Find Academic Supervisors

• High-dimensional Statistics
• Machine Learning
• Signal Processing
• +5 more

Karim Lounici is a Professor at the Centre de Mathématiques Appliquées (CMAP) of Ecole Polytechnique. His research focuses on high-dimensional statistics, machine learning, signal/image processing, probability theory, and optimization. He explores theoretical foundations and practical applications in areas like stochastic processes, quantum tomography, and dynamical systems. His work bridges mathematical rigor with computational methods, addressing challenges in data analysis, uncertainty quantification, and algorithmic efficiency. Recent contributions include equivariant representation learning, robust covariance estimation, and learning Koopman operators for dynamical systems. Publications span topics like neural conditional probability, spatio-temporal Monge alignment, and domain adaptation, reflecting expertise in both statistical theory and applied machine learning. No scientific awards explicitly mentioned. No advising or grant details provided in text. Affiliated with CMAP, a leading applied mathematics research center at Ecole Polytechnique.

Rudy MorelView profile

Research Fellow

• Artificial Intelligence
• Machine Learning
• Physics
• +5 more

Rudy Morel is a Research Fellow at the Center for Computational Mathematics of the Flatiron Institute in New York. His work focuses on AI for Science, developing models like transformers for spatio-temporal systems in physics and stochastic processes in finance. He completed his PhD in Mathematics at École Normale Supérieure (2020-2023), advised by Prof. Stéphane Mallat and co-advised by Prof. Jean-Philippe Bouchaud. His research explores multi-scale processes, compact models, and applications of AI to limited-data domains. Education: PhD in Mathematics, École Normale Supérieure (2020-2023) MS in Probability and Finance, Sorbonne Université (2018-2019) BSc in Mathematics, École Normale Supérieure of Rennes (2015-2018) Research focuses on: AI-driven physics simulations and surrogate models Wavelet scattering spectra for non-Gaussian time-series analysis Path shadowing methods for stochastic prediction Multi-scale data modeling in finance and physics Key collaborations include the Polymathic AI initiative and the EconophysiX chair (CFM/Polytechnique), with visiting research at Rice University (2022).

• Biosensors
• Micromechanics
• Microfluidics
• +3 more

Jasmina Casals Terre is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mechanical Engineering at the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT). She specializes in microfluidics, biosensors, and micromechanics, with a focus on biomedical and environmental applications. Her research combines advanced manufacturing techniques like 3D printing with microfluidic systems for diagnostics, pollution monitoring, and wearable health devices. Education: Industrial Engineer, Master in Science in Aerospace and Mechanical Engineering. She coordinates the Doctoral Program in Mechanical Engineering, Fluids, and Aeronautics. Her work spans over 250+ activities, including 51 indexed journal articles, 32 competitive research projects, and 11 patents. Notable awards include the 15th Anniversary of the International Association for Advanced Materials. Key projects include non-invasive sweat sensing systems, microfluidic devices for liquid biopsy, and environmental microplastic detection. She collaborates with groups like CATMech and MICROTECH LAB, focusing on sustainable technologies and industrial innovation. Her students have developed advanced microfluidic devices and bioengineering solutions, contributing to both academic and industrial applications.

• Remote Sensing
• Hydrology
• Water Resources Management
• +3 more

Prof. Dr. Mehmet Cüneyd Demirel is a Professor in the Department of Civil Engineering at Istanbul Technical University, specializing in Remote Sensing, Hydrology, and Water Resources Management. He has held roles including Associate Professor (2019–2024) and Doctorate Lecturer, with post-doctoral research at institutions like the Geological Survey of Denmark and Greenland and Portland State University. Education includes a PhD from the University of Twente (2008–2014), an MSc in Hydroelectronics from Istanbul Technical University (2001–2004), and a BEng in Civil Engineering from Selçuk University (1997–2001). He has held research positions globally and contributed to projects such as the EU-funded SPACE project and NASA’s Rapid Response Earth Science grant. His research focuses on distributed hydrological modeling, climate change impacts, and remote sensing applications. Key projects include developing drought indices using AI, improving groundwater anomaly estimations, and optimizing pressure management in water distribution systems. He has received awards like the ITU Project Award (2024) and is an Associate Editor for Hydrological Sciences Journal and Stochastic Environmental Research and Risk Assessment . Notable contributions include over 40 publications, 15 supervised theses, and leadership in international collaborations. His work bridges hydrological modeling, remote sensing, and sustainable water management, addressing global challenges in water resources and climate resilience.

