جست‌وجوی استاد و پژوهشگر

Shady Gadoueمشاهده پروفایل

دانشیار

• Power Electronics
• Control Systems
• Electrification Solutions
• +۳ مورد دیگر

Dr. Shady Gadoue is an Associate Professor in the School of Electronic Engineering and Computer Science at Queen Mary University of London. His roles include research leadership in the Centre for Electronics and Centre for Sustainable Engineering. He holds a BSc, MSc, and PhD from Alexandria University and Newcastle University, respectively, and is a Fellow of the Higher Education Academy (FHEA). Dr. Gadoue's research focuses on Power Electronic Converters, Control Systems, and Electrification Solutions for Low Carbon applications. Key areas include Smart Grids, Electric Transportation, and Digital Twins. He has over 23 years of academic and industrial experience, collaborating with Jaguar Land Rover, National Grid, and others. His work has resulted in over 80 IEEE publications, recognized by Stanford University as among the top 2% scientists globally in Energy Engineering (2020–2021). His recent grants include a £49,923 Innovate UK project (2022–2023) for training in power electronics. He teaches modules on electric powertrains and power systems analysis, emphasizing renewable integration and HVDC. Dr. Gadoue’s awards include IEEE’s 'high quality research' designation and top 2% global rankings. His research addresses critical challenges in energy systems, transportation electrification, and sustainable engineering.

Leonardo Morsutمشاهده پروفایل

استادیار

• Synthetic Biology
• Stem Cell Biology
• Regenerative Medicine
• +۳ مورد دیگر

Leonardo Morsut is an Assistant Professor in the Department of Stem Cell Biology and Regenerative Medicine at the Keck School of Medicine, University of Southern California. His research focuses on synthetic biology and tissue engineering, aiming to program mammalian cells for controlled tissue morphogenesis and regeneration. He leads the Morsut Lab, which integrates computational modeling, protein engineering, and stem cell biology in an iterative framework to understand and manipulate tissue self-organization. University: University of Southern California School: Keck School of Medicine Department: Stem Cell Biology and Regenerative Medicine Academic Rank: Assistant Professor Research Interests: Dr. Morsut's work combines synthetic biology with developmental systems to engineer multicellular constructs. His lab specializes in synthetic Notch receptors, genetic circuit design, and controlling spatio-temporal patterning during tissue formation. Key areas include programmable material-to-cell pathways, juxtacrine signaling networks, and computational frameworks for morphogenesis modeling. Recent Publications Trends: His 2024-2025 studies emphasize precise spatial control of differentiation in organoids using synthetic Wnt organizers, programmable material interfaces, and dynamic cell growth feedback. Earlier works focus on transgene silencing mechanisms, mucin-driven morphogenesis, and density-dependent signaling in multicellular systems. Laboratory Mission: The Morsut Lab aims to build functional tissues through synthetic biology tools while training future regenerative medicine leaders. They utilize interdisciplinary approaches from engineering, computational biology, and developmental systems to create programmable multicellular structures.

Valentin Robuمشاهده پروفایل

استاد

• Artificial Intelligence
• Decentralized Energy Systems
• Multi-Agent Systems
• +۴ مورد دیگر

Valentin Robu is a Full Professor of Artificial Intelligence for Decentralized Energy Systems at Eindhoven University of Technology (TU/e). He is a Senior Researcher at CWI (National Research Institute for Mathematics and Computer Science, Amsterdam) and holds a visiting appointment at Princeton University's ECE department. His core expertise lies in multi-agent systems and distributed AI, with a focus on applying AI to energy challenges like smart grids, electric vehicle coordination, and renewable integration. He has authored over 150 peer-reviewed publications and won awards such as the 2019 UK Innovation of the Year Award and the 2018 Low Carbon Transport Award. Education and Career: Robu earned his PhD from TU Eindhoven and CWI (2009). Before his current roles, he was an Associate Professor at Heriot-Watt University (UK), a Senior Research Fellow at the University of Southampton, and held visiting roles at Harvard and MIT. He collaborates globally with institutions like MIT, Harvard, and TU Delft. Research Interests: His work addresses challenges in decentralized energy systems, including AI-driven grid optimization, game-theoretic models for renewable integration, and blockchain applications in energy trading. He focuses on ensuring grid stability, fair resource sharing, and sustainable energy access. Awards: In addition to his Innovation and Low Carbon Transport awards, he has been recognized as a 'Best Reviewer' in 2018. His research impacts UN Sustainable Development Goals related to affordable energy and climate action. Grants and Projects: He leads projects like CESI (UK Energy Systems Integration), CEDRI (India Demand Reduction), and ORCA (Offshore Robotics Hub). His work is featured in media outlets like the BBC, Economist, and World Economic Forum. Labs and Teams: He contributes to TU/e's EAISI (Eindhoven AI Systems Institute) and CWI's Intelligent and Autonomous Systems Group. Collaborations span academia and industry, including Scottish Power Energy Networks and Microsoft Research.

