جست‌وجوی استاد و پژوهشگر

Dario Granaمشاهده پروفایل

استاد

• Rock Physics
• Seismic Reservoir Characterization
• Inverse Problems
• +۱ مورد دیگر

Dr. Dario Grana is the Wyoming Excellence Chair and Professor at the University of Wyoming's Department of Geology and Geophysics. His research focuses on rock physics, seismic reservoir characterization, inverse problems, and geostatistics, with applications in critical zone studies, CO2 sequestration monitoring, and subsurface characterization. Education includes: PhD and MS in Geophysics, Stanford University (2013) MS in Applied Mathematics, University of Milano-Bicocca (2006) BS and MS in Mathematics, University of Pavia (2003, 2005) Research explores rock physics modeling in critical zones, geophysical monitoring of CO2 storage, joint seismic-electromagnetic inversion, and stochastic methods for subsurface characterization. Recent publications emphasize machine learning integration with geostatistical inversion and Bayesian methods for uncertainty quantification. Teaching includes Quantitative Geoscience, Mathematical Methods for Geoscience, Exploration Geoscience, Inverse Theory, and Rock Physics.

Mohamed Elshaerمشاهده پروفایل

پژوهشگر

• Pavement Engineering
• Sustainable Infrastructure
• Transportation Engineering
• +۳ مورد دیگر

Mohamed Elshaer is a Researcher and Research Assistant Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire, part of the College of Engineering and Physical Sciences. His research focuses on climate-resilient pavement systems, emphasizing sustainable solutions for infrastructure durability under environmental stressors. He collaborates on adaptive management strategies involving advanced materials testing and predictive modeling. Elshaer teaches courses such as CEE 736/836 (Asphalt Mixtures & Construction) and CEE 737/837 (Pavement Rehabilitation and Management), reflecting his expertise in transportation engineering. His research interests include pavements, recycling practices, and innovative material applications. His publications highlight work on electrically conductive asphalt for deicing, fiber-reinforced asphalt durability, and predictive modeling of pavement performance. Recent studies address low-temperature cracking in asphalt and the impact of mixing methods on material consistency. Elshaer’s work bridges environmental and civil engineering, addressing challenges like sea-level rise effects on coastal infrastructure and moisture-induced pavement degradation. His contributions advance both theoretical frameworks and practical applications in sustainable infrastructure development.

Ryan Qi Wangمشاهده پروفایل

استادیار

• Urban Informatics
• Human Mobility
• Resilience Engineering
• +۲ مورد دیگر

Ryan Qi Wang is an Assistant Professor of Civil and Environmental Engineering at Northeastern University, jointly appointed with the College of Social Sciences and Humanities. He serves as Associate Research Director for Social Media at the Boston Area Research Initiative (BARI), investigating urban mobility patterns, disaster resilience, and geosocial networks. His research combines urban informatics with complex systems analysis to understand human movement during crises, urban inequalities, and sustainable city development. Recent publications examine mobility responses to environmental hazards, algorithmic biases in mobility models, and data-driven approaches to urban sustainability.

Stefan Radevمشاهده پروفایل

استادیار

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +۳ مورد دیگر

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.

Gianvito Pioمشاهده پروفایل

دانشیار

• Data Mining
• Bioinformatics
• Social Network Analysis
• +۳ مورد دیگر

Gianvito Pio is an Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research spans data mining, bioinformatics, social network analysis, multi-relational data mining, and big data analytics. He holds a PhD in Computer Science from the University of Bari (2015) and has taught courses such as Big Data Management and Analysis and Security in Blockchain Technology . As an expert in heterogeneous network analysis, he develops methods for anomaly detection in cryptocurrency trends, microbiome data interpretation, and legal judgment clustering. He leads the local research unit of the PRIN 2022 project COCOWEARS and contributes to journals like the Machine Learning Journal and Expert Systems with Applications as an associate editor. His recent work includes multi-view learning for risk identification in dynamic networks, spatially-aware models for energy forecasting, and biclustering algorithms for biological data. Collaborations with researchers such as Michelangelo Ceci and Antonio Pellicani highlight his interdisciplinary approach. Gianvito Pio also organizes academic events like the Discovery Science conference and contributes to open-source software tools including HOCCLUS2 and GENERE .

