جست‌وجوی استاد و پژوهشگر

Chih-Yang Tsaiمشاهده پروفایل

استاد

• Supply Chain Management
• Operations Research
• Linear and Non-Linear Optimization
• +۵ مورد دیگر

Dr. Chih-Yang Tsai is a Professor of Operations and Decision Sciences at the School of Business, SUNY New Paltz. He earned his Ph.D. in Operations Research from New York University's Stern School of Business (1993) and an MBA from Tatung University, Taiwan. With over three decades of academic experience, he has held leadership roles including Associate Dean (2006–2013) and Interim Dean (2013–2014). His research focuses on supply chain management, optimization, and simulation, with publications in journals like Decision Support Systems and Mathematical Programming . He teaches courses such as Operations Management, Decision Modeling, and Managing Operations & IT. Notable awards include the W. Edwards Deming Scholarship (1990) and the SUNY New Paltz School of Business Distinguished Service Award (2010). His applied work includes optimizing marketing budgets for American Express and developing analytical tools for regional businesses. He is an active member of INFORMS and the Decision Sciences Institute.

Yubo Songمشاهده پروفایل

پژوهشگر ارشد

• Microgrid Engineering
• Power Electronics
• Power Electronic Converters
• +۳ مورد دیگر

Yubo Song is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, specializing in power electronics, microgrid systems, and reliability engineering. His work focuses on stability validation, control strategies, and the application of neuromorphic computing to power grids. His educational background includes: Ph.D. in Energy Technology from Aalborg University (awarded 2023) M.Sc. in Electrical Engineering from Shanghai Jiao Tong University (awarded 2019) B.Eng. in Electrical Engineering and Automation from Shanghai Jiao Tong University (awarded 2016) Dr. Song's research integrates microgrid engineering, power electronics reliability, and neuromorphic computing to address critical challenges in grid stability. His work pioneers Spike Talk technology for energy-efficient grid communication and develops hybrid modeling approaches for real-time power system simulation. Recent publications demonstrate expertise in damping control of grid-forming inverters, sensitivity analysis for reliability evaluation, and Bayesian risk assessment frameworks. His publication trends reveal a strategic shift toward neuromorphic solutions for power grids, with 2024-2025 works focusing on spike-based communication protocols and event-driven semantic systems. Earlier research centered on stability validation and reliability modeling for microgrids, establishing foundational frameworks still cited in current literature. Dr. Song has secured significant research funding including: Power Sentinel (Villum Foundation, 2024-2025): Developing cyber-physical safeguards for future power grids Reliability and Lifetime Analysis (2022-2025): Creating predictive models for power electronic components Stability and Reliability Validation (PhD project, 2020-2023): Establishing comprehensive microgrid assessment methodologies As Principal Investigator on multiple projects, he mentors early-career researchers while collaborating with industry partners on grid security solutions. His work with the Applied Power Electronic Systems group at AAU Energy focuses on translating theoretical advances into practical grid applications, particularly in converter reliability and neuromorphic grid communication.

Nikolaos Vlastakisمشاهده پروفایل

دانشیار

• Finance
• Financial Econometrics
• Commodity Markets
• +۴ مورد دیگر

Nikolaos Vlastakis is an Associate Professor in Finance at Norwich Business School (NBS), University of East Anglia. He is also a visiting research fellow at Athens University of Economics and Business (Greece). Previously, he held positions as Lecturer in Finance at the University of Essex and Cranfield University. He leads teaching on modules such as Risk Management (BSc) and Economics and Finance (MBA), focusing on Investments, Financial Econometrics, and Data Analytics. His education includes a PhD from Athens University of Economics and Business, an MSc in Engineering-economic Systems (National Technical University of Athens), an MSc in Decision Sciences (Athens University of Economics and Business), and a BSc in Economics and Regional Development (Panteion University Athens). He has received academic distinctions and scholarships from Greek institutions, including NVIDIA’s GPU Grant (2017). Research interests center on information finance, volatility modeling, commodity markets, and forecasting using unconventional datasets (e.g., online activity). Key topics include oil price volatility prediction, term structure of interest rates, and stock market jumps during uncertainty. His work is published in journals like the Journal of Banking and Finance and Journal of Futures Markets , with media coverage in Refinitiv and HedgeWeek. He co-founded the International Symposium in Finance (ISF) and serves on the steering committee of the Financial Management Special Interest Group at the British Academy of Management. Current projects include the Horizon Europe-funded ‘Beyond the Horizon’ initiative (2025–2027), focusing on AI deployment in finance. Awards include Academic Distinction from Panteion University (2000) and multiple Greek State Scholarships. He supervises three full-time PhD students and actively engages in academic reviewing for top journals. Consulting activities span SME financing, asset allocation, and econometric analyses of ICT competitiveness.

