جست‌وجوی استاد و پژوهشگر

Ehsan Miandjiمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

• Gynecologic Cancer Research
• HPV Detection and Prevention
• Women's Health
• +۳ مورد دیگر

Christer Borgfeldt is a Professor at Linköping University's Department of Biomedical and Clinical Sciences. His research focuses on gynecologic cancer, HPV detection, and healthcare quality improvement through data validation studies. He leads projects evaluating clinical registers and screening programs in Sweden and Ethiopia. Research interests include cervical cancer prevention, HPV epidemiology in low-screened populations, and improving diagnostic protocols for gynecological conditions. His work emphasizes clinical data integrity and global health equity.

• Algorithmic development
• Stochastic modelling
• Risk/reliability analysis
• +۹ مورد دیگر

Dr. Tanmoy Chatterjee is a Lecturer in Resilient Design at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Surrey Institute for People-Centred AI and the Institute for Sustainability. Previously, he was a postdoctoral researcher at Swansea University (2018–2022), funded by the EPSRC 'DigiTwin' grant. His research focuses on algorithmic development, stochastic modeling, and machine learning for structural systems, with applications in nonlinear dynamics, metamaterials, and energy harvesting. Education: PhD in Civil Engineering (2014–2018), Indian Institute of Technology Roorkee Masters in Structural Engineering (2011–2013) Key Research Areas: Resilient design of structures under uncertainty Data-driven discovery of nonlinear dynamics Metamaterials for wave propagation control Machine learning for uncertainty quantification Publications: Recent work includes stochastic model updating, Bayesian inference for aeroelastic systems, and PINN-based reliability analysis. His 2025 paper on nonlinear structural joints introduced a novel framework combining experimental data with deep learning. Awards: Recipient of CSIR and Ministry of Education scholarships for his master's and PhD. Supervision: Co-supervised PhD students James Smith (University of Surrey) and Pushpa Pandey (Swansea University). Open to exceptional candidates for funded PhD projects in AI-driven engineering and resilient design. Labs/Teams: Active in Surrey's Centre for Engineering Materials and collaborations with industry partners like Autodesk and UK Atomic Energy Authority.

Atif Mansoorمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• IoT and Sensor Networks
• +۴ مورد دیگر

Atif Mansoor is a Lecturer in Computer Science and Software Engineering at the University of Western Australia, affiliated with the School of Physics, Maths and Computing. He is also part of the Planning and Transport Research Centre within the School of Social Sciences. His research spans interdisciplinary areas including computer vision, machine learning, IoT systems, and probabilistic graphical models. Mansoor has supervised numerous graduate students, contributing to advancements in automated inference, infrastructure monitoring, and image quality assessment. Recent research focuses on parallel Bayesian network inference, IoT-based structural health monitoring, and remote sensing applications for agriculture. His work integrates machine learning with domain-specific challenges in healthcare, urban planning, and environmental science. Mansoor has collaborated on projects funded by initiatives like the iMove CRC, emphasizing practical solutions for infrastructure and transportation systems. His academic contributions include over 50 peer-reviewed publications and active participation in international conferences. Mansoor’s teaching and research reflect a commitment to innovation in computational methods and their societal applications.

Gavin Garnerمشاهده پروفایل

دانشیار

• Human Machine Interface
• Mechatronics
• Advanced Manufacturing
• +۳ مورد دیگر

Gavin Garner is an Associate Professor at the University of Virginia's School of Engineering and Applied Science, Department of Mechanical & Aerospace Engineering. He joined the faculty in 2009 after completing his Ph.D. at UVA and working as a mechanical engineer at AMF Bowling Worldwide. His research focuses on mechatronics, human-machine interfaces, and biomedical engineering applications such as automated tissue fabrication. He advises UVA's ASME and Pi Tau Sigma student chapters. Education: Bachelor's in Physics from Colby College M.S. and Ph.D. in Mechanical & Aerospace Engineering from University of Virginia Research Highlights: Developed FEBE bioreactor systems for tissue engineering Advances in 3D-printed diagnostic devices and microfluidics Interdisciplinary education projects like guitar-building for mechatronics Recent work includes automated biofabrication systems for muscle repair and low-cost diagnostic tools. His 2018 Jefferson Scholars Foundation award supported innovative engineering research. Teaching emphasizes hands-on projects like mechatronics labs, autonomous quadcopters, and bio-manufacturing systems. Key Contributions: Over 15 patents and peer-reviewed publications Student projects include UVA's award-winning ASME competition teams Collaborations with medical researchers for tissue-engineered therapies

