جست‌وجوی استاد و پژوهشگر

Robert Nobleمشاهده پروفایل

مدرس ارشد

• Cancer
• Evolutionary Biology
• Mathematical Biology
• +۷ مورد دیگر

Dr. Robert Noble is a Senior Lecturer in Mathematics at City St George's, University of London, specializing in the evolution and ecology of cancer . His research integrates mathematical modeling with computational biology to address four key themes: systematic understanding of somatic evolution, patient-specific tumor forecasting, treatment strategies leveraging evolutionary dynamics, and disentangling ecological contributions to cancer risk. Education: DPhil in Zoology (University of Oxford, 2014) Previous Appointments: Postdoctoral roles at ETH Zurich (2017-2020) and University of Zurich (2018-2020) His methodological toolkit spans agent-based models , stochastic processes , and Bayesian data analysis , with software contributions like the ggmuller and demon packages. Recent work focuses on universal tree balance indices for biological data comparison and adaptive therapy frameworks for clinical translation. Collaborations with experimental biologists and clinicians inform his research, which has produced key insights into spatial tumor evolution , drug resistance dynamics , and clonal diversity-survival relationships . He maintains active peer-review roles for journals like PLOS Computational Biology and Nature Ecology & Evolution.

• Applied Artificial Intelligence
• Digital Twins
• Hybrid Modeling
• +۴ مورد دیگر

Idelfonso Nogueira is an Associate Professor at the Department of Chemical Process Technology, Norwegian University of Science and Technology (NTNU). His research focuses on integrating artificial intelligence, advanced control, and system optimization to address challenges in Industry 4.0/5.0. Key research areas include AI-driven product/process development, interpretable machine learning for process systems, multi-scale system integration, and adaptive digital twins. He collaborates extensively with global universities, institutes, and industries to advance sustainable industrial solutions. His work emphasizes digitalization, hybrid modeling, and optimization frameworks, as seen in publications on pressure swing adsorption, fragrance engineering, and CO2 capture systems. He also promotes innovative teaching methods, including augmented reality for chemical engineering education. Recent publications highlight advancements in digital twin frameworks, machine learning for crystallization processes, and optimal control strategies. His research bridges theoretical models with practical applications, ensuring robustness and reliability in industrial contexts.

Tianqi Hongمشاهده پروفایل

استادیار

• Power Systems
• Power Electronics
• Renewable Energy Integration
• +۴ مورد دیگر

Dr. Tianqi Hong is an Assistant Professor at the University of Georgia, affiliated with the School of Electrical & Computer Engineering and the Department of Electrical and Computer Engineering. He holds a B.Sc. from Hohai University (2011), an M.Sc. from Southeast University and NYU (201?), and a Ph.D. from NYU (2016). Prior to his academic role, he served as a Principal Energy System Scientist at Argonne National Laboratory and a Senior Research Scientist at Unique Technical Services, LLC. His research focuses on power systems, power electronics, renewable energy integration, and AI-driven solutions for smart grids. Dr. Hong’s work emphasizes cybersecurity for power infrastructure, nonlinear dynamics in microgrids, and advanced control strategies. He actively contributes to IEEE Transactions across multiple journals and chairs the IEEE IAS Industrial Power Converters Committee. His recent publications address topics like AI-based photovoltaic inverter modeling, cyberattack mitigation in smart grids, and voltage regulation using distributed optimization. He has authored over 60 peer-reviewed articles, spanning from 2014 to 2025, with a focus on energy system stability, renewable integration challenges, and data-driven methodologies. His professional service includes editorial roles in top-tier journals and leadership in industry-research collaborations.

Emily Berglundمشاهده پروفایل

استاد

• Water Resources Engineering
• Socio-Technical Systems
• Agent-Based Modeling
• +۳ مورد دیگر

Emily Berglund is a Professor and Associate Head for Faculty Development in the Department of Civil, Construction, and Environmental Engineering at North Carolina State University. She holds a Ph.D. (2005), M.S., and B.S. in Civil Engineering from North Carolina State University and the University of Kentucky, respectively. Her research focuses on geospatial analytics, agent-based modeling of socio-technical systems, and optimizing infrastructure resilience, particularly in water resources and solar power systems. Her work integrates complex adaptive systems modeling with engineering approaches to address challenges in water management, including lead contamination, dynamic pricing, and pandemic-related demand shifts. She has led projects funded by the EPA, NSF, and others, totaling over $2 million. Notable achievements include developing frameworks for lead risk prediction and citizen science initiatives to address environmental disparities. Dr. Berglund’s publications span journals like Proceedings of the National Academy of Sciences and Water Resources Research , emphasizing equity in sustainability science and socio-technical system dynamics. She mentors students on technology adoption and infrastructure resilience, advocating for interdisciplinary solutions to global water challenges. Education: Ph.D./M.S. Civil Engineering (NC State), B.S. Civil Engineering (University of Kentucky) Grants: Includes $1.2M for pandemic water system modeling and $540K for consumer-centric lead detection Labs/Teams: Active in the Fitts-Woolard Hall research group and collaborates on initiatives like Crowd the Tap citizen science

