جست‌وجوی استاد و پژوهشگر

Rosemary Willattمشاهده پروفایل

مدرس

• Sea Ice Physics
• Cryosphere Dynamics
• Climate Science
• +۴ مورد دیگر

Rosemary Willatt is a Lecturer in Experimental Ice and Rock Physics for the Environment at the University College London (UCL) Department of Earth Sciences. Her research focuses on sea ice dynamics in polar regions, combining laboratory experiments with fieldwork and remote sensing techniques. She leads projects involving satellite radar altimetry for monitoring snow and ice thickness, with a particular emphasis on advancing Earth Observation missions through the Polarimetric Synthetic Aperture Radar Altimeter (PoSARA) concept. She has been awarded the inaugural Konrad Steffen Award by ESA for her innovative work on snow depth estimation techniques. Willatt’s work integrates field campaigns (e.g., in the Arctic and Antarctic) with analysis of airborne, satellite, and ground-based radar data. She collaborates with space agencies to optimize satellite mission design and has pioneered novel methods for quantifying snow properties and their impacts on cryosphere processes. Her contributions include advancements in understanding brine migration in snow, L-band microwave scattering, and wind-driven snow redistribution effects on radar signatures. As Principal Investigator for the ESA-funded PoSARA project and Sea Ice Earth Observation at the Centre for Polar Observation and Modelling (CPOM), she bridges experimental physics with large-scale climate monitoring. Her research also emphasizes education, diversity, and sustainability initiatives within the scientific community.

• Life Cycle Engineering
• Ecodesign
• Sustainable Manufacturing
• +۴ مورد دیگر

Wim Dewulf is a full professor at the Faculty of Industrial Engineering Sciences, KU Leuven, and serves as the dean of the faculty. He is a contact person for the Manufacturing Processes and Systems (MaPS) unit at Campus Group T Leuven and holds leadership roles such as division head and member of councils like the University Council and Academic Council. His research focuses on life cycle engineering, ecodesign, sustainable manufacturing, and computed tomography applications in industrial processes. His research interests span sustainable engineering, additive manufacturing (AM), dimensional quality control, and X-ray CT. Recent projects include using deep learning for CT reconstruction, improving AM surface quality via laser remelting, and enabling autonomous demanufacturing of battery-containing products. He actively supervises students in these areas, particularly in laser powder bed fusion and CT metrology. Wim Dewulf is a member of Leuven.AM (KU Leuven Institute for Additive Manufacturing) and SIM² (Institute for Sustainable Metals and Minerals). His work involves advising on circular economy strategies, process optimization, and advanced imaging techniques, with no explicit scientific awards listed in the provided data. He has contributed to education through courses like Applied Sustainability Assessment and Life Cycle Engineering , emphasizing sustainable design and manufacturing. His research teams focus on technology transfer, industrial collaboration, and developing data-driven models for AM and recycling.

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

Mohammed El-Kebirمشاهده پروفایل

دانشیار

• Cancer Genomics
• Computational Biology
• Combinatorial Optimization
• +۴ مورد دیگر

Mohammed El-Kebir is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) , with affiliate appointments in Electrical & Computer Engineering, the Institute of Genomic Biology, and the National Center for Supercomputing Applications. His research focuses on combinatorial optimization algorithms for fundamental challenges in cancer genomics and computational biology , particularly tumor phylogenetics , single-cell sequencing data analysis , and metastasis migration history modeling . PhD in Computer Science, Centrum Wiskunde & Informatica (Netherlands) and VU University Amsterdam (2015) Postdoctoral training at Brown University and Princeton University (2014-2017) El-Kebir's work addresses intra-tumor heterogeneity by developing mathematical models to reconstruct evolutionary trees of cancer mutations from mixed sequencing data. His lab has pioneered methods like SPhyR and MACHINA for phylogeny inference under data limitations, and PhyDOSE for optimizing single-cell sequencing experiments. His research bridges computational challenges with clinical implications for personalized medicine and therapy resistance prediction . Key contributions include: Theoretical foundations for cancer phylogenetic problems (hardness proofs, algorithm design) Single-cell sequencing algorithms accounting for data sparsity and errors Migration graph frameworks for metastasis analysis Interactive tools like ClonArch and CNAViz for tumor data visualization El-Kebir has received multiple accolades including the NSF CAREER Award (2021) and CRII Award (2019) . His group has produced 15+ peer-reviewed publications since 2020, with student collaborators securing prestigious fellowships like the NSF GRFP . He actively contributes to conference program committees (RECOMB, ISMB, WABI) and teaches graduate-level courses in Bioinformatics and Computational Cancer Genomics .

