جست‌وجوی استاد و پژوهشگر

Farid Alizadehمشاهده پروفایل

استاد

• Optimization
• Semidefinite Programming
• Combinatorial Optimization
• +۴ مورد دیگر

Farid Alizadeh is a Professor in the Department of Management Science and Information Systems at Rutgers School of Business, Rutgers University, and is affiliated with the Rutgers Center for Operations Research (RUTCOR). He pioneered semidefinite programming (SDP) in 1990, establishing its foundations and applications to combinatorial optimization. Education: PhD in Computer Science and Engineering, University of Minnesota (1991), Advisor: Ben Rosen Postdoctoral Associate, International Computer Science Institute (ICSI), Berkeley (1991-1993), working with Richard Karp His research spans optimization theory, including SDP, second-order cone programming (SOCP), and their applications in statistical learning (shape-constrained regression, density estimation), combinatorial optimization, and algebraic foundations. Current work focuses on extending simplex-like algorithms to SDP/SOCP for branch-and-bound methods and rule-augmented learning problems where examples incorporate feature-space regions. Scientific Awards: NSF CAREER Award (1995) INFORMS Optimization Society Farkas Prize (2014) Funded by the NSF CAREER award, he mentors graduate students in operations research at Rutgers. His teaching includes Linear Programming, Semidefinite Programming, and Statistical Methods of Business across PhD and Master's programs. As a core RUTCOR member, he collaborates on interdisciplinary optimization projects spanning theoretical computer science, control theory, and statistical applications, maintaining active research labs in both Piscataway (Livingston Campus) and Newark.

Shuang Zhaoمشاهده پروفایل

دانشیار

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
• +۷ مورد دیگر

Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

Gerhard Starkeمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
• +۷ مورد دیگر

Gerhard Starke is a Professor of Numerical Mathematics at the Faculty of Mathematics, University of Duisburg-Essen. He leads the Numerical Mathematics workgroup, focusing on the development and analysis of finite element methods for solving complex mathematical and mechanical problems. His research spans multiple areas of computational mathematics with applications in solid mechanics and optimization. Professor Starke's research interests center around numerical analysis of finite element methods , reconstruction-based a posteriori error estimators , and mixed finite element methods . His work has significant applications in shape optimization , solid mechanics , hyperelasticity , plasticity , and frictional contact problems . His research combines theoretical mathematical analysis with practical computational approaches to solve challenging engineering problems. Analysis of Professor Starke's recent publications (2017-2025) reveals a strong focus on stress-based methods, shape optimization, and advanced finite element formulations. His work demonstrates a consistent progression from fundamental numerical analysis to increasingly complex applications in solid mechanics. Key trends include the development of constrained first-order system least mean approximation for shape optimization, superconvergent DPG approximations in linear elasticity, and stress-based methods for variational inequalities in contact mechanics. Professor Starke has secured significant third-party funding for his research, including current projects funded by the German Research Foundation (DFG) within Priority Programs 1748 and 1962. These projects focus on stress approximation in hyperelastic material models and stress-based methods for variational inequalities in solid mechanics. His research collaborations span multiple institutions including Humboldt University Berlin, University of Duisburg-Essen's Institute of Mechanics, and international partners in Switzerland. Head of Numerical Mathematics workgroup at University of Duisburg-Essen Principal investigator for multiple DFG-funded projects since 1999 Supervisor for numerous doctoral students including Laura Hetzel, Henrik Schneider, and Kemal Suntay Extensive international collaborations with researchers across Europe The Numerical Mathematics workgroup led by Professor Starke maintains active collaborations with mechanics researchers at the University of Duisburg-Essen and international institutions. The group has participated in significant research initiatives including the DFG Research Training Group 615 and multiple Priority Programs. Current work focuses on applying advanced numerical methods to challenging problems in computational solid mechanics and shape optimization.

