جست‌وجوی استاد و پژوهشگر

S. S. Iyengarمشاهده پروفایل

استاد

• High-Performance Algorithms
• Biomedical Computing
• Sensor Fusion
• +۳ مورد دیگر

S.S. Iyengar is a Distinguished University Professor and Ryder Professor at Florida International University's Knight Foundation School of Computing and Information Sciences, where he founded the Discovery Lab. He holds a Ph.D. in Engineering from Mississippi State University (1974), an M.S. from the Indian Institute of Science (1970), and a B.S. from Bangalore University (1968). Research spans high-performance computing, sensor networks, biomedical computing, and AI, with applications in cancer genomics and digital forensics. His innovations include the Brooks-Iyengar algorithm for distributed sensor fusion and cognitive information processing systems. Honors include fellowship in ACM, IEEE, AAAS, and NAI, plus the IEEE Test of Time Research Award for fundamental contributions to distributed computing. He has supervised over 55 PhD students and authored 500+ publications. Current work focuses on DNA mutation prediction, glaucoma monitoring devices, and cybersecurity. Funded by NSF, DARPA, NASA, and others, totaling $65M+ in research funding.

Keri Collinsمشاهده پروفایل

مدرس

• Marine Renewable Energy
• Physical Modelling
• Hydromechanics
• +۲ مورد دیگر

Dr. Keri Collins is a Lecturer in Mechanical Engineering at the University of Plymouth's School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering. Her research focuses on physical modeling for marine renewable energy devices, addressing engineering challenges in creating scale models for research applications. She serves as Admissions Tutor for Mechanical Engineering and Marine Technology programs. Her research interests span marine renewable energy, hydrodynamics, and experimental fluid mechanics, with particular emphasis on wave energy converters and offshore wind turbine technology. She maintains active research in hydrodynamic efficiency and structural analysis of renewable energy systems. Dr. Collins' publications demonstrate strong focus on renewable energy technologies, particularly wind and wave energy systems. Recent work shows increasing application of machine learning for damage detection and optimization in offshore environments. Her experimental research consistently addresses practical challenges in marine energy conversion. She currently supervises multiple research students including Chris Pass (acting DoS), Oscar Best, and Gunay Gazaloglu. Previously supervised PhD students include Qi Ye (Structural analysis of semi-submersible platforms), Daming Wang (Environmental data for wave tank testing), and Ben Howey (Interconnected wave energy arrays). Teaching responsibilities include module leadership for Engineering Design (ENGR107) and Engineering Practice, plus lectures in thermo-fluids, marine renewable energy, and medical engineering. She also supervises honours projects and provides MATLAB tutoring support.

Simonas Saltenisمشاهده پروفایل

دانشیار

• Database Systems
• Spatial Data Management
• Data Engineering
• +۳ مورد دیگر

Simonas Saltenis is an Associate Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. He is part of the Data Engineering, Science and Systems group and the Daisy Center for Data-intensive Systems. His research focuses on database systems, spatial data management, and data engineering with a strong emphasis on temporal and spatial query optimization. He has been involved in key projects such as TimeCenter (active since 2000) and "Effective Shedding and Efficient Processing of Massive Sensor Update Loads" (2008–2011), both funded by Danish research councils. These projects explore database management systems, temporal data handling, and real-time sensor data processing. His research interests include indexing techniques for spatial and temporal data, points-of-interest retrieval, and probabilistic models for energy use prediction. He has also contributed to conference organization, serving as a general co-chair for MDM 2018. Saltenis has supervised at least one PhD student and has actively engaged in peer review for top journals including the VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Information Systems.

Mario A. Roteaمشاهده پروفایل

استاد

• Wind Energy Systems
• Control Theory
• Renewable Energy Optimization
• +۳ مورد دیگر

Mario A. Rotea is a Professor in the Mechanical Engineering department at the Erik Jonsson School of Engineering and Computer Science , University of Texas at Dallas. He serves as Director of UTD WIND and Site Director for WINDSTAR Education . PhD in Control Science and Dynamical Systems from University of Minnesota MS in Electrical Engineering from University of Minnesota BS in Electronics Engineering from National University of Rosario Rotea's research focuses on optimization and control of wind energy systems and control theory . Recent work explores icing prediction, yaw control algorithms, and aerodynamic load management. Publications emphasize wind turbine efficiency , extremum seeking control , machine learning , and experimental validation through wind tunnel tests and field studies. Fellow, IEEE (2007) NSF Young Investigator Award (1993) Seed for Success Award (Purdue, 2004-2005) CT Sun Excellence in Research Award (2006) FRIENDs Dynamo (UT Dallas, 2022-2023) Rotea has directed multiple research initiatives funded by federal grants exceeding $500,000 annually. His work involves collaboration with Sandia National Laboratories, Pacific Northwest National Laboratory, and Purdue University. He leads the UTD WIND research group, focusing on wind energy systems, and contributes to programs like WINDSTAR Education and the Sandia Blade Workshop.

