جست‌وجوی استاد و پژوهشگر

Carl Donovanمشاهده پروفایل

مدرس

• Machine Learning
• Marine Biology
• Ecological Modeling
• +۳ مورد دیگر

Dr Carl Donovan is an Honorary Lecturer in Statistics at the University of St Andrews' School of Mathematics and Statistics. His research focuses on applying statistical and machine learning methodologies to ecological and environmental challenges, including marine conservation, crowd counting, and fisheries science. He has collaborated internationally on projects such as offshore wind energy impact assessments and multilingual geopolitical analysis using social media data. Donovan holds a PhD and has supervised at least one PhD student, Haoyu Liu. His work integrates computer vision techniques with ecological modeling, evidenced by publications in journals like ICES Journal of Marine Science and Ecological Informatics . He contributed to the SAFESIMM software project addressing environmental risks of marine renewable developments. Donovan’s research also extends to species taxonomy, including groundbreaking work on South Asian river dolphin classification. He has been affiliated with institutions such as the Mathematical Institute at the University of St Andrews (North Haugh, KY16 9SS) and collaborates with organizations like the British Antarctic Survey and international marine research groups. His research aligns with UN Sustainable Development Goals related to life below water and responsible consumption.

Yuankai Qiمشاهده پروفایل

مدرس

• Vision-and-Language for Robot Action
• Video Captioning
• Medical Image Processing
• +۸ مورد دیگر

Dr. Yuankai Qi is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing and an Adjunct Lecturer at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on Vision-and-Language for Robot Action, Video Captioning, Medical Image Processing, and Movie Dubbing. He holds a PhD from Harbin Institute of Technology, specializing in visual object tracking, and has won awards such as the DAVIS 2017 Challenge and CAAI Outstanding Doctoral Dissertation. He has published over 60 papers in top-tier venues like CVPR and NeurIPS, and serves as an Area Chair for NeurIPS, ICCV, and ICLR. His current projects include AI-enhanced CT image analysis and generative AI for banking solutions. Dr. Qi collaborates with the Data Horizons, Smart Green Cities, and Frontier AI Research Centres. He teaches COMP 3420 (AI for Text and Vision) and COMP 2250 (Data Communication). His work spans robotics navigation, medical imaging, and video anomaly detection, recognized globally as a Top 2% Scientist in 2024. Education: PhD (Harbin Institute of Technology, China) Key Awards: ACM Multimedia 2024 Best Paper Award, ICPR 2024 Best Student Paper Award Grants: Projects on AI in healthcare and banking (2024–2028) Labs/Teams: Data Horizons Research Centre, Smart Green Cities Research Centre

Dinusha Vatsalanمشاهده پروفایل

مدرس ارشد

• Privacy-Preserving Technologies
• Record Linkage
• Data Mining
• +۴ مورد دیگر

Dr. Dinusha Vatsalan is a Senior Lecturer in Cyber Security at the School of Computing, Macquarie University. She joined in May 2022 and holds a PhD in Computer Science from Australian National University and a BSc (Hons) in Information and Communication Technology from the University of Colombo School of Computing, Sri Lanka. Previously, she was a Research Scientist at Data61, CSIRO. Her research focuses on privacy-preserving technologies, including record linkage, data mining, machine learning, and privacy risk quantification. She has authored over 60 scientific articles and received notable awards such as the Australian government’s Endeavour Postgraduate Research Award, Ruby Payne-Scott Award (CSIRO), and the Manthan Award South Asia (2009). Her work spans privacy-preserving data sharing, adversarial machine learning defenses, and secure data integration across domains like healthcare and mining. Recent publications highlight contributions to trusted data sharing in mineral exploration, privacy-preserving deep learning models, and adversarial training defenses. She is affiliated with the Future Communications Research Centre and actively collaborates internationally on cyber security and privacy initiatives.

