جست‌وجوی استاد و پژوهشگر

Mario Bergesمشاهده پروفایل

دانشیار

• Smart Infrastructure
• Structural Health Monitoring
• Non-Intrusive Load Monitoring (NILM)
• +۴ مورد دیگر

Mario Berges is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. He holds leadership roles as Co-Director of the IBM Smart Infrastructure Analytics Lab and Director of the Intelligent Infrastructure Research Lab (INFERLab). His work focuses on applying information/communication technologies to enhance the operational efficiency and resilience of built environments amid evolving resource constraints and climate changes. Education: PhD in Civil & Environmental Engineering from CMU (2010). Research Interests: Berges' research integrates smart infrastructure systems, energy efficiency, and machine learning. Key areas include non-intrusive load monitoring (NILM), structural health monitoring of pipelines, building automation systems, and urban heat risk modeling. He develops data-driven frameworks for energy disaggregation, sensor placement optimization, and real-time infrastructure diagnostics. Awards: Recognized with the 2010 FIATECH Outstanding Early Career Researcher Award and 2015 Dean’s Early Career Fellowship from CMU. Grants & Labs: Leads INFERLab, collaborating with IBM on smart infrastructure projects. His work spans academic-industry partnerships focused on building analytics, smart grid technologies, and sensor networks. Future Directions: Expanding research into AI-driven energy systems, resilient urban infrastructure, and cross-disciplinary solutions for climate adaptation.

Kwang-Sung Junمشاهده پروفایل

استادیار

• Interactive Machine Learning
• Reinforcement Learning
• Multi-Armed Bandits
• +۷ مورد دیگر

Kwang-Sung Jun is an Assistant Professor at the University of Arizona, Department of Computer Science. His research spans interactive machine learning, reinforcement learning, and learning theory, with a focus on multi-armed bandits, Bayesian optimization, and generalized linear models. Education : Ph.D. in Computer Science from the University of Wisconsin-Madison (2015). Research Trends : Kwang-Sung's recent work (2023-2025) emphasizes bandit algorithms with second-order bounds, adaptive experimentation, and PAC-Bayes frameworks. He explores low-rank structures in regression, explainable reward shaping, and environmental risk modeling via probabilistic assessments of postfire debris-flows. His publications often bridge theoretical guarantees (e.g., regret bounds) with practical applications in machine learning and environmental hazards. Expertise : Interactive machine learning Multi-armed bandits Confidence sequences Reinforcement learning Human-machine hybrid systems

Junier Olivaمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +۸ مورد دیگر

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

• Computational Fluid Dynamics
• Discontinuous Galerkin Methods
• Finite Volume Methods
• +۶ مورد دیگر

Michael Dumbser is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering. His research focuses on computational fluid dynamics, numerical methods for hyperbolic conservation laws, and high-performance computing. He specializes in developing structure-preserving numerical schemes such as discontinuous Galerkin and finite volume methods for continuum mechanics, relativistic fluid dynamics, and multiphase flows. Teaching responsibilities include courses like Calcolo numerico e programmazione , High-Performance Computing for Multi-Functional Metamaterials , and Metodi numerici per l'ambiente . His work emphasizes thermodynamically compatible formulations and adaptive numerical methods for complex physical systems. Recent research trends involve hyperbolic reformulations of classical models (e.g., Navier-Stokes-Korteweg, Einstein equations), staggered semi-implicit schemes for incompressible flows, and GPU-accelerated algorithms. His publications span topics from geophysical fluid dynamics to relativistic astrophysics, with a focus on maintaining physical conservation principles in numerical implementations. No scientific awards are explicitly listed in the provided information. His advising record is not detailed here, though his courses suggest involvement in student mentorship. Research collaborations include projects on metamaterials and computational geophysics. Current initiatives include developing unified models for earthquake rupture dynamics, non-Newtonian fluid simulations, and adaptive mesh refinement techniques. His lab work involves high-performance computing frameworks like ExaHyPE for large-scale wave propagation studies.

Ali Nazemiمشاهده پروفایل

دانشیار

• Hydrology
• Water Security
• Climate Change
• +۳ مورد دیگر

Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.

