جست‌وجوی استاد و پژوهشگر

• Quantum Sensors
• Quantum Gravimetry
• Quantum Magnetometry
• +۴ مورد دیگر

Professor John Close is a Professor and Head of the Department of Quantum Science within the Research School of Physics and Engineering at the Australian National University (ANU). He has held this professorship since 2008 and previously served as Deputy Director of the School from 2012 to 2016. His leadership roles include Chair of the ANU Defence Working Group and Deputy Chair of ANU Academic Board (2015-2017), alongside being a Senior Fellow of the Higher Education Academy. His educational background includes: PhD in Physics from the University of California, Berkeley (1991) Postdoctoral Fellowship at the University of Washington, Seattle (1991-1994) Alexander von Humboldt Fellowship and Max Planck Research Fellowship at the Max Planck Institut für Strömungsforschung, Göttingen (1994-1998) Close's research centers on harnessing quantum fields to develop advanced quantum sensors for fundamental physics and interdisciplinary applications. His experimental and theoretical work spans quantum gravimetry, magnetometry, and Bose-Einstein condensate systems, with direct applications in mapping archaeological sites, volcanoes, aquifers, and mineral deposits. He actively explores quantum wavelet representations, higher-dimensional information processing, motion simulation, and biological quantum sensing through extensive collaborations with Earth Science, Biology, and Industry partners. Analysis of his recent publications reveals a dominant focus on compact mobile quantum sensing platforms, atom interferometry innovations, and sensor fusion techniques. His work bridges quantum physics with practical navigation, geophysical surveying, and environmental monitoring solutions, emphasizing real-world deployment of quantum technologies for precision measurements in gravimetry and inertial navigation. His scientific recognition includes: Alexander von Humboldt Fellowship (1994-1998) Queen Elizabeth II Fellowship (2000) National Teaching Award for Research Led Education (2006) As a former member of the Australian Research Council College of Experts (2015-2018), Close has significantly influenced national research funding while securing grants for interdisciplinary quantum projects. His educational leadership as Deputy Director of the Research School of Physics and Engineering (2012-2016) demonstrates commitment to research-led teaching, recognized by his national teaching award. Close leads the Atom Laser Research Group, driving experimental work in quantum sensor development and Bose-Einstein condensate applications. His team focuses on translating quantum phenomena into deployable technologies for defense, resource exploration, and environmental monitoring through industry and international academic partnerships.

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

• Information Theory
• Data Privacy
• Network Coding
• +۳ مورد دیگر

Professor Parastoo Sadeghi is a distinguished academic in the School of Engineering and Information Technology at the University of New South Wales (UNSW) Canberra, where she serves as Professor of Electrical Engineering. She joined UNSW Canberra in October 2020 after spending 15 years at the Australian National University (2005-2020). Professor Sadeghi received her bachelor's and master's degrees in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1995 and 1997, respectively, and completed her Ph.D. in electrical engineering from UNSW Sydney in 2006. Professor Sadeghi's research spans several cutting-edge areas in information theory and communications, with particular emphasis on information theory , data privacy , network and index coding , wireless communications theory and systems , and spherical signal processing . Her work has resulted in over 200 refereed journal articles and conference papers, plus a book on Hilbert Space Methods in Signal Processing published by Cambridge University Press in 2013. Her recent publications demonstrate a strong focus on differential privacy mechanisms, information leakage analysis, and network coding optimization, with significant contributions to the theoretical foundations of these fields. Professor Sadeghi has held several prestigious positions in the academic community, including serving as Associate Editor for coding techniques for the IEEE Transactions on Information Theory (2016-2019), General Co-chair of the 2021 IEEE International Symposium on Information Theory in Melbourne, and as an elected member on the Board of Governors of the IEEE Information Theory Society (2019-2020). She has been a Senior Member of the IEEE since 2007 and has conducted research visits at leading institutions including the Technical University of Munich (2008) and MIT (2009, 2013, 2020). Her recent work shows increasing focus on privacy-preserving technologies and the theoretical foundations of secure communications, with numerous publications in top-tier venues. Senior Member of IEEE (since 2007) General Co-chair of 2021 IEEE International Symposium on Information Theory Associate Editor for IEEE Transactions on Information Theory (2016-2019) Elected member, IEEE Information Theory Society Board of Governors (2019-2020) Professor Sadeghi actively supervises graduate students in fundamental problems related to information theory, wireless communications, data privacy, and network coding. She offers competitive scholarships of $35,000 AUD for high-achieving PhD students with strong mathematical backgrounds. Her research group maintains strong international collaborations, as evidenced by her frequent research visits to top global institutions and numerous co-authored publications with international researchers. She continues to be an active contributor to the advancement of information theory and its applications to modern communication and privacy challenges.

