جستجوی استاد راهنمای دانشگاهی

Jovica Milanovicمشاهده پروفایل

استاد

• Power System Dynamics
• Power Quality
• Probabilistic Modeling
• +۴ مورد دیگر

Jovica Milanovic is a Professor in the School of Electrical and Electronic Engineering at the University of Manchester, where he leads the Electrical Energy and Power Systems research group. He holds a Dipl.Ing. and MSc in Electrical Engineering from the University of Belgrade, a PhD from the University of Newcastle, and a DSc from the University of Manchester. With over 600 publications, his research focuses on probabilistic modeling of power systems, renewable integration, and power quality. Research Expertise: Probabilistic assessment of uncertain power systems Machine learning applications for grid stability Demand side management in low-carbon networks Global power quality monitoring frameworks Awards & Leadership: FIEEE, FIET, and IEEE PES Distinguished Lecturer Editor-in-Chief of IEEE Transactions on Power Systems Chair of multiple international conferences including PowerTech 2017 Recipient of the Verriest Medal for contributions to power systems

Lyesse Lalouiمشاهده پروفایل

استاد

• Environmental Geomechanics
• Geotechnical Engineering
• Mechanics of Multiphase Porous Materials
• +۲ مورد دیگر

Lyesse Laloui is Full Professor and Director of the Soil Mechanics Laboratory at EPFL's School of Architecture, Civil and Environmental Engineering. A world-renowned expert in geomechanics and sustainability, his research develops nature-inspired solutions for climate change mitigation and renewable energy adoption. His distinguished career includes ERC Advanced and Proof of Concept Grants, two honoris causa doctorates, and leadership roles in Academia Europaea and International Society of Soil Mechanics. Research focuses on: Thermo-hydro-mechanical behavior of energy geostructures Bio-inspired geotechnical solutions for soil improvement Radioactive waste repository safety CO₂ sequestration caprock integrity Urban underground space utilization Recent publications demonstrate innovations in geothermal metro station design, shale behavior characterization for energy applications, and bio-mediated soil stabilization. His laboratory has supervised over 35 PhD graduates now advancing geotechnical engineering globally. Professor Laloui founded 5 EPFL spin-offs including Enerdrape (Swiss Top 100 startup) and serves as European Director of the International Associated Research Centers for Urban Underground Space. His awards include the ASCE Kersten Lecture and InterPore Kimberly-Clark Distinguished Lectureship.

Chao Liمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence in Healthcare
• Medical Imaging
• Precision Medicine
• +۳ مورد دیگر

Chao Li is a Principal Research Fellow at the University of Cambridge's Faculty of Mathematics, specializing in AI-driven healthcare solutions. His research focuses on precision medicine through image-based AI and multi-omics integration, with applications in neurological disease modeling, surgical oncology, and clinical AI safety. He leads the Centre for Mathematical Imaging in Healthcare , advancing translational AI for personalized medicine. Research Themes: Image-based AI for precision mental health AI in surgical and interventional oncology Multi-omics approaches for disease characterization Evaluation of AI innovations for clinical translation Publications highlight advancements in multimodal fusion for diagnostics, histology-molecular marker integration, and neuroimaging analytics for mental health. His work bridges computational mathematics with clinical challenges, emphasizing real-world healthcare impact. Labs/Teams: Active member of the Centre for Mathematical Imaging in Healthcare , collaborating across departments to develop AI tools for clinical deployment.

Michelle Torresمشاهده پروفایل

استادیار

• Political Methodology
• Political Behavior
• Computer Vision
• +۵ مورد دیگر

Margaret Michelle Torres is an Assistant Professor in the Department of Political Science at the University of California, Los Angeles (UCLA). Her research focuses on political methodology, computer vision, causal inference, and survey methodology, with substantive interests in political media communication, participation, and attitude formation. She holds a Ph.D. in Political Science and an A.M. in Statistics from Washington University in St. Louis, alongside a B.A. in Political Science and International Relations from CIDE (Mexico City). Her work bridges computational methods and social science inquiry, emphasizing innovative tools for analyzing visual and textual data. Recent projects explore how ideology influences perceptions of political groups, the role of visual frames in protests, and methodological issues in causal inference. Torres has authored influential papers on topics ranging from election dynamics to the application of machine learning in political analysis. Notable contributions include frameworks for unsupervised visual analysis and critiques of posttreatment variable pitfalls in experiments. Her research frequently intersects with questions of media representation, public opinion, and institutional trust. Torres advises students in quantitative methods and political behavior, though specific advisee names are not listed here.