• Saddlepoint-Based Bootstrap
• Time Series Modeling
• Longitudinal Modeling
• +۳ مورد دیگر

Dr. A. Alexandre Trindade is a Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on developing novel statistical methodologies for complex data structures, with applications spanning finance, physics, and environmental science. He teaches graduate courses in Time Series Analysis and Nonparametric Statistics, and serves as advisor for the undergraduate Actuarial Science Minor program. His primary research areas include: Saddlepoint-Based Bootstrap (SPBB) : Methods for approximate inference in complex models with intractable distributions Time Series & Volatility Modeling : Multivariate autoregressive models, GARCH variants, and asymmetric distributions Spatial & Longitudinal Analysis : State-space models for missing data and spatial regression techniques Tail Risk Quantification : Nonparametric estimation of systemic risk measures like CoVaR Nonparametric Inference : Density estimation and signal detection for high-energy physics His publications demonstrate consistent innovation in statistical theory, particularly in developing resampling-based inference and extending time series methodologies. Collaborative projects include interdisciplinary work with petroleum engineering, nuclear science, and finance. Dr. Trindade maintains active research partnerships with national laboratories and industry, including reliability studies for Boeing and medical device research. He has developed specialized software for multivariate time series analysis and maintains public repositories for statistical computing resources.

Alina Schnake-Mahlمشاهده پروفایل

استادیار

• Public Health Policy
• Social Epidemiology
• Health Disparities
• +۷ مورد دیگر

Alina Schnake-Mahl, ScD, MPH, is an Assistant Professor at Drexel University's Dornsife School of Public Health, jointly affiliated with the Department of Health Management and Policy and the Urban Health Collaborative. Her work focuses on social and political determinants of health inequities, particularly how neighborhood factors, policies, and governance structures influence health disparities. Education: ScD in Public Health, Harvard T.H. Chan School of Public Health MPH in Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health BA in Health: Science, Society and Policy & Politics, Brandeis University Research Interests: Social epidemiology and health services research Health and built environment Policy analysis of occupational and housing policies Methodological innovations in causal inference Urban health inequities Key Research Trends: Dr. Schnake-Mahl's work emphasizes policy evaluation across multiple governance levels, with a focus on paid leave policies, vaccination inequities, and redistricting impacts. Her recent studies highlight how structural policies like paid sick leave correlate with reduced health disparities in urban settings. Labs/Teams: Active contributor to the Urban Health Collaborative, focusing on applied research to inform equitable health policies.

Anindya Royمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Bayesian Analysis
• High-dimensional Estimation and Inference
• +۳ مورد دیگر

Anindya Roy is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on time series analysis, Bayesian methods, high-dimensional statistics, and applications in biomedical and engineering domains. He serves as an Area Editor for Statistical Analysis and Data Mining , and on editorial boards for Sankhya, Ser. A and Statistics in Medicine . His research interests include nonparametric Bayesian approaches, geometric constraints in statistical models, and sequential experimental design. He has received research funding from the National Science Foundation (NSF DMS–2210282). Recent work addresses privacy-preserving data synthesis, causal inference in time series networks, and fetal growth modeling. Key contributions include methodologies for high-dimensional time series with directed acyclic graphs, Bayesian approaches for relational graph estimation, and statistical techniques for medical applications such as cyclotron ion source development and cervical cancer screening risk prediction. Notable collaborative efforts involve proteomic analysis of chemical mixtures, oncology trial design, and biomarker studies. His work bridges theoretical statistics with applied problems in health sciences and engineering, emphasizing interdisciplinary impact.

Jixin Maمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Temporal Representation
• +۳ مورد دیگر

Professor Jixin Ma is Director of the PhD/MPhil Programme and Professor of Computer Science (Artificial Intelligence) at the University of Greenwich's School of Computing and Mathematical Sciences. He leads the Artificial Intelligence Research Group and has published over 200 research papers in top-tier journals and conferences. His research spans artificial intelligence, data science, temporal reasoning, information security, machine learning, and case-based reasoning. Research interests focus on: Temporal representation and reasoning Secure computation and encrypted search Machine learning applications in security Computer vision and pattern recognition He has received multiple awards including 6 Best Paper awards and has secured research grants exceeding £330,000.

