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Maryclare GriffinView profile

Assistant Professor

• Statistical Inference
• Time Series Analysis
• Variable Importance
• +3 more

Maryclare Griffin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research develops statistical methods for complex data analysis, with emphasis on time series modeling, variable importance measures, Bayesian computation, and interpretable machine learning. Current methodological work includes adaptive overdifferencing for non-stationary processes, mixture representations for Bayesian simulation, and structured shrinkage priors for high-dimensional inference. Applications span clinical genomics, environmental monitoring, and biophysical trend analysis. Recent publications demonstrate innovations in time series model identification, optimization algorithms for regularized regression, and variable importance frameworks for nonlinear models. Collaborative work extends to metastasis prediction in melanoma and lightning strike pattern modeling. Holds a PhD in Statistics from the University of Washington and completed postdoctoral training at Cornell's Center for Applied Mathematics. Maintains active research in computational statistics and model interpretability.

Brian Van KotenView profile

Assistant Professor

• Applied Probability
• Numerical Analysis
• Computer Simulation of Molecular Systems
• +4 more

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning. Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths. Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.

Zhiang ChenView profile

Researcher

• Geophysics
• Robotics
• Machine Learning
• +3 more

Zhiang Chen is a Postdoctoral Scholar Research Associate in Geophysics at the California Institute of Technology (Caltech). His work bridges geophysical research with robotics and machine learning for environmental monitoring. Research interests include: Development of low-cost robotic tools (e.g., Shakebot) for seismic studies Integration of UAVs, lidar, and zero-shot deep learning for geologic mapping Analysis of fault scarps, precariously balanced rocks, and volcanic deformation Optimization of environmental monitoring via decision-theoretic and digital twin frameworks Recent publications highlight interdisciplinary applications of robotics and machine learning in geophysics, focusing on 3D mapping of surface deformations, wildfire-induced geohazards, and autonomous detection of geologic features.

Nicolas CharonView profile

Assistant Professor

• Morphological Shape Analysis
• Medical Imaging
• Computational Anatomy
• +7 more

Nicolas Charon is an Assistant Professor in the Department of Mathematics at the University of Houston. He holds a PhD in Mathematics from École normale supérieure de Cachan (2013) and completed postdoctoral research at the University of Copenhagen. His research focuses on geometric methods for medical imaging and computational anatomy, including diffeomorphic registration, functional shape analysis, and Riemannian metrics on curves/surfaces. He has been supported by the NSF DMS-1945224 CAREER Award and has developed software tools like fshapesTk , h2metrics , and ShapeGraph_H2match . Key research areas include varifold-based shape registration, metamorphoses of functional shapes, and sparse methods for diffusion MRI compression. Charon has advised three PhD students and contributed to over 50 publications. His work bridges theoretical mathematics with applications in healthcare, including stroke risk assessment via left atrial appendage morphology analysis and 4D human shape synthesis. Recent projects involve scalable frameworks for PDE solution operators, self-supervised learning of human body scans, and data-driven elastic shape analysis with topological constraints. Charon’s interdisciplinary approach integrates geometry, optimization, and machine learning to advance computational anatomy and medical imaging technologies.

Søren Munch LindhardView profile

Associate Professor

• Construction Management
• Sustainable Building Practices
• Lean Construction Processes
• +3 more

Søren Munch Lindhard is an Associate Professor and Head of Research Group at the Department of the Built Environment within the Faculty of Engineering and Science at Aalborg University. His work focuses on construction management, lean processes, and sustainable practices. He leads projects addressing waste management, worker wellbeing, and circular economy principles in construction. His research emphasizes improving productivity through better project management and risk mitigation, while integrating environmental considerations such as resource efficiency and circularity. Lindhard has contributed to over 60 publications since 2011, including peer-reviewed articles on musculoskeletal disorders in construction workers and exoskeleton adoption. He actively participates in editorial peer review for journals like the Journal of Construction Engineering and Management. Notable projects include optimizing waste systems through behavioral modeling (2025–2027) and investigating Kuwaiti construction workers' health reporting practices. Lindhard also supervises PhD students and collaborates with industry partners to advance sustainable building practices. He has been featured in media discussions addressing construction industry challenges, including bankruptcy prevention strategies and supply chain disruptions. His work bridges academic research with real-world applications, aiming to enhance both operational efficiency and worker safety in the construction sector.