Anna Caiمشاهده پروفایل

مدرس

• Mathematical Biology
• Biomathematics
• Mathematical Modeling
• +۳ مورد دیگر

Dr. Anna Cai is a Lecturer in the Department of Applied Mathematics within the School of Mathematics & Statistics at the University of New South Wales (UNSW), a position she has held since 2011. Prior to this, she served as a postdoctoral researcher and lecturer at the University of California, Irvine from 2007 to 2011. Her educational background: Ph.D., The University of Melbourne, 2008 B.Sc. (First Class Honours), The University of Melbourne, 2004 Dr. Cai's research centers on Mathematical Biology , with specialization in cell migration dynamics , developmental pattern formation , and robustness in biological systems . She employs multi-scale mathematical modeling and stochastic analysis to investigate phenomena ranging from wound healing to zebrafish hindbrain development. Her work bridges theoretical mathematics with experimental biology to decode fundamental mechanisms governing cellular behavior and morphogenesis. Analysis of her 11 publications (2006-2015) reveals an evolutionary trajectory from foundational work on cell migration mechanics to sophisticated studies of developmental robustness. Early research established mathematical frameworks for wound-healing assays and immune cell dynamics, while later work pioneered noise-driven pattern formation models in zebrafish. A unifying thread is her focus on how biological systems maintain precision despite environmental fluctuations. Administrative responsibilities include: Applied representative on the postgraduate review committee UNSW study abroad and exchange course authority Current teaching assignments: MATH2121: Theory and Applications of Differential Equations MATH2018: Engineering Mathematics 2D Prior courses taught: MATH3041: Mathematical Modeling for Real World Systems MATH6781: Biomathematics

• Biomedical Data Computing
• Medical Image Processing
• Image Registration
• +۴ مورد دیگر

Associate Professor Xiu Wang is affiliated with the School of Computer Science at The University of Sydney, where she serves as Associate Director of the Multimedia Lab and a member of the Biomedical & Multimedia Information Technology (BMIT) Research Group. Her research focuses on panoramic data analysis, biomedical computing, image processing, and medical data fusion. Key projects include tumor treatment outcome prediction using deep learning, collaborative learning of multimodal medical imaging, and deformable image registration techniques. She teaches courses such as COMP5214 (Software Development in Java) and supervises graduate students in AI-driven medical diagnostics and imaging. Notable collaborations involve Dr. Hui Cui and Mr. Chaojie Zheng. Her work bridges computer science and healthcare, emphasizing AI applications in oncology, radiology, and neuroimaging. Recent articles highlight advancements in MRI segmentation, PET/CT radiomics, and graph neural networks for disease diagnosis. She actively contributes to interdisciplinary research, integrating computational methods with clinical data for precision medicine.

