جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Statistical Learning Theory
• Optimization
• +۳ مورد دیگر

Professor Yiming Ying is a faculty member in the Faculty of Science at the University of Sydney, where he joined in December 2023. Previously, he held tenured positions at SUNY Albany (Departments of Mathematics & Statistics and Computer Science) and was a Lecturer at the University of Exeter. He completed his PhD in Mathematics at Zhejiang University (2002) and postdoctoral training at CityU Hong Kong, UCL, and University of Bristol. Research Focus His research spans statistical learning theory, optimization algorithms, trustworthy AI, and data science mathematics. Key applications include cancer informatics for early detection. His work aligns with Faculty research strengths in Data and Decisions and Decision-Making for a Sustainable Future. Recent Research Trends Analysis of recent publications shows strong focus on theoretical foundations of machine learning: differential privacy, fairness algorithms, optimization methods for AUC maximization, generalization guarantees, and robust learning techniques for adversarial settings and biological data. Awards and Honors SUNY Chancellor’s Award for Excellence (2023) University at Albany Presidential Research Award (2022) University of Exeter Merit Award (2012) Grants and Advising Significant funding includes current ARC DP250101359 (2025-2028) and multiple past NSF grants. He founded the UALBANY Machine Learning Group and currently advises PhD student Peilin LIU on operator learning.

• Applied Statistics
• Big Data
• Economic Models and Forecasting
• +۲ مورد دیگر

Prof Terry O'Neill serves as Executive Dean at the Bond Business School , Bond University, while also holding the title of Professor and Director at the Centre for Data Analytics. His research spans applied statistics , big data analytics , and financial modeling , with over 80 publications (20% in A* journals). Grants: Lead investigator on five ARC Discovery/Linkage grants totaling $2.99M, including studies on retirement savings, climate change modeling, and financial crisis resilience. Research Trends: Focus on financial literacy , retirement economics , and myelopoiesis , bridging finance with computational biology through collaborations with his spouse Helen O'Neill. Academic Leadership: Established Bond University's University Centre in Actuarial and Financial Big Data Analytics to elevate institutional research prominence in data science.

• Operations Research
• Management Science
• Optimization Algorithms
• +۶ مورد دیگر

Dr. Dmytro Matsypura is an Associate Professor in the Discipline of Business Analytics at the University of Sydney Business School. He holds a BA (Hons) from Kyiv Polytechnic Institute (KPI), an MS (Hons) from KPI, and a PhD from the University of Massachusetts Amherst. His research focuses on optimization methodologies, network science, and their applications in finance, transportation, ecology, and graph theory. He is a recipient of multiple teaching awards, including the Wayne Lonergan Outstanding Teaching Award (Early Career) in 2010. Education: PhD in Management Science, University of Massachusetts Amherst (2006) MS (Hons) in Information Systems, Kyiv Polytechnic Institute (2000) BA (Hons) in Business Administration, Kyiv Polytechnic Institute (1998) Research Interests: Dr. Matsypura’s work spans operations research and management science, with a focus on mathematical optimization and network science. His methodological contributions include developing efficient optimization algorithms, while his applied research addresses real-world challenges in finance, engineering, and ecology. Notable applications include wildfire fuel management, portfolio margining, and credit card fraud detection via graph-based models. Awards and Recognition: Teaching Excellence Award (2008, 2013, 2018) Wayne Lonergan Outstanding Teaching Award (Early Career) (2010) Grants and Projects: Current projects include Bushfire Analytics: Optimization of Fuel Reduction (2023, ARC Discovery Project). His research frequently integrates interdisciplinary collaborations, such as applying graph theory to biomedical problems and cybersecurity. Labs/Teams: Active in the Sydney Environment Institute, contributing to projects at the intersection of analytics and sustainability. Collaborates with industry on fraud detection and supply chain optimization.

• Robust Statistics
• Dimension Reduction
• Meta-Analysis
• +۳ مورد دیگر

Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۷ مورد دیگر

Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.

