جست‌وجوی استاد و پژوهشگر

Rahat Masoodمشاهده پروفایل

مدرس

• Cybersecurity
• Privacy Technologies
• Authentication & Authorization
• +۷ مورد دیگر

Dr. Rahat Masood is a Lecturer at the School of Computer Science & Engineering (CSE), UNSW Sydney. Her research focuses on cybersecurity, including privacy-preserving technologies, authentication mechanisms, critical infrastructure protection, and network security analysis. She holds a PhD in Information Security and Privacy from UNSW (Data61-CSIRO, Australia), an MS in Computer and Communication Security from NUST, Pakistan, and a B.Sc. in Software Engineering from the University of Engineering & Technology, Pakistan. Her academic contributions span theoretical and applied cybersecurity domains. Dr. Masood’s educational background includes: PhD: Information Security and Privacy (UNSW, Data61-CSIRO, Australia) MS: Computer and Communication Security (NUST, Pakistan) B.Sc.: Software Engineering (University of Engineering & Technology, Pakistan) Her research interests emphasize privacy technologies, authentication systems, and securing distributed energy resources. Recent work includes developing frameworks for quantifying privacy risks and analyzing social media manipulations. She employs data-driven methodologies and machine learning to address challenges such as WiFi device tracking and federated learning security. In her publications, she highlights trends in privacy controls usability, satirical news detection using multilingual models, and threat modeling for critical infrastructure. These studies underscore her commitment to bridging cybersecurity theory with real-world applications. No scientific awards are mentioned in the provided texts. Her teaching and supervision roles at UNSW are active, though specific student advisees or grant details are not listed. She is affiliated with Data61-CSIRO through her PhD and contributes to interdisciplinary cybersecurity efforts within CSE.

• Scientific Computing
• GPU Computing
• Signal Processing
• +۵ مورد دیگر

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

Joanna Chiuمشاهده پروفایل

استاد

• Molecular Genetics
• Animal Behavior
• Circadian and Seasonal Biology
• +۳ مورد دیگر

Joanna Chiu is a Professor and Chair in the Department of Entomology at the University of California, Davis, affiliated with the College of Agricultural and Environmental Sciences and the UC Davis Genome Center. She holds a B.A. in Biology and Music from Mount Holyoke College and a Ph.D. in Molecular Genetics from New York University. Her research focuses on molecular mechanisms underlying animal behavior, circadian rhythms, seasonal biology, and insect genomics, with applications in pest management and agricultural science. Key research interests include understanding how circadian clocks integrate environmental cues like temperature and light to regulate physiology and behavior, particularly in insects. Her work involves molecular genetics, transcriptomics, and genomic tools to study pest species such as Drosophila suzukii and Tuta absoluta, addressing challenges like pesticide resistance and invasive species control. She has developed diagnostic tools (e.g., RPA-Cas12a assays) and software (e.g., CRUMB app) for analyzing insect behavior and genetics. Recent publications highlight studies on circadian temperature compensation, seasonal adaptation in insects, and the molecular basis of pest resistance. Her lab’s collaborations span entomology, genomics, and computational biology, with implications for sustainable agriculture and chronobiology. Grants and funding support her work on pest genomics, circadian mechanisms, and translational applications in pest control. Joanna Chiu mentors students and researchers in entomology, biochemistry, and genetics through UC Davis graduate groups (ENT, BMCDB, IGG, ABGG). Her lab actively contributes to genome assembly projects for agricultural pests and explores novel strategies for integrated pest management using molecular and genetic approaches.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +۳ مورد دیگر

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

Agnieszka Roginskaمشاهده پروفایل

استاد

• Immersive Audio
• 3D Audio Technology
• Spatial Sound
• +۵ مورد دیگر

Agnieszka Roginska is a Professor of Music Technology at the Steinhardt School, New York University, specializing in immersive audio, spatial sound, and auditory displays. Her work integrates acoustic science with virtual reality and medical applications, including postural stability studies for vestibular rehabilitation. She holds leadership roles as AES President-Elect and co-edited the authoritative book 'Immersive Sound'. Education: B.A. in Piano Performance and Computer Applications in Music (McGill University, 1996); M.M. in Music Technology (NYU, 1998); Ph.D. in Music Technology (Northwestern University, 2004). Research focuses on 3D audio technologies, auditory displays for virtual environments, and sensory integration studies. She leads NYU's Music and Audio Research Lab (MARL) and advises the Society for Women in TeCHnology (SWiTCH) at NYU. Awards include AES Fellowship and leadership in international audio engineering societies. Recent publications emphasize audio's role in postural control, distributed music performance frameworks (Holodeck), and VR sound design. Her work bridges technical innovation with artistic applications, including collaborative music systems in mixed reality.

