جست‌وجوی استاد و پژوهشگر

Grant Casadyمشاهده پروفایل

استاد

• Landscape ecology
• Disturbance ecology
• Natural resource conservation
• +۳ مورد دیگر

Grant Casady is a Professor in the Department of Biology at Whitworth University, located in Robinson Science Hall. He joined the faculty in 2011 and specializes in landscape ecology, disturbance ecology, natural resource conservation, and remote sensing science. His research focuses on ecological dynamics in disturbed environments, post-wildfire recovery, and vegetation monitoring using advanced remote sensing techniques. Education: Grant Casady holds a Ph.D. from the University of Arizona and both an M.Ag. and B.S. from Oregon State University. His teaching includes courses on environmental science, landscape ecology, ecological measures, and field studies in Central America and East Africa. Research interests emphasize the application of remote sensing and spatial analysis to understand vegetation responses to environmental changes, especially post-wildfire recovery and invasive species management. His work integrates satellite data with field observations to model ecological processes across diverse ecosystems. Publications span topics like sage-grouse habitat selection, land surface phenology modeling, and post-wildfire vegetation dynamics. His Google Scholar profile provides a comprehensive list of publications (https://scholar.google.com/citations?user=D2vv4l8AAAAJ&hl=en&oi=ao). Grants and advising: While specific grants are not listed, his publications indicate sustained research activity. No advisee names are provided in the available text. Labs/Teams: No specific lab affiliations are mentioned, though his work involves collaborations with institutions in Spain, Israel, and across the U.S.

Douglas Eduardo Turattiمشاهده پروفایل

استادیار

• financial econometrics
• empirical finance
• stochastic volatility
• +۳ مورد دیگر

Douglas Eduardo Turatti serves as an Assistant Professor in the Finance department at Aalborg University Business School, specializing in quantitative finance methodologies with emphasis on volatility modeling and macroeconomic applications. Education PhD in Economics and Business, Aarhus University (awarded June 2018) His research centers on financial econometrics and empirical finance, particularly stochastic volatility frameworks, time-varying parameter models, and econometric applications to cryptocurrency markets. Methodologically, he employs efficient importance sampling, simulated maximum likelihood estimation, and Gibbs-sampling techniques to address volatility forecasting challenges in financial time series and inflation dynamics. Recent publications (2020-2025) demonstrate consistent specialization in volatility modeling, with increasing focus on Markov-switching frameworks and cryptocurrency applications. His work bridges theoretical econometrics with practical financial forecasting, appearing in journals including Journal of Forecasting and Journal of Applied Statistics. Scientific Awards No scientific awards documented Dr. Turatti actively disseminates research through international conferences including the International Symposium on Forecasting (2021) and International Conference Computing in Economics and Finance (2023). He participates in the Assistant Professor Circle at AAUBS (2021-2025) but no student advising or grant activities are specified. He operates within Aalborg University's Finance Research Group, contributing to the department's focus on advanced quantitative finance methodologies and financial market analysis.

• Natural product chemistry
• Nanoanalytics
• Botany
• +۶ مورد دیگر

Notburga Gierlinger is an Associate Professor at the Institute of Biophysics , part of the Department of Natural Sciences and Sustainable Resources at the University of Natural Resources and Life Sciences (BOKU), Vienna . Her work integrates plant cell walls , biomechanics , and Raman spectroscopy to explore natural adaptations and sustainable material design. Her PhD research (2003) laid the foundation for her current focus on lignin, suberin, and cutin in plant surfaces and interfaces. Her research interests span wood science , botany , nanotechnology , and biomimetic materials , with a particular emphasis on plant adaptations to extreme environments and sustainable composites. Her publications highlight plant surface chemistry , cell wall mechanics , and nanostructured materials , often leveraging Raman imaging and multivariate data analysis . Recent work explores alpine plant adaptations , self-healing adhesives , and nut shell biomechanics . Scientific Awards : ERC Consolidator Grant (2015) FWF START-Preis (2013) APART Fellowship (2006) Klaus FISCHER-Innovationspreis (2003) Sonderpreis 'Fabrik der Zukunft' (2003) She supervises PhD and Master’s students in projects like Electric Surface Charges in Woodworking , Wood and Walnut Shell-based Particleboards , and Ice Management in Plant Tissues , while serving as a peer reviewer for journals in plant science , biomaterials , and spectroscopy .