Luna Zagoracمشاهده پروفایل

پژوهشگر ارشد

• Cosmology
• Particle Astrophysics
• High-Performance Computing
• +۳ مورد دیگر

Luna Zagorac is a Research Fellow at the Perimeter Institute for Theoretical Physics, focusing on particle cosmology and interdisciplinary astrophysics. Her research bridges theoretical dark matter dynamics, high-performance computing, and cosmic archaeology, including work on ultralight dark matter (ULDM) simulations and ancient Egyptian star maps. She completed her Ph.D. in Physics at Yale University (2022), advised by Nikhil Padmanabhan and Richard Easther. Education: Ph.D. in Physics, Yale University (2022); earlier work involved optical studies of blazars at Colgate University’s Foggy Bottom Observatory. Research emphasizes ULDM phenomenology, soliton dynamics, and interdisciplinary approaches linking astrophysics with archaeology. Key awards include the Leigh Page Award for Teaching Excellence (2021), NASA FINESST Fellowship (2020), and the Franke Graduate Fellowship (2019). She has contributed to developing the CHPLULTRA simulation code for ULDM and pioneered methods to map ancient Egyptian constellations using hieroglyphic sources. Teaching roles include Yale’s Physics Bootcamp and serving as a Teaching Fellow for nine courses. She actively engages in science communication via Astrobites, public lectures, and outreach initiatives like Girls’ Science Investigations. Media highlights include features in the New York Times and podcasts such as H’ad Astra Historia .

Pavel Solinمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Pavel Solin is a Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), within the College of Science. His work focuses on computational mathematics, numerical analysis, and innovative approaches to mathematics and computer science education. He has developed advanced finite element methods (FEM), including hp-FEM and discontinuous Galerkin techniques, with applications in engineering, physics, and porous media modeling. His research also emphasizes integrating computational tools into education, such as self-paced programming courses in Python, SQL, and linear algebra. Dr. Solin is the creator of the Hermes2D library, a C++ framework for adaptive hp-FEM and DG solvers, and has contributed to software for multiphysics simulations. His educational efforts include experimental curricula blending mathematics with programming and STEAM principles. Despite no listed scientific awards, his work has been published in numerous journals, reflecting his expertise in both computational methods and pedagogical innovation. His articles span adaptive methods, numerical simulations of physical phenomena, and educational methodologies. He has collaborated on projects involving electromagnetic stirring, thermal conduction, and fluid dynamics. While no advising or grant details are provided, his research lab and team focus on advancing computational techniques and their practical applications in academia and industry.

Petros Voulgarisمشاهده پروفایل

استاد

• Control Systems
• Autonomous Systems
• Cybersecurity
• +۵ مورد دیگر

Petros Voulgaris is the Victor LaMar Lockhart Professor and Chair of the Mechanical Engineering Department at the University of Nevada, Reno (UNR), where he also directs the Founding Aerospace Program. He is affiliated with the College of Engineering and leads the Learning Autonomy and Control Systems Laboratory. His work focuses on advanced control methodologies for engineering systems, including autonomous vehicles, cyber-physical systems, and resilient networks. Research Interests: Optimal, robust, and distributed control; networked control systems; autonomous systems cybersecurity; and applications in aerospace, robotics, and power systems. His lab explores cutting-edge solutions for challenges like adversarial attacks on autonomous systems and GPS-denied navigation. Prospective Students: Openings for Ph.D. candidates and postdocs with control systems or decision-making expertise. Contact via pvoulgaris@unr.edu for application details. Labs: Learning Autonomy and Control Systems Laboratory Contact: PE Building 204C | (775) 682-5725

