جست‌وجوی استاد و پژوهشگر

• Scholarship of Teaching and Learning (SOTL)
• Higher Education Pedagogy
• Biology Education
• +۳ مورد دیگر

Ivona Mladenovic is a Senior Lecturer in the Department of Biological Sciences at Simon Fraser University (SFU), where she has been involved in teaching since 2003. She specializes in evidence-based pedagogy and focuses on enhancing student learning experiences in first-year biology courses. She played a pivotal role in establishing the SFU Surrey campus’s BISC 101/102 teaching laboratory in 2011 and has developed courses like BISC 106. Her education includes a PhD in Educational Theory and Practice (SFU), a PhD in Medicine (University of Belgrade), and advanced degrees in Endocrinology, Molecular Biology, and Education. She holds certifications from UBC and Vancouver Community College. Research Interests: Mladenovic’s work centers on the Scholarship of Teaching and Learning (SOTL), particularly first-year students’ perceptions of quality teaching in science. She investigates how pedagogical practices can foster inclusive, learning-centered environments. Key questions include understanding students’ definitions of effective teaching and the transferability of pedagogical skills. Grants & Awards: 2019-2020: SFU Institute Grant ($5,000) for studying student learning experiences 2016-2017: SFU Grant ($5,000) on electronics in learning 2012-2013: SFU Teaching Grant ($5,000) for enhancing concept understanding via research 2013-2014: Dean of Graduate Studies Fellowship for her education PhD Teaching & Courses: She currently teaches BISC 202 (Genetics) and BISC 101 (General Biology). Her teaching emphasizes active learning and adapts to diverse student needs. She has authored test banks for major biology textbooks and contributed chapters to post-pandemic education literature.

Douglas Nychkaمشاهده پروفایل

استاد

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +۵ مورد دیگر

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.

Nick Aldredمشاهده پروفایل

مدرس ارشد

• Marine Biofouling
• Biological Adhesion
• Larval Settlement Ecology
• +۲ مورد دیگر

Dr. Nick Aldred is a Senior Lecturer in the School of Life Sciences at the University of Essex, specializing in marine invertebrate biology with expertise in larval settlement ecology and marine bioadhesion. His research focuses on biofouling processes, adhesion mechanisms in biological systems, and biofilm engineering, utilizing barnacles as model organisms. He leads multidisciplinary teams collaborating with defense, industrial, and charitable organizations. Education: BSc (Hons) from University of Wales, Bangor (2002) PhD from Newcastle University (2006) His research investigates how bacterial biofilms influence larval settlement and the polymerisation mechanisms enabling underwater adhesion in marine invertebrates. Current projects include developing mistletoe-inspired adhesives and engineered biofilms for naval applications, funded by DARPA, ONR, and Leverhulme Trust. Publication analysis reveals consistent focus on antifouling technologies, marine adhesive mechanisms, and surface-biology interactions, with recent work expanding into materials science and omics-based adhesion studies across 64+ journal articles. Current PhD supervision: Isabelle Eve Firth (Marine Biology) Jacob Peter Cook (Biological Sciences) As Director of Recruitment and Public Voice Scholar, he actively engages in science communication and institutional leadership.

Shiva Jahangiriمشاهده پروفایل

استادیار

• Big Data Management Systems
• Databases for AI/ML
• Knowledge Graphs
• +۶ مورد دیگر

Shiva Jahangiri is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His research focuses on Big Data Management Systems, Databases for AI/ML, and Query Optimization. He leads the DBIS Lab, which explores database internals, vectorized data processing, and open-source projects like Apache AsterixDB. Education: Ph.D. in Computer Science from the University of California, Irvine; M.S. in Computer Science (Data Science) from the University of Southern California. Current courses taught include Advanced Programming, Advanced Database Systems, and Introduction to Database Systems. He advises Ph.D. and Master’s students on topics like Vector Databases, Query Scheduling, and Resource Management. Recent research trends involve optimizing group-by/aggregation operators, schema inference for semi-structured data, and memory management in complex join queries. His work bridges theoretical advancements with practical implementations in open-source systems. DBIS Lab activities include student participation in senior design projects, directed research, and volunteer roles. The lab emphasizes industry collaboration for hands-on experience in database systems development.