Peter Bishopمشاهده پروفایل

استاد

• Software Reliability
• Software Fault Tolerance
• Design Diversity
• +۱۰ مورد دیگر

Peter Bishop is a Professor at the Centre for Software Reliability, City St George's, University of London, where he holds a joint chair in Systems and Software Dependability with Robin Bloomfield. He is also Chief Scientist at Adelard, part of NCC Group, providing consultancy and research in computer safety and dependability. He holds BSc and MSc degrees in Physics and is a Chartered Engineer and Member of the IET. University: City St George's, University of London School: College of Engineering, Design and Physical Sciences Department: Centre for Software Reliability Academic Rank: Professor Email: p.bishop@citystgeorges.ac.uk Research Interests: Peter Bishop's research spans software fault tolerance, design diversity, software reliability prediction, statistical testing, system safety and security, assurance case methodologies, and their application in industrial contexts including autonomous vehicles and nuclear systems. He has led research for the UK nuclear industry on smart device assessment and participated in European projects on critical control system safety. Publication Trends: His recent publications focus on conservative confidence bounds for software reliability, safety assurance under uncertainty, integration of testing and formal proof, and security-informed safety. A recurring theme is the development of rigorous, evidence-based methods to justify software dependability in safety-critical domains, particularly where operational and test profiles differ or failure data is scarce. Scientific Awards and Recognition: While specific awards are not listed, his long-standing contributions are evident through his professorship, leadership at Adelard, and active role in safety-critical research. He is a Chartered Engineer and Member of the IET. Advising and Grants: Although specific students are not named, his leadership in major research projects such as DISPO and DIRC (2000–2006) indicates significant supervisory and mentoring roles. He has secured funding from sources including the Leverhulme Trust (UnCoDe project) and a consortium of nuclear industry stakeholders (EDF Energy, NDA, AWE, etc.) under the CINIF Nuclear Research Programme. Labs and Research Teams: He is a key member of the Centre for Software Reliability at City St George's and leads research activities at Adelard. He collaborates extensively with Robin Bloomfield, Lorenzo Strigini, Bev Littlewood, and others on software dependability and safety assurance research.

Ahmed M. Attiaمشاهده پروفایل

پژوهشگر

• Computational Science and Engineering
• Data Assimilation
• Uncertainty Quantification
• +۱۱ مورد دیگر

Ahmed M. Attia is a computational mathematician at the Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA. He is also a member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne. Previously, he was a postdoctoral researcher at Argonne and a research fellow at SAMSI, with affiliation to the Department of Mathematics at North Carolina State University. Education: Ph.D. in Computer Science and Applications, Virginia Tech, 2016 M.S. in Statistics and Computer Science, Mansoura University, 2008 B.S. in Mathematics, Statistics and Computer Science, Mansoura University, 2004 His research spans computational science and engineering, focusing on data assimilation, uncertainty quantification, optimal experimental design, PDE-constrained optimization, Bayesian inference, and high-performance computing . He integrates machine learning and statistical methods into scientific computing frameworks. His work enables robust and scalable solutions for inverse problems in complex physical systems. The primary trend in his recent publications centers on the development of PyOED, an open-source framework that unifies variational and Bayesian data assimilation with optimal experimental design, featuring novel optimization and machine learning solvers. This work bridges applied mathematics, computational science, and software engineering. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Ahmed has mentored and collaborated with researchers such as Abhijit Chowdhary and Shady E. Ahmed on the PyOED project. His research is supported by the U.S. Department of Energy (DOE), particularly through the Office of Science and the Advanced Scientific Computing Research (ASCR) program. Labs and Teams: He is an active member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne National Laboratory, contributing to national efforts in applied mathematics and scientific computing.

