جستجوی استاد راهنمای دانشگاهی

• 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.

Arnoldo Frigessiمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

• Survival Analysis
• Nonparametric Regression
• Semiparametric Regression
• +۴ مورد دیگر

Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science

• Probability theory
• Statistics
• Dependence modelling
• +۵ مورد دیگر

Martial Longla is an Associate Professor of Mathematics in the Department of Mathematics at the University of Mississippi, affiliated with the College of Liberal Arts. He has been a faculty member since August 2013 and was promoted to Associate Professor in 2018. His research is centered on probability, statistics, and dependence modeling, with applications across disciplines. His research interests include: Probability theory and limit theorems for dependent data Dependence modelling using copulas Bayesian analysis Statistical distributions and applications Dr. Longla has been supported by prestigious grants and fellowships including funding from the NSF (via Dr. Magda Peligrad), the Laws Fellowship, the University Research Council, and the Taft Center at the University of Cincinnati. He has received 4 summer research grants during his first five years at OleMiss. His recent scholarly focus includes collaborative work with institutions in Cameroon, particularly the University of Yaounde I and the University of Maroua, initiated during his 2019–2020 sabbatical. He is actively involved in mentoring, currently advising four PhD students across institutions. He founded and runs Hope Bamboutos , a charity dedicated to increasing access to STEM education for underrepresented youth in rural Africa. His public engagement includes contributions to political analysis in Cameroon via KMD Radio and the newspaper Quotidien Le Jour . His scientific awards include: Outstanding Beginning Doctoral Student Best Graduate Student Poster Presenter University and city awards for leadership in student rights advocacy Recognition for promoting African culture in Russia Dr. Longla maintains an active research profile and has delivered lecture series in Cameroon during his sabbatical. He is open to interdisciplinary collaborations and continues to publish on copula-based Markov chains, dependence coefficients, and central limit theorems.

• Computational Mechanics
• Structural Crashworthiness
• Additive Manufacturing
• +۵ مورد دیگر

Professor Qing Li is a faculty member at the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney. He obtained his PhD from Sydney in 2000, underwent postdoc training at Cornell University (2000-2001), and held academic roles at James Cook University (2004-2006). He joined Sydney in 2006 via a Sesqui senior lectureship, becoming Associate Professor in 2010 and Professor in 2014. He served as Director of Postgraduate Studies (2007-2012) and Director of Biomedical Engineering (2017-2019). Education: PhD (University of Sydney, 2000), ME (UTS), ME (Hunan) His research focuses on computational design, multidisciplinary optimization of nonlinear and time-dependent multifunctional materials, and biomedical applications. Key areas include additive manufacturing , biomechanics , and machine learning in structural reliability. Recent publications emphasize fracture modeling in biomaterials, reliability analysis, and tissue scaffold design. Recent publications highlight Bayesian learning for robotic systems, probabilistic transformation in reliability analysis, and phase field fracture models for additively manufactured composites. Collaborations with industry partners like Cochlear and Stryker span ARC , NHMRC , and MRFF projects. Awards: Clarivate Highly Cited Researcher (2020), Top 50 Australia Research Leader (2020), APACM Computational Mechanics Award (2016) Fellowships: ARC Future Fellow (2013-2017), ARC Australian Postdoctoral Fellow (2001) He supervises PhD students in projects like epidermal electrodes , Silver Diamine Fluoride remineralization , and virtual surgical planning . His leadership extends to the Centre for Advanced Materials Technology and editorial roles in computational methods journals.

Yuanchang Xieمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic safety
• Traffic operations and simulation
• +۵ مورد دیگر

Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +۷ مورد دیگر

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

Daeyoung Kimمشاهده پروفایل

استاد

• Statistics
• Asymmetric Dependence
• Categorical Data Modelling
• +۴ مورد دیگر

Daeyoung Kim is a Full Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Statistics from The Pennsylvania State University (2008), and M.S. and B.S. degrees in Statistics from Korea University (2002, 2000). His research focuses on statistical dependence modeling via copulas, likelihood inference in categorical data, mixture modeling, and interdisciplinary applications in food science and environmental engineering. He has authored/co-authored numerous publications, including a book on Direction Dependence in Statistical Modeling . His work spans statistical methodology development and collaborative research in environmental and biomedical sciences. Education: Ph.D. in Statistics, The Pennsylvania State University, 2008 M.S. in Statistics, Korea University, 2002 B.S. in Statistics, Korea University, 2000 Research Interests: Kim’s work emphasizes asymmetric dependence analysis, categorical data modeling, and mixture models. He applies these methods to food science (e.g., lipid metabolism) and environmental challenges (e.g., cyanobacterial bloom modeling). His statistical contributions include copula-based regression and likelihood inference techniques for incomplete data. Collaborative Projects: Kim has contributed to studies on antimicrobial additives’ effects on colonic inflammation, lipid metabolism modulation in disease models, and the role of dietary fats in tumorigenesis. His interdisciplinary collaborations span environmental engineering, nutrition science, and biomedical research. Awards: Noted for award-winning teaching and research excellence, though specific honors are not detailed in the provided text.

Boris Berangerمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Environmental Extremes
• Spatial Statistics
• +۳ مورد دیگر

Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.

Elena Di Bernardinoمشاهده پروفایل

استاد

• Spatial Statistics
• Extreme Value Theory
• Environmental Risks
• +۵ مورد دیگر

Elena Di Bernardino is a full Professor at Université Côte d'Azur, leading research in spatial statistics, extreme value theory, and environmental risk modeling. She holds the prestigious Junior membership at the Institut Universitaire de France (2025-2030) and serves as Head of the Probability & Statistics team at the J.A. Dieudonné Laboratory. Her work integrates stochastic geometry, copula theory, and machine learning to address multidisciplinary challenges such as climate risk assessment and wildfire prediction. **Education**: Dual Italian-French national, Elena earned a PhD in Applied Mathematics from Université Lyon 1 (2011) and habilitation (HDR) from Université Pierre et Marie Curie (2017). Her academic journey includes postdoctoral research in Venezuela and Canada, alongside engineering studies at the Polytechnic of Milan. **Research**: Key focus areas include spatio-temporal dependence modeling, multivariate risk measures, and geometric inference on random fields. She leads major national projects like the ANR COCHAIRS initiative under France's PEPR Risques program, and collaborates internationally via Simons-CRM and MITACS grants. **Awards**: Recognitions include the Chercheurs Simons-CRM Prize (2019), PEDR grants (2015, 2019, 2023), and continuous funding through EU and French National Research Agency (ANR) programs. **Leadership**: Currently chairs the Scientific Council of the J.A. Dieudonné Laboratory and oversees strategic initiatives at the 3IA Côte d'Azur Institute. She actively mentors 8 current PhD/postdoc researchers and has supervised over 14 completed doctoral projects. **Teaching**: Teaches advanced courses in extreme value theory, risk modeling, and environmental statistics at both undergraduate and graduate levels.

• Wireless Networks
• Network Scheduling
• Sufficient Dimension Reduction
• +۴ مورد دیگر

Bin Li is an Associate Professor at the School of Electrical Engineering and Computer Science. His research focuses on wireless networks, network scheduling, sufficient dimension reduction, and statistical inference. NSF-funded projects: EAGER: TaskDCL, CAREER: Wireless Collaborative Mixed Reality Networking, CNS Core: Scalable Algorithms for Virtual Reality Over Wireless Networks. Grants include foundational work in AI-driven task training, geospatial digital twins, and joint communication-computation-learning systems. His research spans wireless scheduling algorithms, data freshness optimization, and nonlinear sufficient dimension reduction. Recent work explores Fréchet regression, functional graphical models, and kernel-based hypothesis testing. Articles highlight interdisciplinary applications in computer science, statistics, and mathematics. Statistical methods dominate his contributions, including Bayesian credible sets, copula models, and additive independence frameworks. Collaborations extend to multi-source genomic data analysis and immersive educational platforms via augmented reality. With an h-index of 16 and 74 research outputs, Bin Li’s expertise intersects wireless network optimization and statistical learning. His work addresses challenges in edge computing, cloud offloading, and cyber-physical systems through algorithmic innovation and theoretical rigor.