• Battery Systems Design
• Power Electronics
• Electric Vehicles (EVs)
• +۶ مورد دیگر

Jorge Varela Barreras is an Honorary Research Fellow at Imperial College London (2023) and an EV and Battery Expert at CTAG since 2021. He holds a PhD in Engineering from Aalborg University (2017) and master’s degrees in Electrical Engineering and Power Electronics. His roles include IEEE Senior Member (2024), Maria Zambrano Fellow at UPC (2022), and former postdoc at Imperial College and the University of Oxford. He has over 15 years of R&D experience in battery systems, EVs, and renewable energy applications. Education: M.Sc. in Electrical Engineering (University of Vigo, 2010), M.Sc. in Power Electronics (Aalborg University, 2009), PhD in Engineering (Aalborg University, 2017). Research interests span design, control, and testing of battery systems, EVs, and energy storage. His work focuses on thermal management, battery degradation, and safety, with contributions to hybrid balancing systems and BMS optimization. Notable awards include IEEE Senior Member (2024), Honorary Research Fellowship (2023), and the STFC Batteries Early Career award (2017). He has co-authored over 40 publications, including 16 journal articles, and supervised 2 PhD students. Advising/Grants: Involved in £80M+ projects, 2 as Principal Investigator. Delivered 3 IEEE tutorials, 19 keynotes, and 20 final-year projects. Editor for eTransportation and World Electric Vehicle Journal . Labs/Teams: Collaborations with The Faraday Institution, Imperial Consultants (ICON), and industry partners like Triple Solar. Visiting researcher at RWTH Aachen, FEUP, and INESC TEC.

Nicholas Kisselمشاهده پروفایل

مدرس

• Statistical Machine Learning
• Machine Learning
• Data Science
• +۳ مورد دیگر

Nicholas Kissel is a Special Faculty Lecturer in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His research focuses on Statistical Machine Learning , with applications in health analytics, biomedical research, and algorithmic methodology. His work bridges theoretical statistical frameworks with practical problems in clinical and behavioral sciences. Notable research includes cluster analysis for emotional reactivity subtypes, meta-analyses of ketamine treatment for depression, and injury prediction models for military personnel. He has contributed to studies using actigraphy for sleep pattern analysis and high-dimensional statistical comparisons for cross-validation. While no awards or grants are explicitly listed, his interdisciplinary approach reflects collaboration across health sciences and computational methods.

Pablo Debenedettiمشاهده پروفایل

استاد

• Thermodynamics
• Statistical Mechanics
• Molecular Modeling
• +۶ مورد دیگر

Pablo Debenedetti is the Class of 1950 Professor in Engineering and Applied Science and Professor of Chemical and Biological Engineering at Princeton University. He holds an affiliation with the Princeton Environmental Institute (PEI) and the Andlinger Center for Energy and the Environment. His research focuses on thermodynamics, statistical mechanics, and computational modeling of materials, particularly in water and aqueous solutions, protein thermodynamics, nucleation theory, and phase transitions. Debenedetti's work bridges fundamental science and practical applications in energy, environmental engineering, and biotechnology. He received the Aneesur Rahman Prize for Computational Physics in 2023. His research group employs advanced simulation techniques including molecular dynamics, Monte Carlo methods, and machine learning to investigate phenomena such as liquid-liquid phase transitions in water, ice nucleation mechanisms, and protein folding dynamics in extreme conditions. Debenedetti collaborates across disciplines, contributing to projects ranging from climate modeling to biomedical applications. Debenedetti’s academic leadership includes serving as Dean for Research at Princeton University, overseeing strategic initiatives in research infrastructure and interdisciplinary scholarship. His educational background and career trajectory reflect deep expertise in chemical engineering and physical chemistry, with over 600 publications and numerous patents.