Hao Wangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Statistical Learning
• +۱۲ مورد دیگر

Hao Wang is an Assistant Professor in the Department of Computer Science at Rutgers University, where he leads the Machine Learning Lab. His research spans statistical machine learning, deep learning, and large language models (LLMs), with applications in trustworthy & safe AI, healthcare, recommender systems, computer vision, natural language processing, network analysis, and data mining. He previously held postdoctoral positions at MIT's Computer Science & Artificial Intelligence Lab and received his PhD in CSE from Hong Kong University of Science and Technology (HKUST) under Prof. Dit-Yan Yeung, with earlier studies at Shanghai Jiao Tong University under Prof. Wu-Jun Li. Education: BS (Shanghai Jiao Tong, 2013), PhD (HKUST, 2016) Current Role: Assistant Professor, Rutgers University Research Themes: Bayesian deep learning, causal reasoning, domain adaptation, and safety in AI systems His work emphasizes Bayesian deep learning for interpretable and robust AI, including applications in healthcare (e.g., cancer diagnosis, Parkinson’s disease detection) and foundational contributions to domain adaptation, continual learning, and trustworthy LLMs. Recent publications focus on multimodal LLMs, calibration techniques, and concept-based domain adaptation frameworks. Notable awards include the ACM SIGKDD 2025 Test of Time Award , NIH R01 Award , NSF CAREER Award , and Microsoft Research AI & Society Fellowship . He has also secured grants like NSF IIS-2127918 and Amazon Faculty Research Award . Hao Wang actively recruits PhD students starting in Fall 2025 and collaborates across disciplines in AI, computer vision, and healthcare. His lab's outputs include survey papers on Bayesian deep learning and practical implementations for recommender systems.

• Optimal Transport
• Entropic Distances between Nonnegative Measures
• Gradient Flows
• +۷ مورد دیگر

Giuseppe Savaré is a Full Professor at Bocconi University in Milan. His research focuses on Optimal Transport, Gradient Flows, and Analysis in Metric-Measure Spaces, with contributions to the theory of rate-independent systems and calculus in non-smooth geometries. He has published extensively on topics like Wasserstein spaces, entropy-transport distances, and dissipative evolutions. Affiliations: Bocconi University (Full Professor), Member of the Calculus of Variations and PDE research group. Education: Not explicitly stated in text, inferred via academic rank and publications. His work bridges functional analysis, PDEs, and geometric measure theory. Key themes include the interplay between optimal transport and geometric structures, as well as applications to evolutionary problems in materials science and mathematical physics. Recent contributions include relaxations of unbalanced transport problems and the study of variational principles in metric measure spaces. He has organized workshops on Optimal Transport and Geometric Analysis, and his research has been supported by institutions like the Simons Foundation. His articles explore both theoretical foundations (e.g., gradient flows in Wasserstein spaces) and applied aspects (e.g., crack propagation models).

• Optimal Transport
• Partial Differential Equations
• Calculus of Variations
• +۳ مورد دیگر