Amir ShabaniView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

Amir Shabani is a Lecturer in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). He holds a Ph.D. in System Design Engineering from the University of Waterloo (2011), an M.Sc. in Electrical and Electronics Engineering from Iran University of Science and Technology (2004), and a B.Sc. in the same field (2001). His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Smart City technologies, with emphasis on applications in interactive robotics, affective computing, and IoT systems. Dr. Shabani teaches courses such as SEE 231 (Electronic Devices and Systems), SEE 332 (Power Systems Design and Analysis), and SEE 333 (Network and Communication Systems). His academic interests span System Design (e.g., AI, Robotics, Data Structures) and Electronics (e.g., Embedded Systems, IoT). He actively contributes to smart building automation, renewable energy systems, and human-centric technologies like social robotics for elderly care. His publications address advanced topics including edge computing for social robots, facial emotion recognition, and intelligent occupancy detection in smart environments. His work bridges theoretical computer vision with practical engineering solutions for sustainable energy and urban infrastructure.

• Numerical Linear Algebra
• Scientific Computing
• Image Reconstruction
• +5 more

Misha Kilmer is the William Walker Professor of Mathematics at Tufts University, with a secondary appointment in Computer Science. She also serves as Deputy Director of ICERM at Brown University and co-PI of Tufts TRIPODS Institute. Her research focuses on numerical linear algebra, scientific computing, and image reconstruction/restoration. Kilmer has held leadership roles, including Chair of Tufts' Mathematics Department (2013-2019) and has received prestigious recognition, including SIAM Fellowship (2019) and the Tufts Undergraduate Teaching Award (2001). Education: PhD in Applied Mathematics, University of Maryland (1997) MA in Mathematics, Wake Forest University (1994) BS in Mathematics, Wake Forest University (1992) Research Interests: Kilmer's work emphasizes tensor decompositions, iterative methods, and model reduction. She develops algorithms for high-dimensional data analysis, imaging applications, and inverse problems. Her techniques bridge computational mathematics and engineering challenges, with applications in biomedical imaging, environmental monitoring, and machine learning. Publications Trends: Recent work highlights advancements in tensor-based neural networks, dynamic image reconstruction, and efficient algorithms for large-scale data. Key contributions include randomized algorithms for tensor decompositions and compressed sensing techniques for multi-modal data. Awards: SIAM Fellow (2019) Tufts Undergraduate Initiative in Teaching Award (2001) Advising & Grants: Kilmer has secured funding from NSF, NIH, IARPA, DARPA, and IBM. She co-chairs major SIAM conferences and serves on editorial boards for journals like SIAM Review and La Matematica . Labs/Teams: Active in Tufts' Data Intensive Studies Center (DISC) and co-leads the TRIPODS Institute. Collaborates internationally on projects involving tensor analysis and computational science.

Sepideh MosaferiView profile

Visiting Assistant Professor

• Nonparametric Statistics
• Bayesian Inference and Modeling
• Computational Statistics
• +3 more

Sepideh Mosaferi is a Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her academic background includes a BS and MS from Allameh Tabatabai University in Tehran, Iran, a second MS from the University of Maryland, College Park, and a PhD from Iowa State University. Her research focuses on advanced statistical methodologies with applications in real-world problems. Key areas include nonparametric statistics, Bayesian inference, computational statistics, network modeling, time series analysis, and measurement error modeling. This work emphasizes developing novel statistical models and computational techniques to address practical challenges in diverse fields. In her publications, she explores topics like change point detection in time series, cointegrating regression in econometrics, unbiased prediction under skewed distributions, and small area estimation in environmental and demographic contexts. These contributions highlight her interdisciplinary approach to statistical theory and practice. While no specific scientific awards are listed, her work has been noted for its excellence and innovation. She has no documented advisees or grants tied to her current role. Her affiliation with the Department of Mathematics and Statistics places her within a community focused on award-winning teaching and collaborative research.

