جست‌وجوی استاد و پژوهشگر

Stefano Ermonمشاهده پروفایل

دانشیار

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

Ruben Juanesمشاهده پروفایل

استاد

• Environmental Engineering
• Geophysics
• Energy Resources
• +۳ مورد دیگر

Ruben Juanes is a Professor of Civil and Environmental Engineering and Earth, Atmospheric, and Planetary Sciences at MIT. His research focuses on multiphase flow in porous media, energy resources, and CO₂ sequestration. He holds appointments in both departments and has a strong interdisciplinary focus on geosciences and environmental engineering. His work bridges theory, simulation, and experimentation to address energy and environmental challenges. Education: Ingeniero de Caminos (Civil Engineering), University of La Coruña, Spain (1997) MS in Civil Engineering, UC Berkeley (1999) PhD in Civil Engineering, UC Berkeley (2003) Research interests emphasize fluid dynamics in geologic media, especially CO₂ storage, methane hydrates, and ecohydrology. His group develops computational models to predict large-scale Earth processes, with applications to carbon capture and storage, energy resource management, and subsurface engineering. Notable contributions include advancing understanding of fluid displacement mechanisms, capillary trapping in aquifers, and induced seismicity risks during CO₂ injection. His work has been recognized through awards like the APS Fellowship (2024), DOE Early Career Award (2010), and ARCO Energy Professorship (2008). Advising and Grants: Juanes advises graduate students in CEE and EAPS, focusing on thesis research in multiphase flow and geomechanics. His grants include NSF and DOE funding for projects on subsurface energy systems and induced seismicity. His lab, the Juanes Research Group, collaborates on experimental facilities like the FluidFlower CO₂ storage simulator. Labs/Teams: Active in MIT's Carbon Capture, Utilization, and Storage (CCUS) initiatives and the MIT Energy Initiative (MITEI). Collaborates with industry partners on field-scale CO₂ storage validation and subsurface monitoring technologies.

Niki Kilbertusمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۴ مورد دیگر

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

Ram Rajagopalمشاهده پروفایل

دانشیار

• Smart Grids
• Renewable Energy Integration
• Power Systems Optimization
• +۳ مورد دیگر

Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.

Anwar Haqueمشاهده پروفایل

دانشیار

• Network Security
• 5G and Beyond Networks
• IoT
• +۹ مورد دیگر

Dr. Anwar Haque is an Associate Professor in the Department of Computer Science at Western University, Canada, and a Faculty of Science Distinguished Research Professor. He holds a Ph.D. in Electrical and Computer Engineering and an M.Sc. in Computer Science from the University of Waterloo. Prior to academia, he was Associate Director at Bell Canada. His research focuses on 5G networks, IoT, cybersecurity, AI, and autonomous systems, with over 100 peer-reviewed publications and $15M in collaborative grants. Dr. Haque leads the Western Information & Networking Group (WING) Lab and is the founder/CEO of Bamboo Innovations Inc., a tech startup developing socially responsible smart technologies. Leadership roles include industry expert-in-residence in the Faculty of Science, Undergraduate Chair of the Computer Science Department, and member of Western’s Senate. He has delivered over 30 keynote talks and media features include BBC Earth and The Globe and Mail. Awards include the IEEE CCECE Leadership Award and multiple grants from NSERC, MITACS, and Bell Canada. His work spans network reliability, smart grids, and cybersecurity, with industry partnerships like the Bell-Western 5G Research Centre.

Tapio Schneiderمشاهده پروفایل

استاد

• Atmospheric Dynamics
• Climate Modeling
• Geophysical Turbulence
• +۴ مورد دیگر

Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.