• Applied Mathematics
• Numerical and Computational Mathematics
• Pure Mathematics
• +۷ مورد دیگر

Dr. John Shepherd is an Associate Professor in the School of Science at RMIT University, specializing in applied mathematics, numerical and computational mathematics, and their applications in engineering and environmental systems. His research focuses on analyzing nonlinear problems, particularly in bioreactor dynamics, fluid mechanics, and nuclear energy policy. He has contributed to studies on anaerobic digestion models, reactor stability, and the role of nuclear energy in climate change mitigation. Education: Doctorate in Applied Mathematics (not explicitly stated in text, inferred from title). His work bridges theoretical analysis and real-world applications, such as optimizing methane production in waste digesters and evaluating environmental policies for nuclear energy. He actively supervises research projects, including the analysis of anaerobic digester dynamics. Dr. Shepherd’s publications span interdisciplinary topics, emphasizing the intersection of mathematics, engineering, and environmental science. He engages with policy discussions on nuclear energy’s role in decarbonization, advocating for its integration into clean energy strategies. His research highlights the importance of multiscale analysis in understanding complex systems like bioreactors and fluid flows. Collaborations involve industry and international institutions, reflecting his commitment to practical solutions for sustainability challenges.

• AI for Software Engineering (AI4SE)
• Machine Learning
• Data Mining
• +۵ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.

James Saundersonمشاهده پروفایل

مدرس ارشد

• Convex Optimization
• Semidefinite Programming
• Polynomial Optimization
• +۴ مورد دیگر

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

Fabio Lucianiمشاهده پروفایل

استاد

• genomics
• immunology
• computational biology
• +۶ مورد دیگر

Fabio Luciani is a Professor in Systems Immunology and Machine Learning at the School of Medical Sciences at UNSW Sydney, with visiting fellow positions at the Garvan Institute for Medical Research and Weill Cornell College of Medicine NY, USA. His interdisciplinary research program integrates immunology, genomics, and computational approaches to develop novel immunotherapies for cancer and autoimmune diseases. With a background spanning physics, theoretical biology, and biophysics, Luciani leads an interdisciplinary team with expertise in immunology, mathematical modeling, statistics, and bioinformatics. His research focuses on T cell responses using single-cell genomic technologies to understand immune responses in viral infections, autoimmunity, and CAR T cell therapies. He has developed landmark bioinformatic methods for combining single-cell transcriptome data with antigen receptor sequences and computational models for haplotype reconstruction. Luciani's work demonstrates a strong emphasis on translating molecular and genomic discoveries into clinical interventions, particularly in predicting side effects and identifying solutions for unmet clinical needs in immunotherapy. His research spans multiple collaborative projects including single-cell multi-omics analysis in coeliac disease with Prof Chris Goodnow's team at the Garvan Institute, and CAR T cell therapy response studies with clinicians at Westmead Hospital and Weill Cornell. His scientific contributions include over 140 peer-reviewed publications and successful acquisition of more than $20 million in research funding from organizations including ARC, NHMRC, JDRF, NIH, and private industry partners. He serves as Principal Investigator of the UNSW Future Institute of Cellular Genomics, which has secured $4.5 million over seven years for single-cell technologies in cellular genomics. As an academic supervisor, Luciani currently mentors 3 PhD students, 2 honours students, and 2 postdoctoral researchers, welcoming applications from students interested in bioinformatics, statistics, data science, immunology, and genomics. His work bridges theoretical approaches with experimental immunology to advance precision medicine and transform current immunotherapies into more precise and accessible solutions.

• Applied Statistics
• Big Data Analytics
• Economic Modeling
• +۴ مورد دیگر

Terence O'Neill serves as Professor and Director of the Centre for Data Analytics at Bond University. He established the University Centre in Actuarial and Financial Big Data Analytics to position Bond University as a significant player in the Big Data and Analytics research space. His research interests span applied statistics, big data, economic models, forecasting, and finance. He has published widely in Statistics, Applied Mathematics, Biostatistics, Bioinformatics and Finance, producing over 80 articles with at least 20% in A* ranked journals. His interdisciplinary work uniquely bridges financial analytics with biomedical research, particularly in spleen biology and hematopoiesis. Analysis of his recent publications reveals a strong focus on financial wellbeing among elderly Australians, housing tenure impacts, and financial literacy. His work demonstrates methodological rigor in statistical modeling applied across diverse domains from retirement planning to cellular biology. Professor O'Neill has secured substantial research funding, including ten large Australian Research Council grants totaling $2.99 million, with nine since 2003. He served as lead Chief Investigator on five of these projects and collaborated on seven other externally funded grants. His current research projects include an ARC Linkage Grant on retirement savings drawdown ($267,230), a Discovery Grant on climate change investigations ($300,000), a study of Australian Government securities market ($430,000), and research on political connections ($210,000). His future research interests will focus on data analytics/big data, both theory and application.