Melissa K. Passarelliمشاهده پروفایل

استادیار

• Imaging Mass Spectrometry
• Spatial 'omics
• Secondary Ion Mass Spectrometry (SIMS)
• +۳ مورد دیگر

Melissa K. Passarelli is an Assistant Professor in the Department of Chemistry and Biochemistry at Concordia University, specializing in advanced imaging mass spectrometry techniques. Her work bridges analytical chemistry, biochemistry, and pharmaceutical sciences. Ph.D., The Pennsylvania State University B.Sc., Union College Her research focuses on Imaging Mass Spectrometry (IMS) , including Spatial 'omics , SIMS , and MALDI , for applications in intracellular drug measurement, subcellular chemical imaging, and neurodegenerative disease studies. Recent publications highlight her innovations in 3D OrbiSIMS , correlative imaging methodologies, and single-cell analysis. Key trends include molecular mapping in Alzheimer’s pathology, drug delivery systems, and lipidomics. Her lab collaborates on multi-modal approaches integrating atomic force microscopy (AFM) with single-cell mass spectrometry to evaluate drug uptake and biochemical effects. Funded by a FRQNT grant , this work addresses therapeutic and toxicological challenges. Melissa teaches courses in Analytical Chemistry for Biologists and Imaging Mass Spectrometry , and maintains an active research group with expertise in mass spectrometry-based intracellular analysis.

Aurelio Piazziمشاهده پروفایل

استاد

• Control Theory
• Autonomous Robotics
• Mechatronics Systems
• +۷ مورد دیگر

Aurelio Piazzi is a Full Professor of Systems and Control Engineering at the Department of Engineering and Architecture , University of Parma , Italy, where he has been a faculty member since 1992. He holds a Laurea magna cum laude in nuclear engineering and a PhD in Systems Engineering, both from the University of Bologna. He founded the Active Control Laboratory (ACtLab) in 1995 and currently serves as the director of the Polytechnic Library and president of the Laurea Magistrale in Informatics Engineering. Academic Rank: Professor Department: Department of Engineering and Architecture University: University of Parma Email: aurelio.piazzi@unipr.it Office Location: Campus Scienze e Tecnologie - Padiglione 10, Parco Area delle Scienze, 181/A, 43124 Parma, Italy Personal Website: http://www.ce.unipr.it/people/piazzi His research is centered on control theory , with a focus on inversion-based and optimal feedforward control methods , feedforward-feedback control strategies , and autonomous navigation of wheeled robots and vehicles. He also investigates vision-based control systems and mechatronics . His work bridges theoretical advancements with industrial applications, including food ovens and railway pantograph inspection. The recent publications highlight a strong trend in inversion-based control architectures , feedforward regulation , and trajectory planning for autonomous systems. His work frequently employs polynomial interpolation , spline methods (e.g., eta3, eta4) , and dynamic inversion to achieve smooth, optimal, and robust motion control. The research spans from theoretical equivalence proofs to practical implementations in robotics and industrial automation. He has led significant research projects such as: COOKIES: EU-funded project on vision-based control of food industrial ovens. PAVISYS: Vision-based inspection system for railway pantographs, developed in collaboration with RFI Ferrovie dello Stato. PRIN Projects: National research units on automation and control theory funded by MIUR. He has published over 100 scientific papers in top journals like IEEE Transactions on Automatic Control , Automatica , and IEEE Transactions on Robotics . He is a Senior Member of IEEE and a member of SIAM , reflecting his standing in the international control and applied mathematics communities. He teaches core courses including Fundamentals of Automatic Control and Nonlinear Systems at both undergraduate and graduate levels. He has supervised numerous research students and mentored early-career researchers. His laboratory, ACtLab (Active Control Laboratory) , serves as the primary hub for both teaching and research in automation and control systems at the University of Parma, fostering innovation and practical experimentation.

