جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Transportation Systems
• Renewable Energy
• +۳ مورد دیگر

Joe Severino serves as a Researcher III in Computational Science at the National Renewable Energy Laboratory (NREL), focusing on machine learning applications for transportation and renewable energy systems. His work addresses real-time optimization challenges in traffic coordination, power grid management, and sustainable energy integration through advanced computational methods. His research spans Machine Learning, Transportation Systems, Renewable Energy, Traffic Signal Control, Predictive Modeling, and Road Networks. He develops solutions for traffic state estimation using probe vehicle data, fairness-aware load shedding in power grids, and explainable AI for energy research, frequently employing ensemble methods and adaptive control strategies to enhance model accuracy and equity. Analysis of his 2024-2025 publications reveals a strong trend toward real-time machine learning solutions for critical infrastructure. Key themes include network-wide traffic signal coordination using adaptive control frameworks, fairness optimization in power grid operations, and interpretable AI models for renewable energy systems. His work consistently bridges theoretical machine learning with practical engineering constraints. As part of NREL's Computational Science group, Severino collaborates extensively with interdisciplinary teams to advance computational approaches for sustainable transportation and energy resilience.

Pedro Simões Coelhoمشاهده پروفایل

استاد

• Statistics
• Sampling Techniques
• Structural Equation Modeling
• +۱۲ مورد دیگر

Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +۵ مورد دیگر

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Physical Activity
• Child Health and Development
• Health Promotion
• +۷ مورد دیگر

Hayley Christian is a Professor at The University of Western Australia , holding positions as Senior Research Fellow at the UWA Medical School and Adjunct Associate Professor at the School of Population and Global Health. Her research focuses on improving children's physical activity, health, and well-being through multi-level interventions spanning child, family, social, and built environment domains. PhD in Public Health (with Distinction), The University of Western Australia Chief investigator on 186+ research outputs, 46 grants, and 6 active projects Key research areas include: Physical activity and sedentary behavior in early childhood Health impacts of built environments Companion animals' role in social capital Nature contact benefits for development Policy implementation in childcare settings Her recent publications examine trends in Beach environment impacts on physical activity Child socioemotional development Screen time reduction strategies Physical literacy programs Energy drink consumption patterns Scientific accolades: 2023 School of Population and Global Health Mid-Career Research Award 2022 WA Science Awards Finalist 2020 WA Young Tall Poppy Scientist of the Year She has received major grants including: National Heart Foundation Future Leader Fellowships (2020-2024) NHMRC equipment and partnership grants Healthway and MRFF intervention grants Worldwide Universities Network research funding Hayley leads the PLAYCE (Play Spaces & Environments for Children's Physical Activity) program and serves as: Program Head, Healthy Behaviours and Environments, Telethon Kids Institute Team Head, Child Physical Activity, Telethon Kids Institute Co Director WA Node, ARC Centre of Excellence for Children and Families

Claudia Solis-Lemusمشاهده پروفایل

استادیار

• Statistical Phylogenomics
• Microbiome Analysis
• Network Modeling
• +۵ مورد دیگر