Annika Betkenمشاهده پروفایل

استادیار

• Long Range Mathematics
• Stochastic Volatility
• Simulation Study
• +۳ مورد دیگر

Annika Betken is an Assistant Professor in Mathematics of Operations Research. Her research focuses on advanced statistical methodologies including long-range dependence analysis, stochastic volatility modeling, and machine learning applications. Notable contributions include work on two-sample tests using Wilcoxon rank sums and label shift adaptation in neural networks. She actively engages in academic discourse through conference presentations and publications. Her recent research explores classification scenarios using kernel logistic regression and depth pattern analysis in time series. Betken has delivered invited talks on topics like class probability matching and change-point detection in time series analysis. Her work has been featured in peer-reviewed conferences and journals, contributing to 65 citations and an h-index of 5 as of 2025. Betken has collaborated internationally and contributed to the NAW article in February 2022. Her research spans theoretical advancements in statistical testing and practical implementations in adaptive machine learning systems.

• Payment Systems
• Economic Analysis
• Financial Innovation
• +۳ مورد دیگر

Angelika Welte is an Adjunct Professor in the Department of Mathematics and Statistics at the University of Ottawa's Faculty of Science. Her research focuses on payment systems, economic analysis, and financial innovation, with particular attention to consumer behavior and market dynamics in the context of digital and traditional payment methods. She has conducted extensive studies on merchant acceptance of cash, the impact of payment card pricing, and the adoption of financial technologies like mobile payments. Her work often leverages high-frequency transaction data and survey methodologies to analyze spending patterns during economic disruptions such as the COVID-19 pandemic. Key research themes include the regressive effects of payment card pricing on merchants, the untapped potential of mobile payments in Canada, and the dynamics of ATM card adoption in Italy. Dr. Welte has contributed to policy-relevant analyses through reports like the Bank of Canada's retailer surveys and the 2023 Merchant Acceptance Survey. Her interdisciplinary approach bridges economics, data science, and behavioral analysis to address questions of market efficiency and consumer welfare. Notable collaborations include studies on discounting strategies for payment steering and the cost structures of point-of-sale systems in Canada. Her work frequently appears in peer-reviewed journals and policy reports, emphasizing both quantitative rigor and real-world applicability. While no specific awards or grants are listed, her extensive publication record reflects sustained academic engagement with payment systems and economic behavior.

Lyndal Hulseمشاهده پروفایل

پژوهشگر ارشد

• Wildlife Health
• Veterinary Medicine
• Zoology
• +۳ مورد دیگر

Dr Lyndal Hulse is a Postdoctoral Research Fellow at the School of the Environment, The University of Queensland. Her work focuses on wildlife health, particularly in koalas and other Australian fauna. She holds a PhD in Veterinary Science from The University of Queensland (2021), where her research centered on the impact of Chlamydia on male koala fertility. Education: PhD in Veterinary Science (2021), University of Queensland Previous academic and research training in zoology, microbiology, and wildlife pathology. Research Interests: Chlamydia pecorum infection dynamics and its effects on koala reproduction and general health. Development of diagnostic tools for wildlife pathogens, including non-invasive methods using scat and semen samples. Wildlife epidemiology, particularly in koala populations, focusing on disease spread and conservation challenges. Reproductive biology of monotremes and marsupials, including gender determination and fertility assessment. Grants & Collaborations: Collaborates with institutions like the School of Veterinary Science and School of Agriculture and Food Sciences at UQ. Involved in interdisciplinary projects combining pathology, proteomics, and molecular biology. Labs/Teams: Works within the School of the Environment's research groups focused on wildlife health and conservation biology.