Andrew Linمشاهده پروفایل

مدرس ارشد

• Neuroscience
• Olfactory System
• Neural Circuit Function
• +۳ مورد دیگر

Dr. Andrew Lin is a Senior Lecturer and School Director of One University at the University of Sheffield's School of Biosciences. He holds a PhD from the University of Cambridge and a BA in Biology from Harvard University. His career includes roles as a Lecturer (2019-2022), Vice-Chancellor’s Fellow (2015-2019), and Postdoctoral Fellow at the University of Oxford (2009-2015). Research focuses on how the brain encodes sensory information for memory formation, using Drosophila's olfactory system as a model. Key areas include sparse coding in Kenyon cells, synaptic inhibition/excitation balance, and neural circuit dysfunction links to epilepsy. Teaching includes modules like BMS11004 Introduction to Neuroscience and BMS248 Neural Circuits, Behaviour and Memory. He has secured grants from the European Research Council, BBSRC, and Wellcome Trust. Professional memberships include the FENS-Kavli Network and BBSRC Pool of Experts. Lab research employs techniques like in vivo two-photon imaging, electrophysiology, and genetic manipulation. PhD opportunities are available in neural circuitry and sensory processing.

Hangfeng Heمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reasoning
• +۴ مورد دیگر

Hangfeng He is an Assistant Professor of Computer Science and Data Science at the University of Rochester , affiliated with the Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Pennsylvania and focuses on machine learning and natural language processing. His research emphasizes incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language. Education : PhD in Computer Science from University of Pennsylvania Research Interests : Machine Learning (especially deep learning theory, generalization, and optimization) Natural Language Processing (incidental supervision, language understanding, and multimodal reasoning) Reasoning (inductive biases, temporal data analysis, and model interpretability) Research Trends : His work spans foundational AI research and applied NLP, with recent focus on large language models, multimodal systems, and constraint-aware methods. He explores how models generalize from limited supervision and mitigate biases in real-world data. Key themes include improving model robustness, understanding inductive mechanisms, and applying AI to financial and social text analysis. Labs/Teams : While specific lab affiliations aren’t listed, his work aligns with the Department of Computer Science’s research clusters in AI, machine learning, and data science.

Vytautas Valaitisمشاهده پروفایل

مدرس ارشد

• Macroeconomics
• Labor Markets
• Housing Economics
• +۷ مورد دیگر

Vytautas Valaitis is a Senior Lecturer at the University of Surrey , affiliated with the Department of Economics under the Faculty of Arts, Business and Social Sciences. His research focuses on macroeconomics, labor markets, housing economics, and fiscal policy, with a strong emphasis on heterogeneous-agent models and macro-finance intersections. Max Weber Fellow, European University Institute (previous role) PhD recipient from Duke University (2021) His recent work integrates machine learning into macroeconomic modeling to address multicollinearity in state variables, explores wealth distribution's impact on labor supply, and investigates optimal government debt strategies under geopolitical risk. Publications highlight innovative frameworks for defense financing, inflation control through real/nominal bonds, and housing market effects on asset pricing. Scientific awards include the prestigious Max Weber Fellowship . His research underscores the critical role of structural economic modeling in policy design and market stabilization.