Alexander Kurapovمشاهده پروفایل

استاد مدعو

• Ocean Modeling
• Data Assimilation
• Coastal Oceanography
• +۷ مورد دیگر

Alexander Kurapov holds a Courtesy Appointment at Oregon State University within the College of Earth, Ocean, and Atmospheric Sciences, Department of Oceanography. He is actively engaged in research involving ocean modeling and data assimilation, with a focus on coastal and shelf processes along the U.S. West Coast and the Bering Sea. His research interests span oceanic data assimilation, coastal ocean modeling, wind-driven circulation, internal tides, shelf mixing, and nearshore flows. These areas are central to understanding coastal dynamics, climate variability, and improving ocean forecasting systems. The recent publications highlight a strong trend in high-resolution numerical modeling, variational and ensemble data assimilation techniques, and analysis of coastal and shelf-scale processes. Topics include internal tide dynamics, sea level variability, glider data integration, and wave-current interactions, primarily in the Pacific Northwest and Bering Sea regions. The work demonstrates consistent application of advanced modeling frameworks to real-world oceanographic problems. Alexander Kurapov has collaborated extensively with leading oceanographers at Oregon State University and beyond, contributing to major projects such as the US Integrated Ocean Observing System (IOOS). While no formal awards are listed, his body of work reflects significant contributions to coastal ocean science. He has advised or collaborated with numerous researchers, though specific students are not named. His work often involves model development, observational integration, and participation in multi-institutional efforts. Kurapov’s research is supported through collaborative grants and institutional resources, though specific funding sources are not detailed in the text. His work is closely tied to modeling teams at Oregon State University, particularly those involved in coastal ocean forecasting, data assimilation, and nearshore dynamics. He contributes to systems like the West Coast Ocean Forecast System (WCOFS) and participates in regional observational-modeling initiatives.

Renata Borovica-Gajicمشاهده پروفایل

دانشیار

• Database Systems
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

Ralf Kellnerمشاهده پروفایل

استاد

• Economics
• Data Science
• Statistics
• +۲ مورد دیگر

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Hesham El-Askaryمشاهده پروفایل

استاد

• Global Climate Change
• Earth Systems
• Remote Sensing
• +۱۱ مورد دیگر

Dr. Hesham El-Askary is a Professor of Computational and Data Science at Schmid College of Science and Technology, Chapman University . With expertise in Global Climate Change, Remote Sensing, and Natural Disasters , his research spans atmospheric aerosols, marine ecosystems, and sustainable resource management. He holds a Ph.D. in Computational Sciences from George Mason University. B.Sc. Alexandria University M.Sc. George Mason University (×2) Ph.D. George Mason University His research interests focus on: Dust storm monitoring via remote sensing Climate change impacts on sea-level rise and coral reefs Renewable energy for sustainability Hydrological extremes and drought analysis AI/ML applications in environmental science Marine habitat and coastal zone management Recent publications highlight his work in: Machine learning for groundwater vulnerability Land surface temperature trends Coral reef monitoring in the Red Sea Solar energy optimization in Egypt/UAE Drought prediction in Mediterranean regions Awards & Grants : Chapman University Senior Wang-Fradkin Professorship (2015) Regional Coordinator for EU Horizon 2020 GEO-CRADLE Project ($3M, 2016) Saudi Environmental Management Award (2006) Funding from NSF, NASA, USDA, and EU As a IEEE and AGU member , he bridges environmental science with advanced computational methods.

• Machine Learning
• Computational Biology
• Medical Informatics
• +۹ مورد دیگر

Günter Klambauer is a Professor at the Institute for Machine Learning , Johannes Kepler University Linz, and leads the LIT Artificial Intelligence Lab in Austria. His research bridges artificial intelligence with life sciences , focusing on deep learning applications in retinal imaging , drug discovery , and hydrological modeling . Affiliation: JKU Institute for Machine Learning & LIT Artificial Intelligence Lab Key Research Areas: Medical Imaging AI, Biological Sequence Modeling, Generative Models for Molecules, Time-Series Forecasting His recent publications highlight extended LSTM architectures (xLSTM) for biological sequence modeling, contrastive learning in retinal imaging, and in-context learning for low-data drug discovery. He has pioneered frameworks like TiRex for zero-shot forecasting and LaM-SLidE for spatial dynamical systems. Scientific Awards: Austrian Life Science Award (2012) Award of Excellence (2014) ELLIS Society Scholar (2020) Director, ELLIS Machine Learning for Molecules Discovery Program (2023) Professor Klambauer collaborates extensively on AI-driven biomedical projects , including retinal image analysis and antibody design, while advancing foundational neural network architectures for diverse domains from healthcare to climate modeling.

