جست‌وجوی استاد و پژوهشگر

Michael Mahoneyمشاهده پروفایل

استاد

• Randomized Numerical Linear Algebra
• Machine Learning
• High Dimensional Statistics
• +۳ مورد دیگر

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

Amanda Leachمشاهده پروفایل

استاد پژوهشی

• Pediatric Ear Health
• Otitis Media Research
• Indigenous Child Health
• +۳ مورد دیگر

Professor Amanda Leach AM is a Senior Principal Research Fellow at Menzies School of Health Research, leading the Ear Health Research Program within the Child Health Division. Her research focuses on pediatric ear diseases, particularly otitis media in Indigenous populations, with a strong emphasis on vaccine development, clinical trials, and public health interventions. She leads major initiatives such as the Hearing for Learning Initiative, a partnership aiming to eliminate educational disparities caused by hearing loss in Indigenous children. Academic Title: Research Professor Affiliations: Menzies School of Health Research, Child Health Division Leadership Roles: NHMRC Centre of Research Excellence Lead (2015–2021), Joint Chair of Hearing for Learning Initiative Her research interests span otitis media pathogenesis, vaccine efficacy, and improving healthcare access for Indigenous communities. Key projects include the PREVIX vaccine trials and the Deadly Ears in Deadly Hands initiative. She has been recognized with prestigious awards including the NHMRC Senior Research Fellowship and the Order of Australia (AM). Recent work emphasizes AI-driven diagnosis advancements and systemic improvements in ear health services for remote populations. Over 186 publications highlight her contributions to global pediatric ear health research. Awards: NHMRC Senior Research Fellowship (2011) Telstra NT Business Woman of the Year (2019) Member (AM) of the Order of Australia (2020) Her grants and collaborations include funding from The Balnaves Foundation, NT Government, and Australian Government, supporting large-scale trials and community-based interventions.

Mucha Mkonoمشاهده پروفایل

مدرس ارشد

• Sustainable Tourism
• Ethical Tourism
• Wildlife Tourism
• +۳ مورد دیگر

Dr. Mucha Mkono is a Senior Lecturer at the University of Queensland Business School, specializing in sustainable and ethical tourism. Her research examines tourism's socio-cultural and environmental dimensions, including wildlife tourism, digital activism, and consumer behavior in travel contexts. Her work explores tensions between tourism development and conservation, particularly through critical analyses of power dynamics in African contexts and digital engagement with environmental issues. Research interests include: Ethical conflicts in wildlife tourism and conservation Social media's role in tourism activism and discourse Cross-cultural tourist experiences and perceptions Psychological barriers to sustainable travel behavior Publications exhibit consistent focus on tourism ethics and sustainability, evolving from wildlife and cultural studies toward digital activism and behavioral psychology. Methodologically, they demonstrate expertise in qualitative approaches including netnography, discourse analysis, and projective techniques applied to tourism contexts.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +۳ مورد دیگر

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

• Quantum Computation
• Quantum Programming
• Formal Methods
• +۶ مورد دیگر

Professor Mingsheng Ying (University of Technology Sydney) is a Distinguished Professor specializing in quantum programming , quantum verification , and the foundations of artificial intelligence . He leads the Centre for Quantum Software and Information and co-founded the Quantum Lab . His work bridges quantum computation with formal methods and reasoning under uncertainty. Education : Mathematics, Fuzhou Teachers College (1981) Research Interests His research spans: Quantum programming languages and verification techniques Model checking quantum systems and cryptographic protocols Quantum machine learning robustness Entanglement theory and distributed quantum computation Recent Publications Key contributions include: Quantum error correction verification frameworks Quantum register machine architecture Hamiltonian simulation parallelization Symbolic execution for quantum debugging Robustness tools like VeriQR Awards & Editorial Roles NSF China Distinguished Young Scholar Award (1997) China National Science Award (2008) Co-Editor-in-Chief, ACM Transactions on Quantum Computing Vice President, International Fuzzy Systems Association (2005) Leadership & Grants He oversees 14 active grants (2010–2029) from: Australian Research Council (ARC Discovery Projects) National Natural Science Foundation of China Sydney Quantum Academy Baidu Contract Research His grants fund research in quantum program verification, entanglement classification, and distributed quantum protocols.