Johan Segersمشاهده پروفایل

استاد

• Extreme Value Theory
• Dependence Modelling via Copulas
• Statistical Learning
• +۵ مورد دیگر

Johan Segers is a Full Professor at the Department of Mathematics, KU Leuven , and a Visiting Professor at the Institut de statistique, biostatistique et sciences actuarielles (UCLouvain). His research bridges extreme value theory , copulas , statistical learning , and optimal transport , with applications in finance, environmental risk analysis, and multivariate statistics. Research Trends : Focus on extremal dependence, vine copulas, and Monte Carlo methods. Recent Article Trends : Combines extreme value theory with graphical models and optimal transport for high-dimensional data analysis. Awards & Honors : Prix Adolphe Wetrems (2012–2013) from the Académie Royale des Sciences, des Lettres et des Beaux-arts de Belgique Fellow of the Institute for Mathematical Statistics (IMS) Elected Member of the International Statistical Institute (ISI) Editorial Roles : Associate Editor for Advances in Applied Probability , Bernoulli , Electronic Journal of Statistics , and others. He also contributes to software development, including the R-package spatialTailDep .

• Biostatistics
• Epidemiology
• Infectious Disease Modeling
• +۶ مورد دیگر

Prof. Bouchra Nasri is a Full Professor of Biostatistics at the University of Montreal's Department of Social and Preventive Medicine. She leads the Data Informatics Center of Epidemiology (PathCheck) and co-directs the Réseau de santé numérique. Her work focuses on infectious disease modeling, climate change impacts on public health, and statistical theory for complex data. She holds a FRQS Junior 1 Scholarship in AI-driven health research and has secured over 10 grants from agencies like NSERC and CIHR. Nasri has supervised multiple graduate students and pioneered studies on Lyme disease surveillance via social media, dengue fever modeling, and SARS-CoV-2 seroprevalence in pediatric populations. Her research integrates quantitative methods, multivariate analysis, and AI to address global health challenges. Educations: PhD in Environmental Engineering and Statistics (INRS-ETE, 2017), Postdoc in Statistics at McGill/HEC Key Affiliations: CReSP, CRM StatLab, RRSPQ Her research portfolio includes projects on climate change interventions for urban health equity, mathematical models for antimicrobial resistance, and integrative modeling of emerging infections. She has authored/co-authored over 30 peer-reviewed articles and collaborates internationally on One Health initiatives. Awards: FRQS Junior 1 Scholarship, NSERC Discovery Grants Labs: Data Informatics Center for Epidemiology, Réseau Santé Numérique

Sebastian Engelkeمشاهده پروفایل

دانشیار

• Extreme Value Theory
• Spatial Statistics
• Graphical Models
• +۵ مورد دیگر

Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems. Affiliations: University of Geneva, Research Center for Statistics Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018) Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen. Research Interests Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include: Statistical modeling of multivariate extremes Causal discovery in heavy-tailed distributions Applications in environmental science and climate informatics Key Contributions Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association . Awards & Recognition Lambert Award (2021) Gumbel Lecture (2023) Fields Research Fellowship (2019) Teaching & Supervision Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).

• Time Series Analysis
• Extreme Value Theory
• Financial Modelling
• +۵ مورد دیگر

Thomas Fung is a Senior Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences, serving as Director of Education and Deputy Course Director for the BSc program. He holds a PhD and B.Sc (Advanced Mathematics) from the University of Sydney. His research focuses on integer-valued time series analysis, extreme value theory, and financial modelling, addressing complex data issues like zero-inflation and heavy-tailedness. He teaches courses such as STAT1378 (Coding & Communication in Statistics) and actively supervises postgraduate students on projects like semiparametric models and climate data analysis. Key research projects include flexible models for count time series and improving financial time series analysis via Variance-Gamma processes. His work has been published in journals like Statistics and Probability Letters and Earth and Space Science . Awards include the 2023 Faculty of Science and Engineering Excellent Award for Educational Leadership and multiple Teaching Development Grants. He serves on editorial boards for the Australian and New Zealand Journal of Statistics and R Journal , and holds leadership roles in professional societies like the Statistical Society of Australia NSW Branch. His interdisciplinary collaborations span environmental science, public health, and criminology, reflecting his broad statistical expertise.