Leif Nelsonمشاهده پروفایل

استاد

• Social Psychology
• Decision Making
• Judgment Processes
• +۳ مورد دیگر

Leif Nelson is a Professor at the University of California, Berkeley, affiliated with both the Haas School of Business and the Department of Psychology. His research focuses on social psychology, judgment and decision-making processes, and improving scientific practices in experimental psychology. He emphasizes replicability, methodological rigor, and addressing biases in research. His work spans experimental designs in consumer behavior, prosocial spending, and cognitive biases. Nelson has contributed to debates on replication in social science, publishing extensively on topics like p-curve analysis, specification curve analysis, and combating publication bias. Recent publications highlight his commitment to validating research tools, auditing empirical studies, and advancing transparency in academic publishing. He has no listed advisees but collaborates widely on replication projects and methodological innovations. Professional contact information includes the Department of Psychology administrative email (psychadmin@berkeley.edu) for non-clinical inquiries. His research aligns with UC Berkeley’s interdisciplinary approach to behavioral science and policy-relevant inquiry.

Danlu Guoمشاهده پروفایل

مدرس ارشد

• Hydrology
• Environmental Engineering
• Water Quality Modelling
• +۴ مورد دیگر

Dr Danlu Guo is a Senior Lecturer at the School of Engineering, Australian National University (ANU), since December 2022. She holds a PhD in Hydrology from the University of Adelaide (2017) and has held roles including Research Associate at UNSW Sydney and the University of Melbourne, where she remains an Honorary Fellow. Her research focuses on hydrology and environmental engineering, emphasizing data-driven approaches for catchment water resources, including rainfall-runoff modelling, water quality trends, and climate change impacts. She has received prestigious awards such as the 2022 GN Alexander Medal and the 2019 MSSANZ Early Career Award. Dr Guo leads over $1M in Category 2 funding and contributes to ARC Linkage Projects. Her work bridges academic and applied sectors through roles as Guest Editor for the Journal of Hydrology , Chief Editor of the Australasia Journal of Water Resources , and Chair of MSSANZ's Water Quality Modelling SIG. Education Background: PhD in Hydrology (University of Adelaide, 2017); prior roles include Research Associate at UNSW Sydney (2016–2017) and University of Melbourne (2017–2022). Research Interests include river water quality dynamics, Bayesian modelling, large-sample hydrology, and irrigation efficiency. Her studies have revealed deteriorating water quality in the Great Barrier Reef region and recovery in the Murray-Darling Basin, as well as climate-driven shifts in streamflow and salinity. She develops frameworks for benchmarking irrigation performance using satellite and on-farm data. Grants and Projects: Lead CI for >$1M Category 2 funding; involved in ARC Linkage Projects. Current projects include improving water quality modelling via solute transport studies and enhancing irrigation benchmarking methodologies. Labs/Teams: Active in the Institute for Water Futures (ANU) and collaborates with the University of Melbourne's Infrastructure Engineering. Her research network spans hydrology, environmental engineering, and climate science communities.

Zhang Junyuمشاهده پروفایل

استادیار

• Optimization Algorithms
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

Zhang Junyu is an Assistant Professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore (NUS). He is affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on optimization algorithms, reinforcement learning, and machine learning, with particular expertise in stochastic optimization, decentralized systems, and convex/non-convex analysis. Key research areas include first-order methods for saddle point problems, variance reduction techniques in stochastic optimization, and policy search in continuous control. His work bridges theoretical foundations (e.g., complexity bounds, convergence guarantees) with practical applications in reinforcement learning and distributed systems. Zhang has contributed extensively to publications on primal-dual algorithms, temporal difference learning with deep neural networks, and multi-agent reinforcement learning frameworks. His research emphasizes algorithmic efficiency and robustness, with applications in areas like decentralized actor-critic methods and off-policy learning in constrained Markov decision processes. No scientific awards or grants are explicitly mentioned in the provided text. His academic contributions are centered around advancing optimization theory and its intersections with machine learning, particularly in high-dimensional and non-convex problem domains.