Xiangyi Mengمشاهده پروفایل

استادیار

• Quantum Networks
• Network Science
• Quantum Information
• +۱۱ مورد دیگر

Prof. Xiangyi "X" Meng is an Assistant Professor at Rensselaer Polytechnic Institute's Department of Physics, Applied Physics, and Astronomy. His academic journey includes a B.S. in microelectronics from Peking University and a Ph.D. in physics from Boston University under Prof. Eugene Stanley. He held postdoctoral positions at Northeastern University's Center for Complex Network Research with Prof. Albert-László Barabási and as a Research Associate at Northwestern University with Prof. István Kovács. RPI (2025-present): Assistant Professor Northeastern University (2021-2023): Postdoc Northwestern University (2023-2024): Research Associate His research spans quantum networks, network science, and interdisciplinary applications, with key contributions in: Quantum communication protocols Concurrence percolation theory Quantum field theory applications Computational social science Power grid resilience modeling Multi-community epidemic spreading Prof. Meng has published over 20 papers in top journals like Physical Review Letters, PNAS, and Nature Communications, focusing on quantum advantage in network connectivity, entanglement distribution, and nonlinear network dynamics. His work has been featured in Physics World and Phys.org media outlets. As an educator, he teaches Physics II and Introductory Quantum Mechanics . His team develops quantum-inspired machine learning architectures like entanglement-structured LSTM networks. Current research explores nonconvex entanglement measures and hypergraph immersion techniques for quantum network optimization.

Francesco Ravazzoloمشاهده پروفایل

استاد

• Econometrics
• Energy Economics
• Financial Econometrics
• +۳ مورد دیگر

Francesco Ravazzolo is a Full Professor of Econometrics at the Faculty of Economics and Management, Free University of Bozen-Bolzano, and Head of the Department of Data Science and Analytics at BI Norwegian Business School. He holds a Ph.D. from Tinbergen Institute (2007). His research focuses on commodity markets, energy economics, financial econometrics, and macroeconometrics, with over 40 publications in leading journals such as the Journal of Applied Econometrics and Annals of Applied Statistics. He serves on the editorial boards of the International Journal of Forecasting, Journal of Applied Econometrics, and others. His work addresses policy-relevant issues like energy price forecasting, macroeconomic volatility, and cryptocurrency modeling. He co-founded AIAQUA and COMMODIA, spin-offs from the Free University of Bozen-Bolzano. Key research interests include Bayesian econometric methods, nowcasting techniques, and the application of advanced statistical models to energy and financial markets. His recent work examines gas price caps, smart energy systems, and pandemic impacts on businesses. He has collaborated with institutions like the Reserve Bank of Australia on core inflation measures and co-developed the DeCo toolbox for density forecast combination.

Simon Fooمشاهده پروفایل

استاد

• Photovoltaics
• Multi-junction III-V compound Solar Cells
• Organic/Polymer Solar Cells
• +۵ مورد دیگر

Simon Foo is a tenured Professor of Electrical and Computer Engineering at the FAMU-FSU College of Engineering, part of Florida A&M University and Florida State University. He has held roles including Department Chair (2010–2018) and has been a professor since 2005. His research focuses on photovoltaics and machine learning, with over 150 publications and 50+ graduated students. He leads 20+ funded projects from agencies like NASA, DOE, NSF, and industries like Boeing and Airbus. Education: Ph.D. (1988), M.S. (1985), and B.S. (1983) in Electrical Engineering from the University of South Carolina. Research Interests: Photovoltaics (multi-junction solar cells, quantum dots, perovskites) and machine learning applications in energy systems, control, and healthcare. His lab develops AI-driven solutions for renewable energy optimization and battery management systems. Key Awards: Teaching Incentive Award (1995) Teacher of the Year (2001 & 2010) Engineering Research Awards (2004 & 2008) Two Best Paper Awards (2001 & 2004) Advising & Grants: Mentored over 50 graduate students in photovoltaics and AI. Secured grants from NASA, DOE, NSA, NSF, and industry partners. Current projects include battery state-of-charge estimation and AI for aircraft fire detection systems. Labs & Collaborations: Collaborates with Airbus on aircraft safety systems and maintains an active research group focused on renewable energy and AI integration. His lab is currently full and not accepting students until 2027.