Angel Changمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• 3D Scene Understanding
• +۳ مورد دیگر

Angel Chang is a Hans Fischer Fellow at the Technical University of Munich (TUM), hosted by Prof. Matthias Nießner in the Visual Computing Focus Group. She holds affiliations with Simon Fraser University and Eloquent Labs. Her research bridges natural language processing with 3D scene understanding, focusing on grounding textual descriptions to 3D data through innovations like ScanNet and ShapeNet. Key contributions include dense captioning, object localization in RGB-D scans, and unified vision-language models. Her work addresses challenges in 3D scene reconstruction, CAD model alignment, and interactive environments. Notable projects include SAPIEN for simulated part-based interactions and PartNet, a benchmark for hierarchical 3D object understanding. Chang's research aims to advance AI systems that interpret and generate complex 3D environments using multimodal data. Publications span top venues like CVPR and ACM Transactions on Graphics, demonstrating impact in computer vision, robotics, and machine learning. She collaborates across institutions to develop datasets and frameworks that drive progress in digital scene modeling and human-AI collaboration.

Ira Assentمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Clustering Algorithms
• +۵ مورد دیگر

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

Stefan Rothمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Semantic Scene Understanding
• +۳ مورد دیگر

Stefan Roth is a Professor of Computer Science at Technische Universität Darmstadt, where he leads the Visual Inference Lab. His research focuses on statistical models for visual inference problems, with expertise in computer vision, machine learning, and deep learning applications. Specific areas include semantic scene understanding, image motion estimation, and probabilistic models for low-level vision tasks. Key research areas: Semantic scene understanding and segmentation Optical flow and scene flow estimation Deep learning architectures for vision Probabilistic modeling in computer vision Video analysis and understanding Professor Roth's recent publications explore transformer-based scene graph generation, unsupervised segmentation techniques, and foundation models for depth estimation. His work demonstrates strong interdisciplinary connections between computer vision, machine learning, and autonomous systems, with applications in urban scene understanding and intelligent transportation systems.

Rainer Dyckerhoffمشاهده پروفایل

استاد

• Multivariate Statistics
• Data Depth
• Central Regions
• +۳ مورد دیگر

Rainer Dyckerhoff is an apl. Professor at the Department of Economic and Social Statistics within the WiSo Faculty (Faculty of Management, Economics and Social Sciences) at the University of Cologne. He holds a PhD in Economics (Dr. rer. pol.) from Helmut Schmidt University Hamburg (1994) and a Diploma in Mathematics from Technical University Darmstadt (1988). His research focuses on multivariate statistics, data depth, computational statistics, and decision theory. He has held academic positions at the University of Cologne since 1995, including roles as Scientific Assistant (1995–2001), Academic Council member (2001–2006), and Akademischer Oberrat (since 2006). His work emphasizes methodological advancements in statistical depth functions, central regions, and computational algorithms for multivariate data analysis. Notable contributions include exact computation of halfspace depth and depth-based nonparametric tests. His publications span top journals like Computational Statistics and Data Analysis and Annals of the Institute of Statistical Mathematics . No scientific awards are explicitly mentioned in the provided text. Rainer Dyckerhoff has been affiliated with the Department of Economics at Helmut Schmidt University Hamburg during his early career (1989–1995). His current role combines research leadership and academic service in statistical methodology.

