جست‌وجوی استاد و پژوهشگر

Tzeng Yih Lamمشاهده پروفایل

استادیار

• Forest Measurements
• Forest Sampling
• Quantitative Silviculture
• +۳ مورد دیگر

Dr. Tzeng Yih Lam serves as an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia's Faculty of Forestry. His expertise lies in forest measurements, sampling methodologies, and quantitative silviculture, contributing to sustainable forest management practices through innovative biometric approaches. His educational background includes: PhD in Forest Science (2010) from Oregon State University MSc in Statistics (2009) from Oregon State University MSc in Tropical and International Forestry (2006) from Georg-August-Universität Göttingen BSc in Forestry (2001) from the University of New Brunswick Dr. Lam's research focuses on developing close-range photogrammetry tools for hard-to-measure tree attributes, designing cost-effective probability-based forest inventory systems using auxiliary information, and applying Bayesian filtering for forest growth projection. His work bridges field data collection with advanced statistical modeling to enhance timber production estimation and ecosystem service assessment. Analysis of his 15 most recent publications reveals dominant themes in innovative sampling design (particularly Critical Height Sampling and cluster/nested plots), integration of machine learning with rapid assessment techniques, and sophisticated height-diameter modeling across diverse species. His work consistently emphasizes spatial distribution mapping, LiDAR integration, and error reduction in field measurements. As an active contributor to the UBC Forest Measurements and Biometrics research group, Dr. Lam develops teaching materials for courses including Advanced Regression Analysis and Forest Measurements, advancing methodological training in forest biometrics through R workshops and practical field applications.

• Biostatistics
• Survival Analysis
• Pharmacoepidemiology
• +۵ مورد دیگر

Michal Abrahamowicz, PhD is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and Professor in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University's Faculty of Medicine and Health Sciences. He is affiliated with the Cardiovascular Health Across the Lifespan Program and the Centre for Outcomes Research and Evaluation (CORE) at RI-MUHC. His research focuses on developing innovative statistical methodologies for clinical and biomedical data analysis, particularly in survival analysis. Key areas include modeling time-varying and cumulative effects of risk factors and treatments, and addressing biases in observational studies. He co-founded and co-chairs the international STRATOS initiative (www.stratos-initiative.org), involving over 100 statisticians from 18 countries, aimed at improving analyses of observational studies. From 2011-2019, he served as Principal Investigator of the CAN-AIM network, funded by the Canadian Institutes for Health Research, which brought together over 45 faculty members from 12 Canadian universities for drug safety and effectiveness research. His recent publications demonstrate a strong focus on methodological advances in biostatistics, particularly in survival analysis, causal inference, and flexible modeling techniques. His work spans applications in cardiovascular diseases, cancer epidemiology, pharmacoepidemiology, and arthritis research, with an increasing emphasis on practical implementation of statistical methods in clinical research. Abrahamowicz leads major collaborative research initiatives including large clinical trials and longitudinal population-based studies. His research methodology work directly informs applications in real-world clinical and epidemiological studies, creating a strong bridge between theoretical statistics and practical healthcare research.

• Hydrogeochemistry
• Environmental Chemistry
• Soil Science
• +۳ مورد دیگر

Joel Fabregat-Palau serves as a Postdoctoral Research Associate in the Hydrogeochemistry work group within the Department of Geosciences at the Faculty of Science, University of Tübingen, Germany. His office is located at Schnarrenbergstraße 94-96, 72076 Tübingen. His primary research areas include: Hydrogeochemistry and contaminant transport in soils and groundwater Environmental chemistry of per- and polyfluoroalkyl substances (PFAS) Sorption behavior of antibiotics (e.g., fluoroquinolones) in environmental matrices Development of analytical methods and open-source tools for non-target screening Application of machine learning in environmental modeling Recent publication trends (2021-2025) demonstrate a consistent focus on PFAS and antibiotic contamination in agricultural soils. His work integrates field investigations, laboratory batch experiments, and computational modeling to understand contaminant fate and develop remediation strategies. Notably, he has contributed to open-source software (PFΔScreen) for PFAS data analysis. Scientific Awards: No scientific awards mentioned in the provided information. Advising and Grants: No information available on students advised. No details provided on research grants. Laboratory and Team Affiliation: Dr. Fabregat-Palau is an integral member of the Hydrogeochemistry research team at the University of Tübingen. This team investigates biogeochemical processes in aquatic environments, with emphasis on contaminant behavior, water quality, and sustainable resource management. The group employs advanced analytical techniques and collaborates on interdisciplinary environmental projects.