HongKun ZhangView profile

Professor

• Machine Learning of Dynamical Systems
• Chaotic Billiards
• Ergodic Theory
• +4 more

Prof. HongKun Zhang is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research focuses on hyperbolic dynamical systems, chaotic billiards, and stochastic processes, with recent emphasis on machine learning applications in dynamical systems, financial mathematics, and graph neural networks. She has organized numerous conferences and holds significant grants, including NSF awards and Simons Foundation support. Key research areas include: Machine Learning of Dynamical Systems Chaotic Billiards and Ergodic Theory Graph Neural Networks Financial Mathematics Notable awards/grants: NSF Grant (DMS-2220211) for anomaly detection in human dynamics (2023–2026) Simons Foundation Collaboration Grant (2020) NSF CAREER Award (2012–2017) Her work bridges pure mathematics with applications in finance, physics, and machine learning, with recent publications in Chaos , Physical Review E , and Neural Networks .

Tatiana Landesberger von AntburgView profile

Professor

• Visual Analytics
• Interactive Visualization
• Graph Visualization
• +4 more

Prof. Dr. Tatiana Landesberger von Antburg serves as Professor and Vice Director of Scientific Domain 1 (Theory of Data and Simulation Science) at the Center for Data and Simulation Science (CDS), University of Cologne, leading the Visual Analytics research group within the Institute of Computer Science. Her pioneering work in interactive visualization and visual analytics tackles complex datasets including graphs, movement data, biomedical information, time series, and multivariate data. She develops integrated systems combining machine learning and interactive interfaces to support decision-making across transportation, finance, journalism, and healthcare domains, with particular emphasis on real-world applications in epidemiology and earth sciences. Recent publications (2019-2021) reveal a strong trajectory toward healthcare applications, especially hospital infection control through pathogen transmission analysis, alongside foundational contributions to multilayer network visualization. Her work consistently bridges theoretical innovation with practical implementation in high-impact fields like epidemiology and climate science. Major recognitions include: 2015 Academia Europaea Burgen Scholar Award 2014 IEEE Transactions on Visualization and Computer Graphics Best Reviewer Award IEEE VAST Best Paper Honorable Mention (2020) She directs significant research initiatives funded through: IMfESS – Intelligent Methods for Earth System Sciences RISK Principe – Risk prediction for infection control in hospitals SANE WarmWorld – Exascale Earth System Modeling Her Visual Analytics group actively mentors students and postdoctoral researchers while collaborating internationally on interdisciplinary projects. The laboratory specializes in creating interactive tools for complex data exploration, with ongoing expansion into exascale computing applications for climate modeling and pandemic response systems.

Yu ChengView profile

Professor

• Wireless Network Performance Analysis
• Machine Learning
• Age of Information
• +4 more