Torben Petersمشاهده پروفایل

مدرس

• 3D Computer Vision
• Deep Learning
• Generative Models
• +۲ مورد دیگر

Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.

• Statistical Data Analysis
• Computer Vision
• Digital Health
• +۵ مورد دیگر

Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.

Jonatan Langletمشاهده پروفایل

پژوهشگر ارشد

• Computer Networking
• Network Telemetry
• Programmable Hardware
• +۴ مورد دیگر

Jonatan Langlet is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Software and Computer Systems (SCS) and the Network Systems Lab (NSLab). Holding a Digital Futures fellowship, his work bridges programmable hardware, network telemetry, and machine learning, with a focus on high-speed monitoring and security technologies embedded in network switches and cards. Education: PhD in Computer Science (2024) from Queen Mary University of London, supervised by Prof. Gianni Antichi Teaching: Distributed Systems (QMUL 2021-2022), Data Structures and Algorithms (Karlstad 2019) Research spans three main areas: In-Network Intelligence : Developing machine learning inference capabilities within programmable switches (P4), optimizing neural networks for hardware constraints Telemetry Systems : Creating RDMA-based telemetry pipelines for zero-CPU, line-rate data collection (Direct Telemetry Access, ACM SIGCOMM 2023) 5G Network Innovation : Designing programmable pipelines for 5G user plane functions (IEEE TMC 2022) His publications in top venues like ACM SIGCOMM, SIGMETRICS, and HotNets demonstrate technical depth in programmable hardware, while community roles (TPC member, reviewer) highlight academic engagement. The Digital Futures fellowship supports his ongoing work on large-scale monitoring technologies.

• Machine Learning
• Statistical Learning Theory
• Statistical Science
• +۳ مورد دیگر

Goki Yasuda is an Associate Professor at the Center for Data Science, Waseda University. His research spans statistical science, intelligent informatics, and machine learning, with specialized interests in label noise robustness, semi-supervised learning, and asymptotic analysis of algorithms. He teaches extensively in data science education, including courses like 'Statistics Literacy', 'Data Analysis with Python', and 'Modeling for Data Science' through Waseda's Global Education Center. His research explores foundational machine learning challenges, including: Theoretical analysis of classification under label noise conditions Performance limits of semi-supervised learning with unlabeled data Bayesian inference for misspecified probabilistic models Algorithm optimization for real-world data imperfections Publications demonstrate a strong emphasis on theoretical machine learning (75% of recent work) with secondary focus on applied signal processing for video coding (25% of earlier publications). Research consistently employs statistical methods to address data quality issues in learning systems. He leads research projects including: 'Developing machine learning algorithms for low-quality data' (2022) 'Research on machine learning from data of various quality' (2019) 'Performance evaluation of prediction in half-ranked learning' (2018)

Panagiotis PAPADAKISمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• 3D Shape Analysis
• +۴ مورد دیگر

Panagiotis PAPADAKIS is an Associate Professor (HDR) in Informatics & Robotics at IMT Atlantique Bretagne/Pays de la Loire, Department of Informatics. He leads the RAMBO team at CNRS Lab-STICC and is an associate member of the IRL CROSSING. His research focuses on cognitive robotics, computer vision, and artificial intelligence with applications in assistive robotics, urban search and rescue (USAR), and 3D environment analysis. He has over €1M in research funding and has co-authored numerous top-tier publications in computer vision and robotics journals/conferences. Educations: PhD in Information Technology, National University of Athens (2009) Informatics and Telecommunications Degree, National University of Athens (2005) Research Interests: Robot Control and Navigation 3D Shape Analysis & Matching Urban Search and Rescue (USAR) Robotics Computer Vision for Assistive Technologies Cognitive Robotics and Human-Robot Interaction Key Projects: Leading RAMBO team in CNRS Lab-STICC Coordinated multi-robot assistance systems for smart environments Development of digital twin-driven smart home solutions Teaching: Postgraduate courses in Computer Vision/Perception at University of Rome Programming courses at ENSTA ParisTech Undergraduate Computer Graphics at University of Athens