• Bioinformatics
• Machine Learning in Agriculture
• Protein Engineering
• +۷ مورد دیگر

Prof. Dr. Dominik Grimm is a Professor in the Department of Forestry and Forestry Management at the University of Applied Sciences Straubing (HSWT). He leads the Straubing site for sustainable resource use and is affiliated with the Faculty of Forestry and Forest Management. His research focuses on interdisciplinary applications of machine learning, computational biology, and optimization in agriculture, biotechnology, and environmental science. Key areas include precision agriculture (e.g., UAV-based weed detection in sorghum fields), protein engineering (thermostability prediction), and reinforcement learning for process synthesis. He has pioneered tools like permGWAS2 for genome-wide association studies and EVARS-GPR for seasonal time series forecasting. His work integrates AI with real-world challenges, such as optimizing hospital bed management and improving crop yields through data-driven strategies. Education : Advanced degrees in computer science and bioinformatics (details not explicitly stated). Research interests span computational methods in agriculture, genomics, and combinatorial optimization. He supervises doctoral candidates like Josef Eiglsperger and Jonathan Pirnay. His lab (Grimmlab) develops open-source frameworks for time series analysis and phenotypic data processing. Key projects include automated process design using reinforcement learning and machine learning for perishable product forecasting in horticulture. Publications highlight contributions to protein stability modeling, agricultural robotics, and data stream algorithms. Collaborations involve institutions like the University of Bayreuth and the Helmholtz Center Munich. His work emphasizes practical applications, such as reducing herbicide use through precision agriculture and enhancing biomanufacturing via AI-driven enzyme discovery. Grants and research projects funded include methods for flowsheet synthesis, generative AI in enzyme discovery, and sustainable resource management. He leads initiatives integrating machine learning with horticultural sales prediction and patient bed assignment optimization in hospitals.

Rakhim Aitbayevمشاهده پروفایل

دانشیار

• Numerical Methods for Partial Differential Equations (PDEs)
• Interface Problems
• Orthogonal Spline Collocation Method
• +۵ مورد دیگر

Rakhim Aitbayev is an Associate Professor of Mathematics at New Mexico Institute of Mining and Technology. His research focuses on numerical methods for solving partial differential equations (PDEs), particularly in interface problems, orthogonal spline collocation, iterative methods, and domain decomposition techniques. Research Interests: Numerical methods for interface boundary value problems Orthogonal spline collocation and multigrid methods Iterative solvers for large linear/nonlinear systems Applications to nonlinear elliptic and non-self-adjoint PDEs Finite element and Galerkin schemes Teaching: He has taught courses ranging from Calculus and Linear Algebra to advanced numerical methods, including Computational Fluid Dynamics, PDEs, and COMSOL modeling.

• Origami Mathematics
• Computational Geometry
• Rigid Folding Mechanics
• +۵ مورد دیگر

Thomas C Hull is an Associate Professor of Applied Mathematics at Franklin & Marshall College's Department of Mathematics. Previously, he held roles including Project Associate Professor at the University of Tokyo (2015) and Associate Professor at Western New England University (2008-2023). His research focuses on origami-math intersections, particularly rigid origami's applications in robotics, materials science, and computational geometry. He has been funded by NSF grants, including one exploring origami metamaterials and configuration spaces. Education includes a Ph.D. in Mathematics from the University of Rhode Island (1997), alongside earlier degrees from the same institution and Hampshire College. Hull is the author of seminal works like Origametry: Mathematical Methods in Paper Folding (2020) and Project Origami (2012). His collaborations, such as with Inna Zakharevich proving origami's Turing completeness, have gained international attention in Quanta Magazine . Research interests span rigid origami mechanics, topological kinematics, and algorithmic folding. His NSF-funded work supports student collaborations and explores origami's potential for advanced material systems. Hull teaches courses ranging from calculus to nonlinear dynamics, emphasizing transitions between theoretical and applied mathematics. Grants include NSF awards for configuration spaces of rigid origami and mechanical metamaterials. His work bridges pure mathematics with engineering applications, demonstrating origami's role in solving complex problems from architectural design to biomedical devices.