Alexandra M. Newmanمشاهده پروفایل

استاد

• Applied Optimization
• Transportation Logistics
• Mine Production Scheduling
• +۵ مورد دیگر

Alexandra M. Newman is a Professor in Mechanical Engineering at Colorado School of Mines and serves as the Director of the Operations Research with Engineering Program. Her research focuses on applied optimization across diverse domains including mining, energy systems, healthcare, and sports scheduling. Education: BS in Applied Mathematics, University of Chicago MS in Operations Research, University of California, Berkeley PhD in Industrial Engineering and Operations Research, University of California, Berkeley Her work addresses complex logistical challenges in underground mine planning, renewable energy deployment, and counterinsurgency modeling. Recent publications emphasize microgrid resilience, electric vehicle energy consumption, and optimization of hybrid solar power systems. Notable research trends include: Integration of sustainability metrics in mining and energy systems Advancements in dispatch optimization for renewable energy Application of stochastic modeling to athletic and healthcare scheduling

Timothy Cowanمشاهده پروفایل

پژوهشگر ارشد

• Climate Prediction
• Climate Extremes
• Sub-seasonal Prediction
• +۷ مورد دیگر

Dr Timothy Cowan is a Senior Research Fellow at the University of Southern Queensland's Centre for Applied Climate Sciences, embedded in the Bureau of Meteorology's Seasonal Climate Processes team. His expertise spans climate prediction, monsoon dynamics, El Niño Southern Oscillation, and sub-seasonal forecasting. He holds a BSc(Hons) from the University of Melbourne and a PhD from UNSW. Tim's research focuses on developing climate forecast products for Northern Australia, including the Northern Rainfall Onset and multi-week rainfall burst forecasts. His current project involves creating an Australia-wide cattle heat load forecast system funded by Meat and Livestock Australia. Prior experience includes a decade at CSIRO studying Indo-Pacific climate variability and post-doctoral work at the University of Edinburgh on early 20th-century climate extremes. Professional affiliations include membership in the Australian Meteorological and Oceanographic Society. His work addresses critical challenges in climate risk management for agriculture, particularly drought and heat stress mitigation.

Francesco Audrinoمشاهده پروفایل

استاد

• Computational Statistics
• Financial Econometrics
• Machine Learning
• +۶ مورد دیگر

Francesco Audrino is a Professor of Statistics at the Department of Mathematics and Statistics, University of St. Gallen, affiliated with the School of Economics and Political Science (SEPS). He holds a Diploma in Mathematics from ETH Zurich (specializing in Financial and Insurance Mathematics) and a Ph.D. in Statistics/Finance from ETH Zurich, with a thesis on statistical methods for high-dimensional financial time series. Research Focus: Computational Statistics applied to Economics and Finance, Financial Econometrics, Sentiment Analysis, Volatility Estimation, and Regime-switching Models. His work emphasizes nonparametric methods (e.g., Functional Gradient Descent) and their applications in asset pricing, interest rate dynamics, and risk management. Recent projects include the SentiVol program for sentiment-driven volatility forecasting and causal machine learning analyses of post-earnings sentiment impacts. Selected Projects: Director of the SentiVol SNF Grant (2017–2020) on sentiment analysis for volatility prediction. Co-Director of the SNF Grant on Behavioral Asset Pricing (2010–2013). Leading research on cross-asset dependency structures and causal machine learning applications. Awards & Recognition: Swiss Society of Economics and Statistics Young Economist Award (2007). University of St. Gallen’s Best Researcher Award (2007). Teaching load reduction for outstanding publications (2009–2018). Teaching & Academic Service: Teaches Statistics, Financial Volatility, and Computational Statistics at bachelor’s, master’s, and Ph.D. levels. Served as Elected Member of the Board of Directors of the European Regional Section of the International Association for Statistical Computing (2016–2020). Active in academic networks like the Computational Financial Econometrics (CFE) Network. Labs & Resources: Developed the FGD Code package for nonparametric high-dimensional time series analysis, widely used in volatility and correlation modeling. Maintains real-time volatility forecasts for S&P 500 constituents via the SentiVol project.