Yeonjong Shinمشاهده پروفایل

استادیار

• Applied and Computational Mathematics
• Artificial Intelligence
• Scientific Machine Learning
• +۶ مورد دیگر

Yeonjong Shin is an Assistant Professor of Mathematics at North Carolina State University (NC State), with prior tenure-track roles at KAIST (South Korea) and Brown University (USA). His research focuses on applied and computational mathematics, particularly in artificial intelligence, scientific machine learning, and numerical methods for partial differential equations. He holds a Ph.D. in Mathematics from The Ohio State University (2018) and dual B.S. in Mathematics and B.A. in Economics from Yonsei University (2013), with military service as a KATUSA (2009–2011). Shin’s expertise includes approximation theory, numerical optimization, and uncertainty quantification. He leads research in AI-driven scientific computing, reduced order modeling, and physics-informed neural networks. His work bridges computational mathematics and machine learning, addressing challenges in data-driven dynamical systems and high-dimensional function approximation. He has published extensively in top journals like SIAM Journals, Neural Networks, and Comput. Methods Appl. Mech. Eng. Notable contributions include thermodynamics-informed latent space dynamics (tLaSDI), S-OPT hyper-reduction algorithms, and error analysis for neural network-based PDE solvers. Shin has taught courses at NC State, KAIST, and Brown, including statistical inference, numerical optimization, and computational linear algebra. He actively participates in conferences such as SIAM CSE, ICIAM, and KSIAM, and has delivered invited talks at institutions worldwide.

Kelly Ramsayمشاهده پروفایل

استادیار

• Statistics
• Nonparametric Statistics
• Robust Statistics
• +۹ مورد دیگر

Kelly Ramsay is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science, Toronto, Canada. Her research is focused on developing nonparametric and robust statistical tools for complex data, with a strong emphasis on differential privacy, functional data analysis, and high-dimensional statistics. She bridges theoretical statistics with real-world applications and computational implementation. Education: PhD in Statistics, University of Waterloo (2018–2022) MSc in Statistics, University of Manitoba (2016–2018) BSc (Honours) in Statistics and Actuarial Science, University of Manitoba (2012–2016) Her research interests include differential privacy, robust inference, functional data, changepoint detection, and data depth. She develops methods that are both theoretically sound and computationally feasible, with applications in f-MRI and privacy-preserving data analysis. Her work often integrates simulation studies and real data applications, reflecting her background in statistical consulting and technical analysis. The recent trend in her publications centers on differentially private statistical methods, particularly for multivariate and functional data. She has made significant contributions to differentially private medians, boxplots, scale testing, and changepoint detection, leveraging data depth and robust estimation techniques. Her work combines theoretical guarantees with practical utility in sensitive data environments. Scientific Awards and Funding: NSERC Discovery Grant (2023) NSERC Launch Supplement (2023) Canada Graduate Scholarship - Doctoral (NSERC, 2019) Sprott Scholarship, University of Waterloo (2021) Canada Graduate Scholarship - Masters (NSERC, 2016) SSC Student Presentation Award (2017) Outstanding Research by an M.Sc. Student, University of Manitoba (2019) Kelly Ramsay has extensive experience in statistical consulting and applied data analysis, having worked at the University of Waterloo and Bison Transport. She has contributed to R packages and large-scale web scraping projects. She actively presents her research at major conferences such as ICORS, SSC, JSM, and SIAM/CAIMS. She has collaborated with researchers including Shoja Chenouri, Dylan Spicker, and Aukosh Jagannath. There is no indication of advising graduate students yet, but her collaborative research suggests strong mentorship and teamwork. She is involved in academic seminars and research days at institutions such as McGill University and through CANSSI. Her work is disseminated via arXiv, peer-reviewed journals, and conference proceedings, highlighting her active role in the statistical research community.