جست‌وجوی استاد و پژوهشگر

Bingzhang Chenمشاهده پروفایل

مدرس ارشد

• Biological oceanography
• Theoretical Ecology
• Ecosystem modeling
• +۹ مورد دیگر

Bingzhang Chen is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. He previously held positions as a Chancellor’s Fellow at the same institution, a researcher at the Japan Agency of Marine-Earth Science and Technology (JAMSTEC), and was affiliated with Xiamen University and Mount Allison University. His academic journey began with a PhD from the Hong Kong University of Science and Technology. Education: PhD in Trophic interactions within the microbial food web, Hong Kong University of Science and Technology (Awarded 2009) His primary research interests lie at the intersection of biological oceanography and theoretical ecology, with a strong focus on ecosystem modeling. He investigates how biodiversity, particularly of phytoplankton, influences marine ecosystem functioning such as primary production and the biological carbon pump. A central theme in his work is understanding the differential temperature sensitivity between autotrophs and heterotrophs, a question that bridges statistical analysis, metabolic theory, and Earth system science. His recent publications highlight a consistent trend in developing and applying individual-based models (e.g., PIBM 1.0), analyzing large datasets on plankton thermal responses, and studying the impacts of climate change and anthropogenic activities (like nutrient input) on marine microbial communities across diverse regions from the South China Sea to the North Pacific and Scottish coastal waters. His work often combines modeling with observational data to address fundamental ecological questions. Scientific Awards: David Cushing Prize (2015) from the Journal of Plankton Research New Century Excellent Talent (2012) from the Ministry of Education of China Dr. Chen is actively involved in research supervision, currently guiding five PhD students. He has been the Principal Investigator on multiple research projects funded by organizations such as the Leverhulme Trust, FILAMO, and the National Science Foundation. His expertise in programming (R, Fortran, MATLAB) underpins his methodological approach. He also contributes to the scientific community as an Associate Editor for the prestigious journal Limnology and Oceanography . His work is associated with efforts to understand and model invasive species dynamics, such as the spread of Sargassum muticum in Scottish waters, and he is involved with external advisory groups like the MASTS Marine Artificial Intelligence Forum.

• Robotic Systems
• Intelligent Computing
• Artificial Intelligence
• +۳ مورد دیگر

Nuno Miguel Fonseca Ferreira is a Full Professor at the Instituto Superior de Engenharia de Coimbra (ISEC), part of the Polytechnic of Coimbra, where he currently serves as President of the Scientific Council. His academic career spans over 25 years at ISEC, progressing from Assistant to Professor Coordenador Principal. He has held significant leadership positions including Vice-President of ISEC (2001-2005), Pro-President of the Polytechnic of Coimbra (2009-2010), President of ISEC (2010-2013), and Vice-President of the Polytechnic of Coimbra (2013-2017), where he was responsible for internationalization initiatives. His educational background includes a degree in Electrical Engineering from the University of Porto (1996), a Doctorate in Electrical Engineering from the University of Trás-os-Montes and Alto Douro (2006), and a Habilitation Title (Aggregation) from the same institution (2020). His research focuses on Robotic Systems, with specialization in cooperative robotic systems as evidenced by his Habilitation work. Professor Ferreira's research spans multiple domains of robotics and intelligent systems, with particular emphasis on multi-robot coordination, environmental applications, and medical robotics. His work bridges theoretical control systems with practical applications across diverse fields including forestry, healthcare, manufacturing, and education. He has developed innovative approaches to robotic manipulation, sensor integration, and human-robot interaction, often incorporating advanced techniques from artificial intelligence and machine learning. His recent publications demonstrate a strong trend toward practical applications of robotics in real-world environments, particularly in forestry maintenance, industrial automation, and medical applications. The research shows progression from theoretical control systems to applied robotics in challenging environments, with increasing integration of computer vision, deep learning, and collaborative systems. His work spans both fundamental robotics research and immediate industrial applications, reflecting a balance between academic inquiry and practical implementation. Professor Ferreira has supervised two doctoral theses and participated in numerous research projects with substantial funding. His leadership extends to coordinating 15 of the 33 national and international R&D projects he has participated in, demonstrating significant grant acquisition and management capabilities. His international collaborations through Erasmus+ and other European programs highlight his role in fostering global research partnerships. He is an integrated member of GECAD (Research Group in Engineering and Intelligent Computing for Innovation and Advanced Development), a Portuguese R&D unit classified as Excellent by the Portuguese Science and Technology Foundation. Additionally, he is a member of LASI (Associated Laboratory for Intelligent Systems), the Portuguese laboratory associated with Artificial Intelligence, connecting him to a broader national research ecosystem.