Pengyi Yangمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Stem Cell Biology
• Bioinformatics
• +۴ مورد دیگر

Pengyi Yang is an Associate Professor and University of Sydney Robinson Fellow at the School of Mathematics & Statistics, University of Sydney. He leads the Computational Systems Biology group at the Charles Perkins Centre and holds a conjoint appointment as Unit Head of Computational Systems Biology at the Children's Medical Research Institute (CMRI). His research focuses on computational approaches to understand trans-regulatory networks in stem cells and their applications in regenerative medicine. Yang holds a Ph.D. and has been recognized with awards such as the National Stem Cell Foundation Metcalf Prize (2021). His research spans computational systems biology, machine learning for bioinformatics, and spatial/single-cell omics analysis. Key projects include modeling pluripotency transitions, developing stem cell-derived organoids, and creating computational tools for phosphoproteomics and multi-omics integration. Collaborations include international initiatives like the Laboratory of Data Discovery for Health (InnoHK). Yang advises multiple PhD students and leads grants on topics like stem cell-derived brain organoids and embryonic development modeling. His lab develops tools like Cepo, PhosR, and CiteFuse for omics data analysis. He teaches data science and molecular systems biology at the University of Sydney.

Azizur Rahmanمشاهده پروفایل

استاد

• Data Science
• AI and Machine Learning
• Statistics
• +۷ مورد دیگر

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.

Thomas Britzمشاهده پروفایل

مدرس ارشد

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +۵ مورد دیگر

Dr. Thomas Britz is a Senior Lecturer at the School of Mathematics and Statistics, UNSW Sydney . He is a member of the Combinatorics Research Group and serves as Chief Editor of Parabola and Managing Editor for the Australasian Journal of Combinatorics . PhD in Mathematics (Aarhus University, 2003) Research: Discrete Mathematics, Combinatorics, Graph Theory, Matroid Theory, Coding Theory, Design Theory, and applications to renewable energy, bioinformatics, criminology, and more His research has been supported by grants including an ARC Discovery Grant and international fellowships. He has supervised numerous PhD, Master’s, and Honours students across diverse projects. Recent publications focus on harmonic Tutte polynomials, group divisible designs, and closeness centrality in graphs, reflecting his expertise in foundational and applied combinatorics. Awards : Vice-Chancellor’s Award for Student Wellbeing (2021), Science Dean's Award for Excellence in Student Wellbeing (2021), Vice-Chancellor's Award for Contributions to Student Learning (2015) He contributes to outreach activities , editorial service, and university committees, including the Education Excellence Committee and Sustainability Committee at UNSW.

Pierre Lafaye de Micheauxمشاهده پروفایل

دانشیار

• Applied Statistics
• Biostatistics
• Statistical Theory
• +۱۱ مورد دیگر

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Darfiana Nurمشاهده پروفایل

مدرس ارشد

• Nonlinear time series analysis
• Bayesian inference
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Dr Darfiana Nur is an Adjunct Senior Lecturer at the University of Western Australia (UWA) , affiliated with the School of Physics, Mathematics and Computing and the Department of Mathematics and Statistics . She has a rich academic background with experience at Flinders University and Curtin University, and a strong record in teaching and research. Education: BMath – University of Gadjahmada (UGM), Indonesia (1988) MSc (Research) – University of Western Australia (1993) PhD in Statistics – Curtin University of Technology (1999) Research Interests: Her research focuses on nonlinear time series modelling , particularly Smooth Threshold Autoregressive (STAR) and regime-switching volatility models. She applies Bayesian MCMC algorithms and Hidden Markov Models (HMMs) in genetics and bioinformatics, and explores deep learning for time series forecasting. Her work spans DNA sequence modelling , Bayesian inference , and machine learning applications in complex data environments. She has published extensively in top-tier journals and contributed to both theoretical and applied statistics. Scientific Contributions: She has authored 23 journal publications , 10 refereed conference papers , and 35 non-refereed conference papers . Her recent work includes advanced statistical modelling in GARCH, BEKK, and LASSO frameworks. Supervision: She has supervised 7 PhD students , 1 MMath(Research) , 9 Masters by Coursework , and 7 Honours students across UWA, Flinders University, and the University of Newcastle. Teaching: Her teaching portfolio includes courses in Data Science , Mathematical Statistics , Time Series Analysis , and Bayesian Inference , delivered at both undergraduate and postgraduate levels.