Cristiano Varinمشاهده پروفایل

استاد

• Statistical Methodology
• Composite Likelihood Inference
• Copula Regression
• +۳ مورد دیگر

Cristiano Varin is a Full Professor in Statistics at the Ca' Foscari University of Venice , affiliated with the Department of Environmental Sciences, Computer Science and Statistics (DAIS). He works at the Scientific Campus in via Torino and maintains the DAIS website for research and teaching updates. His research focuses on: Composite likelihood inference - A key methodological contributor with seminal papers in Biometrika and Statistica Sinica Copula regression - With practical implementations in R software Meta-analysis - Including improved likelihood inference techniques Spatial statistics - With applications to environmental data Paired comparison modeling - Applied to sports analytics and behavioral studies Recent publications demonstrate expertise in crossed random effects models (2025), ridge regression for paired comparisons (2024), and thermal comfort range analysis (2023). Scientific recognition includes: Royal Statistical Society Read Paper (2015) on journal citation modeling Gumbel Lecture (2006) by German Statistical Society Ca' Foscari Teaching Award (2020) He has collaborated with institutions including: Swiss National Science Foundation Natural Sciences and Engineering Research Council of Canada Norwegian Council of Research

Vijayanarasimha Hindupur Pakkaمشاهده پروفایل

مدرس ارشد

• Electrical Power Systems Analysis
• Smart Grids
• Microgrid Operation and Control
• +۲۵ مورد دیگر

Vijayanarasimha Hindupur Pakka is a Senior Lecturer in Electrical Power Systems & Smart Grids at De Montfort University, UK, based in the School of Engineering and Sustainable Development within the Faculty of Computing, Engineering and Media. He is affiliated with the Institute of Energy & Sustainable Development (IESD) and the Engineering & Physical Sciences Institute (EPSI), contributing to cutting-edge research in sustainable energy systems and smart grids. Education: PhD in Computer Science, University of Southampton, UK (2005–2009) MSc (Engg) in Electrical Engineering, Indian Institute of Science, Bangalore, India (2002–2004) B.E. in Electrical & Electronics Engineering, Bangalore University, India (1997–2001) PGCertHE, De Montfort University, UK (2016–2017) His research focuses on smart grids, distributed energy integration, microgrid control, voltage regulation, electricity markets, and agent-based modeling. He applies advanced techniques such as machine learning, multi-agent systems, statistical models, and metaheuristics to power system challenges. His teaching includes power system analysis, electrical principles, and power electronics at both undergraduate and postgraduate levels. His recent publications (2022–2024) reflect a strong trend toward intelligent optimization in distribution systems, local energy planning, water quality modeling, and real-time occupancy sensing for energy efficiency. These works span disciplines including electrical engineering, environmental science, and building automation, with a clear emphasis on sustainability, data-driven modeling, and smart infrastructure. Scientific Awards and Recognition: Higher Certificate in Statistics, Royal Statistical Society (2013) Fellow of the Higher Education Academy (FHEA, 2017) Reviewer for EPSRC, Netherlands Organization for Scientific Research, IET, MDPI Energies, and several international journals Vijay Pakka actively supervises MSc and PhD students and has led or contributed to major research grants such as CASCADE, AMEN (EPSRC), and the UKRI-funded CEPREC project. His consultancy includes power and statistical system analysis. He is involved in KTP projects with industry partners like Advanced Infrastructure Technology Ltd and Midas Productions Ltd, focusing on data-driven energy planning and demand optimization. He is a member of IEEE, IET, CIGRE, and the Energy Institute, and regularly presents at international conferences such as CIRED, EEM, and IEEE SmartGridComm. His work bridges technical power systems with socio-economic and behavioral aspects of energy transitions, positioning him at the forefront of interdisciplinary energy research.

• Wireless Sensor Networks
• Brain-Computer Interfaces (BCI)
• EEG Signal Processing
• +۴ مورد دیگر

Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.