Bryan E. Shepherdمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Measurement Error
• +۳ مورد دیگر

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Emily Jensenمشاهده پروفایل

استادیار

• Control Systems
• Power Systems
• Robotics
• +۳ مورد دیگر

Emily Jensen is an Assistant Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado, Boulder. She specializes in the analysis and control of spatially-distributed systems with applications in power grids, satellite constellations, and bio-inspired robotics. Her work focuses on developing theoretical frameworks to optimize system performance under constraints such as communication limitations and nonlinear dynamics. Education: B.S. in Engineering Mathematics & Statistics, UC Berkeley, 2015 M.S./Ph.D. in Electrical & Computer Engineering, UC Santa Barbara, 2020 Professional Appointments: Postdoctoral Researcher at UC Berkeley (2022-2023) Postdoctoral Researcher at Northeastern University (2021) Her research interests center on optimal distributed control, system-level synthesis, and the interplay between system structure and controller design. Key projects include multi-timescale power network control, nonlinear wave dynamics in engineering systems, and distributed parameter systems with limited communication. Dr. Jensen has delivered invited talks at high-profile venues such as the International Symposium on Mathematical Theory of Networks & Systems (2024) and the System Level Synthesis Workshop (2022). She currently advises a team of graduate and undergraduate researchers, including Addie McCurdy, Andy Gusty, and Matt Baughman (co-advised with Prof. Hodge). Awards: UC Regents’ Graduate Fellowship (2016) Zonta Amelia Earhart Fellowship (2019) IFAC Conference Young Author Award Honorable Mention (2022) She teaches ECEN 3300 (Linear Systems) and ECEN 5738 (Nonlinear Control Systems) at CU Boulder. Her work has been published in IEEE journals and conferences, emphasizing theoretical contributions with practical engineering relevance.

Michael Kozdronمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Schramm-Loewner Evolution
• +۴ مورد دیگر

Michael Kozdron is an Associate Professor in the Department of Mathematics & Statistics at the University of Regina , with research focused on probability theory and stochastic processes. He holds a Ph.D. from Duke University (2004) and a B.Sc. from the University of British Columbia (1998). Education : B.Sc. (1998) - University of British Columbia Ph.D. (2004) - Duke University Research Interests : Probability theory and stochastic processes Schramm-Loewner evolution (SLE) Statistical mechanics Random walks and quantum probability Key Article Trends : 2022: Quantum martingales and convergence theorems 2016: Quantum random variables and spectral analysis 2013: SLE convergence rates and loop-erased walk dynamics 2012: Radial SLE Green's functions and quantum conditional expectation 2009-2007: SLE applications in Ising models and Fomin's identity 2005-2004: Excursion measures and planar random walk scaling Additional Activities : He supervises undergraduate and graduate students, maintains a Google Scholar profile , and shares simulations of random processes and lecture notes online.

