جستجوی استاد راهنمای دانشگاهی

• Healthcare Professional Wellbeing
• Empathy and Compassion in Healthcare
• Nursing Education
• +۵ مورد دیگر

Dr. Samantha Jakimowicz is a Senior Lecturer at the University of Technology Sydney's School of Nursing and Midwifery, where she has held academic positions since 2017. She serves as Chair of the Faculty of Health Low Risk Ethics Panel and School Honours Coordinator, while leading the Wellbeing & Resilience Research Group. Her academic journey follows extensive clinical experience as a critical care registered nurse and psychotherapist. Dr. Jakimowicz earned her PhD in Nursing from UTS (2014-2018), Bachelor of Nursing (Hons1) and Bachelor of Nursing from the University of Tasmania, and a Bachelor of Counselling and Human Change from Jansen Newman Institute. She is currently pursuing a Graduate Certificate in Higher Education Teaching and Learning at UTS. Her research program focuses on healthcare professional wellbeing, empathy and compassion in clinical settings, and distress screening for prostate cancer patients. She has developed innovative educational tools including the Virtual Empathy Museum and e-learning modules for distress screening. Her work emphasizes translating research into practical interventions that improve both provider wellbeing and patient outcomes, particularly through Schwartz Rounds and compassionate leadership programs. Dr. Jakimowicz's recent publications demonstrate expertise in mixed-methods research, particularly grounded theory and systematic reviews, with emphasis on aged care culture, nurse wellbeing during pandemic surges, and compassionate healthcare encounters. Her work shows consistent focus on understanding organizational factors affecting nurse wellbeing and developing evidence-based interventions. Her scientific recognition includes the Florence Nightingale Memorial Scholarship Grant ($10,000), NSW Health Funding for Schwartz Rounds UK ($10,000), and a Research Scholarship Award from Illawarra & Shoalhaven Local Health District ($2,500). Dr. Jakimowicz has secured substantial research funding, including $386,000 in external grants and $54,000 in internal university funding since 2017. Major projects include multiple Prostate Cancer Foundation of Australia grants totaling over $340,000 for distress screening initiatives and e-learning development, plus UTS funding for wellbeing interventions across healthcare disciplines. She leads the Wellbeing & Resilience Research Group and is a key member of the Empathy Initiative Research Group, which developed the Virtual Empathy Museum digital teaching resource. Her collaborative approach has established national and international partnerships that have implemented wellbeing interventions like Schwartz Rounds and Compassionate Leadership programs in healthcare facilities.

• Machine Learning
• Reinforcement Learning
• Quantum Machine Learning
• +۲ مورد دیگر

Vaneet Aggarwal is Professor at Purdue University's Edwardson School of Industrial Engineering. His research spans machine learning, reinforcement learning, and quantum computing applications in optimization problems. He develops novel algorithms for complex systems including data centers, networks, and stochastic control environments. Research Focus : Aggarwal's work integrates deep reinforcement learning with combinatorial optimization to solve large-scale engineering problems. Current projects address quantum neural networks, fair resource allocation in bandit settings, and traffic engineering through learning-optimization hybrids. Recent Publications : Recent articles demonstrate consistent focus on theoretical guarantees for learning algorithms, including regret analysis in MDPs, sample complexity of diffusion models, and constrained optimization methods. His quantum machine learning research explores tensor networks for enhanced computational efficiency.

Matthew A Mastenمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Identification
• +۳ مورد دیگر

Matthew A. Masten is an Associate Professor of Economics at Duke University, affiliated with the Trinity College of Arts & Sciences. He holds a PhD from Northwestern University (2013). His research focuses on econometrics, causal inference, and robustness/sensitivity analysis in treatment effect estimation. He has received the NSF CAREER Award (2020–2025) and was tenured at Duke in 2021. His work emphasizes identification under weak assumptions and develops methods for assessing sensitivity to unconfoundedness and unobserved confounding. He has contributed Stata packages like ivcrc and tesensitivity for econometric analysis. His recent articles explore minimax-regret treatment rules, exogeneity assumptions in potential outcomes, and sensitivity analysis techniques. He teaches advanced econometrics courses (ECON 703D, ECON 899) and leads research on correlated random coefficients and instrumental variable models. His grants include foundational work on sensitivity analysis in econometrics. Key awards/grants: NSF CAREER Award (2020–2025), Tenured Faculty Recognition (2021). His research bridges theoretical econometrics with practical applications, emphasizing robustness and methodological innovation.

