جستجوی استاد راهنمای دانشگاهی

Rahul Sarkarمشاهده پروفایل

پژوهشگر ارشد

• Quantum Information Theory
• Microlocal Analysis
• Machine Learning
• +۳ مورد دیگر

Rahul Sarkar is a Postdoctoral Fellow at the University of California, Berkeley, affiliated with the Department of Mathematics . He was previously a Ph.D. student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, graduating in 2022 under the advisement of Biondo Biondi and András Vasy. Research Interests : Quantum information theory, inverse problems, machine learning, microlocal analysis, and numerical methods for PDEs. Scientific Contributions : Developed novel quantum computing algorithms and numerical schemes for geophysical imaging, with applications in seismic tomography and quantum signal processing. Teaching : Taught courses at Stanford including Introduction to Quantum Computing and 3D Seismic Imaging , with roles as instructor and course assistant. Awards : Schlumberger Innovation Fellowship (2019-2020). His work bridges mathematical analysis and quantum computation , with a focus on solving real-world problems through interdisciplinary approaches. He has collaborated with institutions like IBM and Schlumberger to apply quantum algorithms to geoscience and financial optimization.

So, Man Cho, Anthonyمشاهده پروفایل

استاد

• Optimization
• Algorithm Design
• Data Analytics
• +۷ مورد دیگر

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

Tomaso A. Poggioمشاهده پروفایل

استاد

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +۵ مورد دیگر

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +۴ مورد دیگر

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

• Mathematical Analysis
• Fuzzy Set Theory
• Measure Theory
• +۵ مورد دیگر

Lect. dr. Alina Cristiana Gavriluţ is a faculty member at the Faculty of Mathematics, Al. I. Cuza University of Iaşi, Romania. Her research focuses on advanced topics in mathematical analysis, including set multifunctions, non-additive measures, fuzzy integrals, and their applications in physics and complex systems. She has authored/co-authored multiple books and over 30 peer-reviewed papers in journals like Fuzzy Sets and Systems , Entropy , and Reports on Mathematical Physics . Her work explores regularity properties of set multifunctions, integrability in non-additive settings, and interdisciplinary applications in areas like fractal information, quantum mechanics, and neurosciences. She has also contributed to theoretical frameworks connecting mathematical physics with complex systems, including studies on non-differentiable entropy and spacetime manifolds. Dr. Gavriluţ collaborates extensively with researchers in mathematics and physics, notably Maricel Agop and Anca Croitoru. Her research has implications for diverse fields, from image processing to neuronal network modeling. She actively participates in international conferences, presenting findings on set-valued integration, fractal systems, and transdisciplinary approaches to complex phenomena. Education: PhD in Mathematics from Al. I. Cuza University (year not specified). Key Areas: Mathematical analysis, measure theory, fuzzy set theory, complex systems, non-differentiable dynamics. Grants/Awards: Not explicitly listed in the provided texts, but her prolific publication record indicates sustained academic engagement. Labs/Teams: Affiliated with the Mathematics department at Al. I. Cuza University, contributing to research groups in functional analysis and mathematical physics.

Moo Chungمشاهده پروفایل

استاد

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +۵ مورد دیگر

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

Ian Drydenمشاهده پروفایل

استاد

• Statistical Shape Analysis
• Object-Oriented Data Analysis
• Functional Data Analysis
• +۳ مورد دیگر