Yu Cheng is a full Professor at the Illinois Institute of Technology in the Department of Electrical and Computer Engineering within the Armour College of Engineering . He joined the faculty in 2006 and was elevated to IEEE Fellow in 2024. His research focuses on wireless network performance analysis, machine learning applications in networks, age of information optimization, and network security. He has led multiple NSF-funded projects, including the NSF CAREER Award (2011-2015) and a 2018 project on machine learning-driven network optimization. Cheng holds a Ph.D. from the University of Waterloo (2003) and completed a postdoctoral fellowship at the University of Toronto (2004-2006). His educational background also includes M.E. and B.E. degrees from Tsinghua University (1995-1998). He serves as an Associate Editor for IEEE Transactions on Vehicular Technology and has been an IEEE ComSoc Distinguished Lecturer (2016-2017). His research interests include machine learning in wireless networks , cloud computing , age of information , and cybersecurity . Notable achievements include the 2011 NSF CAREER Award and runner-up Best Paper Awards at ACM MobiHoc (2014 and 2018). He has advised numerous Ph.D. students, with current advisees including Bo Yin and Lei Zhou. Graduates include Jin Tang (AT&T Lab) and Xiaohua Tian (Shanghai Jiaotong University). Cheng’s funded research includes projects on optimizing information freshness (NSF 2020-2023) and machine learning for network optimization (NSF 2018-2022). He leads the Future Networking Research Laboratory , focusing on next-generation network architecture and protocols. His work integrates theoretical frameworks with practical implementations, emphasizing scalable solutions for heterogeneous networks and big data challenges.

• Medical Imaging
• Quantitative MRI
• Magnetic Resonance Imaging (MRI) Techniques
• +5 more

Rudolf Stollberger is a Professor at TU Graz's Institute of Biomedical Imaging. His research focuses on advanced MRI techniques, including CEST-MRI, arterial spin labeling, quantitative MRI parameter quantification, and optimal control-based RF pulse design. He holds academic appointments in medical technology and has expertise in high-field MRI (7T), image reconstruction algorithms, and deep learning applications in medical imaging. Education and affiliations are not explicitly detailed, but his work spans interdisciplinary areas such as neuroimaging, perfusion quantification, and biomedical signal processing. His research portal is accessible via http://www.imt.tugraz.at/ . Key research interests include developing robust RF pulses for inhomogeneous fields, improving CEST MRI contrast via optimal control, and enhancing real-time fMRI neurofeedback systems. He has contributed to model-based reconstructions for IVIM and DTI, as well as automated segmentation methods using deep learning. Notable articles from 2025–2023 highlight innovations in MRI sequence optimization, artifact reduction, and explainable AI for medical imaging datasets. His work addresses challenges in high-resolution ASL perfusion imaging, T1 quantification, and lung vessel morphology analysis.

Xiao FuView profile

Associate Professor

• Machine Learning
• Signal Processing
• Optimization
• +5 more

Xiao Fu is an Associate Professor in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds a B.S. (2005) and M.S. (2010) in Communications and Information Engineering and Signal and Information Systems from the University of Electronic Science and Technology of China (UESTC), and a Ph.D. (2014) in Electronic Engineering from The Chinese University of Hong Kong (CUHK). His research focuses on machine learning, signal processing, and optimization, with applications in nonlinear factor analysis, unsupervised learning, and deep neural networks for signal processing tasks. He has received notable awards including the 2022 NSF CAREER Award and the 2024 OSU Promising Scholar Award. His work emphasizes developing robust algorithms for factor analysis (e.g., tensor and matrix factorization), large-scale optimization in data mining, and deep learning techniques for hyperspectral imaging, radio map estimation, and crowd-sourced label analysis. Key research groups affiliated with him include Data Science and Engineering, Artificial Intelligence and Robotics, and Communications and Signal Processing. His recent projects include advancing unsupervised machine learning to reduce reliance on labeled data in AI systems and exploring applications in environmental sensing and medical imaging. Xiao Fu's publications span topics like radio map estimation via latent-domain denoisers, noisy label learning with crowd wisdom, and identifiability in nonlinear mixture models. His research bridges theoretical advancements in optimization and practical applications in wireless communication, ecological networks, and biomedical imaging. Current efforts aim to enhance unsupervised deep representation learning and develop scalable algorithms for high-dimensional data analysis. Awards: NSF CAREER Award (2022), OSU Promising Scholar Award (2024) Grants: NSF-funded CAREER Award project on nonlinear factor analysis tools Labs/Teams: Affiliated with interdisciplinary teams in signal processing, AI, and ecological systems modeling