• Actuarial Science
• Insurance
• Risk Management
• +۳ مورد دیگر

Gee Lee is an Associate Professor in the Department of Statistics & Probability and the Department of Mathematics at Michigan State University. His work bridges actuarial science with advanced statistical and machine learning methodologies, focusing on practical applications for insurance risk modeling. PhD, University of Wisconsin-Madison Associate (ASA), Society of Actuaries His research centers on insurance loss modeling for rate-making and loss reserving, multivariate insurance coverage optimization, dependence structure analysis, and integrating machine learning into actuarial frameworks. Current projects include deep neural networks for claim prediction, unstructured data analysis, and multivariate coverage optimization. Recent publications highlight trends in crop insurance modeling (2025), regularization techniques (2024), multivariate risk retention strategies (2023), textual data analysis (2022), copula regression (2022), and reinsurance game theory (2022). Earlier works explore shrinkage methods, word embeddings, longitudinal claims, healthcare data, and deductible ratemaking. Gee Lee supervises MS and PhD students in actuarial science. Former advisees include Leonard Korreshi (2024), Qiaozhen Qian (2023), and Scott Manski (2020, co-advised). He also supports undergraduate research through MSU’s REU program and directed study courses.

Soutir Bandyopadhyayمشاهده پروفایل

استاد

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +۴ مورد دیگر

Soutir Bandyopadhyay is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He holds a PhD in Statistics from Texas A&M University, an M.Stat from the Indian Statistical Institute, and a B.Sc in Statistics from St. Xavier’s College. His research focuses on spatial and environmental statistics, time series analysis, bootstrap methods, and large sample theory, with applications in energy systems, climate science, and machine learning. Recent work includes advancements in spatial extremes modeling using neural networks, risk analysis in oil and gas flowlines, and statistical frameworks for energy load forecasting. He has organized major conferences including the 2023 IISA Annual Conference and the 2024 workshop on uncertainty in civil/environmental systems. As Director of the Data Science Program at Mines since 2025, he leads interdisciplinary initiatives. Key contributions include development of the National Climate Database (NCDB), bias-correction methods for solar radiation data, and R packages like glmfitmiss for handling missing data in GLMs. His students have achieved notable recognitions, including Fulbright awards and ASA competition honors.

Mahmood Azimi-Sadjadiمشاهده پروفایل

استاد

• Digital Signal Processing
• Machine Learning
• Distributed Sensor Networks
• +۳ مورد دیگر

Dr. Mahmood Azimi-Sadjadi is a full Professor in the Department of Electrical and Computer Engineering at Colorado State University (CSU), affiliated with the Walter Scott, Jr. College of Engineering. He leads the Digital Signal/Image Processing Laboratory , focusing on advanced signal processing, machine learning, and underwater acoustics research. His work spans sonar systems, distributed sensor networks, and remote sensing applications. Educational Background: B.S. in Electrical Engineering, University of Tehran (1977) M.S. and Ph.D. in Electrical Engineering, Imperial College of Science and Technology, University of London (1978, 1982) Research Focus: His research emphasizes statistical signal/image processing, machine learning, and underwater target detection/classification using sonar. He has pioneered methods like manifold-based UXO classification and Bayesian occupancy grids. His lab develops both theoretical frameworks (e.g., canonical correlation analysis) and applied systems (e.g., acoustic tomography). Recent Publications Trends: Recent work includes adaptive machine learning for underwater UXO classification, wildfire prediction via satellite data, and autonomous systems navigation. His publications blend signal processing fundamentals with cutting-edge AI techniques. Awards & Affiliations: 1999 Abell Faculty Teaching Award (College of Engineering) Fellow of CIRA (Center for Information and Remote Sensing Research) Member of IEEE (Life Member) and IEEE Neural Network Society Advising & Labs: Directs the Digital Signal/Image Processing Lab, which collaborates on projects like acoustic source localization and sensor network optimization. His research has been supported by grants from agencies like SERDP (e.g., MR-2416 project).

Yi Huangمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Comparative Effectiveness Research
• +۹ مورد دیگر