Karan S. Suranaمشاهده پروفایل

استاد

• Computational Mathematics
• Computational Mechanics
• Solid Mechanics
• +۷ مورد دیگر

Karan S. Surana is the Deane E. Ackers Distinguished Professor at the University of Kansas. His research focuses on computational mathematics and mechanics, with emphases on finite element methods, error estimation, and constitutive theories for solid and fluid continua. He explores complex phenomena in thermoviscoelastic solids, fluid-structure interactions, and nonlinear continuum mechanics. His work integrates mathematical rigor with computational frameworks to address challenges in multi-physics systems. Dr. Surana has authored influential books, including Advanced Mechanics of Continua , and pioneered the k-version of the finite element method (FEM) for boundary and initial value problems. His contributions extend to shock physics, micropolar continuum theories, and thermodynamic consistency analyses of nonclassical models. Education: Details not explicitly stated in text. Awards: Deane E. Ackers Distinguished Professor title Member of the University of Kansas Council of Distinguished Professors Research Highlights: Development of ordered rate constitutive theories for thermoviscoelastic materials Computational fluid dynamics for compressible flows and polymeric fluids Nonlinear wave propagation in solids and fluids His lab focuses on advancing numerical methods for multi-physics interaction processes, including fluid-solid couplings and high-temperature gas dynamics with real gas models. He investigates material behaviors under extreme conditions, such as high pressure and variable transport properties. Key contributions include mathematical models for shape memory materials and mixture theories, alongside software systems for adaptive finite element computations. Dr. Surana's work bridges theoretical mechanics with practical engineering applications, emphasizing unconditionally stable computational processes.

Arash Mohammadiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Medical Imaging
• Cybersecurity
• +۴ مورد دیگر

Arash Mohammadi is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University), specializing in artificial intelligence, medical imaging, and cybersecurity. His research focuses on advancing AI-driven solutions in healthcare diagnostics, autonomous systems, federated learning, and edge computing. He supervises graduate programs in Information Systems Security, Electrical and Computer Engineering, and related disciplines. Key research interests include medical image classification using deep learning, reliability of autonomous driving systems, and secure communication in smart grids. His work integrates transformer architectures, Bayesian methods, and graph neural networks to address challenges in healthcare, robotics, and network optimization. Notable contributions include frameworks for lung nodule malignancy prediction, ECG-based emotion recognition, and secure federated learning in resource-constrained environments. Dr. Mohammadi has published extensively in top-tier venues such as IEEE conferences and journals, with a focus on practical applications of AI in healthcare and engineering systems. He actively engages in interdisciplinary projects involving biomedical signal processing, human-computer interaction, and resilience engineering for cyber-physical systems.

Devinder Mahajanمشاهده پروفایل

استاد

• Low Carbon Energy
• Clean Fuel Production
• CO2 Mitigation
• +۵ مورد دیگر

Devinder Mahajan is a tenured Professor in the Department of Materials Science and Chemical Engineering at Stony Brook University . He also serves as Graduate Program Director (CME) and Director of the Institute of Gas Innovation and Technology (I-GIT) at the Advanced Energy Research and Technology Center (AERTC). His career spans over four decades, including leadership roles at Brookhaven National Laboratory and international appointments in Italy, Japan, and China. Education : Ph.D. in Applied Chemistry (1979, University of British Columbia), M.Sc. (1976, University of British Columbia), B.Sc. (1972, Panjab University) Dr. Mahajan’s research focuses on low-carbon energy technologies , including CO 2 mitigation, methane hydrate utilization, biomass-to-fuels catalysis, ultra-deep hydrodesulfurization (HDS), extremophiles-mediated hydrogen production for fuel cells, and geothermal energy-minerals extraction. His work bridges chemical engineering, materials science, and environmental policy. His scientific publications (over 318) emphasize clean energy solutions, with recent articles exploring hydrogen storage in graphene materials, pipeline infrastructure for hydrogen blending, CO 2 clathrate hydrates, and photovoltaic/membrane distillation systems. These studies reflect his commitment to decarbonization and sustainable resource utilization. Notable scientific awards include: Marie Curie Senior Researcher (2013-17) Jefferson Science Fellow (2011-12) Outstanding Mentor Award (2009, 2007, U.S. Department of Energy) Crown and Eagle Medal of Honor (2006, RANS) Promising Inventor’s Award (2005, SUNY) Dr. Mahajan actively contributes to professional leadership , serving on editorial boards for the Journal of Renewable and Sustainable Energy and The Open Petroleum Journal . He has organized international workshops on biofuels, methane hydrates, and low-carbon societies, and advised U.S. and Chinese governments on energy policy.