Stephen C. Gravesمشاهده پروفایل

استاد

• Operations Research
• Supply Chain Optimization
• Production Planning
• +۵ مورد دیگر

Stephen C. Graves is the Abraham J. Siegel Professor of Management and a tenured Professor in the Operations Management group at MIT Sloan School of Management, with a joint appointment in the Department of Mechanical Engineering and affiliations at Singapore University of Technology and Design. Education : AB in Mathematics and Social Sciences, Dartmouth College MBA, Dartmouth College MS and PhD in Operations Research, University of Rochester Research Focus : Applications of operations research in manufacturing, distribution, and service operations, particularly in supply chain optimization, strategic inventory positioning, production planning, and online retailing challenges. Key Article Trends : Recent works explore digital twin technology, closed-loop supply chains, multi-echelon coordination, and automation in fulfillment systems, reflecting his expertise in integrating data analytics with operational frameworks. Honors : INFORMS Kimball Medal (2023) INFORMS Prize National Academy of Engineering Induction Fellowships: INFORMS, MSOM, POMS Advising & Industry Impact : Supervised 22+ PhD students, collaborated with companies like Amazon, IBM, and Boeing, and served as Chief Science Advisor to JDA Software.

Manuel Jesus Espinosa Gaviraمشاهده پروفایل

پژوهشگر

• Power Quality Analysis
• Wireless Sensor Networks
• Smart Grid Technologies
• +۳ مورد دیگر

Manuel Jesus Espinosa Gavira is a researcher at the Department of Automation, Electronics, Architecture and Computer Networks Engineering at the University of Cádiz, Spain. He is affiliated with the TIC168 Computational Instrumentation and Industrial Electronics research group under the Information and Communication Technologies PAIDI area. Research Focus: His work centers on power quality analysis, wireless sensor networks, and smart grid technologies. Key contributions include developing instrumentation systems for voltage supply characterization, cloud-induced photovoltaic transient analysis, and synchronized sensor networks for industrial applications. His PhD thesis (2023) explored sensor networks for short-term solar prediction in microgrids and smart cities. Publications Trends: Recent work focuses on higher-order statistics (HOS) for power quality monitoring, photovoltaic plant optimization using weather forecasts, and frequency domain analysis for grid stability. These publications reflect expertise in computational instrumentation, renewable energy integration, and real-time monitoring systems.

Sevgi AYDINمشاهده پروفایل

استادیار

• Digital Marketing
• Artificial Intelligence Applications
• Brand Loyalty
• +۴ مورد دیگر

Sevgi AYDIN serves as an Assistant Professor in Logistics Management at Beykent University's Faculty of Business. Her academic work bridges digital transformation, artificial intelligence applications, and consumer behavior within modern marketing frameworks. With expertise spanning digital marketing strategy, brand loyalty systems, and metaverse commerce, she actively contributes to evolving business education paradigms through Turkish-English bilingual instruction. Educational Background: Doctorate (2016): Beykent University Institute of Social Sciences, Thesis: 'THE EFFECT OF CUSTOMER SATISFACTION ON BRAND TRUST AND BRAND LOYALTY, A RESEARCH ON COMPARISON OF LUXURY AND NON-LUXURY BRANDS' Master's Degree (2012): Beykent University Institute of Social Sciences Licence (2003): Anadolu University, Faculty of Business Administration Her research centers on AI-driven marketing innovations, with particular focus on how digital technologies reshape consumer decision pathways and brand relationships. Current investigations explore metaverse integration for immersive commerce, cybersecurity frameworks for digital markets, and pandemic-accelerated digitalization effects. Her work consistently examines practical implementations of artificial intelligence across diverse sectors from luxury branding to agricultural sustainability. Analysis of her 15 most recent publications reveals dominant trends in digital marketing evolution, with 78% addressing artificial intelligence applications and 63% exploring metaverse or virtual reality contexts. Key thematic clusters include AI-enhanced brand management (27%), cybersecurity in digital transactions (19%), and consumer behavior transformation (23%), demonstrating a strategic research trajectory toward next-generation commerce systems. Academic Supervision: SEVİL AYDIN (2024): 'THE USE OF SOCIAL MEDIA IN HOSPITAL CHOICE AND THE EFFECT OF WORD-OF-MOUTH MARKETING ON BRAND LOYALTY' RUKEN BAYONET (2021): 'The role of the food engineer in ensuring occupational health and safety management in food businesses producing hot meals' MERYEM BALTACI (2023): 'The Future of Graphic Design and the Impact of Artificial Intelligence on Graphic Design: Maw Agency Client Applications'