Yi Huang is an Associate Professor in the Department of Mathematics and Statistics at UMBC since 2007, specializing in biostatistics with a focus on causal inference and public health. She holds a Ph.D. in Biostatistics from Johns Hopkins University (2007) and has been a key contributor to interdisciplinary research at the interface of statistics and public health/medicine/biology. Her work emphasizes methodological innovation in comparative effectiveness research, personalized medicine, and post-marketing safety evaluations. Education: Ph.D. in Biostatistics, Johns Hopkins University Bloomberg School of Public Health (2007) NASA Graduate Fellowship recipient (1999) for aerosol-cloud interaction research Research Interests: Causal inference methodologies, including latent propensity score frameworks Comparative effectiveness research (CER) and meta-analysis Applications in gerontology, environmental health, and maternal/child health Statistical design for evaluating traditional Chinese medicine Grants and Awards: NIH R01 Grant Co-PI ($897,348) for pediatric care coordination study (2015–2020) National Institute of Aging Grant ($77,289) for hip fracture studies (2014–2015) Two FDA Critical Path Grants ($50,000 each) for causal methodology development (2008–2011) NASA Graduate Fellowship (1999) Professional Activities: Member: American Statistical Association, International Biometric Society, American Gerontology Society NIH Grant Reviewer (NIDDK), FDA External Reviewer Editorial Board: Internet Journal of Mental Health Teaching and Mentoring: Developed courses in biostatistics principles, categorical data analysis, and longitudinal methods Advised over 20 graduate students across statistics and interdisciplinary programs Focus on critical thinking and real-world application in pedagogy

Walter Tinssonمشاهده پروفایل

مدرس ارشد

• Experimental Design
• Statistics
• Design of Experiments
• +۳ مورد دیگر

Walter Tinsson serves as a Senior Lecturer in the Department of Mathematics at the University of Pau and Pays de l'Adour, France, where he has maintained continuous academic employment since 2001. His institutional leadership includes serving as Director of the Mathematics Department from 2014 to 2019 and membership on the university's board of directors from 2016 to 2019, demonstrating sustained administrative commitment alongside his teaching and research responsibilities within the Laboratory of Mathematics and their Applications (LMAP). His academic credentials feature: Authorization to direct research (2013), University of Pau and Pays de l'Adour Doctorate in applied mathematics (1998), University of Pau and Pays de l'Adour Tinsson's scholarly focus centers on advanced experimental design theory, with specialized expertise in block designs, small-sample experimental frameworks, generalized linear model applications, and methodological innovations for quantitative and qualitative factors. His research consistently bridges theoretical rigor with practical implementation across diverse scientific domains, particularly emphasizing neighborhood-effect modeling and complex mixture experiment configurations where traditional design approaches face limitations. Publication analysis reveals a concentrated research trajectory from 1997-2014 with significant contributions to spatial interference modeling, mixture-of-mixture methodologies, and optimal crossover design structures. His work demonstrates increasing sophistication in handling constrained experimental regions and complex variance structures, reflecting the field's evolution toward more realistic experimental constraints and multidimensional response surfaces. Walter Tinsson has provided doctoral supervision for Hanen Hanna, whose 2010 thesis addressed mixture designs with two-level factors and external variables under Venezuelan state funding, with Tinsson contributing 80% of the supervisory responsibility. His extensive teaching portfolio spans multiple institutions including ENSAI, ISPED Bordeaux, and IFP Training, covering advanced experimental design and statistical methodology across engineering, biostatistics, and petroleum disciplines. As an active member of the Probability and Statistics Team within LMAP, Tinsson contributes to collaborative research initiatives while maintaining specialized focus on design efficiency criteria and practical implementation challenges for industrial and scientific applications requiring precise experimental control under complex constraints.

Peter Dunnمشاهده پروفایل

دانشیار

• Biostatistics
• Generalized Linear Models
• Statistical Applications
• +۵ مورد دیگر

Associate Professor Peter Dunn is a Biostatistics faculty member at the School of Science, Technology and Engineering, University of the Sunshine Coast. He holds a PhD from the University of Queensland and has expertise in statistical theory, particularly generalized linear models and Tweedie distributions. His work includes developing R packages like 'tweedie' and co-authoring the textbook Generalized Linear Models With Examples in R . Education: PhD (Mathematical Statistics) Qld, BInfTech (Honours) S.Qld, BAppSci UCS Qld, GradCertProfLearning Sunshine Coast. Previously worked as a climate scientist at Queensland Department of Primary Industries and lectured at the University of Southern Queensland before joining USC in 2008. Research Focus: GLMs, statistical education, epidemiology, environmental statistics Contributions: Dunn-Smyth residuals, R packages, educational resources Research interests span theoretical statistics, applied statistical methods in health/ environment, and innovative pedagogy. Awards include the 2012 Office of Learning and Teaching Citation for educational excellence and the EJ Pitman Prize (2002). Teaching areas include epidemiology, biostatistics, and research methodology. He serves on editorial boards and professional societies like the Statistical Society of Australia.