Manar Amayriمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Explainable AI
• +۵ مورد دیگر

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Greg Fasshauerمشاهده پروفایل

استاد

• Meshfree Approximation Methods
• Radial Basis Functions
• Approximation Theory
• +۳ مورد دیگر

Greg Fasshauer is a Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on meshfree approximation methods, radial basis functions, and numerical solutions of partial differential equations. He has authored influential works such as Kernel-based Approximation Methods using MATLAB (2015) and contributed to advancements in approximation theory, spline theory, and computational methods for engineering and biomedical applications. Education: Diplom in Mathematics (1991), University of Stuttgart Staatsexamen in Mathematics and English (1991), University of Stuttgart M.A. in Mathematics (1993), Vanderbilt University Ph.D. in Mathematics (1995), Vanderbilt University Research Interests: Fasshauer’s work emphasizes kernel-based methods, meshfree solvers for electromagnetic and biomedical problems, and high-dimensional function approximation. His contributions include innovative techniques in radial basis functions (RBFs), such as stabilized RBF-FD methods and hybrid kernel approaches, applied to PDEs, M/EEG forward modeling, and energy systems. He also explores theoretical foundations of approximation spaces and convergence properties of numerical schemes. Labs/Teams: His research group develops computational tools for solving complex engineering and scientific problems using meshless methods, with applications in bioelectromagnetics and computational mechanics.

J. Scott Holladayمشاهده پروفایل

دانشیار

• Environmental Economics
• Energy Policy
• International Trade
• +۳ مورد دیگر

J. Scott Holladay is an Associate Professor of Economics at the University of Tennessee and a Fellow at the Howard B. Baker Center for Public Policy. He holds the John and Wanda Wisecarver Faculty Research Fellow title. His research focuses on environmental economics, energy policy, international trade, and experimental economics, with particular emphasis on climate change, renewable energy adoption, and the economic impacts of environmental regulations. Holladay earned a Ph.D. and M.A. in Economics from the University of Colorado, and dual B.S. in Computer Science and B.A. in Economics from Furman University. Key research areas include the environmental and health impacts of coal production, policy design for renewable energy adoption, and the economic drivers behind coal-fired power plant retirements. His work also explores energy infrastructure resilience against natural disasters and the interplay between climate policy and international trade frameworks. Holladay has contributed to debates on net neutrality and the economic consequences of climate treaty negotiations. Notable achievements include the John and Wanda Wisecarver Fellowship and significant contributions to understanding how incentive policies affect solar adoption across socioeconomic groups. His interdisciplinary background in computer science and economics allows innovative approaches to agent-based modeling and reinforcement learning in energy market analysis.

• Power Systems Engineering
• Renewable Energy Integration
• Machine Learning Applications in Energy
• +۳ مورد دیگر

Alessandro Bosisio is a Researcher at the Department of Industrial and Information Engineering, University of Pavia. His research focuses on power distribution systems, renewable energy integration, smart grid technologies, and machine learning applications in energy systems. He has conducted extensive studies on voltage control, fault prediction in distribution grids, and optimization algorithms for grid reliability and resilience. Key research areas include: Impact of extreme weather on power systems Renewables-to-hydrogen systems Machine learning for load forecasting and grid automation GIS-based planning for distribution networks His work emphasizes real-world case studies in Milan and other Italian regions, addressing challenges such as electromobility integration, grid digitalization, and energy transition strategies. Bosisio has also contributed to patents related to distribution network reconfiguration and smart grid automation systems.

YangQuan Chenمشاهده پروفایل

استاد

• Unmanned Aircraft Systems (UAS)
• Mechatronics
• Renewable Energy Systems
• +۷ مورد دیگر

YangQuan Chen is a Professor in the Department of Mechanical Engineering at the University of California, Merced. His research focuses on unmanned aircraft systems, mechatronics, renewable energy systems, cyber-physical systems, and applied fractional calculus. He leads multiple labs, including locations at SE2-273 and 4225 N. Hospital Rd., Atwater, CA. His work integrates advanced control theory, robotics, and digital twin frameworks for applications in environmental monitoring, precision agriculture, and energy systems. Education: PhD (1998) from Nanyang Technological University, Singapore; M.S. (1989) and B.S. (1985) from Chinese institutions. His research emphasizes interdisciplinary approaches, combining fractional calculus with machine learning for real-world challenges like pandemic modeling, UAV-based emission tracking, and smart agriculture. Key research areas include: Unmanned Aircraft Systems (UAS) for environmental and agricultural applications Cyber-physical systems and digital twin frameworks Applied fractional calculus in control systems and epidemiology Machine learning for soil carbon sensing and crop monitoring Recent work explores UAV swarms for methane emission quantification, battery health-aware mission planning, and physics-informed neural networks for state estimation. His lab develops innovative solutions for renewable energy systems, such as adaptive controllers for proton exchange membrane electrolyzers and robust consensus strategies for multi-agent systems. Awards and recognition include prominent citations on Google Scholar and ResearchGate. Ongoing projects involve collaboration with industry and government agencies on smart agriculture, environmental sensing, and energy efficiency.