Xiongcai Caiمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

Yann Quideمشاهده پروفایل

پژوهشگر ارشد

• Psychiatry
• Neurosciences
• Neurobiology
• +۱۰ مورد دیگر

Dr. Yann Quide is a Research Fellow at UNSW School of Psychology and an Affiliated Scientist at Neuroscience Research Australia (NeuRA) Centre for Pain IMPACT. He leads the Neuroimaging, Neurobiology and Mental Health Program within the UNSW NeuroRecovery Research Hub and founded the ENIGMA-Chronic Pain working group, establishing himself as a prominent researcher in the intersection of neuroscience, mental health, and chronic pain. PhD Neuroscience, Université de Tours (France), 2014 MSc Neuroscience, Cognition and Behaviour, Université Paul Sabatier Toulouse III (France), 2007 BSc Cell Biology and Animal Physiology, Université de Tours (France), 2004 Dr. Quide's research focuses on understanding the role of stress and trauma in the development of physical and mental health conditions, with particular expertise in posttraumatic stress disorder, chronic pain mechanisms, and neurobiological alterations. His work integrates functional neuroimaging, structural brain analysis, and clinical interventions to explore how childhood trauma exposure impacts inflammatory responses and stress systems in psychiatric disorders. His extensive publication record reveals a strong emphasis on large-scale collaborative neuroscience through the ENIGMA consortium, with research spanning PTSD neurobiology, chronic pain mechanisms, schizophrenia spectrum disorders, and innovative neuromodulation approaches. His work consistently bridges neuroimaging findings with clinical applications, particularly in developing digital interventions for pain management. 2023: Biological Psychiatry Australia (BPA) – Best Research Paper Award 2018: Biological Psychiatry Australia (BPA) – Neuronal Signalling Award for 2nd Best Abstract 2018: NeuRA Carer Travel Award 2018: Paxinos Prize for best paper published by a NeuRA postdoctoral researcher 2010-2013: Postgraduate scholarship from the French Ministry for Higher Education and Research 2008: Travel fellowship from the France-Quebec Youth Office Dr. Quide has secured significant research funding as Chief or Associate Investigator, including an NHMRC MRFF grant of over $2.2 million for the NeuRoStiM Trial investigating brain-computer interface neuromodulation for spinal cord injury neuropathic pain. His supervision interests span functional neuroimaging, chronic pain, childhood trauma, schizophrenia, bipolar disorder, and inflammation, reflecting his multidisciplinary approach to mental health research. As founder and chair of the ENIGMA-Chronic Pain working group, Dr. Quide has established a major international collaborative network focused on identifying brain correlates of chronic pain through large-scale neuroimaging data analysis across multiple institutions worldwide.

• Neuroscience
• Complex Systems Analysis
• Statistical Physics
• +۳ مورد دیگر

Ben Fulcher is an Associate Professor and Senior Lecturer in the School of Physics at The University of Sydney, leading the Dynamics and Neural Systems Group. His research focuses on applying statistical and physics-based methods to study complex systems, with a strong emphasis on neuroscience. He investigates brain structure and function dynamics, particularly exploring the interplay between genetics, neural connectivity, and information processing mechanisms. Fulcher has pioneered tools like hctsa (highly comparative time-series analysis) for analyzing large-scale neuroimaging and physiological data. Research Interests Complex systems analysis Neural dynamics and connectomics Feature-based time-series analysis Gene-brain connectivity relationships Imaging transcriptomics Recent Work Trends Fulcher's recent articles emphasize time-series analysis applications in neuroscience, including studies on anesthesia effects, brain connectivity, and criticality in neural systems. His work bridges computational methods (e.g., generative models, matrix-product states) with biological insights, often involving interdisciplinary collaborations. Key themes include understanding how brain geometry influences dynamics and translating genetic data into connectomic predictions. Grants & Projects 2024: ARC Future Fellowship for sleep research using complex systems methods 2023: Discovery Project on network neuroscience and AI integration Advising & Labs Fulcher supervises research students and directs the Dynamics and Neural Systems Group, which develops open-source tools like the theft R package for time-series analysis. His lab’s work has been featured in media outlets discussing brain structure-function relationships and comparative neurobiology.

