جست‌وجوی استاد و پژوهشگر

Amos Storkeyمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Generative Models
• +۷ مورد دیگر

Amos Storkey is a Professor at the School of Informatics , University of Edinburgh , with a focus on machine learning, Bayesian methods, and their applications in neuroscience and astronomy. His research spans deep learning, generative models, and stochastic optimization under real-world constraints. Education: MA in Mathematics, Trinity College, Cambridge (1989) Part III (Theoretical Physics), Trinity College, Cambridge (1992) PhD in Neural Networks, Imperial College London (1995) Research Interests: Storkey’s work addresses core challenges in machine learning, including model understanding, efficiency, and transfer learning. Key topics include: Generative Models : Applications in medical imaging (brain/retinal) and music generation. Bayesian & Probabilistic Methods : In healthcare, astronomy, and diffusion processes. Optimization & Reinforcement Learning : Stochastic systems, meta-learning, and few-shot learning. Medical Applications : Structural connectivity analysis in ALS and aging studies. Article Trends: Recent publications emphasize interdisciplinary applications of machine learning, with 5/15 focused on neuroscience (fMRI, ALS, aging), 3/15 on optimization/sampling, and 2/15 on astronomical data analysis. Emerging themes include machine learning markets and generative models for hallucinations.

• Signal Processing
• Digital Communications
• Machine Learning Applications
• +۳ مورد دیگر

Rafael Boloix Tortosa is an Associate Professor in the Department of Signal Theory and Communications at the University of Seville's School of Engineering, where he leads significant research in complex-valued signal processing and its applications. His academic career spans over two decades with continuous involvement in national and international research projects. His research focuses on advancing complex-valued signal processing methodologies, particularly complex Gaussian processes, kernel methods, and widely linear estimation techniques. These theoretical advances are applied to diverse domains including medical imaging (especially burn wound analysis), wireless communications (UWB positioning, OFDM systems), and art conservation. His work bridges fundamental signal processing theory with practical engineering solutions. Professor Boloix Tortosa maintains an active publication record with significant contributions in top IEEE journals, particularly in signal processing and machine learning. His recent work (2025-2019) demonstrates continued innovation in complex-valued machine learning methods, while his earlier publications established foundational work in blind source separation and multiuser detection. He actively participates in multiple research projects including EU-funded initiatives like COURAGEOUS (counter-UAS solutions) and ARIADNE (agricultural drones), alongside national projects in medical imaging and communications. His research group has received consistent funding through the TIC-155 consolidation incentives. His laboratory work centers around the Signal Processing and Communications Research Group, where he develops both theoretical frameworks and practical implementations for signal processing challenges. The group maintains strong connections between theoretical advances and real-world applications across multiple engineering domains.

Eric Sommerladeمشاهده پروفایل

پژوهشگر

• Active Vision
• Surveillance Systems
• Visual Tracking
• +۹ مورد دیگر

Eric Sommerlade is a Post-Doctoral Research Assistant in the Active Vision Group at the University of Oxford Engineering Department . He completed his DPhil in Engineering Science in 2011, focusing on surveillance with active cameras , and previously earned a Diploma in Electrical Engineering from the Technical University of Hamburg. Education : DPhil in Engineering Science (2011), University of Oxford; Diploma in Electrical Engineering, Technical University of Hamburg His research centers on active vision systems for surveillance, particularly visual tracking , camera control algorithms , and information-theoretic approaches to sensor planning. Key themes include multi-camera coordination , trajectory prediction , and real-time SLAM for surveillance simplification. Eric has contributed to 15 publications (2008–2012) spanning conferences like ICRA, CVPR, IROS, and BMVC, with a focus on surveillance systems , visual tracking , adaptive control , and information theory . His work includes probabilistic multi-camera surveillance, cognitive active vision, and entropy-driven exploration strategies. He received funding for his DPhil through the EC grant IST-027110 for the HERMES project under the EU's Sixth Framework Programme. He has collaborated with researchers such as Professor Ian Reid, Ben Benfold, and Nicola Bellotto. Eric has also engaged in teaching activities , including demonstrating Electrical Machines, Computing Labs, and C++ coursework modules. His professional contributions extend to peer reviewing for leading conferences and journals.