James K. Russellمشاهده پروفایل

استاد

• Volcanology
• Petrology
• Glaciovolcanism
• +۵ مورد دیگر

James K. Russell is a Full Professor in Earth, Ocean and Atmospheric Sciences at The University of British Columbia, where he has been faculty since 1999. He directs the Volcanology and Petrology Laboratory and maintains an active research program in volcanology and petrology with significant international collaborations including appointments at LMU Munich, University of Torino, and Monash University. His educational background includes a Ph.D. (1984) and M.Sc. (1980) from the University of Calgary, and a B.Sc.(H) (1976) from the University of Manitoba. He previously worked at Cominco Exploration Ltd in Vernon, British Columbia before joining UBC. Russell's research focuses on understanding magmatic processes through multiple approaches. His primary interests include glaciovolcanism as a tool for paleoclimate reconstruction, predictive modeling of magma transport properties, and high-temperature rock mechanics experiments. His work bridges theoretical modeling with field observations, particularly in the Canadian Cordillera. He has developed influential models for magma viscosity that are widely cited in the volcanology community. His publication record shows consistent high-impact research over several decades, with recent work focusing on ultramafic melt viscosity, kimberlite transport mechanisms, and glaciovolcanic interactions. The research demonstrates a strong emphasis on quantitative approaches combining thermodynamics, rheology, and field-based observations to solve fundamental problems in volcanology. Scientific Awards: 2024 Research Award of the Alexander von Humboldt Foundation, LMU, Munich, Germany 2022 DAAD Research Stay for Visiting Academics Scholarship (3 months) 2019 Visiting Professorship, University Roma Tre 2019 Visiting Fellow at Centre for Advanced Studies, University of Munich 2016 Fellow of Mineralogical Society of America 2013 NSERC Discovery Accelerator Supplement Award 2010 Career Achievement Medal, Volcanology Division, Geological Association of Canada 2008 Peacock Medal, Mineralogical Association of Canada Russell has served in numerous editorial and professional capacities, including as Co-Editor-in-Chief for Journal of Volcanology & Geothermal Research (2016-2022), Associate Editor for multiple prominent journals, and as evaluator for research grants including ERC Consolidator grants and NSERC competitions. He has provided media consultation for National Geographic and Nova/Pioneer Productions. He maintains active laboratory facilities for volcanology and petrology research at UBC, with equipment for high-temperature experiments and magma rheology studies. His research group collaborates internationally with institutions in Germany, Italy, and Australia, reflecting his extensive network built through sabbatical visits and visiting professorships.

Alexey Gorshkovمشاهده پروفایل

استاد مدعو

• Quantum Optics
• Quantum Information Science
• Condensed Matter Physics
• +۶ مورد دیگر

Alexey Gorshkov is an Adjunct Professor at the University of Maryland (UMD) affiliated with the Joint Quantum Institute (JQI) and the Quantum Information and Computer Science Laboratory (QuICS). His primary academic role is in theoretical physics, focusing on quantum optics, quantum information science, and condensed matter physics. He leads a research group exploring quantum magnetism with alkaline-earth atoms, driven-dissipative systems, topological matter, and strongly interacting photons. His work bridges AMO (atomic, molecular, and optical) systems with high-energy and condensed matter physics, emphasizing quantum simulation and novel quantum technologies like precise clocks and quantum computers. Education details are not explicitly listed, but his research collaborations with institutions like JQI and UMD suggest advanced academic training in theoretical physics. His research interests revolve around understanding and controlling quantum many-body systems, particularly in far-from-equilibrium scenarios, entanglement dynamics, and dissipation effects. He has contributed to studies on Rydberg atoms, quantum routing protocols, and error mitigation in quantum simulators. Recent articles highlight his work on quantum protocols for verifying speedups, time-independent information flow, and entanglement dynamics. His group's achievements include demonstrating one-dimensional anyons and developing methods for correlated noise estimation with quantum sensors. Awards and grants are not explicitly mentioned in the provided text, but his prolific publication record indicates sustained research impact. Labs and teams associated with him include the JQI and QuICS, where he collaborates on experimental and theoretical projects. Graduate student and postdoc positions are available in his group, focusing on areas like quantum magnetism and topological systems. His work often involves close ties with experimental groups, emphasizing practical applications of theoretical breakthroughs.