• 3D Computer Vision
• Shape Modeling
• Virtual Humans
• +۴ مورد دیگر

Victoria Fernandez Abrevaya is a post-doctoral researcher at the Max Planck Institute for Intelligent Systems (Perceiving Systems department) in Germany. She holds a PhD from Inria Grenoble (France) under Professors Edmond Boyer and Stefanie Wuhrer, and a MSc in Computer Science from the University of Buenos Aires, Argentina. Her work focuses on 3D reconstruction and understanding of humans from 2D data, with emphasis on facial animation, neural rendering, and generative models. Research interests: 3D computer vision and shape modeling Neural rendering techniques Diffusion models for motion and appearance synthesis Biometric fairness in face analysis Real-time face capture systems Geometry-constrained multi-human rendering Recent work explores occluded face expression reconstruction (OFEr 2025), interactive dynamics modeling (InterDyn 2025), and latent realignment for motion diffusion (Lead 2025). Her SPARK system (2025) enables real-time monocular face capture through self-supervised learning. Prior contributions include FLAME (2023), a popular 3D face model framework, and work on multilinear autoencoders for dynamic facial analysis (2018). She co-developed ImAvatar (2022), an implicit morphable head avatar system from videos.

Guillaume Tochonمشاهده پروفایل

استادیار

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
• +۴ مورد دیگر

Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.

Shakeel Gavioli-Akilagunمشاهده پروفایل

پژوهشگر ارشد

• Changepoints
• Feature Detection
• Multiscale Statistics
• +۴ مورد دیگر

Shakeel Gavioli-Akilagun is a Research Fellow in the Department of Statistics at the London School of Economics and Political Science (LSE), part of the Time Series and Statistical Learning research group. He holds a BSc in Economics and Econometrics from the University of York and an MSc in Statistics (Research) from LSE, where he completed an ESRC studentship. His primary research focuses on changepoints, feature detection, multiscale statistics, and causal inference, with applications in time series analysis and machine learning. His academic contributions include developing algorithms for non-standard change point problems and has published in journals like Electronic Journal of Statistics and Journal of the Royal Statistical Society Series B . He teaches courses such as Distributed Computing for Big Data and Graph Data Analytics at LSE. Recent invited talks include presentations at the Institute of Mathematical Statistics Asia Pacific Rim Meeting (2026) and the EcoSta conference on change point detection (2025).

Rémi CARMINATIمشاهده پروفایل

استاد

• Wave Theory
• Scattering and Transport of Light in Complex Media
• Nano-optics

Rémi CARMINATI is a Professor at ESPCI Paris, affiliated with the Institut Langevin. His research focuses on wave propagation in complex media, light scattering dynamics, and nano-optics. He has contributed to understanding time-varying disorder, polarization depolarization mechanisms, and thermal radiation at micro/nanoscales. Key Research Themes : Wave Theory, Light Scattering in Correlated Media, Nano-optics, Anderson Localization, and Photonic Hyperuniformity. The 15 most recent articles demonstrate expertise in wave dynamics across temporal/spatial disorder, nonlinear photon diffusion, polarization statistics, and applications in thermal imaging, sensing, and metamaterial design. His work bridges theoretical optics with experimental validation using digital holography, photon Doppler velocimetry, and near-field microscopy. Rémi CARMINATI is actively involved in Institut Langevin research, collaborating on projects spanning electromagnetic field correlations, plasmonic nanostructures, and coherence-based imaging through scattering media. His methodological innovations include dwell-time operators for energy storage, reciprocity-based LDOS mapping, and diagrammatic approaches for photon echo analysis.

