جستجوی استاد راهنمای دانشگاهی

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.

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

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +۳ مورد دیگر

Dr. Paolo Tiso is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with George Haller's nonlinear dynamics research group. His work focuses on model order reduction for nonlinear structural mechanics, including light-weight and thermo-elastic systems, flexible multibody dynamics, and contact/friction mechanics. He contributes to advancing methodologies like spectral submanifolds and hyper-reduction techniques for efficient simulation of complex systems. His research emphasizes practical applications such as bolted joint analysis, aeroelastic systems, and structural health monitoring. Notable contributions include benchmark challenges for nonlinear dynamics predictions and advancements in component mode synthesis. Current projects explore geometrically nonlinear systems and their experimental validation. Dr. Tiso's recent publications highlight trends in reduced-order modeling for nonlinear systems, stochastic parameter analysis, and hyper-reduction strategies. He actively engages with industrial challenges, such as optimizing morphing airborne wind energy systems and addressing acoustic fatigue in aerostructures. Collaborations include the ETH Zürich nonlinear dynamics group and international conferences like IMAC. His work bridges computational mechanics and experimental validation, driving innovation in structural dynamics and system identification.

• Nonlinear Dynamics
• Model Order Reduction
• Structural Mechanics
• +۴ مورد دیگر

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

• Process Systems Engineering
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Xiang-Zhao Kong is a Lecturer at the Institute of Geophysics, ETH Zurich, within the Department of Earth and Planetary Sciences (D-EAPS). He holds a PhD in Environmental Engineering from ETH Zurich (2010), where he was awarded the ETH Medal for his dissertation. His career includes postdoctoral research at the University of Minnesota and a Research Fellowship at the University of Queensland before returning to ETH Zurich in 2015. His research focuses on geothermal energy, flow and transport processes in porous media, reactive transport modeling, and subsurface engineering. Key areas include fractured formations, geothermal reservoir optimization, and CO₂ sequestration. He employs advanced computational methods like lattice-Boltzmann solvers and machine learning for subsurface flow modeling and reservoir characterization. Dr. Kong’s work bridges experimental and theoretical approaches, with notable contributions to mineral precipitation dynamics, fluid-rock interactions, and phase transition fracturing. His publications span geothermal systems, carbon capture, and subsurface energy storage. Recent efforts emphasize de-risking CO₂-Plume Geothermal (CPG) technologies and advancing fracture modeling via neural networks. Awards: ETH Medal for PhD Dissertation (2011) Teaching: Leads the 'Groundwater' course (Autumn Semester 2025).

• Geothermal Energy
• Porous Media
• Fluid Dynamics
• +۵ مورد دیگر

Jan Pieter Abrahams is a Professor and Group Leader of the Nanodiffraction group at the Laboratory for Multiscale Bioimaging, Paul Scherrer Institute (PSI) in Switzerland. His work focuses on developing electron diffraction technologies for atomic-resolution imaging of frozen hydrated biological samples, leveraging PSI's detector expertise to advance structural biology beyond conventional microscopy limitations. Current applications target mitochondrial stress mechanisms in neurodegeneration and aging, with active collaborations across international institutions. Research Interests: Abrahams pioneers electron diffraction and cryo-EM methodologies, emphasizing computational-phasing innovations and machine learning integration. His group specializes in: Overcoming dynamical scattering for atomic-level cellular visualization Hybrid pixel detector applications (JUNGFRAU, EIGER) in electron microscopy Deep learning frameworks for diffraction data processing (e.g., DiffraGAN) Structural analysis of protein nanocrystals in disease contexts Mitochondrial protease mechanisms in aging Bacterial cell division and sporulation structures Publication Trends: Recent work (2020–2024) reveals heavy emphasis on AI-driven structural biology, including generative networks for diffraction phasing and lossless data compression. Instrumentation advancements (e.g., Boersch phase shifters) and disease-focused studies (Alzheimer’s amyloid-beta, malaria heme processing) dominate, showcasing interdisciplinary convergence of physics, computation, and biomedicine. Scientific Awards: No specific awards or fellowships documented in source text Advising and Collaborations: Abrahams has mentored PhD students including Thakkar, Pooja; Rheinberger, Jan; Schärer, Martin; and Wennmacher, Julian. His team collaborates with PSI's detector group on sensor development (GaAs/CdTe) and international labs for structural studies. Funding likely stems from PSI instrumentation projects and disease-focused research initiatives. Labs and Teams: He directs the Nanodiffraction group under PSI's Center for Life Sciences, comprising scientists (van Genderen, Latychevskaia) and postdocs (Blum). The lab integrates cryo-EM, electron diffraction, and computational modeling to visualize cellular processes at nanometer scales, with strong ties to detector engineering and pharmaceutical structural analysis.