• Machine Learning
• Cloud Computing
• IT Service Management
• +۳ مورد دیگر

Genady Ya. Grabarnik is a Professor and Graduate Director in the Department of Mathematics and Computer Science at St. John's College of Liberal Arts and Sciences, St. John's University. He holds a Ph.D. from the National Academy of Science. His research focuses on interdisciplinary areas including machine learning, cloud computing, IT service management, and mathematics education. He has contributed to advancements in Kubernetes-based systems, digital twin frameworks, and educational methodologies for STEM prerequisites. His work bridges theoretical foundations with practical applications in cloud infrastructure and automated problem-solving. Education: Ph.D., National Academy of Science Research Interests: Machine Learning and its prerequisites in education Cloud computing, Kubernetes, and microservices IT service management and automation Data-driven methodologies for system optimization Key Contributions: Developed frameworks like Kubetwin for Kubernetes deployments Explored hybrid cloud optimizations via BDMaaS+ Advanced AI-driven ticket resolution systems (e.g., AISTAR ) Studied cross-country educational strategies for mathematics Advising & Grants: No explicit student advising records listed. His grants and collaborations are inferred from his research outputs but not detailed here. Labs/Teams: Not explicitly mentioned, though his work suggests involvement with cloud computing and IT service management research groups.

• Statistical Theory and Modeling
• Lifetime Data Analysis
• Censoring Methodologies
• +۱ مورد دیگر

Dr. Katherine Davies is an Assistant Professor in the Department of Mathematics and Statistics at McMaster University. She holds a B.Sc. (2002) and M.Sc. (2004) in Mathematics and Statistics from McMaster, and a Ph.D. in Statistics (2008) from Western University. Prior to joining McMaster, she spent 14 years at the University of Manitoba, where she was promoted to Associate Professor. Her research focuses on statistical theory and modeling, particularly the analysis of lifetime data, censoring methodologies, and distribution theory with applications in engineering and medical sciences. She has supervised multiple graduate students and maintains international collaborations. Dr. Davies teaches undergraduate and graduate courses including Generalized Linear Models, Applied Regression Analysis, and Introduction to Probability. She is an active member of the Statistical Society of Canada and founded the 'Steering Women into Statistics' outreach initiative.

• Microeconomic Theory
• Decision Making under Risk and Uncertainty
• Behavioral Economics
• +۳ مورد دیگر

Craig Webb is a Senior Lecturer in Economics at the University of Manchester. His research focuses on integrating empirical insights from behavioral economics into mathematical models of decision making under risk, uncertainty, and time. He specializes in revisiting classical microeconomic theory to accommodate deviations like loss aversion and ambiguity aversion while maintaining theoretical simplicity and applicability. Current Courses : ECON10171 Microeconomic Analysis 1, ECON30001 Advanced Microeconomics, ECON80041 Advanced Microeconomic Theory, ECON80150 Advanced Topics in Microeconomic Theory Previous Courses : ECON20000 Managerial Economics I, ECON20351 Microeconomics IIA, ECON20352 Microeconomics IIB, ECON30600 Microeconomics III, ECON31001 Managerial Economics II Research Interests : Craig’s work challenges the classical Subjective Expected Utility model by analyzing behavioral patterns (loss aversion, probabilistic risk attitudes, ambiguity aversion) and developing integrative mathematical frameworks. His recent contributions include preference foundations for discounted utility models and parametric approaches to risk assessment. Scientific Awards : ESRC 1+3 PhD Studentship GLS Shackle Prize Bromley Undergraduate Scholarship Advising : Currently co-supervising Jinrui Pan (PhD Economics) on axiomatic foundations of intertemporal decision making. His research output spans topics like mixture independence, stochastic dominance, and continuous quasi-hyperbolic discounting, with collaborations in behavioral economics and decision theory.