• Multi-Object Tracking
• Signal Processing
• Computer Vision
• +۵ مورد دیگر

Dr. Changbeom Shim is a Research Fellow at Curtin University, affiliated with the School of Electrical Engineering, Computing and Mathematical Sciences under the Faculty of Science and Engineering. His role involves advancing research in multi-object tracking, signal processing, and data analysis. He holds an email address at Curtin University and maintains an active Google Scholar profile and personal website. Shim's research focuses on developing advanced algorithms for multi-object tracking using labeled random finite sets (LRFS), with applications in computer vision, bioinformatics, and geospatial systems. Key areas include trajectory estimation, sensor fusion, and scalable computational methods. His work bridges theoretical frameworks with practical tools like CellTrackVis for biological data visualization and SkyFlow for time-series analysis. His publications from 2017 to 2025 reflect a progression from foundational spatial queries in social networks to cutting-edge multi-sensor object tracking and autonomous systems. The 2024 paper on LRFS overviews highlights his leadership in this field. His research emphasizes efficiency, scalability, and cross-disciplinary applications, with contributions to both algorithmic theory and user-facing tools. Shim collaborates across disciplines, addressing challenges in sensor networks, autonomous robotics, and biological cell tracking. His work often integrates statistical methods with computational techniques, yielding innovations in filtering, visualization, and data exploration. Current trends in his publications emphasize multi-scan/multi-sensor systems and real-world applications like geotechnical property estimation and audio-visual source separation.

Wei Liuمشاهده پروفایل

دانشیار

• Natural Language Processing
• Generative AI
• Knowledge Graph Construction
• +۶ مورد دیگر

Associate Professor Wei Liu is a full-time academic in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA), leading the UWA Centre for Natural and Technical Language Processing. Her research focuses on knowledge discovery from natural language text, generative AI, semantic technologies, deep learning for knowledge graphs, and sequential data mining. She holds a PhD from the University of Newcastle (2003) and an MEng from Huazhong University of Science and Technology (China). Research Interests: Generative AI and Large Language Models (LLM) Multimodal Knowledge Graph Construction Human-AI Collaboration Systems Clinical Data Analysis (Ophthalmology) Technical Language Processing for Maintenance Data Neural-Symbolic Computation Recent Work Trends: Her publications emphasize multimodal systems integration, knowledge graph refinement, and adversarial AI applications. Notable 2025 work includes Docs2KG (human-LLM collaboration), Auto-Regressive Diffusion for 3D interactions, and spherical embeddings for logical queries. Scientific Awards: First Prize in IEEE ICDM/ICBK Knowledge Graph Contest (2020) Best Contribution to Science Award at DICTA 2020 2022 Students Choice Award for academic support Advising & Grants: Leads 25 active grants including ARC Training Centres in Critical Resources and Maintenance Data Science. Collaborates with industry partners like Aspire Digital Group and Minerals Research Institute of Western Australia. Labs/Teams: Directs UWA's Natural and Technical Language Processing Centre, focusing on projects like maintenance work order analysis, geological survey data fusion, and clinical data integration in ophthalmology.

• Mathematical physics
• Statistical mechanics
• Combinatorics
• +۴ مورد دیگر

Tim Garoni is a Professor in the School of Mathematics at Monash University, specializing in mathematical physics and statistical mechanics. His research focuses on discrete models in classical statistical mechanics, spanning mathematical physics, probability theory, combinatorics, and computer science. Education: PhD in Science, University of Melbourne (awarded December 1, 2003) BSc (Hons) in Science, University of Melbourne (awarded December 1, 2000) Professor Garoni's primary research interests lie in equilibrium models including the Ising model, self-avoiding walk, percolation, and the Fortuin-Kasteleyn random-cluster model, as well as non-equilibrium models such as exclusion processes. He has a particular focus on high-dimensional behavior of equilibrium models and properties of Markov chains arising in Monte Carlo methods for discrete statistical mechanics, including questions about mixing times of finite Markov chains. His work often bridges theoretical mathematics with practical applications in areas such as traffic flow modeling. Recent publications show a strong focus on random walks, percolation theory, and high-dimensional systems, with significant contributions to understanding critical phenomena and finite-size scaling. His research also extends to transportation networks, demonstrating the interdisciplinary nature of his mathematical approaches. Professor Garoni is actively involved in multiple research projects, including Self-Interacting Random Walks (2023-2026), Random walks with long memory (2018-2022), and the ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights (2017-2021). He is also organizing academic events such as the AIM Day: Maths AI and Energy meet Industry @ Monash in 2024. As a supervisor, Professor Garoni accepts PhD students working on statistical mechanics and discrete probability projects. His research has resulted in over 49 publications and 8 research projects, demonstrating sustained scholarly productivity in his field.