Azim Ahmadzadehمشاهده پروفایل

استادیار

• Machine Learning
• Space Weather Forecasting
• Solar Physics
• +۴ مورد دیگر

Azim Ahmadzadeh serves as an Assistant Professor in the Department of Computer Science at the University of Missouri–St. Louis within the College of Arts and Sciences. Holding a Ph.D. in Computer Science from Georgia State University (2021), he maintains office hours in 329 ESH on Mondays and Wednesdays from 5:00-6:30 PM via Zoom or in person, with flexible scheduling options for students. Education: Ph.D. Computer Science, Georgia State University, 2021 Dr. Ahmadzadeh's research pioneers machine learning applications for space weather forecasting, specializing in solar flare prediction through innovative time series analysis and computer vision techniques. His work systematically addresses critical challenges including extreme class imbalance in solar datasets, temporal coherence requirements, and data scarcity through novel algorithmic developments. Key contributions include advanced similarity metrics for multivariate time series, synthetic data generation frameworks, and anomaly detection systems specifically designed for solar observations. His publication trajectory (2022-2025) reveals a strategic focus on operationalizing machine learning for heliophysics, with significant output in solar filament detection systems (Elements, MAGFILO), time series analysis innovations (Multiscale Dubuc, Ts-miou), and practical guides for scientific data annotation. This work bridges theoretical machine learning with real-world space weather forecasting needs, evidenced by roadmap documents for SEP monitoring and solar research cyberinfrastructure. Dr. Ahmadzadeh actively develops open resources for the solar physics community including manually annotated datasets, standardized annotation protocols, and ML-ready data pipelines. His research program demonstrates strong interdisciplinary collaboration potential with space agencies and observatories, particularly through contributions to white papers on solar research infrastructure and operational forecasting requirements.

• Time Series Analysis
• Machine Learning
• Solar Physics
• +۳ مورد دیگر

Shah Hamdi serves as an Assistant Professor in the Computer Science Department at Utah State University's College of Engineering. His research bridges machine learning and space physics, with emphasis on time series analysis for solar phenomena prediction and explainable AI systems. His primary research focuses include Time Series Analysis for space weather forecasting, Solar Physics applications in flare and particle event prediction, Explainable AI through counterfactual methods, and Natural Language Processing for social media analysis. He develops novel frameworks for multivariate time series classification, data augmentation of imbalanced datasets, and interpretable model architectures that handle complex spatio-temporal patterns. Analysis of his recent publications reveals three dominant trends: (1) Application of graph neural networks and multimodal fusion to solar flare prediction using photospheric magnetic field data, (2) Development of shapelet-based and saliency-guided counterfactual explanation techniques for time series classification, and (3) Creation of generative models like ChronoGAN and AVATAR for synthetic time series data augmentation. His work consistently addresses challenges in imbalanced data and space weather forecasting accuracy. Dr. Hamdi leads collaborative research initiatives including the CAIG project for synthetic data generation in solar energetic particle events. His grant portfolio demonstrates expertise in securing funding for interdisciplinary space weather and machine learning projects, while his advising focuses on training graduate students in time series analysis and explainable AI methodologies for real-world applications.

• Neurobiology of depression
• Mental health
• Diet and mental health
• +۴ مورد دیگر

Marc Molendijk is an Associate Professor at the Clinical Psychology department, Leiden University within the Social and Behavioural Sciences faculty. His research focuses on the neurobiology of depression, neuroplasticity, and intersections with diet, sexology, stress response, and chronic illness. He coordinates courses in Clinical Psychology, Sexology, and Pre-University College programs. Education: BSc (2006), MSc in Clinical Psychology (2008), PhD (2014, cum laude) at Leiden University. Research Themes: BDNF dynamics in depression, antidepressant pharmacology, stress-mental health links, and machine learning applications in alcohol use disorder prediction. Recent Work: His 2011 Molecular Psychiatry study (co-authored) systematically analyzed serum BDNF levels across 2,044 participants, revealing BDNF as a state marker for depression normalized during remission and specifically elevated by SSRIs/St. John's wort. This work challenges aspects of the neurotrophin hypothesis by dissociating BDNF levels from clinical depression severity. Methodological Expertise: Utilized the Netherlands Study of Depression and Anxiety (NESDA) cohort, including multivariable regression and cross-sectional ANOVA designs, with rigorous controls for covariates like BMI and medication adherence.

