جست‌وجوی استاد و پژوهشگر

• Micromechanics of granular media
• Discrete element modeling (DEM)
• Mechanics of masonry vaults
• +۲ مورد دیگر

Dr. Katalin Bagi is a Professor at the Department of Mechanics of Supporting Structures within the Budapest University of Technology and Economics (BME), Faculty of Civil Engineering . Her academic career spans decades of research, teaching, and leadership in computational mechanics and masonry structures. Teaching: Courses include Mechanics , Strength of Materials , Numerical Models for Structures , Micromechanics of Granular Materials , and Masonry Structural Analysis (1997–2016). Research: Focus on discrete element modeling (DEM) , micromechanics of granular media , and mechanics of masonry vaults , with applications to historical structures and modern engineering challenges. Recent publications explore advanced DEM techniques for validating fan vault geometries, torsion-shear behavior in interlocking joints, and optimization of block assemblages. Her work bridges theoretical analysis with practical simulations to address structural stability and material failure. Scientific Awards include: Korányi Imre Scholarship (1991–1992) Bolyai János Research Scholarships (1998–2001, 2005–2008) Széchenyi István Scholarship (2002–2005) Students: Supervised Tamás Forgács (2016–, Thesis: Masonry Arch Bridges). Labs & Teams: Affiliated with the Pál Vásárhelyi Doctoral School of Civil Engineering and Geosciences and international research collaborations in DEM and structural mechanics.

Sergiy Yakovlevمشاهده پروفایل

استاد پژوهشی

• Combinatorial Optimization
• Geometric Design
• Computational Geometry
• +۵ مورد دیگر

Prof. Sergiy Yakovlev serves as a Research Professor in the Department of Mathematical Modeling at University of Lodz, with room 167 and contact number +48 426313617. His extensive academic contributions span combinatorial optimization, geometric design, and artificial intelligence applications. His research interests focus on Combinatorial Optimization, Geometric Design, Computational Geometry, and AI in Public Health, with significant work in spatial configuration modeling and packing/covering problems. Recent publications demonstrate strong interdisciplinary connections between mathematical theory and practical applications in epidemiology and sensor networks. Analysis of his 15 most recent publications reveals a clear trend toward applying computational geometry and optimization techniques to public health challenges, particularly in modeling epidemic dynamics during the Ukraine conflict and developing AI frameworks for health misinformation detection. His work bridges theoretical mathematics with real-world crisis response systems. While no specific scientific awards are documented in the provided text, his publications appear in high-impact journals including Symmetry (70 points), Environmental and Climate Technologies (100 points), and Cybernetics and Systems Analysis. Prof. Yakovlev actively collaborates on epidemic modeling projects related to the Ukraine conflict, including migration impacts on COVID-19 dynamics and respiratory infection modeling in Kharkiv region. His work involves multiple international research teams across Poland, Ukraine, and Switzerland. He contributes to critical monitoring systems through his research on sensor network reliability for environmental emergencies and forest fire monitoring, with particular attention to failure analysis in hybrid network architectures.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Adversarial Machine Learning
• Computer Security
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He obtained his PhD in Computer Science from the University of Virginia in 2022, advised by Prof. David Evans, and holds an MS in Statistics from UVA and a BS in Mathematics from Tsinghua University. His research focuses on trustworthy machine learning, particularly adversarial robustness, security, and privacy-preserving techniques. Zhang's research interests span: Adversarial machine learning and intrinsic robustness limitations Trustworthy AI systems for security-critical applications Statistical machine learning and optimization methods Privacy-preserving techniques and data poisoning defenses Robust representation learning and generative models His recent publications (2022-2025) demonstrate a strong focus on: Developing new frameworks for adversarial robustness measurement Creating defenses against data poisoning and membership inference attacks Exploring fundamental limits of robust learning Applying diffusion models and generative approaches to security Advancing anti-facial recognition techniques Zhang leads a research group at CISPA investigating robust machine learning systems and has presented work at top venues including NeurIPS, ICLR, ICML, and CCS. While no awards are mentioned in the source material, his research addresses critical challenges in AI security.