Sharon M Weissمشاهده پروفایل

استاد

• Photonics
• Nanotechnology
• Silicon Photonics
• +۳ مورد دیگر

Sharon M Weiss is the Cornelius Vanderbilt Professor of Engineering and holds joint appointments in Electrical Engineering, Materials Science & Engineering, and Physics at Vanderbilt University's School of Engineering. She directs the Vanderbilt Institute of Nanoscale Science and Engineering. Her research focuses on light-matter interaction, silicon photonics, porous silicon biosensors, and nanotechnology. She earned a B.S., M.S., and Ph.D. in Optics from the University of Rochester. Her work spans advanced photonic crystal designs, ultra-sensitive biosensors, and radiation-tolerant optical components for aerospace applications. Recent projects include photonic metacrystals for high-Q cavities and porous silicon sensors for rapid diagnostics. She has pioneered integration of phase-change materials like VO₂ in silicon photonics for ultrafast optical switching. Publications emphasize subwavelength photonics, biosensing innovations, and space-qualified optoelectronics. Her lab develops hybrid waveguides, nanobeam cavities, and AI-enhanced sensing systems. Collaborations include NASA and industry partners for biomedical and defense applications.

Javad Fattahiمشاهده پروفایل

استادیار

• Energy Equity and Inclusion
• Distributed Energy Resources (DER)
• Software-Defined Nanogrids
• +۶ مورد دیگر

Dr. Javad Fattahi is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds roles as an IEEE Senior Member, Professional Engineer (P.Eng.), and member of the Standards Council of Canada. He leads the IEEE 2030.5 smart-grid interoperability verification initiative as its VP and chief editor. Dr. Fattahi earned his BSc in Electrical Engineering from Iran University of Science and Technology (IUT), followed by graduate studies at Sharif University of Technology (SUT). He completed his PhD and postdoctoral research at the University of Ottawa, specializing in energy systems and optimization. His research focuses on energy equity, distributed energy resources (DERs), software-defined nanogrids, game theory, smart mobility, and nonlinear systems. His recent work emphasizes data-driven digital twins for grid stability, V2G integration in residential complexes, and federated Byzantine agreement models for EV infrastructure. Key contributions include transactive energy systems under IEEE standards and energy disaggregation in institutional buildings. Dr. Fattahi has received accolades including IEEE Senior Membership and P.Eng. certification. His lab develops solutions for grid-edge technologies, smart mobility, and resilient energy systems. Graduate opportunities are available via his UniWeb page.

• Heterogeneous Catalysis
• Electrocatalysis
• Sustainable Reactions
• +۳ مورد دیگر

Mathilde Luneau is an Assistant Professor in the Department of Applied Chemistry at the School of Chemistry and Chemical Engineering, Chalmers University of Technology. Her research focuses on heterogeneous catalysis and electrocatalysis for sustainable reactions, employing a multidisciplinary approach encompassing materials synthesis, testing, and advanced characterization. She aims to design efficient catalytic materials for energy and environmental applications. Education : PhD in Chemistry, University of Lyon, France. Postdoctoral Researcher at Harvard University under Prof. Cynthia Friend, investigating dilute alloy catalysts. Research Interests : Luneau’s work emphasizes sustainable reaction pathways using advanced catalytic systems. Her lab explores nanomaterials, bimetallic alloys, and electrochemical methods to enhance catalyst stability and selectivity. Key areas include fuel cell catalyst layers, oxygen-assisted coupling reactions, and additive manufacturing for flow reactors. She employs techniques like X-ray spectroscopy and machine learning to analyze catalyst structures and performance. Publications : Her recent studies highlight advancements in platinum nanoparticle synthesis, dilute PdAu catalysts, and Ti-6Al-4V anodes for electrochemical reactors. Trends reflect a focus on optimizing catalytic selectivity, stability under varying conditions, and scalable production methodologies. Labs/Teams : Luneau leads a research group at Chalmers dedicated to sustainable catalytic materials. Her team collaborates on projects like biogas processing and hydrogen production, leveraging interdisciplinary expertise in chemistry, engineering, and computational methods.

Tomer Galantiمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Large Language Models
• +۵ مورد دیگر