Jochen Rangerمشاهده پروفایل

پژوهشگر

• Item Response Theory
• Response Time Analysis
• Psychometrics
• +۵ مورد دیگر

Jochen Ranger is a Researcher at the Department of Methodology within the Institute of Psychology, Faculty of Philosophy I at Martin Luther University Halle-Wittenberg. His work focuses on psychometric modeling, particularly integrating response times and accuracy in test analysis. Key Research Areas: Item Response Theory (IRT), response time modeling, diffusion models, hierarchical models, and cognitive processes in test-taking. Contributions: Developed statistical methods (e.g., Hausman test for model fit, proportional hazards models) and computational tools (e.g., R scripts for LBA and diffusion models). Collaborations: Frequent co-authorship with J.-T. Kuhn, C. Szardenings, and T. Ortner on IRT extensions and cognitive modeling. His publications from 2012–2020 highlight advancements in IRT calibration, model robustness, and applications to personality and educational testing. Notable works include studies on the speed-accuracy trade-off, dyscalculia assessment, and neuroleptic treatment decisions.

Christian Heineمشاهده پروفایل

پژوهشگر

• Data Visualization
• Topology-Based Methods
• Scientific Computing
• +۳ مورد دیگر

Christian Heine is a researcher at the Institute of Computer Science , University of Leipzig. His work focuses on advanced data visualization techniques, particularly those grounded in topological and geometric analysis of scalar fields, ensemble data, and high-dimensional datasets. Key Research Areas: Topological visualization, scalar field analysis, medical imaging, and uncertainty quantification. Methodologies: Bayesian inference, fiber trajectories, volume rendering, and dynamic workflows. Applications: Meteorological data analysis, medical diagnostics, and interactive visualization systems. He has published extensively on these topics, with recent work addressing spatio-temporal trends in climate data and noise-robust visualization techniques. His research often integrates interdisciplinary approaches, bridging computer science and applied sciences.

• Marketing Models Building
• Multivariate Data Analysis
• Marketing Dynamics
• +۷ مورد دیگر

Stanislav Stakhovych is a Senior Lecturer in the Department of Marketing at Monash University's Faculty of Business and Economics. With a PhD from University of Groningen (2010), he specializes in quantitative marketing methodologies and strategic consumer behavior analysis. Current academic affiliation: Senior Lecturer at Monash University Education: PhD in Marketing (University of Groningen, 2010) His research focuses span marketing models building, Bayesian econometrics, and spatial modeling, with applications in customer retention, churn prediction, and e-commerce analytics. Recent work examines online retail dynamics, complaint recovery frameworks, and personalized incentive effectiveness. Key research trends include: Application of Bayesian methods to marketing problems Development of customer base analysis techniques Integration of geospatial modeling in marketing strategy Mobile marketing analytics Predictive modeling for B2B customer retention

Michael Francis Wanglerمشاهده پروفایل

دانشیار

• Pediatric genetics
• Peroxisomal disorders
• Medical genetics
• +۴ مورد دیگر

Dr. Michael Francis Wangler is an Associate Professor at Baylor College of Medicine within the Graduate School of Biomedical Sciences and Department of Molecular & Human Genetics . His research focuses on pediatric genetics and peroxisomal disorders , using Drosophila models and genomics to study Mendelian disease mechanisms. Co-Director of Model Organisms Screening Center (MOSC) for Undiagnosed Diseases Network Active in clinical genetics at Texas Children's Hospital Research interests include: Deciphering molecular pathogenesis of rare diseases Genomics and metabolomics in peroxisome biogenesis disorders Functional validation of de novo variants Model organism applications in clinical genetics Recent publications highlight work on: DDX39B , RBBP5 , and DNMT3A variant-associated syndromes Biomarker discovery in peroxisomal diseases Neurodevelopmental disorder mechanisms via Drosophila Laboratory: Duncan Neurological Research Institute (Room DNRI-1050, Houston, TX)