• Computational Biology
• Structural Biology
• NMR
• +۱۲ مورد دیگر

Bruce Randall Donald is the James B. Duke Distinguished Professor of Computer Science and Biochemistry at Duke University, with additional professorships in Chemistry and Mathematics. He is a member of the Duke Cancer Institute and Faculty Network Member of the Duke Institute for Brain Sciences, as well as a Bass Fellow. Donald received his B.A. from Yale University and Ph.D. from the MIT Artificial Intelligence Laboratory, and has held academic positions at Cornell University, Interval Research Corporation, and Duke University since 2006. Education B.A., Yale University Ph.D., MIT Artificial Intelligence Laboratory Donald's research spans Computational Biology , Structural Biology , NMR , MEMS , Robotics , and Physical Geometric Algorithms . His work focuses on algorithmic solutions for structural proteomics, protein design, and rational drug development, integrating computational methods with experimental validation. Recent publications emphasize AI-driven drug discovery and predicting cancer resistance mutations , with key articles in Cell , PLoS Computational Biology , and Nature Communications Biology . His lab employs ensemble-based modeling and non-proteinogenic amino acid design to tackle challenges in infectious diseases and oncology. Scientific honors include the Presidential Young Investigator Award , Guggenheim Fellowship , and fellowships from the ACM , AAAS , and IEEE . Donald's lab is supported by the NIH Outstanding Investigator Grant and a $3M MIRA award . Advising highlights: Over 30 PhD and Master’s students trained, with alumni leading roles at institutions like MIT, Stanford, and companies such as Genentech, NVIDIA, and Ten63 Therapeutics. His lab maintains state-of-the-art dry and wet facilities at Duke's Levine Science Research Center and French Family Science Center.

Dan B Goldmanمشاهده پروفایل

دانشیار مدعو

• Computer Graphics
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Dan B Goldman is an affiliate associate professor at the University of Washington's Computer Science and Engineering department, while serving as a Principal Engineer and AI/ML architect for Lucasfilm & ILM. His career bridges academia and industry, with prior roles at Adobe's Creative Technologies Lab (2007-2015) and Google (2015-2024), where he led computer vision R&D for Project Starline. He is a member of the Visual Effects Society and holds a PhD in Computer Science (2007) from the University of Washington. Research Focus: Computer graphics, computer vision, artificial intelligence, and human-computer interaction Notable Projects: Content-Aware Fill (Photoshop), Project Starline (Google), HyperNeRF (neural radiance fields), and Nerfies (deformable 3D modeling) Academic Legacy: Mentored by David Salesin, with an Erdős-Bacon number of 6 (via Rose Byrne and David Salesin's academic lineage). His work appears in top venues like SIGGRAPH, CVPR, and ECCV, covering topics from neural rendering to 3D reconstruction. Industry Impact: Pioneered technologies used in major films (Star Wars, Jurassic Park) and Adobe Photoshop features. His recent work at Google Labs (2022-2024) focused on generative media, reflecting his dual expertise in creative tools and computational methods.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Statistics
• +۳ مورد دیگر

Marco Cuturi is a Professor of Statistics at ENSAE (École Nationale de la Statistique et de l'Administration Économique), part of Institut Polytechnique de Paris, where he has been affiliated since 2016, working part-time since 2018. He is also a CREST Permanent Member and a Research Scientist at Apple ML Research in Paris, having previously worked at Google Brain from 2018 to 2022. His academic journey includes positions as an Associate Professor at Kyoto University's Graduate School of Informatics (2010-2016), a lecturer at Princeton University's ORFE department (2009-2010), and postdoctoral research at the Institute of Statistical Mathematics in Tokyo (2005-2007). Dr. Cuturi earned his Ph.D. from École des Mines de Paris in 2005 and holds a master's degree from ENS Cachan, having graduated from ENSAE in 2001. His research centers on the application of optimal transport theory to machine learning and statistics, with significant contributions to computational optimal transport, kernel methods, and time series analysis. His influential work includes the development of the Sinkhorn distance for fast computation of optimal transport, soft-DTW for time series alignment, and various applications of optimal transport in machine learning. His research program explores how geometric structures can be leveraged in statistical learning, with recent work focusing on entropic optimal transport solvers, Gromov-Wasserstein distances, and applications to single-cell biology and multimodal learning. Dr. Cuturi's publications reveal a consistent trajectory of developing theoretically grounded yet computationally efficient methods that bridge optimal transport theory with practical machine learning applications across diverse domains including computer vision, natural language processing, and computational biology. Among his professional recognitions is a Notable Paper Award at AISTATS 2020 for his work on regularization in optimal transport. He serves as an Associate Editor for JMLR and has held senior area chair positions at NeurIPS and ICML. Dr. Cuturi has organized major machine learning events including the Machine Learning Summer School in Kyoto (2012, 2015) and co-organized workshops on optimal transport at major conferences. Dr. Cuturi has supervised numerous doctoral students including Nina Vesseron, Théo Uscidda, Charlotte Bunne, and Othmane Sebbouh, with research spanning optimal transport applications in generative modeling, representation learning, and biological data analysis. His current research group at ENSAE and Apple continues to push the boundaries of optimal transport methodology and its applications to cutting-edge machine learning problems.