Shiguo Jiangمشاهده پروفایل

دانشیار

• Geospatial Information Technologies
• Land Use/Land Cover Change
• Forest Ecosystem Dynamics
• +۴ مورد دیگر

Shiguo Jiang is a tenured Associate Professor in the Department of Geography, Planning, and Sustainability at the University at Albany's College of Arts and Sciences. Currently on sabbatical during Fall 2025, he maintains active research leadership through the Spatial Analysis Laboratory. His affiliations extend to the PhD Program in Information Science (College of Emergency Preparedness, Homeland Security and Cybersecurity) and the MS Program in Biodiversity, Conservation, and Policy (Department of Biological Sciences), reflecting his interdisciplinary approach. Dr. Jiang's educational background includes a PhD from Ohio State University (2012), an MS from Peking University (2004), and dual BS/BA degrees from Peking University (2001). His research integrates geospatial technologies with spatial statistics to investigate land use dynamics, forest ecosystems, and human-environment interactions. Key application areas include urban systems analysis, climate change modeling, and crime pattern detection, with notable fieldwork in the Maya Forest of Belize. His publication portfolio reveals an evolution from urban fractal analysis (early Beijing studies) toward integrated socio-ecological research, with recent emphasis on crime geospatial modeling and climate-ecosystem interactions. The 15 most recent articles demonstrate methodological innovation in remote sensing reconstruction and spatial statistics, applied across environmental, criminological, and public health domains. Dr. Jiang actively mentors graduate students across departments including Geography, Sociology, Information Science, and Criminal Justice. While currently unable to fund Graduate Research Assistants, he collaborates with students who secure external funding. His Spatial Analysis Laboratory requires technical proficiency in programming (Python/R/Matlab) and geospatial software, welcoming undergraduates through postdoctoral researchers with relevant skills.

• Hydrology
• Hydrogeology
• Climate Change Impact
• +۳ مورد دیگر

Nathan Young is an Assistant Professor in the Department of Sustainable Resources Management at SUNY College of Environmental Science and Forestry (ESF). His research focuses on physical hydrology and environmental science, with a particular emphasis on understanding how heat, water, and solutes move through the environment in response to climate change and landscape alterations such as permafrost thaw, sea-level rise, and environmental hazards like wildfires and landslides. He integrates fieldwork, numerical modeling, and quantitative analysis to improve predictive models for sustainable water management. Education includes a Ph.D. in Geology and Environmental Science (2019, Iowa State University), an M.S. in Earth and Environmental Science (2014, Wright State University), and a B.A. in Geology and Sociology/Anthropology (2012, Earlham College). Research interests span hydrogeology, climate change impacts, permafrost dynamics, and groundwater flow modeling. His work often involves advanced tools like MATLAB, R, Python, and numerical models such as MODFLOW and HydroGeoSphere. He emphasizes fieldwork in student mentorship, valuing hands-on experience and quantitative skills. Teaching includes courses like Watershed Hydrology (FOR 340) and Watershed Ecology and Management (FOR 442). Current advisee Ford Ford is pursuing an MS in Environmental Science. Awarded funded graduate assistantships and undergraduate research positions are available to qualified candidates with academic excellence, research experience, and enthusiasm for hydrological studies. His lab focuses on cryohydrogeological systems, particularly in Arctic regions like Nunavik, Québec, and investigates the interplay between thermal regimes, permafrost degradation, and subsurface hydrology.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Ole Madsenمشاهده پروفایل