Claudia Solis-Lemus is an Assistant Professor at the University of Wisconsin-Madison with dual appointments in the Department of Plant Pathology within the College of Agricultural and Life Sciences and the Wisconsin Institute for Discovery. Her work bridges statistical methodology development with biological applications, focusing on creating computationally efficient models that maintain biological interpretability and theoretical guarantees. Dr. Solis-Lemus earned her PhD in Statistics and MA in Mathematics, both from the University of Wisconsin-Madison. Her academic journey has positioned her at the intersection of statistics, computational biology, and evolutionary genomics. Her research program centers on three interconnected areas: statistical phylogenomics, microbiome analysis, and the application of deep learning in biological contexts. In statistical phylogenomics, she develops novel methods to reconstruct phylogenetic networks that can account for reticulate evolutionary processes like hybridization and horizontal gene transfer. Her work in microbiome analysis focuses on creating statistical frameworks to model microbial communities as networks and use these networks to predict host phenotypes. In deep learning applications, she addresses challenges in applying neural networks to biomedical data by developing methods with statistical guarantees and improved interpretability. Analysis of Dr. Solis-Lemus's recent publications reveals a strong emphasis on developing computationally efficient methods for analyzing complex biological networks. Her work spans from theoretical advances in phylogenetic network inference to practical software implementations for microbiome analysis. A notable trend is her focus on creating methods that balance statistical rigor with computational tractability, particularly for large-scale biological datasets. Her research increasingly integrates machine learning approaches with traditional statistical methods to address challenges in evolutionary biology and microbiome research. Dr. Solis-Lemus has received significant recognition through competitive grants including: NSF CAREER Award (DEB 2144367) for "Towards Scalable and Robust Inference of Phylogenetic Networks" USDA Individual Hatch grant for "Enhanced interaction and network statistical models for microbiome data" DOE Computational Tool Development award for "Harnessing the power of big omics data" USDA-NIFA hatch project 1023699 for "Novel interaction and network statistical models for microbiome data" NSF IntBIO Collaborative Research DEB Award 2316269 USDA NIFA award for "Unraveling The Microbial Mechanisms That Mediate Disease Resurgence In Plants" Dr. Solis-Lemus actively mentors a diverse research group including postdoctoral researchers, PhD students, MS students, and undergraduates. Her lab is supported by multiple grants totaling millions of dollars, enabling research that spans from theoretical statistics to practical applications in agriculture and medicine. She has successfully secured funding from NSF, DOE, USDA, and industry partners, demonstrating the broad relevance of her work across multiple domains. The Solis-Lemus Lab operates as an interdisciplinary team bringing together statisticians, biologists, computer scientists, and domain experts. The lab maintains strong collaborations with researchers across UW-Madison and nationally, particularly in plant pathology, microbiology, and evolutionary biology. They develop open-source software tools that are widely used in the scientific community, including PhyloNetworks for phylogenetic network analysis and CMiNet for microbiome network construction.

• Distributed Data Management
• Cloud Computing
• Digital Cultural Heritage
• +۴ مورد دیگر

Renata Słota is a Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, where she serves as Vice-Dean of the Faculty of Development. Her office is located at D-17, ul. Kawiory 21, room II, 3.34, with contact details including phone +48 12 328 33 35 and email rena@agh.edu.pl. She also contributes to academic governance as a member of the Disciplinary Council for Technical Information Technology and Telecommunications. Her research centers on distributed data management and cloud computing , with pioneering work in digital cultural heritage systems , blockchain-based decentralized authority , and scientific workflow automation . Notable contributions include integrated frameworks for 3D Cultural Heritage objects management, consensus mechanisms for collaborative data sharing, and multi-cloud data access solutions. Her work bridges theoretical advances with practical applications in cultural preservation and scientific computing. Analysis of her 2020-2026 publications reveals dominant trends in decentralized data governance , cultural heritage digitization , and hybrid cloud workflow management . Key themes include blockchain applications for organizational structures, semantic interoperability for quality-oriented data access, and reproducibility frameworks for computational experiments. Her research consistently addresses real-world challenges in global data accessibility and fault tolerance. Professor Słota actively participates in major research initiatives including the Onedata project for global data federation and the Scalarm platform for distributed parameter studies, demonstrating leadership in data-intensive computing environments and collaborative infrastructure development.