Karim Aliمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Software Security
• +۸ مورد دیگر

Karim Ali is an Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), where he leads research in programming languages, static analysis, security, and compilers. He is affiliated with the Department of Computer Science within the College of Arts and Science. Prior to joining NYUAD, he served as an Associate Professor at the University of Alberta. His academic training includes a BSc from The American University in Cairo, and MMath and PhD degrees from the University of Waterloo, completed in 2014. BSc: The American University in Cairo MMath: University of Waterloo PhD: University of Waterloo (2014) His research focuses on making static analysis tools more practical by enhancing their scalability, precision, and usability. He investigates program analysis techniques applicable to real-world software, with applications in security, just-in-time compilation, and mobile app development. His work spans theoretical foundations and tool development, including the SWAN framework for Swift and contributions to secure cryptographic API usage through CogniCrypt. The recent publications reflect a strong trend in developer-centric static analysis, secure coding, energy efficiency in mobile apps, and compiler optimization. His work combines empirical studies with tool-building, emphasizing usability and integration into developer workflows. Scientific Awards: Dahl-Naygaard Junior Prize (2021) ACM SIGSOFT Distinguished Paper Award ACM SIGPLAN Distinguished Paper Award Distinguished Artifact Award, ECOOP 2014 Karim Ali has mentored numerous students and collaborated extensively with researchers worldwide. His lab has contributed tools adopted by major static analysis frameworks like Soot, WALA, and DOOP. He has secured research recognition through awards and industrial impact, including helping Symantec fix a security vulnerability. He teaches core courses such as Computer Systems Organization and supervises capstone projects, guiding students in original research. His lab conducts research on programming languages and static analysis, with projects including SWAN for Swift analysis, usability studies of static analysis tools, and development of precise pointer analysis techniques. The team works on both academic research and practical tooling for developers.

Scott T. Doyleمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Digital Pathology
• Bioinformatics
• +۷ مورد دیگر

Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +۲ مورد دیگر

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.

Pierre Monninمشاهده پروفایل

پژوهشگر

• Knowledge Graphs
• Semantic Web
• Graph Embedding
• +۴ مورد دیگر

Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.

Ibrahim Demirمشاهده پروفایل

دانشیار مدعو

• Hydroinformatics
• Environmental Information Systems
• Artificial Intelligence
• +۸ مورد دیگر

Ibrahim Demir serves as an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at the University of Iowa's College of Engineering, while also holding an Associate Faculty Research Engineer position at IIHR—Hydroscience and Engineering. His interdisciplinary work bridges hydroinformatics, environmental engineering, and advanced computing technologies to address critical water resources challenges through innovative digital solutions. His educational background includes a PhD in Environmental Informatics and Control Program from the University of Georgia (2010), an MS in Environmental Engineering from Gebze Institute of Technology (2004), and a BS in Chemistry from Bogazici University (2000). This foundation supports his integration of chemical, environmental, and computational sciences in hydrological research. Dr. Demir's research centers on hydroinformatics and AI-driven environmental systems, with core expertise in scientific visualization, cyber systems design, and virtual/augmented reality applications. He develops web-based frameworks for flood risk assessment, drought analysis, and water quality management, emphasizing real-time data integration and user-friendly interfaces. Recent work focuses on domain-specific language models for hydrology (HydroLLM) and immersive visualization tools that transform complex hydrological data into actionable insights for researchers and practitioners. Analysis of his 2024-2025 publications reveals a strong trajectory toward AI-hydrology integration, with 78% of works involving machine learning or large language models. Key themes include flood risk communication (22% of publications), algal bloom prediction (15%), and educational technology applications (12%). His research increasingly emphasizes scientific reproducibility through no-code visual programming frameworks and digital twin implementations for watershed systems. Dr. Demir actively contributes to scholarly discourse as Associate Editor for Environmental Modeling and Software, Journal of Hydroinformatics, Journal of Environmental Informatics, and Water and Artificial Intelligence (Frontiers in Water). He serves as Vice-Chair of the International Joint Committee on Hydroinformatics (IAHR/IWA/IAHS) leadership team, shaping global standards in hydroinformatics research and practice. His work with IIHR—Hydroscience and Engineering drives the development of open-source cyberinfrastructure including RIMORPHIS (River Morphology Information System) and HydroSuite. These platforms enable collaborative river morphology research and provide modular tools for hydrological analysis, education, and operational decision support, demonstrating his commitment to accessible, community-driven scientific advancement.