• Public Health
• Digital Health
• Clinical Research
• +۱۱ مورد دیگر

Apurv V Soni is an Assistant Professor at the UMass Chan Medical School , holding appointments in the Department of Medicine under the T.H. Chan School of Medicine and the Morningside Graduate School of Biomedical Sciences . His work spans multiple roles in Population and Quantitative Health Sciences , Masters in Clinical Investigation , MD/PhD Program , Population Health Sciences , and Translational Science . BA in Biology/Neurobiology from Boston University MD and PhD in Clinical & Population Health from University of Massachusetts Medical School Soni's research focuses on digital health interventions , SARS-CoV-2 diagnostics , and public health equity , particularly in India and underserved U.S. populations. His work addresses atrial fibrillation screening , COPD management , and health disparities through wearable technology and machine learning. Recent publications highlight trends in telemedicine , long COVID studies, and AI in healthcare . While no formal awards are listed, his collaborations with institutions like Harvard Chan School of Public Health and University of Massachusetts demonstrate his interdisciplinary impact. He actively mentors and collaborates on clinical trials and global health initiatives , including the SMART-India program for atrial fibrillation screening and Test Us Bank biorepository. His affiliations include teams in cardiovascular digital health , infectious disease , and health services research .

• Bayesian Statistics
• Financial Econometrics
• Computational Statistics
• +۳ مورد دیگر

Wilson Chen is a Senior Lecturer at the University of Sydney. He holds a PhD in Financial Econometrics from the same institution. Prior roles include a post-doctoral fellowship at the University of Technology Sydney and an Assistant Professorship at the Institute of Statistical Mathematics in Japan. His research focuses on advancing computational Bayesian methods and statistical tools for financial time series analysis. Education: PhD in Financial Econometrics, University of Sydney Research Interests: Wilson develops efficient computational techniques for Bayesian inference, with applications to financial data analysis. His work emphasizes MCMC optimization, quantile function models, and the integration of machine learning with statistical methodologies. Recent efforts include improving sampling efficiency in Bayesian frameworks and exploring variational approaches for complex posterior distributions. Publications: His work spans themes in Bayesian computation, financial econometrics, and machine learning. Notable contributions include optimizing MCMC thinning, semiparametric GARCH models, and Stein-based sampling techniques for probabilistic inference. Awards: No scientific awards explicitly mentioned. Advising & Grants: Currently supervising three PhD students: Peiwen JIANG (Modelling Complex Posteriors in Bayesian Inference), Wen PENG (Bayesian Neural Networks for Volatility Dynamics), and Yuning ZHANG (Stochastic Loss Reserving). Grant details are not specified in the provided texts. Labs/Teams: No specific lab or collaborative team affiliations mentioned.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۵ مورد دیگر

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

Alan T. Arnholtمشاهده پروفایل

استاد

• Statistical Simulation
• Linear Models
• Computational Statistics
• +۳ مورد دیگر

Alan T. Arnholt is a Professor in the Department of Mathematical Sciences at Appalachian State University. He holds a B.S. from the U.S. Military Academy at West Point, an M.S., and a Ph.D. from the University of Northern Colorado. His research interests include statistical simulation, linear models, computational statistics, and reproducible research. He has contributed extensively to statistical education through R packages like BSDA and PASWR, emphasizing open-source tools and pedagogical innovation. His work spans statistical theory, software development, and interdisciplinary applications, including contributions to medical research and sports science. Arnholt has been a faculty member since 1993 and actively promotes project-based learning in statistics education. Key academic contributions include the development of the BSDA package, which provides datasets and functions for statistical analysis, and his work on regression methods like HRLR regression. He has also published on statistical pedagogy, emphasizing practical applications and interactive tools like Shiny apps for teaching concepts such as statistical power. His collaborations extend to fields such as anesthesiology and sports biomechanics, showcasing the interdisciplinary reach of his statistical expertise. Arnholt’s grants and advising focus on enhancing quantitative education and integrating technology into curricula. He is a co-author of influential textbooks and resources, further solidifying his impact on statistical education and practice.