• Insurance
• Risk Management
• Actuarial Science
• +۴ مورد دیگر

Nilüfer Dalkılıç is an Associate Professor in the Department of Insurance and Risk Management at the Personal School of Applied Sciences, Dumlupinar University, Turkey. Her institutional email is nilufer.dalkilic@dpu.edu.tr and work phone is 0 (274) 265 20 31. She actively contributes to academic research and institutional projects within the Turkish higher education framework. Her research spans Insurance, Risk Management, Actuarial Science, and Financial Risk Analysis with specialized focus on Digital Insurance (InsurTech), Pension Systems, and Macroeconomic Factors. Recent work examines disaster risk financing in earthquake contexts, historical evolution of insurance systems, and Solvency II regulatory adaptation in Turkey. She employs quantitative methodologies including regression analysis, system simulation, and data envelopment analysis. Analysis of her 2014-2024 publications reveals consistent empirical investigation of the Turkish insurance sector, emphasizing non-life insurance performance, pension fund dynamics, and digital transformation impacts. Her work bridges theoretical frameworks with industry applications, particularly in regional economic development contexts. Dr. Dalkılıç served as Executive on two significant research projects: the Dumlupinar University-funded 'Kütahya Insurance Intern Career Pool' (2016-2017) and the Ministry of Development-commissioned 'Kütahya, Afyonkarahisar, Uşak Provinces Project Development Action Plan' (2015-2016), demonstrating applied research engagement with governmental and regional stakeholders.

David Lorenzمشاهده پروفایل

مدرس ارشد

• English Linguistics
• Language Variation and Change
• Grammaticalization
• +۵ مورد دیگر

David Lorenz is a Senior Lecturer and Associate Professor at Lund University's Centre for Languages and Literature, specializing in English Linguistics. His work spans language variation, grammaticalization, and phonetic reduction in spoken language. Teaching: Courses in English language, academic writing, and linguistic theory/methods. Research: Focus on usage-based language dynamics, corpus analysis, and experimental linguistics. Recent publications explore epistemic modality, multi-word sequences, and statistical modeling. Projects include CRMWSE (2022-2025) on language cognition and upcoming work on TikTok hate speech rhetoric (2025). Contact: david.lorenz@englund.lu.se | +46 76 720 10 15 | SOL:H317b, Lund University.

• Applied Mathematics
• Blockchain Technology
• Cybersecurity
• +۳ مورد دیگر

Halil Snopçe is a Part-Time Full Professor at the Faculty of Contemporary Sciences and Technologies within South East European University . His academic and professional work spans applied mathematics , blockchain technology , cybersecurity , and parallel computing . Doctor in Computer Science and Applied Mathematics (2007-2011), SEEU Master in Mathematics (2003-2007), University of Tirana Bachelor in Mathematics (1992-1997), St. Cyril and Methodius University Snopçe’s research focuses on mathematical transformations , systolic arrays , and blockchain applications in education and credentials verification. His work also addresses cybersecurity challenges , data mining , and parallel processing for computational efficiency. Recent publications highlight trends in blockchain technology for academic systems, parallel algorithms , and educational technology during the pandemic. His applied mathematics contributions include numerical analysis and linear algebra solutions. Snopçe has no listed scientific awards in the provided information. He has collaborated on student advising and interdisciplinary research projects, though specific advisees are not detailed.

Abel Patrick Abelمشاهده پروفایل

استادیار

• Energy Economics
• Financial Risk Analysis
• Real Estate Finance
• +۳ مورد دیگر

Abel Patrick Abel is an Assistant Professor at the University of Passau, with research expertise spanning Energy Economics , Financial Risk Analysis , and Climate Policy . His work bridges empirical econometrics and policy evaluation, focusing on energy markets, real estate bubbles, and credit risk dynamics. Key Research Areas: Energy economics, financial market inefficiencies, climate policy, and quantitative risk modeling. Recent Publications: Analysis of hydropower valuation under climate risk, energy market regulations during global crises, and speculative bubbles in real estate and crypto assets. Training & Affiliations: Affiliated with energy law research initiatives and the Climate Area of the Florence School of Regulation (FSR), contributing to policy dialogues on climate neutrality and public acceptance. Methodological Focus: Leverages econometric modeling, stochastic processes, and bubble detection frameworks.