استاد

• Robotics
• Automation
• Industry 4.0
• +۶ مورد دیگر

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Giles Hookerمشاهده پروفایل

استاد

• Machine Learning
• Functional Data Analysis
• Differential Equations
• +۹ مورد دیگر

Giles Hooker is a Professor of Statistics and Data Science at the Wharton School, University of Pennsylvania, with his office located at 409 Academic Research Building, 265 South 37th Street, Philadelphia, PA 19104. He teaches courses including STAT4700 (Data Analy & Stat Comp), STAT5030, STAT9916 (Sem in Adv Appl of Stat), STAT9950 (Dissertation), and STAT9999 (Independent Study). Dr. Hooker's research focuses on the interfaces of statistical inference with models from machine learning and applied mathematics. His primary interests include Machine Learning , Functional Data Analysis , Differential Equations , Computational Statistics , and Statistical Ecology . He specializes in uncertainty quantification in random forests and ordinary differential equations models, as well as robust statistics. Much of his work is application-driven, particularly in ecology, epidemiology, environmental sciences, and medicine. His recent publications (2021-2024) reveal a strong emphasis on interpretable machine learning, statistical stability in model distillation, and ecological modeling. He has developed significant software packages including fda for functional data analysis in R and Matlab, and CollocInfer for fitting differential equations to data, complete with extensive documentation. Dr. Hooker has presented at conferences including the Conference on Applied Statistics in Ireland (2023) where he delivered a short course on Inference and Interpretation in Machine Learning. His methodological contributions span from profiled estimation of differential equations to functional data analysis techniques.

• Brain-Computer Interfaces
• Electroencephalography (EEG)
• Biomedical Engineering
• +۷ مورد دیگر

Jane E. Huggins is a Research Professor in the Department of Biomedical Engineering and Physical Medicine and Rehabilitation at the University of Michigan. Her work focuses on developing EEG-based brain-computer interfaces (BCIs) into clinical tools for individuals with physical impairments, addressing challenges like signal processing, task design, and in-home technical support. University of Michigan Appointments: Biomedical Engineering (2024–Present), Physical Medicine and Rehabilitation (2024–Present) Research Interests: Jane’s research bridges BCIs with augmentative and alternative communication (AAC) systems, emphasizing accessibility, commercial readiness, and integration with established clinical infrastructure. Her projects explore event-related potentials (P300), neural prostheses, and user-centered design for equitable healthcare technologies. Grant Funding Highlights: She has secured major grants from NIH, NSF, General Motors, and Cerebral Palsy Alliance Research Foundation to advance BCI applications in accessibility, autonomous vehicles, and lifelong assistive technology deployment. Professional Contributions: Jane actively presents at international BCI conferences and collaborates with the UMDBI Lab on clinical trials and software innovations for BCI systems.

• Asymptotic Statistics
• Bayesian Variable Selection
• Nonparametric Inference
• +۳ مورد دیگر

Minerva Mukhopadhyay is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. She earned her PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata, and completed postdoctoral work at Duke University under Professors David Dunson and Sandeep Dave. Her research focuses on Asymptotic Statistics, Bayesian Variable Selection, and Nonparametric Inference, with applications to high-dimensional data analysis. Her academic work includes developing distribution-free tests for high-dimensional data, consistency analysis of Bayesian variable selection methods, and novel approaches for sparse linear models. She has contributed to journals like Biometrika , Annals of the Institute of Statistical Mathematics , and Statistica Sinica , reflecting her expertise in theoretical and computational statistics. Research Trends: Her recent articles emphasize scalable Bayesian methods for ultra-high-dimensional data, random projections for predictive modeling, and nonparametric extensions using Gaussian processes. Scientific Awards: Best Student Paper Award (IISA 2015) Pillar Iglesias Travel Award (ISBA 2016) O’Bayes Travel Award (2017) Academic Roles: Previously taught at Bethune College (Kolkata) and Duke University, with experience in interdisciplinary statistical research at ISI. Skills: C, R, MATLAB, and journal reviewing for TEST , JSPI , Stat , and CSDA .