Paul R Moorcroftمشاهده پروفایل

استاد

• Ecological Dynamics
• Biosphere-Atmosphere Interactions
• Animal Movement Ecology
• +۳ مورد دیگر

Paul R Moorcroft is a Professor of Organismic and Evolutionary Biology at Harvard University and an Affiliate in the School of Engineering and Applied Sciences (SEAS). His research focuses on ecological dynamics of terrestrial plant communities, biosphere-atmosphere interactions, and mechanistic models of animal movement. He collaborates with institutions like NASA JPL on projects such as the ECOSTRESS mission, which investigates environmental processes using satellite data. His lab, the Moorcroft Lab, emphasizes fieldwork and interdisciplinary approaches, as seen in recent collaborations in Italy and Southeast Asia. Key research areas include tropical forest ecology, carbon cycling in peatlands, and the application of remote sensing technologies to environmental monitoring. His work bridges theoretical models with empirical data, addressing global challenges like climate change and biodiversity loss. Recent publications highlight contributions to quantifying carbon emissions from peatland drainage and improving satellite-based mapping of forest functional traits. His advising activities include mentoring graduate students like Rhemi Toth. Collaborations with NASA and academic partners underscore his role in advancing Earth systems science through interdisciplinary research.

Zongyi Liمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Scientific Computing
• Partial Differential Equations
• +۵ مورد دیگر

Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science

Sanjay Purushothamمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Optimization Theory
• +۵ مورد دیگر

Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.

Emily Soمشاهده پروفایل

استاد

• Disaster Risk Reduction
• Earthquake Engineering
• Urban Resilience
• +۵ مورد دیگر

Professor Emily So serves as Deputy Head of the School of Arts and Humanities at the University of Cambridge and directs the Cambridge University Centre for Risk in the Built Environment (CURBE). A chartered civil engineer with extensive field experience, she holds leadership roles in the Open-Oxford-Cambridge AHRC Doctoral Training Partnership and chairs the Faculty EDI Committee. Her research focuses on urban risk and resilience , particularly in earthquake-prone regions. Combining structural engineering with epidemiological approaches, she develops innovative casualty estimation models and engages directly with affected communities worldwide. Her work spans seismic safety, disaster epidemiology, and remote sensing applications for rapid damage assessment. Professor So's publication trends reveal strong emphasis on machine learning for disaster risk modeling , with recent work featuring graph neural networks, deep clustering for urban morphology, and LSTM-based population forecasting. Her research bridges engineering, social sciences, and data science to address resilience in developing nations. 2010 Shah Family Innovation Prize (Earthquake Engineering Research Institute) Fellow of the Institution of Civil Engineers (FICE) Scientific Advisory Group for Emergencies (SAGE) member advising UK government As Director of CURBE, she leads interdisciplinary collaborations with EEFIT, Global Earthquake Model (GEM), World Bank, and USGS. Her field investigations following major earthquakes inform practical solutions for vulnerable communities, notably contributing to the 2017 World Building of the Year design in China. Current work includes sabbatical research for 2025-2026 focused on decolonizing architectural approaches to disaster resilience. Professor So maintains active roles in professional organizations and international disaster response frameworks, with her CURBE team developing methodologies now implemented globally for seismic safety improvements.

Gavin McNicolمشاهده پروفایل

استادیار

• Soil Biogeochemistry
• Biosphere-Atmosphere Exchange
• Wetland Ecology
• +۳ مورد دیگر

Gavin McNicol is an Assistant Professor in the Department of Earth and Environmental Sciences at the University of Illinois at Chicago (UIC). His research focuses on soil biogeochemistry and its connection to Earth’s climate system, particularly methane emissions from wetlands, temperate rainforests, and waste systems. He employs field measurements, laboratory experiments, and data science to study greenhouse gas dynamics across scales. McNicol teaches courses on data science (EAES 420) and climate-ecosystem interactions (EAES 579). His work integrates machine learning with satellite remote sensing to monitor global wetland methane emissions. Key collaborations include FLUXNET-CH4, CRMRCN, and the SOIL NGO in Haiti. McNicol holds a BS from the University of Stirling (2010) and a PhD from UC Berkeley (2016). Research interests span wetland methane budgets, carbon cycling in coastal ecosystems, and climate impacts of sanitation systems. He has secured grants from NASA, DOE, and NSF, totaling over $87,000. His team, the McNicol Lab, emphasizes interdisciplinary approaches combining fieldwork, lab analysis, and computational modeling. Recent publications (2023–2025) address global methane upscaling, tropical wetland monitoring gaps, and boreal-Arctic methane feedbacks. McNicol’s work bridges local measurements to global climate models, with applications in climate change mitigation and sustainable development.