• Structural Biology
• Electron Microscopy
• Protein Crystallography
• +۵ مورد دیگر

Maléna Bastien Masse serves as a Lecturer at the Department of Architecture within EPFL's School of Architecture, Civil and Environmental Engineering (ENAC), and as a Scientist at the Structural Exploration Lab (SXL). Her research focuses on advancing circular economy practices in construction through systematic building reuse methodologies. Her primary research interests include: Developing standardized assessment frameworks for building component reuse potential Creating digital inventories of construction systems (e.g., DISCS database with 102 Swiss buildings) Exploring concrete rubble as structural material through digital fabrication techniques Documenting historical reuse practices in Swiss construction heritage Designing interdisciplinary pedagogy for sustainable building with reused materials Recent publications demonstrate strong emphasis on data-driven approaches to circular construction, with 9 of 10 articles published in 2025 addressing methodological innovations in reuse assessment, material flow analysis, and digital process integration. Her work consistently bridges historical analysis with contemporary engineering solutions. Research is supported by the Swiss National Science Foundation (FNS 105216_192693), EPFL ENAC, and collaborative industry partnerships. She actively contributes to critical practice through conference installations and physical prototypes that demonstrate viable reuse systems. Her laboratory work centers on the Structural Exploration Lab (SXL), where digital tools are developed to enhance precision in deconstruction and material recovery processes. Current projects focus on creating waste-negative concrete structures and optimizing supply chains for reused building components.

• Circular Economy in Construction
• Building Reuse and Recycling
• Sustainable Structural Engineering
• +۳ مورد دیگر

Junchao Hu is a Research Fellow at the Department of Psychology, Faculty of Philosophy, University of Fribourg. His work bridges cognitive psychology , neuroscience , and machine learning , focusing on sensory integration and developmental disorders. Education : Doctoral degree in progress at University of Fribourg Email : junchao.hu@unifr.ch ORCID : 0000-0002-6296-5639 His research explores how auditory stimuli influence visual attention, threat detection mechanisms, and computational approaches to speech disorders. Key methods include eye-tracking and machine learning to decode cognitive processes and stuttering patterns. Recent publications highlight cross-modal sensory interactions (2024), pre-conscious gaze biases toward emotional stimuli (2024), and AI-driven stuttering detection (2022). These works span neuroscience , psycholinguistics , and computational modeling . Scientific Awards : SNSF Ambizione Scholarship As a graduate researcher, he collaborates with interdisciplinary teams at the intersection of psychology, technology, and health sciences.

• Cognitive Psychology
• Neuroscience
• Developmental Psychology
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Matthew Wodrich is a Research and Teaching Associate at the Institute of Chemical Sciences and Engineering (ISIC-GE) and Computational Molecular Design Laboratory (LCMD) under the School of Basic Sciences at Ecole Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on computational chemistry, homogeneous catalysis, and asymmetric synthesis, with significant contributions to reaction mechanism modeling and enantioselectivity prediction. He actively contributes to teaching programs at EPFL's SCGC-ENS division. Key Research Areas: Computational Reaction Modeling, Enantioselectivity Prediction, Volcano Plot Analysis, Metal-Ligand Cooperative Catalysis, Photocatalysis, and Machine Learning in Chemistry. Recent Article Trends: His 2022-2025 work emphasizes computational catalyst optimization , solvent effects in reaction selectivity , and machine learning-driven enantioselectivity analysis , with applications in transition metal catalysis, hypervalent iodine chemistry, and CO 2 reduction.