Audun Theodorsenمشاهده پروفایل

دانشیار

• Plasma Physics
• Nuclear Fusion
• Turbulence
• +۳ مورد دیگر

Audun Theodorsen is an Associate Professor at the Department of Physics and Technology, UiT The Arctic University of Norway, specializing in Complex Systems Modeling within plasma physics and nuclear fusion. His research focuses on turbulence , stochastic modeling , and intermittent fluctuations in fusion plasmas, particularly in the scrape-off layer of tokamaks like Alcator C-Mod. He also contributes to understanding cosmic dust dynamics using data from Solar Orbiter and Parker Solar Probe. Key Collaborators: Odd Erik Garcia, Samuel Kociscak, Ingrid Brigitte Mann, Ralph Kube, Juan Manuel Losada Teaching: Courses in Statistical Physics , Stochastic Modeling , and Nonlinear Dynamics His recent publications include stochastic models for plasma filaments and Bayesian analyses of dust impact ionization , spanning journals like Physics of Plasmas , Astronomy and Astrophysics , and Journal of Geophysical Research: Atmospheres . His work bridges fusion energy research and space science , emphasizing universal statistical patterns across diverse physical systems. For details on his research projects, see ITER and Fusion Research Center Norway .

• Stochastic Control
• Optimal Investment
• Hedging in Incomplete Markets
• +۸ مورد دیگر

Michael Monoyios is an Associate Professor in Financial Mathematics at the University of Oxford's Mathematical Institute and a Tutorial Fellow in Applied Mathematics at Lady Margaret Hall. His academic journey includes a BSc and PhD in Theoretical Physics from Imperial College London and postdoctoral work at the Niels Bohr Institute. Prior to academia, he worked as a derivatives trader in London. His research centers on stochastic control and optimization in financial contexts, including portfolio management under transaction costs, hedging in incomplete markets, and dual methods for pricing. He also explores insider information, asymptotic expansions, and machine learning applications in finance. His publications predominantly focus on mathematical finance , with recurring themes of duality theory, optimal consumption, and dynamic programming. Recent work integrates neural networks for portfolio generation and investigates stability in recursive utility models. Awards and honors: Leverhulme Research Fellowship (2004-2005) University of Oxford Teaching Award (2007) Departmental Teaching Award (2014) He tutors undergraduates in Applied Mathematics and has organized workshops at the Isaac Newton Institute and London Mathematical Society.

Mahya Mohammadi Kashaniمشاهده پروفایل

پژوهشگر

• Underwater Robotics
• Risk Assessment
• Probabilistic Planning
• +۳ مورد دیگر

Mahya Mohammadi Kashani is a Researcher in Software Engineering at the IT University of Copenhagen, specializing in risk assessment for underwater robotics. She serves as an Early Stage Researcher in the REMARO (Reliable AI for Marine Robotics) network, a European Commission-funded project advancing trustworthy AI for marine applications. Her academic background includes: PhD in Computer Science (2021-2024), IT University of Copenhagen: Focused on statistical assessment of plans via probabilistic optimization of reliability. MSc in Artificial Intelligence and Robotics (2016-2019), Shahid Rajaee Teacher Training University: Researched search-based image annotation using deep models. BSc in Software Engineering (2010-2014): Developed filter drivers for disk access control at low-level driver hierarchies. Her research develops Bayesian-inference-based methods for risk-averse decision-making in underwater robotics, creating probabilistic models to assess operational risks and select reliable robot plans. Previously, she investigated pattern recognition algorithms, sparse reconstruction/coding techniques, and search space reduction for automatic image annotation systems. Her publications (2022-2025) establish a cohesive framework for risk-aware marine robotics, addressing fault recovery, risk quantification, and plan assessment through probabilistic optimization and belief-based planning models. No scientific awards were documented in the source material. Funded by the REMARO project (2020-2025), she contributes to European Commission research on trustworthy AI, active learning, and image segmentation for underwater robotics as a co-investigator. Her collaborative work spans risk-averse planning methodologies and reliability engineering for autonomous marine systems. Within the REMARO network, she collaborates on advancing reliable AI for marine robotics through active learning techniques and image segmentation solutions for challenging underwater environments.