• Stochastic Control
• Financial Mathematics
• Optimization
• +۶ مورد دیگر

Professor Harry Zheng is a Professor of Mathematics at Imperial College London's Faculty of Natural Sciences, Department of Mathematics. He is affiliated with the CFM-Imperial Institute of Quantitative Finance and Mathematical Finance. His research focuses on stochastic control, optimization, and financial mathematics with applications in banking, numerical methods, and statistics. Key research areas include stochastic control theory, mean field games, financial derivative pricing, and machine learning integration in financial systems. His work addresses optimal investment strategies, risk management, and systemic risk modeling in dynamic markets. Notable contributions include applying deep learning to solve high-dimensional stochastic control problems and analyzing governance dynamics using mean field frameworks. Recent publications span innovations in neural network-based solvers for Hamilton-Jacobi-Bellman equations, robust optimization under uncertainty, and behavioral finance models incorporating transaction costs and prospect theory. Professor Zheng collaborates with institutions like the CFM-Imperial Institute to advance quantitative finance methodologies. His research bridges theoretical advancements with practical applications in portfolio management, risk assessment, and computational finance.

David Mguniمشاهده پروفایل

مدرس

• Multi-Agent Systems
• Reinforcement Learning
• Game Theory
• +۳ مورد دیگر

Dr. David Mguni is a Lecturer in Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on multi-agent systems, reinforcement learning, game theory, and decision-making under uncertainty. He holds a PhD in applied mathematics from University College London and prior degrees from the University of Cambridge and the University of London. Before academia, he was a Principal Scientist at Huawei, leading the Multi-Agent Systems team in London. His work bridges theoretical foundations with practical applications, particularly in strategic decision-making and autonomous systems. He teaches the ECS656U/ECS796P Distributed Systems course and supervises students in reinforcement learning, game theory, and optimization. Research interests include non-zero-sum game frameworks, safe reinforcement learning (e.g., SAUTE RL), and reward shaping techniques. His publications span venues like AAMAS, NeurIPS, and IJCAI, addressing challenges in multi-agent coordination, adversarial environments, and algorithmic efficiency. Collaborations involve designing scalable methods for large systems and integrating language models into decision-making processes. David is part of Queen Mary’s Centre for Multimodal AI, contributing to projects on socially-attentive policies, risk-aware systems, and distributed optimization. His recent work emphasizes safety in multi-agent learning (e.g., SAUTE RL), variance reduction in estimator models, and frameworks for managing adversarial scenarios. He actively seeks PhD candidates to explore reinforcement learning and game-theoretic applications.

Sanowar Khanمشاهده پروفایل

استاد

• Mathematical modelling
• CAD of sensors and actuators
• Computational electromagnetics
• +۸ مورد دیگر

Professor Sanowar Khan is a Professor and Associate Dean (SSE) at the School of Science and Technology , City St George's, University of London , where he has been a faculty member since 1989. He holds a PhD and MSc (with Distinction) in Electrical Engineering from Peter the Great St Petersburg Polytechnic University, Russia. He previously served as Deputy Dean from 2008 to 2019. His research spans mathematical modelling, computational electromagnetics, finite element analysis, magnetic shape memory materials, sensor and actuator design, tomographic imaging, and measurement systems . His work integrates advanced numerical methods with practical instrumentation and biomedical applications, including hemodynamics and smart sensors. Recent publications show a strong trend in finite element modeling of electromagnetic phenomena , particularly in power systems (e.g., skin and proximity effects in cables) and biomedical engineering (e.g., carotid artery hemodynamics). He also contributes significantly to signal processing and smart material-based sensing . His editorial leadership includes being Editor-in-Chief of Sensor Review and former Associate Editor of Measurement and related journals. Fellow of the Institution of Engineering and Technology (IET) Fellow of the Institute of Measurement and Control (InstMC) Honorary Professor, Peter the Great St Petersburg Polytechnic University Founder Member, International Compumag Society (ICS) Liveryman, Worshipful Company of Scientific Instrument Makers Freeman of the City of London He advises on instrumentation standards and policy through his editorial and professional roles. He has no known PhD or Master’s students listed. He leads no specific lab or research team mentioned in the text but collaborates widely, particularly with researchers in biomedical engineering and electromagnetics.