• Computational Chemistry
• Homogeneous Catalysis
• Organic Reaction Mechanisms
• +۴ مورد دیگر

Dr. Surya Gupta is a PostDoc researcher at the University of Basel's Department of Environmental Sciences, Faculty of Science, working within the FG Alewell research group. He joined the university in April 2022 after completing his Ph.D. at ETH Zurich. His research focuses on the intersection of soil science, hydrology, and remote sensing applications, with particular emphasis on digital soil mapping and the relationship between soil properties and erosion processes. Education: Ph.D. in Environmental Sciences (2018-2021), ETH Zurich M.Tech in Remote Sensing and GIS (2013-2015), Indian Institute of Remote Sensing, Dehradun B.Tech in Agricultural Engineering (2009-2013), Punjab Agricultural University, Ludhiana Dr. Gupta's research primarily centers on soil hydraulic properties and their applications in environmental modeling. His work involves developing advanced methods for global and national digital mapping of soil properties, particularly saturated hydraulic conductivity and van Genuchten parameters. He investigates the complex relationship between soil erosion and soil hydraulic properties, examining how incorporating hydraulic properties changes soil erosion modeling outcomes. A significant portion of his research focuses on machine learning applications in soil science, where he works on reducing clustering and overfitting in algorithms while developing Pedo-Transfer Functions (PTFs) and Covariate-based GeoTransfer Functions (CoGTFs). His methodological approach combines extensive field data with remote sensing datasets and sophisticated computational techniques to address critical environmental questions related to soil health and water management. Analysis of Dr. Gupta's recent publications reveals a strong focus on global-scale soil property mapping using machine learning approaches. His research demonstrates increasing sophistication in integrating legacy soil data with modern environmental covariates to produce high-resolution global datasets. A notable trend is his work bridging soil physics with practical applications in erosion modeling and agricultural management, particularly in how soil hydraulic properties influence crop responses to climate variability. His publications span top-tier journals in soil science, hydrology, and environmental modeling, indicating strong recognition within these interdisciplinary fields. Dr. Gupta has demonstrated exceptional productivity with numerous first-author publications in high-impact journals. His collaborative network is extensive, working with researchers across multiple institutions in Switzerland, Europe, and India. While no specific major grants are mentioned in the provided text, his publication record suggests involvement in significant research projects addressing global soil and water challenges. As part of the Department of Environmental Sciences at the University of Basel, Dr. Gupta contributes to the institution's strong research profile in environmental systems science. His work aligns with the department's focus on understanding complex Earth system processes and human-environment interactions, particularly through the integration of field observations, remote sensing, and computational modeling approaches.

• Soil Science
• Hydrology
• Remote Sensing Applications
• +۵ مورد دیگر

Mark Robinson is a Professor at the Department of Molecular Life Sciences, University of Zurich, and affiliated with the Swiss Institute of Bioinformatics. He leads the Robinson Research Group, focusing on Computational Biology Bioinformatics Single-Cell RNA Sequencing Statistical Genomics His work bridges computational method development with applications in cancer immunology, epigenetics, and developmental genetics. Key research contributions include Development of bioinformatics tools like pubassistant.ch, scDblFinder, and DESpace Advancements in spatial transcriptomics and single-cell data analysis Studies on epigenetic aging and tumor microenvironment dynamics Notable collaborations span institutions in Switzerland, Germany, and international agricultural pest research groups. His recent publications (2023-2025) emphasize Spatial omics data interpretation Interdisciplinary collaboration frameworks Optimized tissue processing methods Computational benchmarks for reproducible research While no specific scientific awards are mentioned in the data, his software tools and methodological papers demonstrate significant impact on open science and bioinformatics communities.