Randall Balestrieroمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Self-Supervised Learning
• +۹ مورد دیگر

Randall Balestriero is an Assistant Professor of Computer Science at Brown University , with a focus on practical deep learning theory , self-supervised learning , and generative AI . His research spans multiple domains including computer vision , bioacoustics , geophysics , and quantitative finance . PhD in Electrical and Computer Engineering (2021), Rice University MA in Mathematics (2016), Université Paris 6 Pierre and Marie Curie BA in Mathematics (2014), Université du Sud Toulon Var His work develops novel theoretical solutions to guide practitioners, improve state-of-the-art methods like batch normalization and generative networks , and address challenges in self-supervised learning and AI fairness . Recent publications focus on topics such as: Masked Autoencoders and Spatial Correlations Grokking and Geometric Characterization LoRA Vulnerabilities and Robustness Latent Space Prediction and Joint Embedding He teaches CSCI 2952X - Research Topics in Self Supervised Learning and has contributed to open-source projects like MCCV for cluster job management.

Philip Kleinمشاهده پروفایل

استاد

• Algorithms
• Approximation algorithms
• Combinatorial optimization
• +۶ مورد دیگر

Philip Klein is a Professor of Computer Science at Brown University specializing in algorithms and combinatorial optimization. His work focuses on developing efficient algorithms for complex optimization problems, particularly those involving graphs and networks. He earned his BA in Applied Mathematics from Harvard University (1984), followed by an MS (1986) and PhD (1988) in Computer Science from MIT. After a postdoctoral fellowship at Harvard, he joined Brown University where he has established himself as a leading researcher in theoretical computer science. Professor Klein's research centers on approximation algorithms, combinatorial optimization, and graph theory with particular emphasis on planar graphs. His work includes foundational contributions to the Steiner tree problem, traveling salesperson problem, and network flow algorithms. He has developed polynomial-time approximation schemes for numerous problems in planar graphs, bridging theoretical computer science with practical applications in network design and geographic analysis. His recent publications reveal a continued focus on planar graph optimization, with significant contributions to sparsest cut problems, redistricting algorithms, and vehicle routing. The research demonstrates a consistent pattern of developing efficient approximation schemes for NP-hard problems in specialized graph classes. National Science Foundation Presidential Young Investigator Award Philip J. Bray Award for Teaching Excellence Fellow of the Association for Computing Machinery Hoopes Prize Professor Klein has secured substantial research funding throughout his career, including multiple NSF grants totaling over $600,000. His teaching portfolio includes foundational courses like CSCI 0170 (Computer Science: An Integrated Introduction) and advanced graduate courses focused on algorithms for planar graphs. His current research explores applications of graph algorithms to geographic clustering problems and redistricting, continuing his tradition of connecting theoretical computer science with real-world challenges.

Daniel Junمشاهده پروفایل

استاد

• Structural Analysis
• Structural Mechanics
• Finite Element Methods
• +۵ مورد دیگر

Daniel Jun is a Professor of Structural Mechanics & Structural Analysis at Ruhr West University of Applied Sciences (Hochschule Ruhr West) in Mülheim an der Ruhr, Germany, where he also serves as Dean of Faculty 3 and Head of the Institute of Civil Engineering. With over a decade of experience in academia since joining the university in 2012, Professor Jun has established himself as a leading expert in structural engineering, particularly in the areas of cooling tower design, power plant construction, and reinforced concrete structures. Professor Jun's academic journey began at Ruhr-Universität Bochum, where he earned his Dipl.-Ing. (Master's degree) in Civil Engineering with a specialization in Structural Engineering in 1997. He continued his studies at the same institution, completing his Dr.-Ing. (PhD) in 2002 with a dissertation titled "Adaptive Simulationsstrategien für nichtlineare Finite-Element-Simulationen von Schalentragwerken" (Adaptive Simulation Strategies for Nonlinear Finite Element Simulations of Shell Structures). 2002: Dr.-Ing., Ruhr-Universität Bochum 1992-1997: Dipl.-Ing. in Civil Engineering, Ruhr-Universität Bochum Professor Jun's research spans structural analysis, structural mechanics, and computational methods with particular emphasis on cooling tower stability, power plant construction, and reinforced concrete structures. His recent work increasingly addresses sustainability aspects in construction, including carbon emission analysis. His publications consistently focus on practical engineering challenges, especially in power generation infrastructure, where he has contributed significantly to cooling tower design, maintenance, and lifecycle management standards. Over his career, Professor Jun has supervised numerous Bachelor's and Master's theses covering diverse topics from structural reinforcement techniques to AI applications in civil engineering. His professional activities include serving as a consulting engineer registered with Ingenieurkammer-Bau NRW and active participation in industry working groups. Member of the VDI advisory board "Structural Analysis" Deputy Chairman of the Structural Engineering Committee Consulting Engineer registered with Ingenieurkammer-Bau NRW Active participant in VGB Power Tech working groups Member of Bauforumstahl e.V. (Steel Construction Forum) Professor Jun teaches Structural Mechanics I & II, Structural Analysis I & II, and Numerical Methods in Engineering. His teaching combines theoretical foundations with practical applications, preparing students for real-world engineering challenges in construction and infrastructure development.