• Geostatistics
• Bayesian Methods
• Digital Soil Mapping
• +۷ مورد دیگر

Luc Steinbuch is a Research Associate at Wageningen University & Research specializing in Geostatistics and Digital Soil Mapping . His work bridges Bayesian statistical frameworks with environmental science applications. Key Research Areas: Geostatistical modeling, luminescence detection, AI in academia, sustainability education Notable Projects: Bayesian geostatistics for soil mapping (2014-2021), crosstalk analysis in luminescence data (2025), AI implications for academic work (2023 keynote) Recent publications demonstrate methodological innovations in Geoderma (2024) and Radiation Measurements (2025), focusing on improving spatial data accuracy through machine learning and Bayesian approaches. His work impacts both fundamental scientific understanding and educational practices in soil science. As a contributor to open-source software development, Steinbuch has created tools for luminescence data analysis. He actively engages in academic communication, evidenced by his 2023 media spotlight and 2025 Zenodo report on sustainability education.

Melody Ghahramaniمشاهده پروفایل

استاد

• Time Series Analysis
• Biostatistics
• Environmetrics
• +۳ مورد دیگر

Melody Ghahramani is a Professor in the Department of Mathematics and Statistics at the University of Winnipeg. Her research focuses on time series analysis, environmetrics, and biostatistics with applications in finance and infectious disease modeling. She holds a Ph.D. in Statistics from the University of Manitoba and has held academic positions in Winnipeg since completing her postgraduate studies. Education: Ph.D. in Statistics, University of Manitoba M.Sc. in Statistics, Simon Fraser University Bachelor's Degree in Mathematics and Statistics, University of Manitoba Research interests emphasize statistical methodologies for zero-inflated and overdispersed data, environmental modeling of temporary streams in Canadian prairies, and biostatistical applications in healthcare and epidemiology. Her work bridges theoretical statistics with practical applications in finance and ecological systems. Publications highlight advancements in time series regression, volatility modeling, and cryptocurrency trading strategies. She has contributed to statistical frameworks for analyzing count data and financial market dynamics. Her applied research includes modeling long-term survival outcomes in healthcare and environmental water resource systems. No formal awards are listed, though her extensive publication record reflects sustained academic contribution. She advises courses in statistics and supervises student research in mathematical and statistical methodologies. Contact details: Office 6L25, Lockhart Hall, reachable at m.ghahramani@uwinnipeg.ca or 204.789.1414.

Courage Krahمشاهده پروفایل

پژوهشگر

• Environmental Science
• Food Waste Management
• Economic Analysis
• +۳ مورد دیگر

Courage Krah is a PhD Student at Pharos University , affiliated with the School of Management, People and Organisations . His research focuses on Household Food Waste in Ireland , particularly its Environmental and Economic Burdens , under the supervision of Dr. Anushree Priyadarshini and Dr. Paul Hynds. His work involves Quantifying household food waste via cross-sectional surveys Applying optimized Generalised Linear Models (GLMs) for demographic and geographic profiling Assessing greenhouse gas emissions and resource use with OpenLCA and life cycle costing (LCC) Recent publications and presentations highlight his expertise in Environmental and economic assessment of food waste Machine learning for consumer behavior analysis Pest management in agricultural crops He presented at the 52nd Annual Research Conference (UCD, 2024) and the TU Dublin Graduate Research Symposium (2024), and participated in stakeholder discussions at the Towards Halving Food Waste in Europe Conference (Netherlands, 2024).

Felix Abramovichمشاهده پروفایل

استاد

• Statistical Learning
• High-dimensional Inference
• Sparsity
• +۵ مورد دیگر

Felix Abramovich is a Professor in the Department of Statistics and Operations Research at Tel Aviv University. His research focuses on theoretical foundations of statistical and machine learning, specializing in high-dimensional inference, sparsity, model selection, and nonparametric estimation techniques. Professor Abramovich's work spans statistical learning theory, classification methods, wavelet-based estimation, and inverse problems. His research develops minimax-optimal procedures for sparse high-dimensional data and explores the interplay between Bayesian methods and frequentist optimality. Key themes include adaptive estimation under sparsity constraints, theoretical analysis of classification algorithms, and wavelet applications in nonparametric statistics. Analysis of Abramovich's recent publications reveals a strong emphasis on classification methodologies, high-dimensional regression, and deep learning theory. His work consistently develops minimax-optimal procedures, examines sparsity patterns in complex data structures, and bridges Bayesian and frequentist frameworks. The research demonstrates increasing focus on modern machine learning challenges including neural network theory and multiclass classification. Professor Abramovich actively recruits students and post-doctoral researchers with strong mathematical statistics backgrounds to work on fundamental problems in statistical learning theory and high-dimensional inference.