Farshid Agharebparastمشاهده پروفایل

دانشیار

• Data Communications
• Mobile Computing
• Wireless Networks
• +۳ مورد دیگر

Farshid Agharebparast serves as an Associate Professor Teaching in the Department of Electrical and Computer Engineering at the University of British Columbia's Faculty of Applied Science. His academic career is anchored in data communications networks and mobile computing, with active contributions to the Communication Systems Research Group specializing in Data Communications. Education: BSc, University of British Columbia MASc, University of British Columbia PhD, University of British Columbia Dr. Agharebparast's research explores stochastic network calculus applications for wireless performance evaluation, cross-layer design in fading channels, and QoS provisioning. His work bridges theoretical frameworks like min-plus algebra with practical challenges in 4G handover privacy, bandwidth allocation in WiMAX, and traffic shaping for unreliable channels. These investigations address critical needs in next-generation mobile infrastructure where latency guarantees and resource efficiency are paramount. His teaching encompasses core engineering subjects including Circuit Analysis (ELEC 201), Operating Systems (CPEN 331), Computer Networking (ELEC 331, CPEN 400N), and multiple design studio courses (ELEC 291, CPEN 291). This portfolio demonstrates deep engagement with both foundational theory and hands-on implementation across electrical and computer engineering curricula. Scientific Awards: No awards documented in source materials Through design studio courses and undergraduate teaching assistantships, Dr. Agharebparast mentors emerging engineers in practical system development. His publication trajectory from 2001-2022 indicates sustained research activity in network performance modeling, though specific grant funding details remain unreported. His work consistently targets real-world wireless challenges including video streaming optimization and privacy-preserving mobility management. Affiliated with UBC's Communication Systems Research Group, he contributes to a collaborative ecosystem focused on data communications innovation. Current investigations appear centered on stochastic performance bounds for modern wireless architectures, extending his two decades of scholarship in network calculus applications.

Christian Rohrbeckمشاهده پروفایل

مدرس ارشد

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +۳ مورد دیگر

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

• Network Science
• Evolutionary Dynamics
• Complex Systems
• +۵ مورد دیگر

Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.

Bodhisattva Senمشاهده پروفایل

استاد

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +۴ مورد دیگر

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor (2008) M.Stat., Indian Statistical Institute, Kolkata B.Stat., Indian Statistical Institute, Kolkata Research Interests: Nonparametric function estimation Optimal transport applications in statistics Empirical Bayes and multiple testing High-dimensional statistical inference Statistical methods in astronomy Key Contributions: Developed multivariate distribution-free tests using optimal transport Advanced convex regression methods in multidimensions Contributed to nonparametric maximum likelihood estimation in mixture models Explored statistical applications in stellar abundance clustering His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.

• Calculus of Variations
• Partial Differential Equations
• Mathematical Analysis
• +۳ مورد دیگر

Edoardo Mainini is an Associate Professor at the University of Genova, specializing in mathematical analysis and applied mathematics. His research focuses on calculus of variations, partial differential equations, elasticity theory, optimal transport, and nonlinear dynamics. He has contributed to studies on fractional equations, material science, and stochastic processes. His work often bridges pure and applied mathematics, addressing problems in mechanics, probability, and geometric analysis. Mainini has collaborated extensively with researchers such as M. Kružík, D. Percivale, and U. Stefanelli, producing influential papers in journals like Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal. . His recent articles (2020–2025) address topics ranging from fractional linear equations to Bayesian nonparametric models. Education includes a PhD in Mathematical Analysis from the University of Genova (2010) and a thesis on Infinite-dimensional porous media equations and optimal transportation . He has organized international conferences and seminars on variational methods and geometric structures. His research emphasizes rigorous mathematical frameworks for physical phenomena, including the linearization of elasticity models and the study of ground states in diffusion-dominated systems. Mainini’s contributions to carbon nanotube geometries and optimal transport theory have been recognized through invited talks at major events, such as the International School of Mathematics “Guido Stampacchia” . His work often explores the interplay between discrete and continuous models, with applications to materials science and geometric optimization.