Ian Dryden is a Professor of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a Ph.D. from the University of Leeds (1989) and has held academic positions at Florida International University, University of Nottingham, University of Chicago, and multiple stints at USC. His research focuses on statistical methodology for highly-structured data, including shape analysis, functional data analysis, and object-oriented data analysis. He has authored over 150 publications and two influential books: Statistical Shape Analysis (2016) and Object-Oriented Data Analysis (2022). Dr. Dryden’s work spans applications in medical imaging, corpus linguistics, environmental science, and Bayesian spatial modeling. Recent projects include using remote sensing for peatland monitoring and Bayesian hierarchical models for urban deprivation analysis in Tanzania. He has secured over $20M in grants as PI/Co-I and is an elected Fellow of the Institute of Mathematical Statistics. His research outputs emphasize methodological innovation, with key contributions to shape trajectory analysis, manifold-valued data regression, and Riemannian manifold smoothing splines. Collaborative work includes interdisciplinary projects in genetics, pharmacology, and environmental science, reflecting his broad statistical expertise and impact across disciplines.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Călin-Adrian POPA is a Full Professor at the Department of Computer and Software Engineering, Politehnica University of Timişoara. He holds a Ph.D. in Computer Science and Information Technology (2015) from Politehnica University Timişoara, and additional degrees in Mathematics from West University of Timişoara (B.Sc. 2013, M.Sc. 2015). His research focuses on artificial intelligence, machine learning, and neural networks, with specializations in complex-valued systems (octonion, quaternion, Clifford algebra), stability analysis, and synchronization of neural networks. He has been awarded the 'Profesor Bologna' Distinction (2017). Research interests include: Advanced neural network architectures Deep learning for computer vision Fractional-order systems Nonlinear dynamics and control Applications in robotics and astrophysics His publications span 2014–2025, emphasizing theoretical contributions to neural networks and practical applications in computer vision and robotics. He supervises a large cohort of PhD students in AI-related fields.

Dirk Arnoldمشاهده پروفایل

استاد

• Evolutionary Computation
• Optimization
• Constraint Handling
• +۱۲ مورد دیگر

Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research focuses on evolutionary computation, optimization, and surrogate modeling, with applications in machine learning and digital media. He is actively involved in teaching and research, contributing to major conferences and journals. PhD, University of Dortmund (2001) MSc, Simon Fraser University (1997) Diplom, University of Dortmund (1995) His research interests lie in evolutionary algorithms, particularly evolution strategies, and their application to noisy and constrained optimization. He investigates constraint handling, surrogate modeling, and parameter adaptation mechanisms. His work bridges theoretical analysis and practical applications in image processing, tone mapping, and robotics. Recent publications show a strong trend toward surrogate-assisted optimization, constrained evolutionary algorithms, and applications in computer vision and graphics. His work is consistently published in top venues such as GECCO, PPSN, and IEEE Transactions on Evolutionary Computation. Scientific Awards: Best Paper Award, Continuous Optimization Track, GECCO 2015 Best Paper Award, ES/EP Track, GECCO 2013 Best Paper Award, PPSN XII 2012 Best Paper Award, ES/EP Track, GECCO 2010 Best Paper Award, PPSN IX 2006 Best Student Paper Award, ACM Symposium on Document Engineering 2014 Arnold has advised numerous students, many of whom are co-authors on his publications. He has received research grants supporting work in evolutionary computation and optimization, though specific grant details are not listed. He is a key member of research clusters in Human-Computer Interaction, Visualization & Graphics, and Algorithms & Bioinformatics. He leads a research group focused on evolutionary algorithms and optimization, collaborating with researchers such as H.-G. Beyer, N. Hansen, and S. Brooks. Future work includes advancing constrained and mixed-integer evolutionary optimization, improving surrogate models, and expanding applications in computer vision and HCI.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

• Meta-learning
• Deep Learning
• Bayesian Deep Learning
• +۳ مورد دیگر

Boyan Gao is a Postdoctoral Researcher at the University of Oxford's Institute of Biomedical Engineering, part of the Engineering Science faculty. His research focuses on meta-learning, deep optimisation, and Bayesian deep learning, with applications in clinical machine learning. He is affiliated with the Computational Health Informatics (CHI) Lab under Prof. David Clifton, exploring optimiser and loss function learning for novel tasks. Education: PhD in Deep Learning and Machine Learning from the University of Edinburgh (supervised by Prof. Timothy Hospedales) Research Interests: Boyan's work bridges theoretical advancements in meta-learning with practical clinical applications. Key areas include gradient-based meta-learning, optimisation algorithm adaptation for unseen tasks, and Bayesian approaches in deep learning. His recent projects address challenges in medical image segmentation, physiological signal analysis, and scalable edge-based language models. Labs & Collaborations: He collaborates within the CHI Lab, focusing on clinical machine learning applications. His work intersects with digital health initiatives, leveraging AI for healthcare diagnostics and patient monitoring.