Pratika Dayalمشاهده پروفایل

دانشیار

• Galaxy Formation
• Epoch of Reionization
• 21cm Cosmology
• +۳ مورد دیگر

Pratika Dayal is an Associate Professor & Rosalind Franklin Fellow at the Kapteyn Astronomical Institute, University of Groningen. Her research focuses on galaxy formation in the first billion years of the Universe, cosmic reionization, and dark matter. She leads major projects like the UNCOVER survey with the James Webb Space Telescope (JWST) and collaborates internationally with initiatives like the Square Kilometre Array (SKA). Education: Doctorate in Astrophysics from SISSA, Trieste, Italy (2006-2010). Postdoctoral roles at Leibniz Institute (Potsdam), University of Edinburgh, and Durham University. Research Interests: Early galaxy formation, interplay between galaxy evolution and reionization, astrobiological habitability, and dark matter's role in cosmic structure. Her work combines theoretical models, simulations, and observational data from Hubble, ALMA, and JWST. Grants & Awards: ERC Starting Grant (2016), NWO VIDI Grant (2018), Addison Wheeler Fellowship (2015). She chairs astronomy committees and is a member of the International Astronomical Union. Teaching: Teaches 'Galaxy Formation and Evolution' and 'Quantum Universe Seminar' at the Master's level. Supervised over a dozen PhD, master's, and bachelor's students across institutions like Groningen, Edinburgh, and Cambridge. Outreach: Leads public lectures, stargazing events, and chairs the Young Academy of Groningen's outreach committee. Engages in international collaborations and science communication initiatives.

• Climate Finance
• Environmental Economics
• ESG Investments
• +۲ مورد دیگر

Lavinia Rognone is a researcher at the University of Edinburgh Business School , specializing in climate finance and ESG investment dynamics. Her work bridges environmental economics with financial market analysis. Research Focus : Climate risk pricing, ESG fund behavior, clean energy markets, and gold trading under climate uncertainty. Collaborations : Extensive partnerships with institutions including European Central Bank , Copenhagen Business School , and University of Florida . Her recent publications analyze: (1) ESG institutional trading patterns (2025), (2) climate risk impacts on sustainable funds (2023), (3) cross-asset climate information transmission (2022), and (4) macroeconomic implications for ECB policy frameworks (2021). Key methods include text-based risk analysis, INGARCH modeling, and multi-analyst comparative studies.

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

Yun Liمشاهده پروفایل

استاد

• Statistical Genetics
• Multi-omics Dissection
• Genetic Dissection of Complex Diseases
• +۸ مورد دیگر

Dr. Yun Li is a Professor in the Department of Genetics and Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), affiliated with the UNC School of Medicine. He also holds an adjunct position as an Associate Professor in Applied Physical Sciences. His research focuses on developing statistical methods for genetic and genomic studies, including genotype imputation (e.g., MaCH software), multi-omics integration, and analysis of complex diseases such as metabolic, autoimmune, cardiovascular, and neuropsychiatric disorders. He leads the Li Lab, which has contributed to projects like the 1000 Genomes Project, TOPMed, and BACPAC consortium. Dr. Li’s work emphasizes methodological advancements for low-coverage sequencing, epigenetic studies, single-cell RNA-seq, and chromatin organization analysis. Education: Ph.D. in Biostatistics from the University of Michigan (2009). Leadership includes roles in the NIH PRIMED consortium and the 4DN Predictive Modeling Working Group. The lab has developed tools such as HUGIn for Hi-C data exploration and CUE for DNA methylation imputation. Collaborations span multi-omic studies, precision medicine, and population genetics across diverse populations. Research interests span statistical genetics, genomic data integration, and translational applications. The lab actively participates in consortia addressing genetic dissection of complex traits, leveraging single-cell and spatial transcriptomics, and chromatin 3D architecture.