Daniele De Martiniمشاهده پروفایل

مدرس

• Mobile Robotics
• Autonomous Systems
• Robot Navigation
• +۵ مورد دیگر

Daniele De Martini is a Departmental Lecturer in Mobile Robotics at the Oxford Robotics Institute (ORI) and a College Lecturer in Engineering Science at Pembroke College, University of Oxford. He co-leads the Mobile Robotics Group (MRG) with Professor Paul Newman. His academic journey began in Pavia, Italy, where he earned his BSc in Mechanical Engineering, followed by an MSc in Mechatronic Engineering from Politecnico di Torino, and completed his PhD in Robotics at Università degli Studi di Pavia under the supervision of Prof. Tullio Facchinetti. De Martini's research focuses on robust navigation and scene understanding for mobile robots, with particular emphasis on enabling robot operation in challenging weather conditions and diverse scenarios. His work spans from odometry and localization to detection and segmentation, utilizing various sensing technologies including vision, laser, and radar systems. A significant portion of his research explores FMCW scanning radar technology as a more robust alternative to traditional visual perception systems, especially in adverse weather conditions. He also investigates techniques to enhance the training of perception modules using machine learning approaches, believing that learning and adaptation are paramount for achieving true robot autonomy. His publication record demonstrates a clear progression from early work on energy management and scheduling algorithms to his current focus on mobile robotics and perception. The analysis of his recent publications reveals strong emphasis on radar-based perception, robust localization methods, scene generation for autonomous driving simulation, and long-term autonomy systems. His work bridges the gap between theoretical robotics and practical field deployment, as evidenced by projects like The Hulk vehicle platform designed for weather-proof outdoor operations. As an educator, De Martini teaches Structures and Mechanics at Pembroke College and contributes to the academic mission of the Department of Engineering Science. His research group actively develops solutions for real-world robotic challenges, with applications ranging from urban mobility safety assessment to long-term autonomous inspection systems.

• Statistical Science
• Data Science
• Machine Learning
• +۷ مورد دیگر

Nick Whiteley is a Professor in the School of Mathematics at the University of Bristol and serves as Director of the Institute for Statistical Science. He also held a Heilbronn Chair in Data Science and previously directed the EPSRC Centre for Doctoral Training in Computational Statistics and Data Science. His research spans statistical science and data science with interdisciplinary applications. Education : Ph.D. (University of Cambridge, 2009), M.Eng. (University of Oxford, 2003) Whiteley's research focuses on sequential Monte Carlo methods , Bayesian inference , hidden Markov models , and nonlinear filtering . His work connects statistical theory with practical applications in epidemic modeling, graph representation, and temporal data analysis. His recent publications emphasize scalable algorithms for high-dimensional problems and theoretical foundations of approximation methods. Key article trends include epidemic modeling , dynamic network analysis , stochastic processes , and machine learning theory . Sub-fields span from computational statistics to geometric methods in AI. Whiteley has received prestigious recognition through the Heilbronn Chair and Brunel Research Fellowship.

• Robotic Ultrasound
• Implicit Neural Representations
• Medical Image Segmentation
• +۲ مورد دیگر

Dr. Mohammad Farid Azampour is a Researcher at the Chair of Computer Aided Medical Procedures within the Department of Informatics at the Technical University of Munich (TUM). His work bridges robotics, medical imaging, and deep learning, with affiliations extending to the German Heart Center Munich (DHM) and collaborations across TUM's NARVIS and IFL labs. Research Focus: Azampour specializes in physics-inspired AI for healthcare, including robotic ultrasound systems, neural representations for medical image reconstruction, and anatomy-aware deep learning. His projects emphasize real-time surgical guidance, weakly supervised learning for clinical data, and multi-modal registration (e.g., CT-US/MRI fusion). Teaching & Supervision: He coordinates courses such as Medical Augmented Reality , Advanced 3D Computer Vision , and Innovation Generation in Healthcare . Azampour actively mentors students through IDPs, theses, and practical projects in computer-aided medical procedures. Publications: His recent work (2020-2025) demonstrates consistent focus on enhancing medical imaging through neural networks (e.g., Ultra-NeRF for ultrasound, diffusion models for simulation) and improving surgical robotics via real-time navigation/registration. Key themes include reducing annotation dependency, leveraging physics-based models, and enabling intraoperative decision-making.