Li Hanمشاهده پروفایل

استاد

• Data Science
• Computational Biology
• Protein Dynamics
• +۲ مورد دیگر

Li Han is Professor of Computer Science at Clark University's Becker School of Design and Technology, where she also directs the Data Science program. She holds a Ph.D. from Texas A&M University and M.S./B.S. degrees from Xi'an Jiaotong University. Her research spans computational protein studies, data science, and robotics, with current focus on protein dynamics and allosteric mechanisms. Her publications demonstrate consistent focus on computational approaches to biological systems, particularly protein dynamics, folding mechanisms, and conformation analysis. Recent work (2022) examines allosteric pathways in ubiquitin ligases using advanced simulation techniques. Methodological contributions include dimensionality reduction for protein conformation spaces and novel algorithms for molecular simulation. She teaches diverse courses including Introduction to Data Science, Algorithms, and Robotics, and advises student computing organizations. Her research has received funding from NSF and NIH.

Ruisheng Suمشاهده پروفایل

استادیار

• Deep Learning
• Medical Image Analysis
• Neurovascular Imaging
• +۴ مورد دیگر

Ruisheng Su is an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), part of the Medical Image Analysis group (IMAG/e). He leads the SWITCH stroke workshop at MICCAI and collaborates with institutions like Erasmus MC, University of Bern, and Philips. His research focuses on deep learning for neurovascular image/video analysis, including applications in stroke, aneurysms, and image-guided interventions. He aims to develop trustworthy AI methodologies for medical professionals. Academic Background: Joined TU/e's IMAG/e group in 2024. Previously, he was a post-doctoral researcher at Erasmus MC’s Biomedical Imaging Group Rotterdam, specializing in AI for endovascular stroke interventions. Before 2020, he worked as a software engineer at ASML and a research scientist at Philips Research. Holds a Master’s in Electrical Engineering from Technical University of Munich (TUM), with thesis work at Philips on patient activity monitoring. Research Highlights: Active in developing AI-based techniques for image registration, segmentation, and quantification in neurovascular diseases. Key projects include CAVE (artery/vein segmentation), AngioMoCo (motion correction), and autoTICI (automatic reperfusion scoring). His work contributes to UN Sustainable Development Goals in health and well-being. Awards: Van Leersum Grant (2022) for visiting Harvard Medical School and DAAD AiNet Fellowship (2023) for collaborations in Berlin/Munich. Teaching: Courses include 'Medical Image Analysis' spanning from 2013 to present and ongoing courses starting 2024.

Serkan Gugercinمشاهده پروفایل

استاد

• Model Reduction
• Dynamical Systems
• Numerical Analysis
• +۶ مورد دیگر

Serkan Gugercin is the Class of 1950 Professor of Mathematics and Deputy Director of the Division of Computational Modeling and Data Analytics (CMDA) at Virginia Tech's College of Science. He is also affiliated with the Department of Mechanical Engineering. His research focuses on model reduction, dynamical systems, numerical analysis, and scientific computing, with applications in engineering and data-driven methods. Gugercin has held prestigious titles such as the A.V. Morris Professorship (2016–2019) and received awards like the NSF Early CAREER Award (2007) and Alexander von Humboldt Fellowship (2016). He earned his Ph.D. in Electrical Engineering from Rice University (2003) and has secured over $5.5M in research funding. His work bridges numerical methods, control theory, and optimization, emphasizing high-fidelity reduced models for complex systems. Education: B.S., Middle East Technical University (1997); M.S. and Ph.D., Rice University (1999, 2003). Key contributions include co-authoring the textbook *Interpolatory Methods for Model Reduction* (SIAM) and advancing structure-preserving interpolation techniques for nonlinear systems. Research areas include data-driven modeling, parametric systems, and energy-based approximation methods. Labs/Teams: CMDA Program, part of Virginia Tech’s Academy of Integrated Science. Collaborates on interdisciplinary projects involving power networks, fluid dynamics, and mechanical systems. Active in editorial roles for SIAM Journal on Scientific Computing and Systems & Control Letters.

Sylvain Arlotمشاهده پروفایل

استاد

• Statistical Learning
• Non-Parametric Statistics
• Data-Driven Tuning of Learning Algorithms
• +۵ مورد دیگر

Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.