• Control Systems
• Signal Processing
• Automation Engineering
• +۱ مورد دیگر

Professor Minyue Fu is an Honorary Professor in the School of Engineering at the University of Newcastle, Australia, specializing in Electrical and Computer Engineering. With over 30 years of research experience, he has established himself as a leading expert in control systems and signal processing, having published over 500 research papers with an H-index of 55. His academic journey began with a Bachelor's degree from the University of Science and Technology of China, followed by M.S. and Ph.D. degrees from the University of Wisconsin-Madison. Prof. Fu's research interests span a broad range of topics in control theory and signal processing. His work consistently focuses on fundamental theoretical problems with practical applications in diverse fields including power systems, sensor networks, multi-agent systems, and cyber-physical systems. He has made significant contributions to distributed control algorithms, stochastic systems, quantization effects in control, and networked systems. His recent publications (2021-2024) demonstrate continued productivity and relevance in the field, with research spanning decentralized optimal control, anomaly detection in cyber-physical systems, cart-pole control systems, and mean-field games. These works reflect his ability to bridge theoretical control concepts with practical engineering challenges, particularly in the context of modern networked and distributed systems. Fellow of IEEE (2004) Fellow of IFAC (2022) Fellow of Engineers Australia Fellow of Chinese Association of Automation (2018) Throughout his career, Prof. Fu has held significant editorial positions including Editor of IEEE Transactions on Signal Processing (2010-2014) and Associate Editor for several prestigious journals. His research has been supported by numerous grants, though specific details aren't provided in the current text. His laboratory work has focused on practical implementations of control algorithms in various systems, demonstrating the real-world applicability of his theoretical contributions. Prof. Fu has also supervised numerous students throughout his career, though specific names aren't listed in the available information.

Ellis Patrickمشاهده پروفایل

دانشیار

• Bioinformatics
• Biomedical Data Science
• Statistical Bioinformatics
• +۶ مورد دیگر

Associate Professor Ellis Patrick is an applied statistician and bioinformatician affiliated with the University of Sydney's School of Mathematics and Statistics, the Westmead Institute for Medical Research, and the Sydney Precision Data Science Centre. His research focuses on developing bioinformatics tools to analyze high-dimensional biomedical data, with applications in disease treatment, precision medicine, and understanding molecular drivers of conditions like Alzheimer’s, MS, and cancer. He holds a PhD from the University of Sydney and postdoctoral experience at Harvard Medical School and The Broad Institute. Education: PhD in statistical bioinformatics from the University of Sydney. Postdoctoral training included joint appointments at Brigham and Women’s Hospital, Harvard Medical School, and The Broad Institute of MIT and Harvard. Research Interests: Specializes in statistical bioinformatics, spatial omics, and high-dimensional data analysis, with emphasis on translational research in infectious diseases, organ transplantation, cardiovascular disorders, neurodegeneration, and oncology. His work combines computational methods with clinical data to uncover disease mechanisms and improve therapeutic strategies. Grants & Collaborations: Recent grants include PrecisionGO (MRFF-funded), White matter lesion transcriptomics (National MS Society), and studies on coronary artery disease and graft failure prediction. Collaborates widely with medical researchers, leveraging interdisciplinary teams to tackle complex health challenges. Labs & Teams: Leads the Bioinformatics Cluster at the Sydney Precision Data Science Centre, integrating computational and experimental approaches to advance precision health initiatives.

Michael Burkeمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne

• Material Science
• Mathematical Modeling
• Fractal Networks
• +۵ مورد دیگر

Professor Tony Roberts is the Head of School in the School of Mathematical Sciences at Queensland University of Technology (QUT). He holds a PhD from the Australian National University and is a Fellow of the Australian Mathematics Society. His research focuses on the interplay between material microstructure and macroscopic properties, with emphasis on topology optimization, random structure modeling (e.g., Gaussian fields, percolation models), and material property analysis such as conductivity, diffusion, and fluid flow. He develops computational methods for analyzing experimental techniques like 3D statistical reconstruction and small-angle scattering. His recent work includes optimizing piezoelectric materials for robotics, studying diffusion dynamics in fractal networks, and modeling material failure mechanisms. Key contributions span multi-functional piezoelectric components, anisotropic elastic properties of additively manufactured alloys, and fracture mechanics in perforated materials. Awards include his fellowship in the Australian Mathematics Society. Supervision interests include structural optimization, diffusion in random media, and porous material failure modeling. Education: PhD (Australian National University) Affiliations: Faculty of Science, School of Mathematical Sciences Research Themes: Material science, computational modeling, fracture mechanics, stochastic systems