Tomer Galanti is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on machine learning, deep learning, large language models, and artificial intelligence. He holds a B.S., M.S., and Ph.D. in Computer Science and Mathematics from Tel Aviv University (2015–2021), followed by a postdoctoral position at MIT (2021–2024). His work spans theoretical foundations of neural networks, optimization, generalization bounds, and practical applications in language modeling and self-supervised learning. Education: Postdoctoral Associate, Massachusetts Institute of Technology (MIT), 2021–2024 Ph.D., Computer Science, Tel Aviv University, 2021 M.S., Computer Science, Tel Aviv University, 2017 B.S., Mathematics and Computer Science, Tel Aviv University, 2015 Research Interests: Machine and deep learning, large language models, artificial intelligence, generalization bounds, neural collapse phenomena, and self-supervised learning. His recent work explores optimization techniques (e.g., SGD with weight decay), distributed inference systems, and theoretical guarantees for neural network performance. Articles Trends: His publications emphasize theoretical advancements in neural network dynamics, generalization, and optimization. Notable themes include sparse neural networks, speculative inference acceleration, and fairness in language models. He also investigates representation learning and decision tree integration in language modeling frameworks. Advising & Grants: No advising or grant details are explicitly listed in the provided materials. His research is supported through institutional and collaborative efforts in computational and theoretical machine learning.

Luana Ruizمشاهده پروفایل

استادیار

• Large-scale Graph Machine Learning
• Graph Neural Networks
• Signal Processing
• +۶ مورد دیگر

Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships

• Geotechnical Engineering
• Soil Mechanics
• Earthquake Engineering
• +۳ مورد دیگر

Prof. Alper SEZER is a faculty member at Ege University's Department of Civil Engineering (Faculty of Engineering), specializing in Geotechnics. His research focuses on geotechnical engineering, earthquake effects, soil mechanics, and sustainable development. He has conducted extensive studies on soil liquefaction, seismic site effects, and post-earthquake damage assessments in regions like Antakya, Izmir, and Adıyaman. His work integrates computational methods (e.g., genetic algorithms, artificial neural networks) with experimental geotechnics to improve soil characterization and disaster resilience. He has collaborated on projects analyzing valley effects, ground motion amplification, and the mechanical behavior of soils under cyclic loading. Education: Details not explicitly provided in text, inferred through academic publications and position. Research interests include geotechnical hazard mitigation, soil stabilization techniques (e.g., polymer-modified soils), and the application of fractal analysis to soil properties. His studies address practical challenges such as sulfate resistance in cement-stabilized soils and freeze-thaw resistance of fiber-reinforced materials. He has contributed to microzonation studies and seismic risk assessments in urban areas like Izmir Bay. Publications emphasize field reconnaissance findings from major earthquakes (e.g., 2020 Samos, 2023 Türkiye earthquakes) and laboratory experiments on soil behavior under dynamic loading. Awards and recognitions are not explicitly listed in the text. Grant activities and advising details are not provided, but his extensive publication record suggests sustained research funding. He is affiliated with geotechnical laboratories at Ege University, focusing on experimental testing and computational modeling in geotechnical engineering.

• 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.

• Lithium-ion Battery Technologies
• Redox Flow Batteries
• Bioenergy
• +۵ مورد دیگر

Dr. Jacob Joseph Lamb is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Energy and Process Engineering within the Faculty of Engineering. He serves as the Study Program Manager for the Bachelor of Engineering in Renewable Energy (BIFOREN) and leads the Digital Energy Systems laboratory group. His roles include WP8 leadership in the FME Battery initiative and Theme Leader for Efficient Energy Use at ENERSENSE. Education: PhD in Energy and Process Engineering, NTNU (2013–2016) MSc in Chemical Engineering, University of Otago, New Zealand (2011–2012) BSc in Chemical Engineering, University of Otago, New Zealand (2008–2010) Research Interests: Dr. Lamb focuses on sustainable energy systems, particularly lithium-ion battery technologies , redox flow batteries , bioenergy , and digitization of energy systems . He explores sensor technologies for real-time monitoring and optimization of energy storage devices. His work integrates machine learning with physics-based models to enhance battery performance and reliability. Key Projects: FME Battery: Norwegian research initiative for advanced battery technologies ReZinc: Zinc-air flow battery development for stationary storage HeaLiSelf: Self-healing lithium-ion batteries using optical sensing and data analytics Computational studies on pre-lithiation processes and cold-climate battery performance Publications: His recent work emphasizes lithium-ion battery digitalization , SoC estimation , and electrode structuring . He bridges experimental and computational methods to address challenges in energy storage scalability and safety. Affiliations: ENERSENSE (Efficient Energy Use Theme) NTNU Thermal Engineering Laboratories