• Quality of Life
• Coping Mechanisms
• Aging and Health
• +۱۲ مورد دیگر

Anne-Sofie Helvik is a Professor at the Norwegian University of Science and Technology (NTNU) within the Department of Public Health and Nursing . Her research focuses on quality of life, coping mechanisms, aging, impairment, and disabilities, with a particular emphasis on older adults. Email: anne-sofie.helvik@ntnu.no Location: Øya Helsehus, 153.03.001, Øya, Håkon Jarls gate 11 Her work spans multiple domains: Health Promotion: Central to her research is improving health outcomes in older populations. Substance Misuse: Investigates alcohol and drug use among older adults and its impact on health. Mental Health: Studies depression, pain, and dementia in aging populations. Sensory Function: Explores olfactory and hearing impairments in older adults. Healthcare Systems: Evaluates treatment, care, and postoperative follow-up in both pediatric and geriatric contexts. Her methodological approach combines both quantitative and qualitative research , with a strong emphasis on longitudinal studies and population-based analyses. Currently, she supervises two doctoral students: Heidi Sivertsen – Researching depression among older community residents. Ingrid Torvik Heian – Investigating olfactory functioning. Her research unit, Allmennmedisinsk Forsknings Enhet (AFE) , focuses on aging and health in older adults, accessible via NTNU’s aging and health page.

• Hardware Security
• Machine Learning
• 3D IC Design
• +۴ مورد دیگر

Wafi Danesh is an Assistant Professor in the Department of Engineering Programs at SUNY New Paltz, part of the School of Science & Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Missouri Kansas City (2022). Prior to academia, he served as a Senior Engineer I - Design at Microchip Technology Inc. (2022-2023). His research focuses on hardware security, leveraging machine learning for FPGA Trojan detection and secure 3D IC design. Teaching interests include System-on-Chip Design, Digital Logic Fundamentals, and Computer Architecture. Education: PhD in Electrical and Computer Engineering, University of Missouri Kansas City, 2022 Research Interests: Dr. Danesh explores cutting-edge methods to enhance hardware security, including AI-driven approaches for IoT device protection and thermal management in 3D integrated circuits. His work bridges machine learning and physical hardware vulnerabilities, emphasizing FPGA security and PUF-based solutions for wireless systems. Publications Trends: His articles span FPGA Trojan detection via NLP and unsupervised learning, thermal challenges in 3D ICs, and neuromorphic computing innovations. Recent work highlights automated security tools and multi-valued computing for energy efficiency. Awards: None explicitly listed in the provided materials. Advising & Grants: No formal advisees or grants are mentioned. His professional activities center on research and teaching.

Rajen Shahمشاهده پروفایل

استاد

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +۳ مورد دیگر

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Steven Paul Reiseمشاهده پروفایل

استاد پژوهشی

• Quantitative Psychology
• Latent Variable Modeling
• Structural Equation Modeling
• +۳ مورد دیگر

Steven Paul Reise is a Research Professor in Quantitative Psychology at the University of California, Los Angeles (UCLA), where he has been a faculty member since 1998. He co-directs the Advanced Quantitative Methods training program, supporting graduate students in measurement, latent variable modeling, and modern experimental design. Dr. Reise earned his Ph.D. in Psychology from the University of Minnesota (1990), mentored by IRT pioneer David Weiss and personality researcher Auke Tellegen. Dr. Reise specializes in applying latent variable measurement models—including Structural Equation Modeling (SEM) and Item Response Theory (IRT)—to personality, psychopathology, and health outcomes research. His methodological work focuses on hierarchical models, MIMIC modeling, person-fit statistics, and bifactor modeling. He has been recognized with prestigious awards, including the Raymond B. Cattell Award (1998), UCLA Psychology Department Distinguished Teaching Award (2008), and University of California Campus-Wide Distinguished Teaching Award. His recent publications explore multidimensionality in SEM, bifactor modeling, person-fit indices, and applications to psychopathology and health outcomes. Dr. Reise collaborates on projects spanning gene-to-phenotype modeling, impulsivity, and advanced psychometrics. Scientific Awards : Raymond B. Cattell Award (1998), UCLA Psychology Teaching Award (2008), UC Campus-Wide Teaching Award, Outstanding Paper of the Year (2012).

Romain Quidantمشاهده پروفایل

استاد

• Nanophotonics
• Optomechanics
• Plasmonics
• +۴ مورد دیگر

Prof. Dr. Romain Quidant is a Full Professor in the Department of Mechanical and Process Engineering at ETH Zürich, where he also serves as Head of the Institute for Energy and Process Engineering. His research focuses on nanophotonics, optomechanics, and plasmonics with applications in quantum optics, biomedical engineering, and thermal control systems. He leads a multidisciplinary team exploring light-matter interactions at the nanoscale, particularly in levitated nanoparticles and plasmonic therapies. Key research interests include quantum optomechanical systems, plasmonic nanothermometry, and targeted photothermal therapies. His work bridges fundamental physics with practical applications such as precision measurement, medical imaging, and energy-efficient materials. Recent studies highlight advancements in optical trapping techniques, thermal wavefront shaping, and robotic surgery guidance using fluorescent nanothermometry. Prof. Quidant’s publications showcase innovations in reconfigurable meta-surfaces, optofluidic platforms for high-throughput analysis, and adaptive thermal microscopy for brain imaging. His lab develops integrated systems for medical diagnostics, environmental sensing, and quantum-enabled technologies. These efforts have been applied to cancer treatment optimization and novel materials for energy systems.