• Coding Theory
• Combinatorics
• Error-Correcting Codes
• +۲ مورد دیگر

Anina Gruica is a Research Fellow in the Algebra group at the Technical University of Denmark (DTU), specializing in coding theory with emphases on density questions, combinatorial structures of error-correcting codes, and DNA-based data storage systems. Her work bridges theoretical mathematics and practical applications in next-generation storage technologies. Her research program centers on Coding Theory and Combinatorics, investigating the geometric and probabilistic properties of codes in metric spaces. Key interests include rank-metric codes, MRD codes, and combinatorial optimization for DNA storage efficiency, where she develops novel approaches to random access and coverage depth challenges through algebraic and geometric frameworks. Dr. Gruica's publication portfolio reveals a strong interdisciplinary trajectory, with increasing focus on DNA storage applications since 2022. Her work combines deep theoretical insights in combinatorial geometry with practical storage system design, resulting in high-impact publications across SIAM journals, IEEE conferences, and arXiv preprints that address both classical coding problems and emerging biological storage constraints. She has received competitive recognition including: ALCOCRYPT conference travel award (February 2023) DIAMANT PhD travel grant (2000 EUR, January 2023) DIAMANT visitor grant (1900 EUR, October 2021) SIAM Travel Award (August 2021) As co-organizer of the Postgraduate International Coding theory Seminar (PICS), Dr. Gruica actively mentors junior researchers while maintaining extensive collaborations with A. Ravagnani, J. Sheekey, and E. Yaakobi. Her conference presentations at venues like ISIT and SIAM AG23 demonstrate her leadership in translating theoretical advances to storage applications. She contributes to DTU's Algebra group research ecosystem, focusing on algebraic structures for coding theory and cryptography. Her current projects, evident from 2024-2025 preprints, explore combinatorial geometry for DNA storage efficiency and advanced rank-metric code constructions, positioning her at the forefront of coding theory's application to biological data systems.

Pavel Krivitskyمشاهده پروفایل

پژوهشگر

• Social Networks
• Computational Statistics
• Infectious Disease Epidemiology
• +۷ مورد دیگر

Dr. Pavel Krivitsky is a researcher at the School of Mathematics and Statistics of the University of New South Wales (UNSW) Sydney . His work focuses on developing methods and models for analyzing complex network data, with applications in epidemiology, social sciences, and national security. He maintains popular R packages like ergm and contributes to the Statnet Project and uDASH (UNSW Data Science Hub). PhD in Statistics, University of Washington (2009) MS in Statistics, University of Washington (2006) BS in Biometry and Statistics, Cornell University (2003) His research spans social network analysis , computational statistics , and stochastic modeling , particularly for unusual or indirectly observed network data. Recent publications highlight advancements in multi-criteria recommender systems, network sampling techniques, and corrections for international datasets. Scientific Awards : 2024 Mid-career Impact Award for Fundamental Research (UNSW Science) 2023 Journal of the American Statistical Association Discussion Paper 2019 Freeman Award for social structure research 2019 Richards Award for open-source software development Dr. Krivitsky supervises PhD students Saman Forouzandeh (2021–2025) and Yun Pan (2022–). He teaches courses like MATH3831 (Statistics in Social and Market Research) and DATA5002 (Data Visualisation), previously instructing MATH5855 (Multivariate Analysis) and DATA1001 (Fundamentals of Data Science).

Jason Atnipمشاهده پروفایل

پژوهشگر

• Dynamical Systems
• Ergodic Theory
• Random Dynamical Systems
• +۵ مورد دیگر

Dr. Jason Atnip is a researcher in the School of Mathematics and Statistics at UNSW Sydney. His work focuses on applied and statistical aspects of dynamical systems and ergodic theory, using functional analytic and spectral methods to prove stochastic, fractal geometric, and extreme value theoretic properties of dynamical systems. His research has applications in ocean and atmospheric models, particularly in understanding extreme weather events. He also engages in mathematical outreach to high school students. University: UNSW Sydney School: School of Mathematics and Statistics Academic Rank: Researcher Email: j.atnip@unsw.edu.au Research Interests: Dr. Atnip's research spans dynamical systems, ergodic theory, random dynamics, and open systems, with a focus on spectral techniques. His work connects mathematical theory to real-world applications in climate modeling and extreme event prediction. He also develops outreach activities to demonstrate the beauty of mathematics to younger audiences. Publication Trends: Dr. Atnip's recent articles (2025–2018) emphasize extreme value theory, thermodynamic formalism, random systems, and fractal geometry. His work often integrates spectral methods with applications in geophysical models. Location: He is based at The Red Centre, UNSW Sydney, NSW 2052, Room 2075.

