جست‌وجوی استاد و پژوهشگر

Taha Boussaidمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Data Science
• +۶ مورد دیگر

Taha Boussaid is a researcher at INSA Lyon 's College of Engineering, affiliated with the Department of Energy and Environment and laboratories CETHIL (Centre for Energy and Thermal Sciences of Lyon) and LIRIS (Image and Information Systems Laboratory). His work focuses on physics-based machine learning to optimize district heating networks under uncertainty, with a systemic approach integrating local policies and international collaboration. PhD Candidate in Probabilistic Machine Learning & Predictive Control Optimization of Distributed Energy Systems (2022–Present) MSc in Mechanical Engineering from University College London (2021), evaluating microchannel heat sinks and nanofluids for power electronics Lecturer and tutor for graduate and undergraduate courses in statistics, machine learning, mathematics, and physics His research interests span Artificial Intelligence , Data Science , Renewable Energy , Turbomachinery , and Numerical Modelling . Recent publications highlight graph neural networks , physics-guided ML , and surrogate models for energy system simulation. Scientific Awards : Best Collaboration Award (2024) at the Energy and Informatics International Forum Best Paper Award (2023) at the International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impacts of Energy Systems He contributes to open-source projects on GitHub, including satellite image classification and energy system simulation frameworks. His teaching experience includes advanced statistical tools, deep learning models (LSTM, GRU), and mathematics for undergraduates.

Weiwei Zhanمشاهده پروفایل

استادیار

• Geohazard assessment and mitigation
• Geotechnical earthquake engineering
• Soil liquefaction
• +۱۱ مورد دیگر

Weiwei Zhan is an Assistant Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), College of Engineering. He holds a Ph.D. in Civil Engineering from Clemson University (2020) and a B.S. in Geological Engineering from Chengdu University of Technology, China (2014). He is the director of the Geosystems Engineering and Intelligence Laboratory (GEI), which focuses on intelligent and systematic solutions for geohazard mitigation and infrastructure resilience. Ph.D. in Civil Engineering, Clemson University, USA, 2020 M.Sc. in Civil Engineering (non-thesis), Clemson University, USA, 2019 B.Sc. in Geological Engineering, Chengdu University of Technology, China, 2014 Dr. Zhan's research lies at the intersection of geotechnical earthquake engineering, geohazards, and data science. His primary interests include landslide hazard assessment and mitigation , soil liquefaction triggering and consequence assessment , earthquake site response and ground motion modeling , explainable machine learning and deep learning , geospatial modeling and uncertainty quantification , and multi-hazard infrastructure resilient design . His lab leverages interdisciplinary techniques from geotechnics, geology, geophysics, geodesy, and data science to advance smart cities and resilient infrastructure initiatives. His recent publications reflect a strong trend in applying machine learning and signal processing to geohazard detection. Key themes include accelerogram-based liquefaction detection , remote-sensing-based landslide early warning using satellite and UAV imagery , probabilistic seismic analysis of earthen levees , and geospatial modeling of seismic hazards . His work emphasizes uncertainty quantification, real-time hazard assessment, and the development of open-source tools like the OpenLIQ geospatial liquefaction database. Scientific awards and honors include: National Scholarship, Ministry of Education of China (2016) Travel Grants from NHERI SimCenter and EERI (2022) Top 10 Bachelor’s Thesis at Chengdu University of Technology (2014) Honorable Mentions in International and National Mathematical Modeling Competitions Dr. Zhan has served as a reviewer for numerous journals and as a convener for major conferences such as SSA and IAEG. His lab, GEI, conducts research on full-cycle landslide hazard management, near-real-time liquefaction detection, earthquake ground-motion modeling, and geospatial analytics. He has secured research support through travel grants and symposium funding, and his ongoing work includes developing globally applicable models for lateral spreading and improving ground-motion models using geospatial proxies.

• Number Theory
• Cryptography
• Analytic Number Theory
• +۵ مورد دیگر

Carl Pomerance is a Professor in the Department of Mathematics at Dartmouth College. His research spans various areas of number theory, including analytic number theory, cryptography, and computational methods. He is renowned for his work on prime numbers, pseudoprimes, and the distribution of arithmetic functions. Pomerance has authored or co-authored numerous influential books, including Prime Numbers: A Computational Perspective , and has edited volumes on cryptology and computational number theory. His research interests include the study of arithmetic functions, such as Euler's totient function and Carmichael's function, as well as topics in algebraic number theory, combinatorics, and algorithms. Pomerance has delivered invited talks at major conferences and institutions worldwide, focusing on themes like additive number theory, multiplicative functions, and the interplay between number theory and cryptography. Key contributions include advancements in primality testing algorithms (e.g., the quadratic sieve), the study of Carmichael numbers, and investigations into pseudoprimes and their distribution. He has also explored topics such as covering congruences, elliptic curves, and the properties of algebraic numbers.