Alessio Figalli is a Full Professor and Director of the Forschungsinstitut für Mathematik (FIM) at ETH Zurich. His research focuses on optimal transport, partial differential equations, and geometric measure theory. He has held positions at leading institutions, including the University of Texas at Austin and École Polytechnique. Educational Background PhD in Mathematics (2006–2007): Scuola Normale Superiore di Pisa and École Normale Supérieure de Lyon Master's (2004–2006): University of Pisa and Scuola Normale Superiore Bachelor's (2002–2004): University of Pisa and Scuola Normale Superiore Research Interests Figalli's work bridges pure and applied mathematics, with contributions to optimal transport, free boundary problems, and stability of geometric inequalities. His research has applications in economics, meteorology, and physics. Notable contributions include: Advances in regularity theory for free boundary problems Quantitative stability estimates for functional and geometric inequalities Applications of optimal transport to economics and machine learning Scientific Recognition Recipient of the Fields Medal (2018), EMS Prize (2012), and Stampacchia Gold Medal (2015). He is a member of prestigious academies, including the Accademia Nazionale dei Lincei and the Academia Europaea. Teaching and Mentorship Figalli has taught courses on analysis, PDEs, and optimal transport at ETH Zurich and the University of Texas. He mentors numerous PhD students and postdocs, contributing to the next generation of mathematicians. Grants and Editorial Work He leads the ERC-funded RSPDE project and serves on editorial boards for journals like Archive for Rational Mechanics and Analysis and Duke Mathematical Journal .

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +۳ مورد دیگر

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Evelyn Ramplerمشاهده پروفایل

استادیار

• Analytical Chemistry
• Mass Spectrometry
• Metabolomics
• +۳ مورد دیگر

Evelyn Rampler is an Assistant Professor (Tenure Track) at the University of Vienna's Faculty of Chemistry, Department of Analytical Chemistry. Her research focuses on advancing mass spectrometry-based methodologies for lipidomics and metabolomics, with particular expertise in ganglioside profiling, metabolic pathway analysis, and analytical workflow optimization. She leads projects addressing challenges in quantitative lipidomics, sustainable metabolite extraction, and the application of novel mass spectrometry tools for biomarker discovery. Her academic contributions include pioneering work on TRAM (Triple Acquisition Mass Spectrometry) for integrated metabolomics and lipidomics analysis, FAIMS-enhanced lipidomics workflows, and taxonomically-informed metabolite identification tools like microbeMASST. She has organized international conferences such as APMRS-CEEPC 2024 and actively promotes gender equity in analytical chemistry through advocacy initiatives. Key research areas span metabolic engineering using isotope labeling, sustainable agricultural practices for bioactive compound production, and drug discovery targeting cancer metabolism. Rampler has secured research funding for projects like 'Prone Adiposity,' exploring adipogenic differentiation mechanisms in mesenchymal stem cells through multiomics approaches. Her lab develops innovative LC-MS methodologies to enhance metabolite coverage and analytical throughput while maintaining high resolution and accuracy. Notable collaborations include work with institutions on microbial metabolomics, plant lipidomics, and stem cell glycolipid analysis. Her team's activities emphasize translational applications of analytical chemistry in environmental science, pharmacology, and biomedical research, with over 55 peer-reviewed publications and frequent conference presentations since 2012.

• Fluid Mechanics
• Machine Learning
• Chaotic Systems
• +۳ مورد دیگر

Luca Magri is a Reader in Data-Driven Fluid Mechanics at Imperial College London's Department of Aeronautics. He holds affiliations as a Fellow of The Alan Turing Institute and a Hans Fischer Fellow at the Technical University of Munich (TUM). His research focuses on physics-constrained machine learning, chaotic systems, and fluid mechanics. Magri’s work bridges computational methods with fluid dynamics, addressing challenges in turbulence, combustion, and acoustics through innovative approaches like reservoir computing and adjoint-based optimization. Education & Career PhD in Engineering, University of Cambridge (2015) Postdoctoral Fellow, Stanford University Center for Turbulence Research (2015–2017) Lecturer at Cambridge University Engineering Department (2017–2018) Current: Reader at Imperial College London since 2018 Research Interests Magri’s research integrates machine learning with fluid dynamics to model chaotic systems, optimize combustion processes, and analyze turbulence. Key areas include: Physics-informed neural networks for extreme event prediction Adjoint-based methods for thermoacoustic stability Bayesian data assimilation in nonlinear systems Publications & Awards His impactful work has been recognized with awards such as the ERC Starting Grant (2019), Royal Aeronautical Society Fellowship (2022), and multiple fellowships from Stanford and Cambridge. Over 50 publications span journals like Journal of Fluid Mechanics and Proceedings of the Royal Society A . Grants & Collaborations Magri leads projects funded by the EU Horizon 2020, UKRI, and the ERC. He collaborates globally, including with TUM’s Institute for Advanced Study and Cambridge’s Engineering Department.