Suckjoon Junمشاهده پروفایل

استاد

• Chromosome segregation
• Cell growth and division
• Bacterial cell cycle
• +۲ مورد دیگر

Dr. Suckjoon Jun is a Professor in the Department of Physics at the University of California, San Diego. He holds a PhD from Simon Fraser University (2004) and leads a research program focused on fundamental questions in biological physics, particularly chromosome segregation, bacterial cell cycle regulation, and cell size control. His lab combines theoretical and experimental approaches to investigate physical principles governing biological systems. Key research areas include the thermodynamic basis of chromosome segregation in bacteria, quantitative relationships between cell growth and division, and mechanical determinants of cell morphology. His group develops novel tools like the 'mother machine' for single-cell analysis and applies concepts from polymer physics to understand chromosome organization. Recent work has established fundamental principles of bacterial cell size homeostasis. Dr. Jun's honors include the NSF CAREER Award and Pew Scholarship. His research has revealed how physical constraints like cell boundary confinement influence chromosome positioning and led to mechanistic models of cell size control validated across bacterial species.

Paul Savariappanمشاهده پروفایل

استاد آموزشی

• Queueing Theory
• Reliability Analysis
• Statistical Inference
• +۳ مورد دیگر

Paul Savariappan is an Associate Teaching Professor in the Department of Statistics at North Carolina State University (NC State), where he has served since January 2018. Previously, he held roles including Visiting Associate Professor at the University of Wisconsin-Madison (2015-2016), Associate Professor at Luther College (2012-2017), and faculty positions at Loyola College, India (1986-2003). He holds a Ph.D. in Statistics from the University of Madras (2000), an M.S. in Biomathematics from Marquette University (2006), and multiple advanced degrees from Indian institutions. Education: Ph.D. in Statistics, University of Madras, 2000 M.S. in Biomathematics, Marquette University, 2006 M.Phil. in Statistics, University of Madras, 1991 M.Sc. in Statistics, Loyola College, 1984 B.Sc. in Statistics, St. Joseph’s College, 1982 Research Interests: Dr. Savariappan specializes in Queueing Theory , focusing on tandem queues, blocking mechanisms, and service rate analysis. His work in Reliability Analysis involves bivariate/trivariate exponential models and system dependability. He applies statistical methods to engineering and biological systems, with a strong emphasis on Bayesian inference and stochastic processes. Articles & Trends: His publications emphasize theoretical and applied aspects of queueing systems and reliability engineering. Recent work explores statistical inference for complex queueing networks and multivariate exponential distributions in system reliability. Earlier contributions address bulk arrival queues and maintenance optimization strategies. Awards: Three-time recipient of NC State’s “Thank a Teacher” award “Thank an Advisor” recognition from the Advising Professional Development Committee Advising & Grants: He advises over 60 students annually and co-advises master’s theses (e.g., Fernando David Soler Diaz’s work in Animal Science). He contributed to a USDA NIFA grant (2025) and developed NC State’s ST525 course for the Agriculture and Data Science certificate program. His service includes department diversity initiatives and peer teaching evaluations. Labs & Collaborations: Active in interdisciplinary projects, including data science collaborations with Duke University’s Data Fest. His work bridges statistics with engineering, biology, and agriculture through applied statistical methodologies.