• Nonlinear Dynamics
• Quantum Science
• Turbulence
• +۳ مورد دیگر

Daniel Lathrop is a Professor of Physics and Geology at the University of Maryland (UMD), and a Fellow of the American Physical Society. He joined UMD in 1997 following postdoctoral roles at Yale and faculty positions at Emory University. His research spans nonlinear dynamics, quantum science, and geophysical fluid dynamics. Lathrop directs the Nonlinear Dynamics Laboratory, focusing on experiments simulating Earth’s core (e.g., the 3-meter liquid sodium spherical Couette experiment) and superfluid helium phenomena. Education: B.A. in Physics (UC Berkeley, 1987), Ph.D. in Physics (University of Texas at Austin, 1991). Research emphasizes turbulent flows in rotating systems, magnetic field generation (dynamo effects), and quantum fluid behavior. His lab integrates machine learning for prediction of magnetic field evolution and turbulence dynamics. Collaborations include developing UAV-based geophysical sensors for landmine detection and advancing stochastic computing hardware using magnetic tunnel junctions. Awards include the NSF Presidential Early Career Award (1997), APS Stanley Corrsin Award (2012), and UMD Distinguished Scholar-Teacher designation. He served as Director of the Institute for Research in Electronics and Applied Physics (2006–2012). Advising: Supervised numerous graduate students in experimental physics and geophysics. Active in interdisciplinary projects combining fluid dynamics, quantum science, and machine learning. Labs/Teams: Nonlinear Dynamics Laboratory, Quantum Materials Center, and Institute for Research in Electronics & Applied Physics (IREAP). Research themes include planetary magnetic field modeling, turbulence in extreme conditions, and novel computing hardware inspired by physical systems.

Kat Wilsonمشاهده پروفایل

استادیار

• Coastal Geomorphology
• Aeolian Sedimentology
• Quaternary Climate and Sea Level Change
• +۳ مورد دیگر

Kat Wilson is an Assistant Professor in the Department of Earth and Environmental Sciences at Boston College. She can be contacted at Devlin 320B, via telephone at 617-552-3640, or email at kathleen.wilson@bc.edu. She holds an ORCID ID (0000-0002-2810-7585) and maintains academic affiliations through her research lab. B.A., University of Pittsburgh B.S., University of Pittsburgh M.S., University of Florida Ph.D., The University of Texas at Austin Dr. Wilson specializes in coastal geomorphology and sedimentology, focusing on the interplay between terrestrial/nearshore sedimentary processes and climatic controls on coastal landscapes. Her research spans three key themes: Morphodynamic feedbacks in coastal systems across modern to Late-Pleistocene timescales Coastal hazards from cyclones and sea level rise Interdisciplinary coastal management and resilience strategies She leads the Coastal Morphology, Processes, and Sedimentary Systems (COMPASS) Lab, which integrates field observations, numerical modeling, and remote sensing analysis. Current projects extend from present-day coastal changes to Late Pleistocene sedimentary records.

Greg Hamerlyمشاهده پروفایل

استاد

• Machine Learning
• Unsupervised Learning
• Data Clustering
• +۱۰ مورد دیگر

Greg Hamerly is a Professor and former Interim Chair (2022–2025) in the Department of Computer Science at Baylor University’s School of Engineering and Computer Science. He holds a PhD from the University of California, San Diego (2003) and specializes in machine learning, particularly unsupervised learning and data clustering, with real-world applications in medical imaging and ecological monitoring. PhD, University of California San Diego (2003) MS, University of California San Diego (2001) BS, California Polytechnic State University (1999) His research centers on the development and application of efficient machine learning algorithms. Key areas include improving k-means clustering techniques, federated learning, and applying AI to early detection of retinoblastoma through leukocoria in photographs and monitoring invasive aquatic species using video data. He has also contributed to computational thinking curriculum development. His recent publications reflect strong trends in unsupervised and self-supervised learning, with a focus on algorithmic efficiency, robustness, and real-world deployment in healthcare and environmental science. His work often combines clustering, deep learning, and computer vision for practical applications. Dubious awards (as humorously noted on personal website) Chief Judge for ICPC South Central USA regional competition (2018, 2019) Site Director for ICPC South Central USA regional competition (2010–2013, 2015–2019) Member of ICPC Live Analytics team at ICPC World Finals (2011–2015) Participant in ICPC World Finals as contestant (2000, 2001) Greg Hamerly has advised numerous graduate students, including PhD candidates now at Texas A&M Kingsville and Meta. He has secured research funding from the NSF, NIH, Department of the Interior, and Texas Parks and Wildlife Department. He is also deeply involved in competitive programming, having coached students and developed the Competitive Learning course at Baylor with over 300 original problems. He leads research projects on eye disease detection and invasive species monitoring, often in collaboration with interdisciplinary teams. His lab integrates algorithmic innovation with practical deployment, emphasizing accessibility and real-world impact.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