• Computational Biology
• Bioinformatics
• Statistical Genomics
• +۴ مورد دیگر

Matteo Favero is a Lecturer and Scientist at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He conducts research at the ETHOS Lab, focusing on data-driven interventions to enhance environmental and social sustainability in the built environment. His work integrates computational tools with human-centric approaches to optimize building performance and occupant well-being. Education: PhD in Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 2022 MSc in Building Engineering, Politecnico di Milano, 2015 BSc in Building Engineering, Politecnico di Milano, 2012 Research Interests: Favero's research explores occupant behavior modeling, statistical analysis of thermal comfort, and human-building interactions. He emphasizes multi-domain studies to bridge gaps between energy efficiency, indoor environmental quality, and user satisfaction. His work supports the development of adaptive building controls and sustainable design guidelines through empirical data and interdisciplinary methodologies. Publication Trends: Recent articles (2020–2024) prioritize occupant-centric building operations, statistical rigor in comfort studies, and standardized documentation for behavior models. Themes include predictive algorithms for thermal preferences, validation of human-in-the-loop methods, and critical reviews of multi-domain research practices, reflecting a consistent focus on enhancing data reliability and practical applicability in sustainable building science. Awards: 2022 Best Paper Award: A guideline to document occupant behavior models for advanced building controls (Building and Environment) 2022 Best Paper Award: Quality criteria for multi-domain studies in the indoor environment (Building and Environment) Labs and Teams: He is a core member of the ETHOS Lab at EPFL, an interdisciplinary group advancing sustainability through computational and engineering solutions for human-oriented built environments.

• Building Sustainability
• Thermal Comfort
• Occupant Behavior Modeling
• +۳ مورد دیگر

Raffaella Buonsanti serves as an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Basic Sciences, holding multiple leadership roles including Director of SCGC Administration and Member of SB School Direction. She leads the Laboratory of Nanochemistry for Energy (LNCE) at EPFL Valais Wallis, focusing on cutting-edge nanomaterials research for energy applications. Her institutional affiliations span the Institute of Chemical Sciences and Engineering (ISIC), Swiss Center for Electronics and Microtechnology (SCGC), and Center for Digital Scholarship (CDS). Her research centers on nanomaterials synthesis for energy conversion , with particular expertise in colloidal nanocrystals, CO 2 electroreduction, and quantum dot applications. Key focus areas include: Designing tunable catalysts for CO 2 -to-fuel conversion Atomic-scale control of metal-oxide interfaces Stability mechanisms in electrocatalytic systems Data-driven nanocrystal shape prediction Hybrid quantum dot-molecular systems for energy transfer Current work emphasizes overcoming copper catalyst stability challenges through oxide coatings and liquid metal nanoparticle engineering. Analysis of her 15 most recent publications (2024-2025) reveals dominant research themes in electrocatalysis (73% of articles), nanomaterials synthesis (60%), and energy conversion (53%). Key technical advances include c-ALD-grown oxide shells for quantum dots, liquid gallium-based catalysts, and data-driven nanocrystal shape control. Her work consistently bridges fundamental surface science with industrial CO 2 electrolysis applications. She actively mentors 9 current PhD students and has supervised 11 graduates, with advisees researching copper nanocatalysts, CO 2 reduction mechanisms, and colloidal nanomaterials. Her teaching portfolio includes Introduction to Chemical Engineering Laboratory Works , Introduction to Transport Phenomena , and Nanomaterials for Chemical Engineering Applications . The LNCE laboratory under her direction develops colloidal synthesis methods for energy applications, with recent work focusing on solid-liquid electrocatalysts and quantum dot hybrid materials. Current projects emphasize scalable nanofoundry approaches and industrial implementation of CO 2 conversion technologies.

• Nanomaterials Synthesis
• Electrocatalysis
• CO2 Electroreduction
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر