent publications (2024-2025) reveal a concentrated effort in developing real-time intraoperative imaging solutions, particularly focusing on optical coherence tomography-elastography fusion with AI-driven computational models to address surgical cavity assessment and tumor elasticity characterization in breast cancer procedures. He maintains active collaboration with the Harry Perkins Institute of Medical Research, driving translational projects that convert laboratory innovations into clinical tools for improving breast cancer surgical outcomes through precision imaging.
Professor Victor Solo serves as Director of Research with the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW). With an extensive academic career spanning over four decades since earning his PhD from the Australian National University in 1979, he has established himself as a leading expert in multiple interdisciplinary fields. University: University of New South Wales School: School of Electrical Engineering and Telecommunications Department: Electrical Engineering and Telecommunications Position: Professor and Director of Research Professor Solo received his BSc from the University of Queensland, followed by a BSc (first class honors) and BE (first class honors) from UNSW, culminating in a PhD from ANU in 1979. His educational background provided the foundation for his diverse research career spanning engineering, mathematics, and biomedical applications. His research interests encompass a wide range of theoretical and applied topics, with particular emphasis on Systems and Signal Processing, Control Theory, and Ill-Conditioned Inverse Problems. He has made significant contributions to Econometrics and Time Series Analysis, developing innovative approaches to System Identification. His work extends into biomedical domains through research in Medical Imaging and Computer Vision, as well as Neuroengineering through studies of Neural Coding and Point Processes. Professor Solo's interdisciplinary approach bridges theoretical mathematics with practical applications across engineering and medical fields. Analysis of Professor Solo's recent publications reveals a strong focus on advanced statistical modeling techniques, particularly in time series analysis and point process modeling. His work consistently addresses stability and identifiability challenges in complex models, with recent publications exploring Vector Autoregressive models, Hawkes processes, and stochastic differential equations on manifolds. The research demonstrates a progression from foundational theoretical work to increasingly sophisticated applications in network modeling and biomedical signal processing. Professor Solo has maintained an exceptionally productive research career with publications spanning from 1981 to the present, demonstrating remarkable longevity and adaptability in his research focus. His work shows consistent contributions across multiple high-impact journals including IEEE Transactions on Signal Processing, Automatica, and Neural Computation. While specific awards are not listed in the available information, his sustained publication record in top-tier journals indicates significant recognition within his fields of expertise. As Director of Research, Professor Solo likely oversees research strategy and development within the School of Electrical Engineering and Telecommunications. His extensive publication record suggests active supervision of graduate students and postdoctoral researchers, though specific names of advisees are not provided in the available information. His research has likely attracted substantial grant funding given the scope and duration of his work across multiple domains.
Prof Zhen (Jeff) Luo is a Professor at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS). Since 2012, he has led the Advanced Metamaterials & Metastructures (AMM) and Engineering Computation and Optimisation (ECO) research groups, focusing on multi-disciplinary engineering innovations. Expertise : Advanced materials design, topology optimization algorithms, additive manufacturing, and computational mechanics. Education : PhD in Mechanical Engineering from Huazhong University of Science and Technology (2005). His research bridges Mechanical, Structural, Aerospace, and Biomechanical Engineering , developing cutting-edge metamaterials and computational methods. Recent work includes two-scale lattice optimization , stochastic bandgap analysis , and machine learning-aided virtual modeling for structural reliability. Key contributions span 3D-printed heat sinks , frequency-selective surfaces , and hydrogen storage systems . As a World’s Top 2% Scientist (Stanford, 2019–present), he has secured over AUD $7 million in grants, including from the Australian Research Council (ARC) and National Intelligence Discovery Research Grants (NI220100074). Awards : IAAM Scientist Award, IAAM Fellow. Leadership : Editorial roles in Structural and Multidisciplinary Optimization , Frontiers in Bioengineering and Biotechnology , and organizing roles in 21 international conferences.
Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.
Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.