Nir Lipovetzkyمشاهده پروفایل

دانشیار

• AI Planning
• Search
• Learning
• +۶ مورد دیگر

Nir Lipovetzky is an Associate Professor in Artificial Intelligence at the School of Computing and Information Systems, University of Melbourne. He is affiliated with the Agent Lab and Digital Agriculture, Food and Wine Lab. His research focuses on AI planning, search, and applications in autonomous systems, particularly in agriculture. He holds a PhD from Universitat Pompeu Fabra (2012) and a BSc in Computer Science (2004). Education: PhD in Artificial Intelligence, Universitat Pompeu Fabra (2012) MEng in Artificial Intelligence, Universitat Pompeu Fabra (2007) BSc in Computer Science, Universitat Pompeu Fabra (2004) Graduate Certificate in University Teaching, University of Melbourne (2020) Research interests include AI planning, constraint programming, operations research, and intention recognition. He has developed novel width-based planning algorithms and contributed to planning competitions. Recent work includes adaptive goal recognition and reinforcement learning for decision-making under uncertainty. Publications span topics like count-based novelty exploration, process mining for goal recognition, and human-like goal inference models. Awards include the Best Student Paper Award at AAMAS 2024 and recognition in planning competitions. Advises 10+ PhD and Master’s students on topics like exploration methods, agricultural robotics, and multi-agent systems. Supervises Farm.bot, an open-source platform for AI-driven agriculture. Serves as Conference Chair for ICAPS 2025 and has held roles in AAAI, IJCAI, and ICAPS committees.

• Statistical Ecology
• Wildlife Population Dynamics
• Bayesian Statistics
• +۴ مورد دیگر

Eleni Matechou is an Honorary Academic in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. Her academic background includes a BSc in Statistics from Athens University of Economics and Business, an MSc in Statistics with Applications in Medicine from the University of Southampton, and a PhD jointly conducted at the University of Kent and the Max Planck Institute for Demographic Research. She has held positions such as Research and Teaching Fellow at Victoria University of Wellington, Departmental Lecturer at the University of Oxford, and is currently a member of the Statistical Ecology @ Kent (SE@K) group and the National Centre for Statistical Ecology (NCSE). Her research focuses on statistical ecology, particularly developing models for wildlife populations, migration patterns, and environmental DNA (eDNA) monitoring. Key areas include Bayesian and frequentist approaches to capture-recapture models, mixture models, and integrating citizen science data for ecological inference. She is actively involved in methodological advancements for biodiversity assessment and conservation, emphasizing the use of innovative technologies like robotics and eDNA. Professionally, Eleni chairs the Environmental Statistics Section of the Royal Statistical Society and serves as Book Reviews Editor for the Journal of Agricultural, Biological and Environmental Statistics . Her work bridges statistical rigor with ecological applications, addressing challenges in monitoring biodiversity and informing conservation strategies. Collaborations span institutions globally, reflecting her interdisciplinary approach to solving ecological problems.

Steve Shirtliffeمشاهده پروفایل

استاد

• Field crop agronomy
• Crop Phenotyping
• Crop Imaging
• +۳ مورد دیگر

Steve Shirtliffe is a Professor in the Department of Plant Sciences at the University of Saskatchewan’s College of Agriculture and Bioresources. His research focuses on field crop agronomy, crop phenotyping, precision agriculture, and weed science. He holds a Ph.D., M.Sc., and B.Sc. from the University of Manitoba. Dr. Shirtliffe’s work integrates remote sensing, machine learning, and field experimentation to improve crop yields and agricultural sustainability. He has pioneered applications of UAV-based imaging for crop analysis, including canola yield prediction and weed management. His research emphasizes precision agriculture, crop lodging prediction, and mechanical weed control systems. He has received prominent awards including the ASA Professor of the Year (2008) and University of Saskatchewan’s Provost’s Outstanding Teaching Award (2011-12). Courses taught include PLSC 375 (Current Topics in Agronomy), PLSC 401 (Sustainable Crop Production), and PLSC 898 (Plant Population Biology). Recent research highlights include: Development of AI-driven field boundary delineation systems Integration of SAR and optical data for crop phenology mapping Optimized deep learning models for edge device yield prediction Long-term weed dynamics studies in organic/conventional systems His work addresses critical challenges in Prairie agriculture through innovative technology applications and systems-level crop management strategies.