Ahmad Haidarمشاهده پروفایل

دانشیار

• Artificial Pancreas
• Diabetes
• Biomedical Engineering
• +۹ مورد دیگر

Ahmad Haidar is an Associate Professor in the Department of Biomedical Engineering at McGill University , with an associate membership in the Division of Endocrinology . His work focuses on developing and clinically testing advanced algorithms for the artificial pancreas , a medical device designed to automate glucose regulation in Type 1 Diabetes patients. The device integrates glucose sensors, insulin pumps, and control algorithms to optimize hormone delivery based on real-time data. Research Areas: Artificial pancreas, diabetes, systems dynamics and control, medical diagnostics and therapeutics, Bayesian estimation, glucose physiology, biomedical devices. Dr. Haidar’s recent publications (2024–2025) emphasize innovations in closed-loop insulin delivery , integration of GLP-1 receptor agonists like semaglutide, and machine learning for personalized dosing. His studies include clinical trials on hybrid and dual-hormone systems, sensor calibration methods, and strategies to reduce carbohydrate-counting burdens. Many articles explore real-world applications during exercise, meals, and long-term use.

Jiebiao Wangمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Computational Biology
• +۷ مورد دیگر

Jiebiao Wang is an Assistant Professor in the Department of Biostatistics at the University of Pittsburgh School of Public Health. He develops computational methods for omics data analysis, focusing on cellular deconvolution, statistical genetics, and proteomics modeling. Education : BSc/MSc in Statistics from Renmin University of China, PhD in Biostatistics from University of Chicago, Postdoc in Statistics/Data Science at Carnegie Mellon University. Research Interests include: Cellular deconvolution methods (EnsDeconv, HiDecon, scMD, MIND, bMIND) Proteomics data analysis with mvMISE models Statistical genetics applications in gene expression imputation (MixRF) and autism studies Scientific Awards : Research Poster Award, ICSA Applied Statistics Symposium (2023) Student Research Award, New England Statistics Symposium (2023) Teaching : Courses on causal inference (BIOST 2068) and omics data methods (BIOST 2154).

Ioannis Karatzasمشاهده پروفایل

استاد

• Stochastic Analysis
• Mathematical Finance
• Probability Theory
• +۳ مورد دیگر

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability at Columbia University, holding appointments in both the Department of Mathematics and Department of Statistics. His work bridges Stochastic Analysis , Mathematical Finance , and Optimization , with a focus on utility maximization, market equilibrium, and stochastic control problems. Research Interests: Stochastic processes, financial mathematics, optimal stopping, singular control, and portfolio optimization. His contributions include foundational work on martingale methods in incomplete markets and the development of stochastic portfolio theory. Books: Co-authored Brownian Motion and Stochastic Calculus and Methods of Mathematical Finance , both standard references in stochastic analysis and quantitative finance. Teaching: Taught advanced courses like Stochastic Methods in Finance and Probability , covering topics from arbitrage theory to optimal consumption. Doctoral Students: Advised 18 PhD students, including notable researchers in stochastic control, financial economics, and optimal stopping.

• Statistical Signal Processing
• Machine Learning
• Optimization
• +۶ مورد دیگر

Ali Pezeshki is a Professor at Colorado State University with joint appointments in the Department of Electrical and Computer Engineering and the Department of Mathematics . His research spans statistical signal processing , machine learning , optimization , and applied harmonic analysis , with applications to radar, bioimaging, and network systems. B.Sc. and M.Sc. in Electrical Engineering from University of Tehran (1999, 2001) Ph.D. in Electrical Engineering from Colorado State University (2004) He has served on the editorial board of IEEE Access (2012-2018) and received the IEEE SP Society Best Young Author Award (2011). His students include PhD candidates like Yifan Yang and alumni such as Pooria Pakrooh (Qualcomm) and Wenbing Dang (Argo AI). His recent publications focus on greedy optimization strategies , submodular functions , adaptive radar , and single-pixel imaging . Keywords across his work include Compressed Sensing , Network Learning , and Statistical Estimation .