Yaniv Planمشاهده پروفایل

دانشیار

• Applied Probability
• Compressive Sensing
• Matrix Completion
• +۳ مورد دیگر

Yaniv Plan is an Associate Professor of Mathematics at the University of British Columbia. His research focuses on applied probability, compressive sensing, matrix completion, and mathematical foundations of machine learning. He co-organizes the interdisciplinary group Mathematics of Information, Learning, and Data (MILD). His work bridges high-dimensional probability with applications in signal processing and data science. He teaches advanced courses such as Probability in High Dimensions and Compressed Sensing , emphasizing theoretical foundations like concentration inequalities, random matrix theory, and optimization. His research has led to advancements in 1-bit compressed sensing, matrix completion, and robust recovery algorithms. Notably, he received the NIPS 2018 Best Paper Award for contributions to learning Gaussian mixtures via sample compression. Plan’s publications address topics like sub-Gaussian matrices, weighted matrix completion, and non-smooth stochastic gradient descent. His work often combines rigorous mathematical analysis with practical applications in compressed sensing and sparse recovery. He collaborates widely, with co-authors including Roman Vershynin, Emmanuel Candès, and Mary Wootters. His research has been supported by grants exploring compressed sensing, high-dimensional data, and algorithmic robustness.

• Seismology
• Ambient Seismic Noise
• Seismic Interferometry
• +۶ مورد دیگر

Dr. Anne Christine Obermann is a Senior Researcher and Lecturer at the Swiss Seismological Service (SED), ETH Zurich. She holds a PhD in seismology from ISTerre (Grenoble, France), completed in 2013 under Professors Michel Campillo and Eric Larose. Her research focuses on seismic monitoring techniques, including ambient noise interferometry and coda wave analysis, with applications to volcanic eruptions, earthquake precursors, and crustal deformation studies. She has applied these methods to case studies such as Piton de la Fournaise volcano in Réunion Island and the 2008 Wenchuan earthquake in China. Education: PhD in Seismology (ISTerre, Grenoble, 2013) Key Techniques: Probabilistic inversion for crustal deformation localization, numerical modeling of coda wave sensitivity Her work bridges numerical analysis and field applications, addressing challenges in resolving subsurface changes linked to hazardous events. Recent studies include 3D sensitivity kernel implementations for concrete fracturing analysis under tension.

Nicolas Boulleمشاهده پروفایل

استادیار

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +۴ مورد دیگر

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

Alex Skillenمشاهده پروفایل

مدرس

• Computational Fluid Dynamics (CFD)
• Machine Learning
• Magnetohydrodynamics
• +۴ مورد دیگر

Dr. Alex Skillen is a Lecturer in Engineering Simulation and Data Science at the Department of Mechanical and Aerospace Engineering, University of Manchester. His research focuses on the intersection of Computational Fluid Dynamics (CFD) and machine learning, with applications in magnetohydrodynamics, environmental flows, and subcooled boiling phenomena. He actively contributes to interdisciplinary projects such as the Fluids Research Group and Physics-informed Deep Learning for Fusion Thermal Hydraulics. Education: PhD in Mechanical Engineering from the University of Manchester (2012), investigating overset grid methods for Navier-Stokes equations. Research Interests: CFD algorithm development, turbulence modeling, machine learning integration in flow simulations, and numerical analysis of multiphase phenomena. His work aligns with UN Sustainable Development Goals related to affordable and clean energy, and industry innovation. Collaborations include international projects on thermal hydraulics in nuclear systems and turbulence super-resolution using generative models. He has contributed datasets for turbulence research and developed open-source tools for fluid-structure interaction simulations. Advising: Supervised one PhD thesis titled 'The overset grid method applied to the solution of the incompressible Navier-Stokes equations in two and three spatial dimensions.' Active in mentoring within the Fluids Research Group. Labs/Teams: Core member of the Fluids Research Group and collaborator on the Exascale Partitioned Fluid-Structure Interaction (ParaSiF_CF) framework development.