Arie Hendrik Havelaarمشاهده پروفایل

استاد

• Microbial Risk Assessment
• Epidemiology of Foodborne Diseases
• Public Health
• +۳ مورد دیگر

Dr. Arie Hendrik Havelaar is a Professor at the University of Florida's Department of Animal Sciences and affiliated with the Institute for Risk Assessment Sciences at Utrecht University. His work focuses on microbial risk assessment and epidemiology of foodborne diseases, with a strong emphasis on low-resource settings. He has held roles at the Dutch National Institute for Public Health and the Environment and currently contributes to systems approaches linking food production and human health. His research spans food safety in Ethiopia, Burkina Faso, and Florida, addressing pathogens like Campylobacter, Salmonella, and norovirus surrogates. Dr. Havelaar's studies integrate quantitative methods, such as structured expert judgment for disease burden estimation and machine learning for environmental pathogen modeling. His work bridges public health, agriculture, and environmental science, often emphasizing One Health frameworks. Key projects include the CAGED study on Campylobacter colonization in Ethiopian infants, the Global Burden of Animal Diseases (GBADs) initiative, and interventions to improve produce safety in Ethiopia and Burkina Faso. His research outputs highlight pathways for reducing foodborne disease burdens through policy-relevant frameworks and evidence-based interventions. Collaborations span universities, governments, and NGOs, with a focus on global health equity and sustainable development. Despite extensive publications, no specific awards are listed in the provided texts.

Nick Barnesمشاهده پروفایل

استاد

• Computer Vision
• 3D Vision
• Biological Vision
• +۴ مورد دیگر

Nick Barnes is an Interim Director of the School of Engineering and Professor at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. He advises the Bandalang Studio and leads research in computer vision, prosthetic vision, and biomedical engineering. His work focuses on 3D vision, probabilistic methods, and saliency detection, with clinical applications in retinal prosthesis development and bushfire detection systems. Research interests include biological vision replication, prosthetic vision systems (e.g., Bionic Vision Technologies), and computer vision algorithms for medical devices. Collaborative projects involve partners like Optus Bushfire Research Centre, Bionic Vision Technologies, and CSIRO Health. His team’s recent work includes advancements in retinal prosthesis functionality (face/chair detection, obstacle avoidance), clinical trials, and AI-driven medical imaging (polyp segmentation, colonoscopy automation). Notable student achievements include Sahir Shrestha’s DICTA 2021 Best Paper Award and Cedric Scheerlinck’s JG Crawford Prize for PhD Thesis. Advising spans over 20 students across PhD and Master’s programs, with active collaborations on weakly supervised learning, point cloud analysis, and generative models. Key projects include ANU-Optus Bushfire Research and clinical trials for suprachoroidal retinal prostheses. Research outputs emphasize multimodal systems, uncertainty-aware algorithms, and biomedical applications, bridging computer vision theory with clinical and real-world deployment.

Kevin Songمشاهده پروفایل

استاد

• Econometrics
• Nonparametric Statistics
• Semiparametric Models
• +۳ مورد دیگر

Kevin Song is a Professor at the Vancouver School of Economics (VSE), part of the Faculty of Arts at the University of British Columbia (UBC). He holds a B.A. from Seoul National University and a Ph.D. from Yale University. Prior to joining UBC in 2011, he served as an Assistant Professor at the University of Pennsylvania. His research focuses on econometric theory, particularly nonparametric and semiparametric models, optimal inference for nonregular and set-identified parameters, and structural models analyzing economic agent interactions. Education Background: B.A., Seoul National University, South Korea Ph.D., Yale University, USA Research Interests: Inference on nonparametric/semiparametric models Optimal estimation of nonregular parameters Set-identified parameters Structural models in economic interactions Recent Work Trends: His publications emphasize methodological advancements in econometric inference, including diffusion over networks, stable matching theory, and partial identification. His work bridges theoretical rigor with practical applications in service markets, education policy, and large-scale network analysis. Grants and Collaborations: Collaborations with institutions like the Lima Summer School and contributions to research centers such as the Stone Centre on Wealth and Income Inequality highlight his interdisciplinary reach. He oversees research streams in the VSE and contributes to policy discussions through structured empirical methodologies.

