جست‌وجوی استاد و پژوهشگر

David Ascherمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Structural Biology
• +۳ مورد دیگر

David Ascher is Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute, Deputy Director of Biotechnology at The University of Queensland, and Head of Systems and Computational Biology at Bio21 Institute. He holds honorary positions at Cambridge University, FIOCRUZ, and the Tuscany University Network. His educational background includes: Bachelor of Biotechnology Bachelor of Science (Honors) Bachelor of Laws Doctor of Philosophy David's research focuses on computational biology and bioinformatics, particularly in modeling biological data to understand fundamental processes. His work centers on developing tools to unravel the genotype-phenotype link, using computational and experimental approaches to assess the effects of mutations on protein structure and function. His group has created a platform of 40 widely used programs for variant effect prediction, which are applied in clinical settings for hereditary diseases, rare cancers, and drug-resistant infections. His recent publications span structural biology, genomics, and drug discovery, with a strong emphasis on protein dynamics, mutation impact prediction, and computational tools for biomedical research. The work often bridges basic science and clinical applications. Scientific awards and fellowships include: Anders Young Investigator Award (2017) Dr Álvaro Romanha Award (2017) Jack Brockhoff Early Career Researcher Award (2016) Dr Antoniana Ursine Krettli Award (2016) Dr Naftale Katz Award (2015) TJ Martin Award (2014) Bionomics Best Thesis Award (2014) NHMRC Investigator Fellowship (2020–2024) MDHS Research Fellowship (2019) NHMRC CJ Martin Fellowship (2014–2018) Victoria Fellowship (2013) Churchill Memorial Trust Fellowship (2013) David Ascher leads the Computational Biology and Clinical Informatics laboratory, which develops computational tools for clinical applications. His work is supported by major grants including the NHMRC Investigator Fellowship. He has not publicly listed his advisees, but his laboratory likely mentors students and researchers in computational biology.

• Cybersecurity and Privacy
• Artificial Intelligence
• Distributed Computing
• +۶ مورد دیگر

Dr. Keshav Sood is a Senior Lecturer at Deakin University's School of Information Technology, where he leads research in cybersecurity, AI, and next-generation networks. His affiliations include roles as Graduate Research Coordinator and South Asia Country Coordinator. He completed his PhD at Deakin University and a post-doctoral fellowship at the University of Newcastle. Research Interests: Dr. Sood focuses on securing distributed systems, with emphasis on: Federated learning for intrusion detection in IoT/5G networks Biometric privacy in immersive technologies (VR/AR) RF fingerprinting for IoT device authentication Quantum-resistant software-defined networks Adversarial robustness in voice authentication systems His publications consistently explore AI-driven security frameworks, with recent work addressing data sparsity in IoT sensors, cross-domain IIoT authentication, and phishing mitigation using large language models. Awards: Professor of IT Award (2016) IEEE TNSE Excellent Reviewer (2023) Deakin HDR Supervision Award (2023) Course Team Award for Industry Certification Alignment (2021) Supervision & Grants: Dr. Sood currently advises 8 graduate researchers and has secured $655,313 in competitive funding. Key projects include: Smart Farming Cyber Resilience (DFAT Maitri Grant) Secure Access for Critical Infrastructure (Cyber CRC) IoT Data Integrity for Defense Systems (Australian Defence) He leads the Deakin Cyber Research and Innovation Centre, focusing on scalable security solutions for industry partners.

• Robotics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Zhang Fangyi is a research fellow at Queensland University of Technology's School of Electrical Engineering and Robotics, specializing in robotics, computer vision, and machine learning. With a PhD completed in 2018 titled 'Learning real-world visuo-motor policies from simulation,' Zhang has established a strong research trajectory focusing on bridging the gap between simulation and real-world robotics applications. Zhang's research interests center around robotic perception and manipulation, with particular expertise in sim-to-real transfer techniques, tactile sensing systems, and graph neural networks. Their work spans multiple domains including robotic grasping, fabric manipulation, face clustering algorithms, and graphene-based sensor development. A consistent theme throughout Zhang's research is the development of robust systems that can effectively transition from simulated environments to real-world applications. The publication record shows a clear evolution from foundational work in sim-to-real transfer (2015-2019) toward more specialized applications in tactile sensing and material science (2021-2024). Recent work demonstrates expanding interests into graphene-based sensor technology while maintaining core expertise in robotic perception. Zhang frequently collaborates with leading researchers at QUT including Peter Corke, with whom they've published multiple papers on robotic grasping and tactile sensing. Zhang's research has practical applications across multiple domains including assistive robotics, sensor development, and computer vision systems. Their work on laser-induced graphene sensors shows particular promise for next-generation tactile interfaces and wearable technology.