Simon Christمشاهده پروفایل

پژوهشگر

• Computational Biology
• Biophysics
• Reproducible Science
• +۴ مورد دیگر

Dr. Simon Christ is a Researcher at the Department of Computational Biology within the Institute of Cell Biology and Biophysics at Leibniz University Hannover's Faculty of Natural Sciences. His office is located in Building 4160, Room 110 at Herrenhäuser Str. 2, 30419 Hannover. He can be contacted via phone (+49 511 762 16174) or email (christ@cell.uni-hannover.de). Christ holds a Doctor rerum naturalium (PhD) from the University of Potsdam (2020) and a Master of Science from Humboldt University of Berlin (2014). His doctoral research focused on morphological transitions of vesicles under nonuniform conditions, while his master's thesis examined non-Markovian systems in statistical physics. His research focuses on computational approaches to biological systems, including: Development of reproducible scientific workflows and open-source tools Agent-based simulations of biological processes Numerical solutions of differential equations in biological contexts Computational modeling of membrane dynamics and vesicle behavior Research software engineering practices and tools He is proficient in multiple programming languages including Julia, C/C++, MATLAB, Mathematica, Python, and R, and is a certified instructor for The Carpentries. Christ's recent publications demonstrate a strong focus on research software engineering, computational biology methods, and biophysical modeling of cellular systems. His work frequently appears in computational biology, biophysics, and software engineering journals, with consistent contributions to open science practices and reproducible research methodologies. No specific awards or student advising relationships are mentioned in the available documentation. His current work appears to be conducted within the computational biology research group at Leibniz University Hannover, with collaborations extending to other institutions including the University of Potsdam and Max Planck Institute.

Dionissios T. Hristopulosمشاهده پروفایل

استاد

• Geostatistics
• Spatial Random Fields
• Spartan Spatial Random Fields
• +۱۵ مورد دیگر

Dionissios T. Hristopulos is a Professor and Head of the Geostatistics Laboratory at the School of Mineral Resources Engineering, Technical University of Crete, Greece. His research spans geostatistics, spatial random fields, environmental modeling, stochastic hydrology, and porous media mechanics. He has developed innovative Spartan Spatial Random Field (SSRF) models rooted in statistical physics, enabling efficient spatial interpolation and simulation. PhD in Physics, Princeton University (1991) MA in Physics, Princeton University (1988) Diploma in Electrical Engineering, National Technical University of Athens (1985) Hristopulos' research interests focus on the development and application of geostatistical methods in mineral resources, environmental monitoring, petroleum reservoirs, and GIS. He investigates spatial anisotropy, groundwater dynamics, earthquake return times, and the mechanical properties of heterogeneous materials. His work bridges statistical physics and geostatistics, particularly through SSRF models and renormalization group methods for upscaling transport properties in porous media. The most recent publications highlight his focus on geometric anisotropy detection in environmental data, non-parametric estimation methods, and applications of Spartan random fields in environmental time series and spatial data interpolation. His work increasingly integrates machine learning concepts with geostatistical modeling, especially for automatic mapping and ecological monitoring using remote sensing. Marie Curie success story (European Commission, 2010) for SPATSTAT project Hristopulos has secured research funding from national and EU programs, including the Marie Curie Transfer of Knowledge (SPATSTAT) and the INTAMAP STREP project. He has mentored several Master’s and PhD students, including Manos Varouchakis (PhD candidate on groundwater monitoring) and Manolis Petrakis (Master’s on anisotropy characterization). He collaborates with researchers in the US, France, Slovakia, and the UK. He serves on the editorial board of Stochastic Environmental Research and Risk Assessment and has contributed software tools for anisotropy detection in MATLAB and R. His research group, the Geostatistics Laboratory at TUC, focuses on machine learning and geostatistics, developing computational tools for environmental data analysis, spatial interpolation, and simulation. The lab emphasizes practical applications in hydrology, ecology, and mineral resources, supported by strong theoretical foundations in statistical physics and stochastic modeling.