Antonio Peyracheمشاهده پروفایل

دانشیار

• Applied Economics
• Econometrics
• Productivity Analysis
• +۳ مورد دیگر

Associate Professor Antonio Peyrache is a Deputy Head of School at the School of Economics, University of Queensland, within the Faculty of Business, Economics and Law. His research focuses on applied economics, econometrics, and productivity/efficiency analysis with particular emphasis on production systems, public sector efficiency, and systemic risk modeling. He leads the Centre for Efficiency and Productivity Analysis (CEPA), driving advancements in efficiency measurement methodologies. Key research interests include judicial system efficiency, banking systemic risk, and multilevel production networks. Recent work explores homothetic production technologies and optimal organizational structures for public institutions. Featured projects include analyzing productivity in Australian horticulture and European judicial systems. His expertise spans both theoretical contributions (e.g., decomposition frameworks) and applied policy analysis (e.g., healthcare delivery efficiency). Publications span journals like European Journal of Operational Research and Omega, with a focus on operational research techniques and their policy applications. He has directed projects funded by the Asian Productivity Organization and collaborated on EU-KLEMS growth accounting initiatives. Professional roles include editorial contributions and leadership in academic productivity analysis.

Chun Wangمشاهده پروفایل

استاد

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +۳ مورد دیگر

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

Scott Moffatمشاهده پروفایل

استاد

• Spatial Cognition
• Navigation
• Cognitive Aging
• +۲ مورد دیگر

Scott Moffat is a Professor of Psychology at Georgia Institute of Technology, affiliated with the School of Psychology. He holds a Ph.D. in Psychobiology & Clinical Neuropsychology from the University of Western Ontario (1998). His research focuses on the cognitive neuroscience of aging, particularly the interplay between hormonal systems (e.g., cortisol, testosterone, insulin) and spatial cognition, navigation, and brain aging. He employs neuroimaging (fMRI/PET), behavioral assessments, and biochemical assays to study these topics. A key innovation is his use of virtual reality environments to evaluate spatial memory in older adults. Research interests emphasize stress physiology, including cortisol’s role in hippocampal atrophy and Alzheimer’s risk, testosterone’s impact on cognitive aging, and insulin’s effects on neural function. His work highlights individual differences in cognitive aging and the biological mechanisms underlying such variability. He has published extensively on spatial navigation strategies, age-related neural changes, and the application of virtual technologies for cognitive assessment. Labs/Teams: Director of the Cognitive Neuroscience of Aging Lab at Georgia Tech’s Center for Advanced Brain Imaging. His lab investigates brain-behavior relationships in aging using multimodal neuroimaging and endocrine measures.

Andrew Thomas Campbellمشاهده پروفایل

استاد

• Ubiquitous Computing
• Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Andrew Thomas Campbell is a Professor and Albert Bradley 1915 Third Century Professor in the Department of Computer Science at Dartmouth College. His research focuses on ubiquitous computing, machine learning, and mental health, particularly using mobile and wearable sensors to assess and manage mental illnesses. He leads the StudentLife project, which tracks college students' mental health over four years, and co-directs the HealthX Lab. Campbell's work has received prestigious awards, including the ACM UbiComp 10-Year Impact Award for pioneering mobile sensing in mental health. He previously held tenure as an Associate Professor at Columbia University and has industry experience at Google and Verily. His research spans $42M in grants from NIH, NSF, and corporate partners, emphasizing technology-driven solutions for mental health challenges. Education: B.Sc. from Aston University, M.Sc. from City University, Ph.D. from Lancaster University. He teaches CS 1 Introduction to Programming and mentors numerous students. Awards include the Dean of the Faculty Award for Mentoring (2025) and multiple ACM Test of Time Awards. His lab collaborations involve跨学科 teams addressing mental health through AI and sensing technologies.

Naratip Santitissadeekornمشاهده پروفایل

مدرس ارشد

• Data Assimilation
• Inverse Problems
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.

Hao Chenمشاهده پروفایل

دانشیار

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +۷ مورد دیگر

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.