Jonathan Gryakمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Applied Algebra
• +۵ مورد دیگر

Jonathan Gryak is an Assistant Professor of Computer Science and Data Science at Queens College and the CUNY Graduate Center, where he also serves as Deputy Executive Officer. He leads the Interdisciplinary Data Science Lab (IDSL), focusing on developing novel AI/ML methods for biomedical applications that leverage underlying problem morphology and operational constraints. Education: B.S. in Mathematics and Computer Science (Dual), Rensselaer Polytechnic Institute M.S. in Mathematics, Fairfield University M.Phil. in Computer Science, CUNY Graduate Center Ph.D. in Computer Science, CUNY Graduate Center (2017) Gryak's research centers on Nonlinear Algebraic Data Analysis, combining tensor analysis, numerical algebraic geometry, deep neural networks, and invariant theory to create more accurate models of complex real-world data. His Sequential Algebrogeometric Analysis (SAGA) approach captures disease progression through heterogeneous, longitudinal medical data. His work bridges the gap between theoretical mathematics and practical clinical applications, particularly in cardiac care, traumatic brain injury, and food allergy diagnostics. His recent publications demonstrate a strong emphasis on interpretable machine learning methods, especially those employing tropical geometry, for critical healthcare applications. These include predicting heart failure therapy needs, detecting acute respiratory distress syndrome, and improving oral food challenge outcome prediction. His research consistently focuses on methods that incorporate domain knowledge, handle asynchronous data, and leverage intrinsic data structures while maintaining clinical interpretability. Scientific Awards: CUNY Faculty Fellowship Publication Program (2024) NSF grant for improving treatments for heart failure patients through machine learning Gryak actively mentors PhD and undergraduate students in the IDSL, guiding research that spans from theoretical mathematical developments to clinical implementations. His lab collaborates with medical professionals to translate AI techniques into commercial applications that impact patient care. Current projects include multimodal data integration for clinical decision support across various conditions including cancer, traumatic brain injury, and inflammatory bowel disease. The Interdisciplinary Data Science Lab under Gryak's leadership develops end-to-end solutions, from novel mathematical techniques to industry and clinical partnerships for commercialization. Recent work has produced automated systems for endoscopic disease assessment, hematoma segmentation, and cardiac event prediction, with multiple patents stemming from this research.

• Deep Learning
• Explainable AI
• Uncertainty Analysis
• +۲ مورد دیگر

Kristoffer Wickstrøm is an Associate Professor in the Machine Learning Group at UiT The Arctic University of Norway, where he serves as research leader for interpretability at SFI Visual Intelligence. His work focuses on deep learning, with emphasis on explainability, uncertainty modeling, and learning with limited data. Deep Learning Explainable Artificial Intelligence Uncertainty Analysis Medical Image Analysis Small Data Learning His recent research trends include: Explainable AI for medical imaging and time series Uncertainty quantification in neural networks Physics-informed learning for PET imaging Representation learning for healthcare applications Robustness in clinical AI systems Sea ice forecasting with deep learning Collaborations include work with: Prof. Gustau Camps-Valls (University of Valencia) Prof. Marina M.-C. Höhne (Technical University of Berlin) Prof. Robert Jenssen (UiT) Prof. Michael Kampffmeyer (UiT)

• Life, Health and Medical Informatics
• Bioinformatics
• Genomics
• +۵ مورد دیگر

Takahiro Mimori is a Researcher at the Faculty of Science and Engineering , Waseda University. His work focuses on Life, Health and Medical Informatics with extensive contributions to genomic data analysis , phylogenetic inference , and computational methods for healthcare applications . Current affiliation: Waseda Research Institute for Science and Engineering Previous roles: RIKEN (2019-2025), Tohoku Medical Megabank Project His research spans algorithms for structural variant detection , HLA typing , and multiomics integration , particularly in maternal and cancer studies. Recent work includes computational frameworks for microdroplet barcode design and hyperbolic phylogenetic tree embeddings . Key tools developed: HLA-VBSeq, STR-realigner, AP-SKAT, iSVP Major projects: Tohoku Medical Megabank, Maternity Log Study His publications demonstrate expertise in machine learning applications to medical diagnostics and genomic variant analysis , with over 29 peer-reviewed papers and 796 Scopus citations.