• Autonomous Systems
• Artificial Intelligence Safety
• System Identification
• +۵ مورد دیگر

Laura Lützow is a Researcher at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Cyber Physical Systems group. She earned her bachelor's degree in Mechatronics from TU Ilmenau (2020) and a master's degree in Robotics, Cognition, and Intelligence from TUM (2022). Her work focuses on enhancing the safety and reliability of autonomous systems through set-based system identification, uncertainty quantification, and conformance checking. B.Sc. in Mechatronics, TU Ilmenau (2020) M.Sc. in Robotics, Cognition, and Intelligence, TUM (2022) Laura's research emphasizes formal methods for analyzing and improving the safety of autonomous systems. Her work includes developing mathematical frameworks for reachability analysis and uncertainty quantification, particularly in dynamic environments with complex constraints. Laura's publications highlight advancements in reachability analysis, system identification, and uncertainty quantification. Key themes include conformal prediction for autonomous systems, zonotope reduction techniques, and motion planning under dynamic obstacles. Her work bridges theoretical control systems research with practical applications in robotics and AI safety. She has taught courses on verification, controller synthesis, formal methods, and fundamentals of artificial intelligence at TUM. Additionally, she was a visiting researcher at Stanford University's Intelligent Systems Laboratory (January-July 2025).

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.

• Statistics
• Epidemiology
• Machine Learning
• +۲ مورد دیگر

Paul Eilers is a researcher in the Epidemiology department at Erasmus MC , with extensive contributions to statistical methodology and biomedical applications. His work spans survival analysis, computational statistics, and signal processing. Specializes in advanced statistical modeling with splines Active in medical informatics and epidemiological research Collaborates internationally on multidisciplinary projects Research Interests : Develops innovative statistical techniques for complex biomedical data, focusing on: Spline-based smoothing algorithms Survival analysis with multiple time scales Multidimensional P-splines for neuroscience Deconvolution methods in NMR spectroscopy Computational efficiency in determinant derivatives Application of statistics to clinical research Scientific Output : Demonstrates expertise in statistical modeling through 115 research outputs, including recent work on: Quantile regression techniques Semiparametric proportional hazards models Adaptive smoothing for neuronal activity analysis Modern approaches to statistical computing Collaboration Network : Maintains active collaborations across disciplines, particularly in: Medical statistics applications Computational method development Chemometrics and bioinformatics Cross-institutional research projects

Daniel McGibneyمشاهده پروفایل

دانشیار

• Operations Research
• Business Analytics
• Clinical Trials Management
• +۳ مورد دیگر

Daniel McGibney is an Associate Professor at the University of Miami's Miami Herbert Business School in the Management Science department. He serves as Co-director of the MS in Business Analytics Program and focuses on applying data-driven methodologies to operational research and clinical trial optimization. University: University of Miami School: Miami Herbert Business School Department: Management Science Email: dxm1023@miami.edu His research bridges operational research and business analytics, emphasizing probabilistic modeling for clinical trials and practical data science applications. Recent work explores global site selection optimization with risk-based constraints and educational frameworks for linear regression in business contexts.

• Civil Engineering
• Hydraulic
• Coastal Sedimentation
• +۳ مورد دیگر

Rifat Tür is an Assistant Professor at Akdeniz University , Faculty of Engineering, Department of Civil Engineering. His work focuses on hydraulic engineering, coastal structures, and drought forecasting. Education: PhD (2006-2010) in Civil Engineering, Gazi University MSc (2000-2003) in Civil Engineering, Akdeniz University BSc (1996-2000) in Civil Engineering, Süleyman Demirel University Research Interests: Coastal Engineering and Sedimentation Drought Monitoring and Forecasting Wave Height Prediction Artificial Intelligence in Civil Engineering Maritime Risk Assessment Port Handling Capacity Modeling Recent publications emphasize machine learning for climate prediction, coastal structure design, and drought resilience. He has contributed to TUBITAK-funded projects on coastal sedimentation and served as an Assistant Editor for the International Journal of Engineering & Applied Sciences .