• Combinatorics
• Graph Theory
• Asymptotic Enumeration
• +۷ مورد دیگر

Brendan McKay is a Professor in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of mathematics and computing. His work is deeply rooted in combinatorics, graph theory, and probabilistic methods in discrete structures. His research interests span a wide range of topics in discrete mathematics, including random graphs , asymptotic enumeration , Hamiltonian cycles , planar and bipartite graphs , matrix theory , and computational combinatorics . His work often combines theoretical depth with algorithmic applications, particularly in the analysis of complex networks and discrete systems. The recent publications (2015–2025) reflect a sustained focus on asymptotic enumeration techniques, structural properties of graphs, and algorithmic challenges in graph isomorphism and distance queries. Key themes include the use of probabilistic tools (e.g., cumulant expansions, martingales), enumeration under degree constraints, and symmetry-preserving operations on maps. His collaborations frequently involve researchers such as Mikhail Isaev, Catherine Greenhill, and Qing Wang. Scientific Awards and Recognitions: Fellow, Australian Mathematical Society (2000 → …) Fellow, Australian Academy of Science (1997 → …) McKay has led and co-led several major research projects, including Deep Learning for Graph Isomorphism and Hypergraph models for complex discrete systems , demonstrating leadership in both theoretical and applied directions. He has not explicitly listed advisees, but his role as Principal Investigator (PI) on multiple projects indicates active mentorship and grant leadership. His work often involves interdisciplinary applications in bioinformatics and network science. He is associated with research groups and projects focused on graph algorithms , random discrete structures , and computational combinatorics , often in collaboration with the mathematical sciences community at ANU and beyond.

Donald Cartwrightمشاهده پروفایل

پژوهشگر

• Harmonic Analysis
• Group Theory
• Probability
• +۳ مورد دیگر

Donald Cartwright is an Honorary Reader in the School of Mathematics and Statistics at the University of Sydney. He is affiliated with the Carslaw Building (Room 523) and focuses on harmonic analysis, group actions on trees and affine buildings, and random walks on graphs and groups. His research bridges abstract mathematical structures with probabilistic methods, contributing to the understanding of complex dynamical systems. Email: donald.cartwright@sydney.edu.au Research Areas: Harmonic Analysis, Group Theory, Probability

• Construction Safety Management
• Structural Health Monitoring
• Artificial Intelligence Applications in Civil Engineering
• +۳ مورد دیگر

Associate Professor Nima Gorjian at UniSA STEM, University of South Australia, is a leading researcher in construction safety management, structural health monitoring, and AI applications in civil engineering. His work spans knowledge-driven approaches, ontology development, and renewable energy systems. Key research areas include: Construction safety management and hazard identification Ontology and knowledge graph modeling Self-sensing materials for infrastructure monitoring Energy optimization in wastewater networks AI-driven fault diagnostics for machinery Recent publications focus on automation of job hazard analysis, smart composite materials, and predictive maintenance strategies. His affiliations include collaborations with SA Water Corporation and Queensland University of Technology.

Sarat Mokaمشاهده پروفایل

مدرس

• Probability Theory
• Data Science
• Statistics
• +۲ مورد دیگر

Sarat Moka is a Lecturer in the School of Mathematics & Statistics at The University of New South Wales and holds an Honorary Lecturer position at Macquarie University. His research spans Probability Theory, Data Science, Statistics, Deep Learning, and Monte Carlo Simulation. Education: PhD (Tata Institute of Fundamental Research, 2017), Master of Engineering (Indian Institute of Science, 2008), Bachelor of Engineering (Andhra University, 2006) Previous Roles: Research Fellow at Macquarie University (2021–2023), ACEMS Postdoctoral Research Fellow at The University of Queensland (2017–2021), Scientist at Indian Space Research Organization (2008–2010) His research integrates theoretical and applied approaches, focusing on rare event simulation, optimization algorithms, and statistical modeling. Recent publications emphasize continuous optimization for machine learning tasks (e.g., best subset selection, change point detection) and Monte Carlo methods. Scientific awards include the prestigious ACEMS Postdoctoral Research Fellowship. His collaborative work involves institutions like UNSW, Macquarie University, and The University of Queensland.