Shaodi Youمشاهده پروفایل

دانشیار

• Physics Based Vision
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Shaodi You is an Associate Professor at the University of Amsterdam (UvA), working in the Computer Vision Research Group within the Institute of Informatics, Faculty of Science. He received his Ph.D. and M.E. degrees from The University of Tokyo, Japan in 2015 and 2012, and his bachelor's degree from Tsinghua University, P. R. China in 2009. His research focuses on three main areas: Physics Based Vision, Perception Based Vision and Learning, and 3D geometry. His work specifically addresses challenging problems such as rain and water drop detection/removal, liquid optics, dehazing, and non-rigid 3D geometry. He is particularly known for his work on adherent raindrop detection and removal in video, which was presented at CVPR 2013 and later published in IEEE TPAMI. His recent publications (2024-2025) demonstrate a strong focus on physics-based vision problems, including rain location prior learning for deraining, generalized segmentation for foggy scenes, and hyperspectral image processing. His research integrates deep learning techniques with physics-based models to solve challenging computer vision problems in adverse conditions. Many of his recent works address weather-related vision challenges, building on his earlier foundational work on raindrop detection. Dr. You serves as an area chair for major computer vision conferences (CVPR, ICCV, ECCV, and NeurIPS) and is an associate editor of the International Journal of Computer Vision (IJCV). He is the founder of the International Workshop on Physics Based Vision meets Deep Learning (since 2017), which has become an important venue for research at the intersection of physics-based modeling and deep learning in computer vision. He has supervised students and supported the CSC scholarship program. His research group has developed several important datasets and projects, including GTAV-NightRain, Atlantis, and BUJT-UvA hyperspectral datasets, which have contributed significantly to the computer vision community.

• Statistical Machine Learning
• High-Dimensional Statistics
• Likelihood-Free Inference
• +۳ مورد دیگر

Summary Ann B. Lee is a Professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University (CMU). She serves as Co-Director of the Ph.D. Program in Statistics. Previously, she was the J.W. Gibbs Assistant Professor at Yale University and a visiting researcher at Brown University. Her research focuses on developing statistical methods for complex data in physical sciences, including trust-worthy uncertainty quantification, likelihood-free inference, and applications in astronomy, climate science, and hurricane dynamics. Education: Ph.D. in Physics from Brown University (2002); M.Sc./B.Sc. in Engineering Physics from Chalmers University of Technology (Sweden). Key Research Interests: - Scientific Machine Learning - Uncertainty Quantification (UQ) - Likelihood-Free Inference - Tropical Cyclone Analysis - High-Dimensional Data Modeling She leads the STAMPS (STAtistical Methods for Physical Sciences) research group, which bridges classical statistics and machine learning. Recent work includes methods for estimating ocean thermal responses to hurricanes, probabilistic forecasting, and diagnostics for generative models. Her team collaborates with climate and astrophysics communities, hosting public webinars and symposia. Advising: Supervised over 15 PhD students, including graduates now in academia and industry. Current advisees include Luca Masserano and Alex Shen. Labs/Teams: Co-directs the STAMPS Research Center at CMU, launching in Fall 2024 as a university-wide initiative.

Young-Sun Leeمشاهده پروفایل

دانشیار

• Psychometrics
• Educational Measurement
• Item Response Theory
• +۳ مورد دیگر

Young-Sun Lee is an Associate Professor of Psychology and Education at Teachers College, Columbia University. Their academic affiliations include the departments of Human Development and Measurement, Evaluation, and Statistics. They hold a Ph.D. in Educational Psychology (Educational Measurement & Statistics) from the University of Wisconsin-Madison, an M.A. and B.A. from Ewha Womans University in Seoul, South Korea. Lee's research focuses on psychometrics, including Classical Test Theory, Item Response Theory, and Cognitive Diagnosis Modeling, alongside educational and psychological measurement. Their work emphasizes applied statistics in assessment design, model validation, and educational software development. Publications span cognitive diagnostic frameworks, latent variable modeling, and applications in special education and healthcare. Lee has co-edited the Handbook of Diagnostic Classification Models and contributed to technical manuals for educational assessment tools like mCLASS:MATH. Their research bridges theoretical statistical models with practical applications in education and clinical settings.