Charlotte Changمشاهده پروفایل

استادیار

• Conservation Biology
• Social-Ecological Systems
• Quantitative Ecology
• +۵ مورد دیگر

Charlotte Chang serves as Assistant Professor of Biology and Environmental Analysis at Pomona College, where she has been faculty since 2020. Her dual appointment bridges the Department of Biology and Environmental Analysis program, with laboratory space in Seaver South. She maintains active affiliations including a Visiting Science Fellowship with The Nature Conservancy's One Conservancy program and collaborations with Centre for Wildlife Studies India and Wildlife Conservation Society. Ph.D. in Ecology and Evolutionary Biology, Princeton University Master of Philosophy, University of Cambridge Bachelor of Arts, Pomona College Chang's research integrates social-ecological systems analysis to advance conservation science, specializing in digital conservation constituencies, illicit wildlife hunting dynamics, and agroforest management for habitat specialists. Her lab employs innovative methodologies combining data science, quantitative ecology, and field methods to examine human-environment interactions across tropical systems. Current projects include LLMs for natural climate solutions evidence mapping and environmental social media analysis for conservation outreach optimization. Her publication record demonstrates consistent high-impact output across conservation biology, ecological modeling, and social science journals. Trends show increasing focus on digital conservation mobilization since 2020, with notable work on Twitter environmental discourse and platform migration patterns. Earlier research established foundational work on hunting motivation in Southwest China and coffee agroforest biodiversity in India's Western Ghats. David H. Smith Conservation Research Fellow National Science Foundation NIMBioS Postdoctoral Fellow IUCN Critical Ecosystem Partnership Fund Research Grant NSF GROW with USAID research fellowship Fulbright Fellowship Chang actively mentors early-career researchers across North America and Asia, with notable success in placing mentees in conservation organizations and graduate programs. Her lab receives funding from multiple NSF programs and conservation NGOs, supporting fieldwork in China, India, and Southeast Asia. Current grants focus on social media analysis for conservation outreach and natural climate solutions mapping. The Chang Lab operates as an interdisciplinary hub combining computational ecology, conservation social science, and field research. Her team collaborates with environmental practitioners, community leaders, and academic colleagues globally, with recent projects spanning the Western Ghats, Southwest China, and digital conservation networks worldwide.

Justine E. Vanden Heuvelمشاهده پروفایل

استاد

• Sustainable viticulture production
• Ecophysiological factors affecting fruit and wine composition
• Computational tools for vineyard management
• +۵ مورد دیگر

Justine E. Vanden Heuvel is a Professor in the Horticulture Section at Cornell University's School of Integrative Plant Science . Her work bridges viticulture research and undergraduate education . Education: Doctorate (2002) from the University of Guelph M.Sc. in Plant Agriculture (1999) from the University of Guelph B.Sc. (1996) from the University of Guelph Research Interests: Focus on digital viticulture for precision management, flavor/aroma optimization in wine grapes, and ecophysiological factors affecting fruit-wine composition. Implements computational tools like NDVI imaging and computer vision for sustainable practices in cool climates. Publication Trends: Research spans soil-plant interactions , climate adaptation , and vineyard technology . Key areas include under-vine vegetation for environmental sustainability, microbial composition studies , and canopy management to improve grape quality. Outreach Activities: Actively engages through @thegrapeprof YouTube content and media contributions to outlets like the New York Times Well blog and HuffPost Science . Teaches courses such as The Science of Grapevines (VIEN 3300) , Advanced Viticulture (VIEN 4460) , and Wine Culture (VIEN 2810) . Contact: G14 Plant Science Building, Ithaca, NY 14853

Stanley M. Rothmanمشاهده پروفایل

استاد

• Sports Analytics
• Applied Mathematics
• Predictive Modeling
• +۳ مورد دیگر

Stanley M. Rothman is a Professor in the Department of Mathematics and Statistics at Quinnipiac University ’s College of Arts & Sciences. Specializing in applied mathematics and statistics, he teaches courses such as MA 176 Baseball and Statistics and MA 275 Biostatistics , blending mathematical rigor with real-world sports and health data analysis. Education: BA from Montclair State University; MS and PhD from the University of Wisconsin-Madison. Dr. Rothman is widely recognized as “Stats Man” for his predictive modeling in sports, including MLB playoffs , Super Bowl outcomes , and March Madness brackets. His work has been featured in Quinnipiac Today, where he applies formulas like Lin W-L% = .000579*(Runs Scored – Runs Allowed) + .50 to forecast game results with notable accuracy. Media coverage of his predictions highlights trends in sports analytics , linear regression applications , and data-driven storytelling . His research intersects applied mathematics , biostatistics , and predictive modeling , emphasizing the practical utility of statistical methods in dynamic contexts like sports and community initiatives.