Leonard Lyeمشاهده پروفایل

استاد

• Environmental Engineering
• Statistical Hydrology
• Water Resources Engineering
• +۷ مورد دیگر

Leonard Lye is a Professor in the Department of Civil Engineering at the Faculty of Engineering and Applied Science, Memorial University. His expertise lies in environmental engineering, statistical hydrology, and the design of experiments, with a strong emphasis on water resources and engineering education. He has been a key academic figure at Memorial since 1988. Research Interests: Dr. Lye's research focuses on statistical methods in hydrology, including flood frequency analysis, drought assessment, and risk modeling for marine and environmental systems. His work integrates design of experiments (DOE) into engineering education and practical applications. He has developed innovative statistical models for ocean outfalls, hull motion analysis, and rare event risk estimation, such as in the Deepwater Horizon case. His contributions span civil, environmental, and ocean engineering, with a strong statistical foundation. His recent publications reflect a consistent trend in applying advanced statistical and probabilistic methods to environmental and engineering challenges, particularly in hydrology, waste management, and marine safety. These works demonstrate interdisciplinary collaboration and a focus on real-world problem-solving using response surface methodology, Bayesian analysis, and Monte Carlo simulations. Scientific Awards: PEGNL Teaching Award (2001) President's Award for Distinguished Teaching (2003) Government of Alberta's Bronze Teamwork Award (2012) Glenn Roy Blundon Award (2013) Recreation NL Bridging the Gap Award (2014) PEGNL Award for Community Service (2014) Tetra Society Lifetime Achievement Award (2014) President's Award for Exceptional Community Service (2014) CSCE Service Award (2014) Advising and Grants: While specific students are not listed, Dr. Lye has mentored numerous graduate researchers through his extensive publication record and leadership in research projects. His work has been supported by academic and governmental funding, particularly in environmental and water resources engineering. He has also led initiatives in engineering education and community-based innovation, including the Shad Valley program and entrepreneurial ventures. Labs and Teams: Dr. Lye has been actively involved with the Shad Valley program at MUN since 2003 and has served as Program Director since 2007. He is also the founder of the local chapter of the Tetra Society of North America, where he collaborates with engineers and volunteers to design assistive devices for people with disabilities. His entrepreneurial work with Com-Adv Devices Inc. involves product development teams focused on fitness and educational tools.

Damu Dingمشاهده پروفایل

پژوهشگر

• Network Security
• DDoS Detection
• Programmable Network Devices
• +۵ مورد دیگر

Dr. Damu Ding is a researcher specializing in network security and programmable data planes, currently affiliated with the Department of Computer Science and Engineering at the University of Bologna. With a PhD completed in 2021 from the same institution, Dr. Ding has established a robust research profile focused on leveraging programmable network devices for security applications and network monitoring. Dr. Ding's research interests center around network security mechanisms, particularly DDoS detection systems implemented directly within programmable switches using the P4 language. Their work bridges theoretical network security concepts with practical implementations in modern data plane programmable networks. Key areas of investigation include network traffic entropy analysis, flow cardinality estimation, volumetric attack detection, and in-network machine learning applications. Analysis of Dr. Ding's publication record from 2019-2024 reveals a consistent research trajectory focused on pushing security functionality into the network data plane. The publications demonstrate increasing sophistication in leveraging programmable switches for security applications, moving from basic heavy-hitter detection to complex DDoS mitigation systems and in-network machine learning. This research sits at the intersection of networking, security, and systems, with strong practical implications for modern network infrastructure. Dr. Ding has established productive collaborations with researchers including Marco Savi, Domenico Siracusa, and Noa Zilberman, resulting in numerous publications in top networking venues including IEEE Transactions on Network and Service Management, IEEE Transactions on Dependable and Secure Computing, and INFOCOM. Their work has been published in both journal and conference formats, demonstrating impact across the networking research community. As a researcher who completed their PhD relatively recently (2021), Dr. Ding represents an emerging voice in the network security and programmable networking space, with a growing body of work that addresses critical challenges in modern network security architectures.

Dominik Baumannمشاهده پروفایل

استادیار

• Systems and Control Theory
• Machine Learning
• Communication Networks
• +۳ مورد دیگر

Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.

Nina Effenbergerمشاهده پروفایل

پژوهشگر

• Climate Science
• Machine Learning
• Renewable Energy
• +۳ مورد دیگر

Dr. Nina Effenberger is a Postdoctoral Researcher in the Climate Physics group at ETH Zurich, led by Professor Reto Knutti. She previously completed her Ph.D. at the University of Tübingen under Nicole Ludwig’s Machine Learning in Sustainable Energy Systems group and was affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Her work bridges climate science, machine learning, and energy modeling to address challenges in renewable energy systems under climate change. Her research focuses on multi-decadal wind power forecasting using climate models (CMIP6) and Gaussian Processes, with a particular emphasis on turbine-location-aware downscaling techniques. She has also contributed to mental health advocacy in academia, including co-authoring a 2023 publication in Plos One on stress and well-being among Ph.D. students. Her recent work includes arXiv preprints on climate data selection and temporal resolution impacts in wind power predictions. Dr. Effenberger is an alumnus of the sustainAbility Ph.D. initiative and trained in mental health first aid. She can be contacted via email at nina.effenberger@env.ethz.ch .