John Leonardمشاهده پروفایل

استاد

• Autonomous Mobile Robotics
• Simultaneous Localization and Mapping (SLAM)
• Self-Driving Cars
• +۳ مورد دیگر

John J. Leonard is the Samuel C. Collins Professor of Mechanical and Ocean Engineering at MIT, serving as Associate Department Head for Research in the Department of Mechanical Engineering. His work focuses on navigation and mapping for autonomous systems, particularly self-driving cars and underwater robotics. He pioneered research in Simultaneous Localization and Mapping (SLAM), addressing uncertainties in robotic navigation. Leonard holds a B.Eng. from the University of Pennsylvania (1987) and a Ph.D. from the University of Oxford (1991). His research interests include long-term visual SLAM in dynamic environments, autonomous vehicle safety, and human-robot collaboration. He leads projects funded by institutions like Toyota, exploring Level 2.99 autonomous systems that enhance safety through advanced perception and decision-making. Notable contributions include work on trajectory prediction, underwater scene reconstruction, and certifiable robot perception algorithms. Leonard’s research bridges academia and industry, addressing societal challenges like traffic safety and underwater exploration. He emphasizes ethical considerations in autonomous systems and collaborates with policymakers to shape regulations. His educational efforts focus on training the next generation of robotics engineers through courses and mentorship.

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

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

Christiane Lemieuxمشاهده پروفایل

استاد

• Quasi-Monte Carlo Methods
• Low-discrepancy Sequences
• Computational Finance
• +۳ مورد دیگر

Christiane Lemieux is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo and Associate Dean of Operations and Academic for the Faculty of Mathematics. Her research focuses on quasi-Monte Carlo methods, low-discrepancy sequences, and their applications in computational finance, machine learning, and risk management. Education includes a Ph.D. in Computer Science from Université de Montréal (2000), M.Sc. in Mathematics from Université de Montréal (1996), and B.Sc. in Actuarial Science from Université Laval (1994). Her research explores quasi-Monte Carlo methods for multidimensional integration, low-discrepancy sequence constructions, dependence concepts for point set characterization, machine learning-based construction paradigms, and applications in finance and risk management. Recent publications demonstrate strong emphasis on combining quasi-Monte Carlo methods with machine learning techniques, particularly graph neural networks for low-discrepancy point set generation and randomized methods for various computational applications. Awards include the Outstanding Performance Award from University of Waterloo (2012, 2018, 2022) and Journal of Complexity IBC Young Researcher Award (2004). She actively supervises graduate students and postdoctoral researchers, and has led several NSERC and MITACS grants supporting her research program.

Paul Hagelsteinمشاهده پروفایل

استاد

• Harmonic Analysis
• Geometric Maximal Operators
• Fourier Series Convergence
• +۱ مورد دیگر

Paul Hagelstein is Professor of Mathematics at Baylor University's College of Arts & Sciences. Specializing in harmonic analysis, his research focuses on geometric maximal operators, Fourier series convergence, and interpolation theory. He earned his PhD from the University of Chicago and completed postdoctoral work at Princeton University before joining Baylor in 2003. His research explores fundamental operators in harmonic analysis including Tauberian conditions for geometric maximal operators and Solyanik estimates. Recent work examines differentiation properties of weighted bases and applications to control theory. Professor Hagelstein teaches across all levels of mathematics education, from introductory precalculus to graduate-level complex analysis and advanced topics in real variable theory.

Wanli Qiaoمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• Geometric Data Analysis
• Machine Learning
• +۲ مورد دیگر

Wanli Qiao is an Associate Professor in the Department of Statistics at George Mason University. His research focuses on modern nonparametric statistics, geometric data analysis, machine learning, and extreme value theory, with applications to molecular biology. He holds a faculty position at the Fairfax campus, located in the Nguyen Engineering Building. His work is supported by the National Science Foundation. Qiao's research emphasizes statistical inference on geometric structures embedded in point clouds, particularly studying protein energy landscapes' geometric and topological properties. Key areas include density ridges, cluster tree analysis, and algorithmic development for geometric data analysis. His methodologies bridge statistical theory with computational practice, addressing challenges in high-dimensional and functional data. Selected publications highlight contributions to clustering algorithms (e.g., hill-climbing consistency, mean-shift adaptations) and theoretical advancements in density estimation, confidence region construction, and extreme value analysis on manifolds. Collaborations span computational biology and statistical physics, demonstrating interdisciplinary impact.

Demetris P. Gerogiannisمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +۷ مورد دیگر

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.