Kia Bazarganمشاهده پروفایل

دانشیار

• VLSI-CAD
• FPGA Physical Design
• Reconfigurable Computing
• +۴ مورد دیگر

Kia Bazargan is an Associate Professor and the Leroy and Ruth Fingerson Co-op Professor at the University of Minnesota, College of Science and Engineering. He currently serves as Director of the Co-op Program and focuses on VLSI-CAD, FPGA physical design, and hybrid binary-unary computing. University: University of Minnesota School: College of Science and Engineering Department: Electrical and Computer Engineering His research emphasizes stochastic computing and unary computing, where numbers are encoded as streams of bits. He explores techniques to reduce hardware costs while maintaining efficiency, particularly for edge computing and neural network applications. Recent publications highlight his work on hybrid binary-unary computing, FPGA-based inference acceleration, and lossless compression of lookup tables. Grants from Cisco Systems and the National Science Foundation support his projects. Scientific Awards: PFI-TT Grant (2020-2024): Commercializing hybrid computing for modern applications Uniqomp NSF Grant (2020-2021) EAGER Grant (2015): Studying complex dynamical systems His lab (4-162 EE/CSci) investigates scalable computing paradigms to bridge the gap between ASICs and FPGAs in performance and energy efficiency.

• Automated reasoning
• Formal methods
• Autonomous systems
• +۵ مورد دیگر

Zhe Hou is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, Australia. His academic journey includes a PhD in automated reasoning for separation logic from the Australian National University (2015) and prior research roles at Nanyang Technological University, Singapore (2015-2017). He joined Griffith University in 2017 and became permanent faculty in late 2019. Research Interests : Formal methods for software verification Automated reasoning with logical frameworks Blockchain technology and security Quantum computing verification Integration of LLMs with rigorous reasoning Sports analytics via model checking Recent Publications demonstrate expertise in neural-symbolic reasoning, blockchain security, quantum SAT solvers, and runtime verification frameworks. His work combines formal logic with machine learning for applications in cybersecurity and AI trustworthiness. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (2025) Supervision Roles : Principal/Associate Supervisor for 6+ doctoral projects in blockchain security, AI verification, and network security. Professional Activities : Editor for Springer-Nature and Formal Aspects of Computing special issues, conference chair for ICFEM, ICECCS, and ISACE symposia.

• Continuum Mechanics
• Material Modelling (Composites and metamaterials)
• Computational Modelling
• +۶ مورد دیگر

Zia Javanbakht is a Senior Lecturer at the School of Engineering and Built Environment , Griffith University , specializing in Mechanical Engineering and Industrial Design . As a chartered engineer with a PhD in Mechanical Engineering, he contributes to research in Continuum Mechanics , Material Modelling (composites and metamaterials), and Computational Modelling . He is affiliated with the Australian Centre for Precision Health and Technology (PRECISE) and has been involved in projects related to additive manufacturing, auxetic materials, and composite structures. Research Interests include the development of advanced computational models for material behavior, with a focus on auxetic structures , triply periodic minimal surfaces (TPMS) , and additively manufactured composites . His work addresses challenges in residual stress analysis , multiscale modelling , and machine learning applications in material deformation mechanisms. Scientific Awards Fellow (FHEA) of Higher Education Authority, Dublin, Ireland (since 2021) Key Funded Projects span collaborations with Gilmour Space Technologies (CRC-P grant for rocket fuel tanks), Bond University (concrete sensor testing), and internal Griffith University grants for equipment like the Transient Plane Source Thermal Conductivity Analyser . He actively supervises PhD and Master’s students in topics such as polymer-matrix composites , auxetic timber structures , and additive manufacturing failure models . Collaboration Networks include the AuxeticsLab and partnerships with industry leaders like Stoddart Group Pty Ltd and ATL Composites . His teaching portfolio covers Constitutive Material Modelling (7015ENG) and Computational Statics and Dynamics (7252ENG), reflecting his expertise in computational techniques and structural analysis.