Hildegard Meyer-Ortmannsمشاهده پروفایل

استاد مدعو

• Theoretical Physics
• Complex Systems
• Statistical Physics Out-of-Equilibrium
• +۷ مورد دیگر

Prof. Dr. Hildegard Meyer-Ortmanns is an Adjunct Professor of Physics at the School of Science, Constructor University, Bremen, Germany. Her research focuses on theoretical physics, complex systems, and nonlinear dynamics, with applications in biology, cognitive processes, and energy systems. She holds a PhD in Physics from the University of Hamburg (1983) and a Habilitation from the University of Heidelberg (1993). Previously, she was a Full Professor at Jacobs University Bremen (2002–2020) and held positions at institutions including CERN and the Max-Planck Institute. Research Interests: Statistical Physics Out-of-Equilibrium and Nonlinear Dynamics Role of Topology in Biological and Cognitive Systems Resilient Design of Power Grids Evolutionary Game Theory and Multi-Scale Dynamics Funded Projects: DFG-grant ME-1332/30-1: Topological phases in nonlinear oscillatory systems BMBF Project CoNDyNet 2: Decentralized power grid dynamics DFG-grant ME-1332/28-2: Coupled heteroclinic networks Her work bridges physics, biology, and engineering, emphasizing interdisciplinary approaches. Recent articles explore heteroclinic networks in brain dynamics, power grid stability, and stochastic processes in ecosystems. She actively organizes international conferences and workshops on complexity science and energy systems.

• Macroeconomics
• Economic Dynamics
• Cyclical Development
• +۹ مورد دیگر

Sara Casagrande is a Tutor in the Department of Economics and Management at the University of Trento, affiliated with the Faculty of Economics. She has held teaching roles since 2014, instructing courses such as Investment Instruments and Derivatives, Macroeconomics and Monetary Policy, and Global Economic Scenarios. Her academic career includes post-doctoral research and research assignments at the University of Trento, focusing on projects like the PRIN 2022 initiative analyzing post-pandemic European governance and Eurozone institutional reforms. Education: Higher Diploma in Accounting (2007), Bachelor’s in Economics and Business Management (2010), Master’s in Finance (2012), and PhD in Economics and Management (2017), all from the University of Trento. Research interests span macroeconomics, European integration dynamics, institutional variety, and econometric modeling. She has organized and participated in numerous conferences, including the EASET Conference and events at the Jean Monnet European Centre of Excellence, focusing on comparative economic systems and EU policy analysis. She currently serves as an External Fellow at CefES (University of Milano Bicocca) and on the Scientific Advisory Committee of the EASET Conference. Her recent work emphasizes algorithmic modeling of economic systems, EU core-periphery divides, and the role of comparative economics in post-pandemic policy design. She has contributed to special journal issues on globalization and European integration and serves as a referee for journals like the European Journal of Law and Economics.

ROBERTO IGLESIAS RODRIGUEZمشاهده پروفایل

استاد

• Artificial Intelligence
• Mobile Robotics
• Reinforcement Learning
• +۴ مورد دیگر

Roberto Iglesias Rodríguez is a Professor at the Department of Electronics and Computing, Higher Polytechnic School of Engineering at Universidade de Santiago de Compostela. He holds a PhD from the same institution (2003) with a thesis on vector quantification models. His research focuses on artificial intelligence, mobile robotics, reinforcement learning, and fuzzy logic control. He leads the GSI Group (Intelligent Systems Group) within the Center for Research in Intelligent Technologies (CITIUS). Key research areas include: Robot navigation and path planning Reinforcement learning and hybrid learning systems Fuzzy control systems in dynamic environments Computer vision for robotics Federated learning and distributed robotics Recent work emphasizes federated learning applications in robotics, non-IID data handling, and adaptive systems. Notable contributions include a self-organized multi-camera network for robot deployment (2013) and combining reinforcement learning with genetic algorithms (2006). His research bridges theoretical advances with practical implementations in autonomous systems. Publications span over 30 years, with impactful work in journals like Robotics and Autonomous Systems, IEEE Transactions, and Lecture Notes in Computer Science. Active collaborations include institutions like The University of Texas at Austin and Maastricht University.