Udo FeuerhakeView profile

Researcher

• Spatio-temporal Data Analysis
• Geoinformatics
• Urban Digital Twin
• +5 more

Dr.-Ing. Udo Feuerhake is a Researcher at the Institute of Cartography and Geoinformatics (IKG) within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His work focuses on spatio-temporal data analysis, urban digital twins, and transportation engineering. He holds a M.Sc. in Computergestützte Ingenieurwissenschaften from Leibniz University Hannover (2010) and has been with IKG since 2011. Research interests include voxel-based urban modeling, trajectory analysis, and mobility patterns. Notable projects involve integrating 3D geodata for urban digital twins, smartphone-based vehicle detection, and developing interactive web applications for spatial visualization. He collaborates on projects like the USEfUL logistics tool and contributes to traffic analytics dashboards (TA-Dash). Feuerhake leads and co-leads multiple theses, including work on 3D navigation path planning, parking occupancy visualization, and AR/MR applications. His research emphasizes real-world applications in smart cities, emergency response systems, and sustainable urban logistics. Key Projects: Urban Digital Twin development, smartphone sensor platforms, trajectory pattern mining Teaching: Involved in thesis supervision and module development for spatial analysis and visualization Awards: None explicitly mentioned

• Sustainable Supply Chain Management
• Transportation Management
• Operational Research
• +3 more

Dr. Sadeque Hamdan is a Senior Lecturer in Data Analytics at Bangor Business School, Bangor University, UK. His academic work focuses on optimization problems in sustainable transportation and supply chain management, with applications in both theoretical and practical domains. Education Postgraduate Certificate in Higher Education, University of Kent (2023) PhD in Complex Systems Engineering, University of Paris-Saclay (2020) MSc in Engineering Management, University of Sharjah (2015) BSc in Civil Engineering, University of Sharjah (2013) Research Focus Dr. Hamdan's research spans several interconnected domains: sustainable supply chain operations, transportation management, and operational research applications. His work employs advanced data analytics to solve complex optimization problems in logistics, aviation management, and green transportation systems. Publication Trends Recent publications (2023-2025) demonstrate a strong focus on sustainable transportation optimization, particularly electric vehicle charging systems, maritime logistics, and air traffic management. Methodologically, Dr. Hamdan frequently applies heuristic algorithms, multi-objective optimization, and combinatorial approaches to solve complex supply chain and transportation problems. Projects & Advising Dr. Hamdan leads the ongoing research project 'Sustainable Aviation and Emerging Technologies: Maximizing Operational and Resource Efficiency' (2025-2026). He actively supervises postgraduate students, though specific student names are not listed in available materials.

Isadora ANTONIANO VILLALOBOSView profile

Associate Professor

• Statistics
• Probability Theory
• Bayesian Inference
• +5 more

Isadora Antoniano Villalobos is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. Her research focuses on statistical models and methods for complex data structures, particularly Bayesian inference for intractable models, extreme value theory, and risk analysis. She leads projects such as RISE (Rethinking and Innovating Statistics for Extremes) and SMARTMUS-E, addressing environmental and energy challenges. Her teaching spans courses like Applied Probability and Statistics for Empirical Research in Management across various degree programs. She collaborates internationally, speaks English, Spanish, and Italian fluently, and contributes to editorial boards in statistical journals. Research Interests : Bayesian methods, extreme value analysis, statistical modeling of spatio-temporal data, risk assessment, and applications in environmental science, hydrology, and social sciences. Her work emphasizes flexible models for complex datasets and interdisciplinary collaborations. Recent Projects : RISE: PRIN-funded project on extreme value analysis (2023-2026) SMARTMUS-E: EU Horizon Europe project on museum energy management (2023-2025) Bayesian Nonparametric Mixture Models for the Dependence of Extremes (BayMEx, 2019-2021) Teaching : Courses include Applied Probability for Computer Science , Statistics for Language Sciences , and Statistics for Empirical Research in Management . She has taught at undergraduate and graduate levels since 2019.