• X-ray tomography
• Computed tomography
• Ghost imaging
• +۶ مورد دیگر

Dr. Andrew Kingston is a Postdoctoral Fellow in the Department of Materials Physics at the Australian National University, where he contributes to the X-ray tomography and applications research group. His work focuses on advancing imaging technologies with applications across materials science, geology, and potentially medical fields. Dr. Kingston's research spans X-ray tomography, computed tomography, ghost imaging, and digital image processing. He has developed novel techniques for high-fidelity X-ray micro-tomography and image reconstruction algorithms that address challenges in image quality, radiation dose reduction, and complex material analysis. His work bridges theoretical developments with practical applications in materials characterization and geological analysis. Analysis of Dr. Kingston's publication trend shows consistent innovation in X-ray imaging technologies, particularly in ghost imaging techniques and micro-tomography. His work demonstrates increasing sophistication in handling beam hardening effects, motion correction, and spectral information extraction, reflecting the evolving complexity of modern imaging challenges. Dr. Kingston actively collaborates with researchers across multiple institutions, contributing to the advancement of X-ray imaging methodologies. His work with the X-ray tomography and applications group at ANU has produced significant contributions to the field, particularly in developing methods that improve image quality while addressing practical constraints like radiation exposure and computational efficiency.

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

• 5G/6G Physical Layer signal processing algorithms
• Machine Learning techniques for Physical Layer signal processing
• Ultra-Reliable Low-Latency Communications
• +۵ مورد دیگر

Dr. Beeshanga Abewardana Jayawickrama is a Senior Lecturer and Data Science Engineering Course Director at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). He holds a BEng in Telecommunications Engineering (Hons I) and a PhD in Electronic Engineering (Wireless Communications) from Macquarie University, Sydney, Australia, completed in 2011 and 2015 respectively. As a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), he maintains active research and industry collaborations. His educational background includes: BEng in Telecommunications Engineering (Hons I), Macquarie University (2011) PhD in Electronic Engineering (Wireless Communications), Macquarie University (2015) Dr. Jayawickrama's research focuses on cutting-edge wireless communication technologies with particular emphasis on 5G/6G Physical Layer signal processing algorithms, Machine Learning techniques for Physical Layer signal processing, Ultra-Reliable Low-Latency Communications, Non-Terrestrial Networks, Compressed Sensing (Sub-Nyquist Sampling), and spectrum sharing. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and industry collaborations. He has published over 40 prestigious conference and journal papers while developing algorithms that have been incorporated into commercial 5G base stations. Analysis of his recent publications reveals a strong focus on satellite communications, particularly cognitive GEO-LEO satellite networks, where he explores spectrum sharing, beam design, and interference management. His research increasingly integrates machine learning techniques with traditional signal processing approaches, especially for spectrum sensing and channel estimation in next-generation wireless systems. The trend shows growing emphasis on practical implementation and experimental validation of theoretical concepts. His scientific recognition includes: Macquarie University Medal in Engineering Vice-Chancellor's Commendation for Academic Excellence Outstanding Teacher Award (2021) Multiple competitive scholarships from Macquarie University and CSIRO In terms of teaching and supervision, Dr. Jayawickrama has taught numerous undergraduate and postgraduate subjects including Advanced Telecommunication Engineering, 4G/5G Mobile Technologies, Communication Systems, and Engineering Research Thesis. His current research is supported by significant grants including the AI-SSPCAS project (CSIRO), Smart Flood and Storm Intelligence Sensing Initiative (NSW Department), and CogSat: Cognitive Satellite Radio (SmartSat CRC). Previously, he has secured research funding from Intel Corporation and Nokia Research Centre. Dr. Jayawickrama has held leadership roles including Course Director for Data Engineering since 2021 and UTS IEEE Student Branch Counsellor from 2017-2020. His industry experience includes research positions at Ericsson in Sweden (working on 5G New Radio receiver algorithms) and Intel Labs in the USA (working on Licensed Shared Access and Citizens Broadband Radio Service).