Mengshuo Jiaمشاهده پروفایل

پژوهشگر

• AI4Science
• Uncertainty Modeling
• Probabilistic Analysis
• +۴ مورد دیگر

Dr. Mengshuo Jia is a Senior Scientist and Principal Investigator at ETH Zürich, affiliated with the Power Systems Laboratory (PSL) within the Department of Electrical Engineering. He holds a Ph.D. from Tsinghua University (2016–2021) and a B.Eng. from North China Electric Power University (2012–2016). His roles include Guest Lecturer for 'Optimization in Energy Systems' and Associate Editor for IEEE Systems Journal and IET Renewable Power Generation. Education: Ph.D. in Electrical Engineering, Tsinghua University (2016–2021) B.Eng. in Electrical Engineering, North China Electric Power University (2012–2016) Research focuses on AI4Science , Uncertainty Modeling , Probabilistic Analysis , Stochastic Optimization , and Data-Driven Power Systems . Notable contributions include the RePower LLM-driven research platform and the DALINE toolbox for power flow linearization. His work bridges AI advancements with energy system challenges, enhancing autonomous research and grid optimization. Recent publications emphasize LLM applications in energy systems, small modular reactor integration, and hydrogen supply chain optimization. Over 10 peer-reviewed articles since 2022 highlight interdisciplinary innovation. Awards: 2023 ESI Hot Paper (top 0.1%) and Highly Cited Paper (top 1%) 2023 China First Prize of High-influence Papers 2022 Springer Thesis Award Advising and grants include leadership in Swiss National Science Foundation projects and editorial roles in top-tier journals. Collaborations emphasize data-driven methodologies and privacy-preserving distributed algorithms. Labs: Active in the Power Systems Laboratory (PSL) at ETH Zurich, advancing research in energy system optimization and AI integration.

Sebastian Kozerkeمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Cardiac Mechanics
• Perfusion Imaging
• +۳ مورد دیگر

Prof. Dr. Sebastian Kozerke is Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, leading the Professorship for Biomedical Imaging. His research program develops advanced magnetic resonance imaging methods for cardiac applications, focusing on ultra-fast dynamic imaging of perfusion, cardiac mechanics, and microstructure analysis. Key innovations include k-t undersampling techniques and parallel imaging methods that significantly advance spatiotemporal resolution in medical imaging. Professor Kozerke's research spans multiple domains including perfusion imaging, diffusion tensor imaging for myocardial microstructure, and real-time metabolic imaging using dynamic nuclear polarization. Current investigations explore hyperpolarized 13 C pyruvate metabolic imaging, neural network applications for cardiac analysis, and low-field MRI techniques. His work consistently bridges fundamental physics with clinical translation. Publications demonstrate leadership in cardiovascular MRI innovation. Recent work establishes consensus standards for hyperpolarized MRI studies (2025), develops deep learning frameworks for flow quantification (FlowMRI-Net), and advances microstructure analysis in cardiomyopathy (2025). Earlier foundational work includes contributions to diffusion imaging, parallel MRI methods, and cardiac DTI techniques. Professor Kozerke teaches core courses including 'Biomedical Imaging' (227-0385-10L), 'Biomedical Engineering' (227-0386-00L), and leads the 'Seminar on Biomedical Magnetic Resonance' (227-0980-00L). He founded EXCITE Zurich, a joint center for experimental and clinical imaging technologies. Career progression includes: PhD and Venia legendi from ETH Zurich, research at King's College London, co-founding GyroTools (2003), professorship at King's College London (2008), University of Zurich (2010), and ETH Zurich dual appointment (2014).