Aurélien Garivierمشاهده پروفایل

استاد

• Machine Learning
• Statistics
• Sequential Decision Making
• +۵ مورد دیگر

Aurélien Garivier is a Professor at Ecole Normale Supérieure de Lyon , affiliated with the UMPA and LIP laboratories. He serves as Deputy Director of UMPA and leads the Master 2 Fundamental Computer Science program at ENS Lyon. His research and academic activities span multiple domains within Machine Learning , Statistics , and Sequential Decision Problems . Local chair of COLT 2025 in Lyon Co-organizer of workshops on Graph Representation Learning and Reinforcement Learning Former director of the Department of Mathematics at the Faculty of Science and Engineering (2016-2018) He has contributed to comparative studies of programming languages for statistical tasks, such as the Baum-Welch algorithm speed analysis in R/Python/Matlab, and co-authored the Perfect Simulation of Processes with Long Memory (2015), which introduced CIAFTP algorithms for stationary processes with transition kernels. His leadership extends to organizing educational initiatives like the UT3-INSA-UT1 Big Data Challenges and Enter the World of AI exhibition. He is also co-host of the SciDoLySE group (Data Science in Lyon and Saint-Etienne).

Stilian A. Stoevمشاهده پروفایل

استاد

• Stochastic Processes
• Time Series Analysis
• Long-range Dependence
• +۴ مورد دیگر

Stilian A. Stoev is a Professor of Statistics and serves as Bridge Program Director and Associate Director of the PhD Program at the University of Michigan's Department of Statistics. He holds a Ph.D. in Mathematics and Statistics from Boston University (2005) and an M.Sc. in Mathematics from Sofia University (1998). His research focuses on stochastic processes, long-range dependence, heavy-tailed distributions, and extreme-value theory with applications in finance, oceanography, and computer networks. He teaches courses such as Statistics 412 and has contributed to methodological advancements in statistical inference for stochastic processes. Research Interests: His work addresses time series analysis, limit theorems for Markov chains, long-range dependence modeling, and the estimation of Hurst parameters using wavelets. He explores applications in stochastic finance and communication networks, emphasizing both theoretical developments and practical implementations through computational tools like MATLAB and R. Grants & Advising: As a program director, he oversees academic and research initiatives. His advising responsibilities include mentoring PhD and Master's students in statistical theory and applications. No specific grants are detailed in the provided texts. Labs/Teams: No specific laboratory affiliations are mentioned, though his research involves interdisciplinary collaborations typical in statistical methodology development.

Markos Konstantakisمشاهده پروفایل

پژوهشگر ارشد

• Augmented Reality
• Virtual Reality
• Digital Culture
• +۷ مورد دیگر

Markos Konstantakis is a PhD candidate at the Department of Cultural Technology and Communication, University of the Aegean, focusing on Augmented Reality (ACUX thesis). He holds a Master’s in Information Systems from the Hellenic Open University and degrees in Marine Mechanical Engineering from NTUA. As a Research Fellow-Lecturer at the University of Athens, he teaches courses like Network Engineering, MATLAB, and Digital Culture under the e-learning program. He also works as a Research Assistant at Panteion University under the ELKE research fund. His research spans Virtual/Augmented Reality, Digital Culture, Cultural Heritage Management, and Human-Computer Interaction. Key research interests include: Development of digital twins for cultural spaces using photogrammetry and laser scanning Augmented Reality applications for cultural tourism and education Personalized visitor experiences through frameworks like ACUX and AI-driven recommendation systems Integration of IoT and serious games in cultural heritage preservation His recent work emphasizes: Smart Glasses for cultural heritage navigation OpenLab initiatives for archaeological conservation (e.g., Ancient Tiryns) Recommender systems leveraging LLMs like ChatGPT for museums Design of mobile interfaces for cultural tourism optimization Collaborations include projects like TRACCE (content personalization), CHATS (tangible interfaces), and TRIPMENTOR (gamification in tourism). His contributions bridge technological innovation with cultural preservation, emphasizing user-centric design approaches.

Ilse C.F. Ipsenمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +۸ مورد دیگر

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.