S. Alireza Behnejadمشاهده پروفایل

دانشیار

• Design and Construction of Spatial Structures
• Formex Configuration Processing
• Structural Performance of Bamboo
• +۴ مورد دیگر

Dr. S. Alireza Behnejad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Surrey, UK. He holds a PhD in Geometry of Spatial Structures (2018) and serves as Director of the Spatial Structures Research Centre founded in 1963. His roles include Programme Leader for Undergraduate Civil Engineering and member of the University Ethics Committee. Behnejad is Editor-in-Chief of the International Journal of Space Structures and a key member of the International Association for Shell and Spatial Structures (IASS), including organizing the 2020/21 virtual IASS conference. Behnejad’s research focuses on spatial structures, bamboo engineering, and pedagogical methods. Notable projects include the Bamboo For Sustainable Construction initiative (with global partners) and the DAD Project , promoting hands-on learning through physical models. He has been awarded the Vice-Chancellor's Teacher of the Year (2021) for enhancing student learning. His work bridges historical architectural forms (e.g., Iranian Karbandi vaults) with modern engineering challenges, emphasizing sustainability and education. Behnejad collaborates with institutions worldwide, including Brazil’s University of São Paulo and China’s Southwest Jiaotong University. He supervises postgraduate students on topics like spatial structure conceptual design and leads efforts to improve engineering education through full-scale physical models and international student exchanges.

Rajan Jagpalمشاهده پروفایل

پژوهشگر

• Composite Materials
• Manufacturing Processes
• Hydrogen Storage Technologies
• +۳ مورد دیگر

Rajan Jagpal is a Researcher in the Department of Chemical Engineering at the University of Bath, affiliated with the Centre for Integrated Materials, Processes & Structures (IMPS), The Foundry Centre for Digital Manufacturing & Design, and the Centre for Sustainable Energy Systems (SES). He holds a PhD in Automated Composite Manufacturing (2022) supervised by Prof. Evangelou, Prof. Loukaides, and Dr. Butler. His research focuses on composite materials processing, hydrogen storage technologies, UAV photogrammetry applications, and sustainable manufacturing systems. Key research contributions include optimizing non-crimp fabric preforming via magnetic clamping and Bayesian algorithms, developing freeze-cast porous composites for hydrogen storage, and advancing UAV-based manufacturing quality control. He collaborates on projects like the High Speed Microtomography initiative (2022–present) investigating carbon fabric deformation mechanisms. His work addresses UN Sustainable Development Goals related to affordable clean energy and industry innovation. He actively engages in public outreach, contributing to school engagement activities from 2018–2019. Jagpal is currently accepting doctoral students for research aligned with his expertise in advanced manufacturing and materials science.

Ilya Shpitserمشاهده پروفایل

دانشیار

• Causal Inference
• Missing Data
• Graphical Models
• +۵ مورد دیگر

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University (JHU), within the Whiting School of Engineering. He is also a member of the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on causal inference, missing data, graphical models, algorithmic fairness, and semi-parametric inference, with applications in healthcare, public health, and criminal justice. Shpitser holds a BA in Computer Science and Mathematics from UC Berkeley (1999), and MS (2004) and PhD (2008) in Computer Science from UCLA. Postdoctoral work included Harvard University and the University of Southampton. He has received awards such as the Causality in Statistics Education Award (2017) and the NSF CAREER Award (2020). His work addresses disparities in algorithmic bias, causal pathway analysis, and high-dimensional observational data. Key contributions include developing causal inference methods for missing data and fairness, as well as software tools like Ananke. He serves as an associate editor for the Journal of Causal Inference and the American Journal of Epidemiology. His research is funded by NSF, NIH, DARPA, and the Office of Naval Research. Shpitser advises numerous PhD students, many of whom have transitioned to academic and industry roles. His interdisciplinary collaborations span biostatistics, computer science, and public health, emphasizing real-world applications of causal methods.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۹ مورد دیگر

Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.