• Power Systems
• Energy Transition
• Grid Reliability and Risk Engineering
• +۳ مورد دیگر

Dr. Blazhe Gjorgiev is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on power systems reliability, energy transition modeling, and grid risk engineering. He leads the Reliability and Risk Engineering group, addressing challenges in grid resilience, renewable integration, and cascading failure mitigation. His work bridges machine learning applications with physical grid systems, emphasizing data-driven approaches for infrastructure optimization. Key research interests include: Power grid reliability and vulnerability analysis Renewable energy integration and policy implications Data-driven modeling of transmission and distribution systems Machine learning for fault detection and grid operation Recent publications highlight advancements in anomaly detection for power line inspection, European energy transition policies, and graph-based power grid benchmarking. His work often intersects with socio-technical issues, such as energy inequities and infrastructure resilience in aging systems. Dr. Gjorgiev collaborates on projects like the Nexus-e platform for energy-economic assessments and the Cascades-Risk model for expansion planning. His research has practical implications for grid modernization, policy design, and sustainable energy systems.

Todorovski Mirkoمشاهده پروفایل

دانشیار

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +۶ مورد دیگر

Associate Professor Todorovski Mirko is affiliated with the Faculty of Electrical Engineering and Information Technologies at Ss. Cyril and Methodius University in Skopje, Macedonia. His primary appointment is in the Department of Power Systems, where he specializes in power systems engineering, grounding systems, and renewable energy integration. He leads research activities at the Transmission Power Systems Institute, focusing on enhancing grid reliability and optimizing energy infrastructure. His research interests span multiple domains, including power system planning, high voltage engineering, lightning protection, and smart grid technologies. Key areas of exploration include radial distribution network design, robust optimization for energy storage systems, and the impact of renewable energy sources on grid dynamics. Recent publications highlight trends in addressing uncertainty in power demand, optimizing feeder routing with battery storage, and mitigating lightning-induced risks in communication tower grounding systems. His work frequently employs advanced computational methods, such as Monte Carlo simulations and stochastic optimization, to address real-world grid challenges. While no formal grants or awards are listed, his contributions to the Macedonian power system's flexibility assessment and grid modernization efforts reflect significant applied research impact. He maintains active involvement in academic teaching, instructing core courses like High Voltage Networks and Earthing Systems in Power Grids. His research often intersects with industry needs, such as improving grid resilience through decentralized charging solutions for electric vehicles and enhancing voltage regulation in low-voltage distribution networks. Ongoing projects explore the integration of distributed generation and advanced grid control strategies.

• Chemical Engineering
• Hemolysis
• Cardiovascular Diagnostics
• +۳ مورد دیگر

Krystian Jędrzejczak is an Assistant Professor at the Division of Industrial Process Intensification within the Faculty of Chemical and Process Engineering at Warsaw University of Technology. His research focuses on applying chemical engineering methodologies to cardiovascular disease diagnostics, particularly in hemolysis risk assessment and atherosclerosis modeling using computational fluid dynamics (CFD) and experimental techniques. Education: PhD in Chemical Engineering (2025) Master of Science (2020) Bachelor of Science (2019) Research interests revolve around interdisciplinary applications of chemical engineering principles to biomedical challenges. Key areas include: hemolysis mechanisms in blood vessels, computational modeling of cardiovascular diseases, and experimental validation of medical simulations. His work bridges engineering and medicine to improve diagnostic accuracy and surgical planning. Recent articles emphasize CFD-based risk assessment in coronary arteries, carotid stenosis, and atherosclerotic lesions. These studies highlight advancements in hemodynamic analysis and personalized healthcare solutions through 3D-printed cardiovascular phantoms. Advising: Supervised 2 promoted theses (no grant information available). His research is conducted within the Division of Industrial Process Intensification, collaborating on biomedical device development and medical simulation technologies.

Helene Reyمشاهده پروفایل

استاد مهمان

• International Macroeconomics
• International Finance
• Financial Stability
• +۳ مورد دیگر

Helene Rey is a Visiting Professor of Economics at London Business School , with affiliated roles at CEPR and NBER. She is a Fellow of the Econometric Society, British Academy, European Economic Association, and American Academy of Arts and Sciences. Specializes in international macroeconomics and finance Focuses on financial stability and global monetary systems Her research interests include: International capital flows Exchange rate dynamics Global financial cycles Monetary policy independence Scientific recognition includes: 2024 Yrjö Jahnsson Prize 2022 Carl Menger Prize 2019 Grand Prix de l'Economie 2013 Bernácer Prize She contributes to policy discussions at institutions like Banca d'Italia and the Bank for International Settlements, with notable work on the global financial cycle and its implications for monetary sovereignty.