Pete Santagoمشاهده پروفایل

استاد

• Biomedical Informatics
• Pattern Recognition
• Machine Learning
• +۵ مورد دیگر

Dr. Pete Santago is a Professor in the Department of Computer Science at Wake Forest University. He was previously a faculty member at the Wake Forest University School of Medicine for 24 years, where he established an engineering group in Radiology and co-founded the Biomedical Engineering Department. His research spans biomedical informatics, pattern recognition, machine learning, and image/signal processing, with current interests in stochastic processes, data mining, and computer organization. Education: Ph.D. in Electrical Engineering from North Carolina State University Education: M.S. in Computer Science from Virginia Tech Dr. Santago’s research bridges biomedical engineering and computer science, with applications in medical imaging, data analysis, and educational innovation. His work emphasizes practical solutions for image processing, diagnostic tools, and hands-on STEM learning environments like the WakerSpace makerspace. His 15 most recent publications highlight expertise in medical imaging (e.g., PET, CT, MRI), algorithm development (segmentation, reconstruction), and educational initiatives. Techniques such as convex sets, MAP estimation, and geodesic active contours recur in his contributions to biomedical informatics and computer science. As Faculty Director of WakerSpace, Dr. Santago focuses on service work and student mentorship, guiding independent projects that integrate data mining and computer organization principles.

Julien Albertمشاهده پروفایل

پژوهشگر

• Explainable Artificial Intelligence
• Human–Computer Interaction
• Recommender Systems
• +۴ مورد دیگر

Julien Albert is a researcher within the Namur Digital Institute (NaDI) and the Research Center on Information Systems Engineering at the University of Namur . He completed his master’s degree in computer science with a data-science focus in 2020 and is active in explainable AI, recommender systems, and human–computer interaction. Education: M.S. in Computer Science (data-science focus), University of Namur, 2020 – thesis on scientific-literature recommendation systems. Research Interests: Albert investigates how machine-learning decisions can be made transparent and trustworthy, with a particular emphasis on explainability for computer-vision models and fairness-aware personalized ranking . His work combines rigorous algorithmic development with human-centered empirical studies, bridging machine-learning research and interactive-systems design. Recent publications reveal a clear strand on explainable AI (XAI): evaluating saliency-based explanations, designing human-in-the-loop studies, and accelerating interpretation of nonlinear embeddings. A parallel track focuses on recommender systems , exploring both explanation interfaces powered by large language models and bias-aware Bayesian ranking algorithms. A third emerging theme tackles civic technologies , studying requirements for idea-browsing tools on digital participation platforms. He has (co-)supervised at least three master’s students and regularly participates in international venues such as the Francophone Conference on Human–Computer Interaction and the European Symposium on Artificial Neural Networks, indicating a growing footprint in the XAI and HCI communities.

Jon Cockayneمشاهده پروفایل

دانشیار

• Bayesian Numerical Methods
• Probabilistic Numerical Methods
• Differential Equations
• +۴ مورد دیگر

Jon Cockayne is an Associate Professor at the University of Southampton specializing in Bayesian Numerical Methods , Probabilistic Numerical Methods , and Uncertainty Quantification . His research focuses on integrating probabilistic approaches into numerical algorithms, particularly for solving linear systems , differential equations , and MCMC output analysis . He has contributed to BayesCG (Bayesian Conjugate Gradient) and Computation-Aware Gaussian Processes . Notable collaborations include work on Statistical Finite Element Methods , Probabilistic Iterative Methods , and Calibrated Numerical Algorithms for industrial applications like Hydrocyclone Equipment state estimation.

• Computational statistics
• Sequential Monte Carlo methods
• Forward-backward filters
• +۵ مورد دیگر

Paul Fearnhead is Professor of Statistics at Lancaster University since 2001, where he started as a lecturer. His academic journey includes a DPhil with Peter Clifford and a post-doctoral position with Peter Donnelly at the University of Oxford. He has been Editor of Biometrika since January 2018. Education : DPhil in Statistics (Oxford), Post-doctoral research (Oxford) Research Interests : Computational statistics with focus on sequential Monte Carlo methods, forward-backward filters, and changepoint detection algorithms; population genetics including bacterial inference for Campylobacter jejuni; Bayesian inference for diffusions and continuous-time stochastic processes. Research Funding : Lead investigator for EPSRC grants on Statistical Scalability (2016-2022), Health Sciences Bayesian Data Science (2018-2023), and CoSInES program (2018-2023) involving multiple UK institutions. Scientific Awards : 2006 Adam's Prize 2007 Royal Statistical Society Guy Medal in Bronze His collaborative work includes teaching the 2012 Modern Computational Statistics course through the Graduate Training Programme and interdisciplinary research on US college basketball ranking systems.

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +۵ مورد دیگر

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.