Juan Encisoمشاهده پروفایل

استاد

• Irrigation System Design
• Water Management
• Deficit Irrigation
• +۳ مورد دیگر

Juan Enciso is Professor of Biological and Agricultural Engineering at Texas A&M University, specializing in irrigation system design, water management, and precision agriculture technologies. His research develops UAS-based remote sensing methods for crop monitoring, yield prediction, and resource optimization. Current projects include AI-driven detection of crop contaminants, wastewater utilization for bioenergy crops, and citrus yield estimation using multi-temporal datasets. Enciso's work bridges engineering solutions with sustainable agricultural practices, particularly in water-scarce regions. He has extensively studied deficit irrigation strategies, salinity management, and UV treatment of agricultural water. Recent publications demonstrate advanced applications of deep learning for volunteer plant detection and plastic contaminant mapping in agricultural fields.

• Traffic Conflict Analysis
• Connected and Automated Vehicles
• Human Factors and Driving Behaviour
• +۵ مورد دیگر

Professor Md. Mazharul Haque is a distinguished academic and researcher in transportation engineering at Queensland University of Technology (QUT), Australia. He serves as Head of the School of Civil & Environmental Engineering and holds a Professorial rank. His expertise spans econometrics, artificial intelligence, and traffic safety. He has secured over $3 million in research funding, published over 110 peer-reviewed articles, and achieved an h-index of 27 (Scopus) and 33 (Google Scholar). His work is recognized through prestigious roles like Associate Editor of Accident Analysis & Prevention and ASCE Journal of Transportation Engineering . Research focuses include Traffic Conflict Analysis, Connected and Automated Vehicles (CAVs), Human Factors in Driving, and Black Spot Identification. He co-founded the startup Advanced Mobility Analytics Group (AMAG), developing AI-driven road safety tools. Awards include the TRB Outstanding Paper Award (2020, 2016) and the John Kirby Award (2014). He has advised multiple PhD students on topics like CAV integration, safety modeling, and traffic flow optimization. His grants address real-time risk assessment, safety evaluation of traffic signals, and CAV behavioral impacts. Education includes a PhD from the National University of Singapore (2005–2009) and degrees from Bangladesh University of Engineering and Technology (BUET). Professional memberships include TRB Committees, ASCE, and editorial roles in leading journals. Key contributions include pioneering AI-based video analytics for crash risk forecasting and advancing econometric models for transportation systems.

Les Mayhewمشاهده پروفایل

استاد

• Actuarial Science
• Demography
• Statistics
• +۱۳ مورد دیگر

Les Mayhew is a part-time Professor of Statistics at Bayes Business School (formerly Cass), City, University of London, within the Faculty of Actuarial Science and Insurance. He is an Honorary Fellow of the Institute of Actuaries and a member of the Royal Economic Society. His career spans academia, government, and consultancy, with significant contributions to demography, pensions, health, and long-term care policy. His research interests include demographic methods, population ageing, the economics of ageing, and the exploitation of administrative data for population estimation and policy evaluation. He has pioneered the use of large administrative datasets and Geographical Information Systems in public sector research, particularly in health and local government applications. His work often bridges actuarial science, statistics, and public policy. The recent articles reflect a strong focus on health inequalities, longevity risk, pension sustainability, and innovative financing models for long-term care. Key themes include the impact of smoking on healthy life expectancy, gender convergence in survival, deprivation and lifespan inequality, and asset-based solutions for care funding. His research combines rigorous statistical analysis with practical policy implications. Twice winner of the Bayes research prize Winner of the University Project of the Year prize Joint winner of the University Research Impact Award Honorary Fellow of the Institute of Actuaries Les Mayhew advises central government, healthcare providers, and local authorities on policy evaluation, resource allocation, and service commissioning. He is the Managing Director of Mayhew Harper Associates Ltd., a research consultancy specializing in data linkage. He has supervised and collaborated with numerous researchers and students, though specific names are not listed. He has held visiting appointments at IIASA and the University of Newcastle, and previously served in senior roles at the Office for National Statistics and the Department of Social Security. He has led projects on population estimation using administrative data, transport policy (including the London congestion charge), and the economic impacts of population ageing. His future work appears focused on levelling up health outcomes, sustainable care systems, and the economic implications of longevity.