Alexandros GelastopoulosView profile

Research Fellow

• Mathematical Modeling
• Probability Theory
• Stochastic Processes
• +4 more

Alexandros Gelastopoulos is a Research Fellow at the Institute for Advanced Studies in Toulouse with a PhD in Mathematics from Boston University (2019). His research employs mathematical modeling to study biological and social systems, focusing on probability theory, stochastic processes, and reinforcement dynamics in social contexts like cumulative advantage and ranking-based behaviors. Primary research interests include analyzing how popularity rankings influence social systems, studying brain oscillations for memory functions, and exploring decision-making paradoxes. His work often bridges mathematical rigor with behavioral sciences to uncover mechanisms driving social inequalities and neural computations. His publication trends show strong emphasis on social dynamics (e.g., lock-in effects, reinforcement processes) and computational neuroscience (e.g., neural rhythms, memory substrates), using stochastic modeling and empirical validation across disciplines. European Commission's Seal of Excellence (2024) Collaborates extensively with international researchers on projects involving mathematical sociology and behavioral experiments. Teaches game theory and mathematical courses while developing expository materials on real analysis and information theory.

Alina ChertockView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Conservation Laws
• +4 more

Alina Chertock serves as the Head of the Department of Mathematics at North Carolina State University (NCSU), where she leads academic and research initiatives in computational mathematics. Her work focuses on developing advanced numerical methods for hyperbolic conservation laws, fluid dynamics, and models involving uncertainty quantification. Chertock’s research integrates finite-volume, particle, and hybrid methods to address complex phenomena such as chemotaxis, magnetohydrodynamics, and shallow water systems. She has contributed to high-resolution schemes for stiff detonation waves and stochastic collocation techniques for nonlinear PDEs with uncertainties. Her research interests span computational fluid dynamics, numerical analysis, and applied mathematics, with applications in geophysical flows, multiphase systems, and biological models. Notable contributions include well-balanced path-conservative schemes for rotating flows and divergence-free flux methods for magnetohydrodynamics. Chertock collaborates actively with institutions on NSF-funded projects, including structure-preserving methods for atmospheric and shallow water models. Her publications emphasize adaptive algorithms, error mitigation in stochastic systems, and the integration of machine learning for wave equation analysis. Chertock’s work bridges theoretical developments with practical applications, addressing challenges in computational efficiency and accuracy across diverse scientific domains.

Johnny JakobsenView profile

Associate Professor

• Composite Materials
• Wind Turbine Engineering
• Structural Mechanics
• +3 more

Johnny Jakobsen is an Associate Professor in the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science. His research focuses on composite materials, wind turbine engineering, and structural mechanics, with a particular emphasis on failure phenomena in advanced composites and sandwich structures. He has contributed to projects addressing localized effects in sandwich components, automated composite manufacturing, and fatigue analysis. His work spans theoretical modeling, experimental validation, and industrial applications, including collaborations on optimizing draping processes for wind turbine blades and developing novel reinforcement methods for composite components. Jakobsen has authored or co-authored over 70 publications since 2005, with recent contributions focusing on automated manufacturing, composite reinforcement strategies, and material characterization under complex loading conditions. Notable projects include the Integrated Design and Processing of Lightweight Composite and Sandwich Structures and studies on peel stopper designs for sandwich beams. His research has been supported by industry partnerships and has led to patents, such as the Method of Manufacturing a Fibre Reinforced Composite Component . Jakobsen also serves on academic review committees and has been featured in media for contributions to structural engineering advancements.

LI YuchenView profile

Associate Professor

• Artificial Intelligence
• Data Mining
• Graph Mining
• +6 more

Yuchen Li is an Associate Professor at the School of Computing and Information System (SCIS) at Singapore Management University (SMU), where he also holds the Lee Kong Chian Fellowship for Research Excellence. He earned his Ph.D. in Computer Science from the National University of Singapore (NUS) in 2017. His research focuses on social analytics, high-performance graph mining, and fintech applications leveraging large language models (LLMs). He has led significant projects funded by MoE, including 'Next-Gen Competitive Intelligence' and 'CONQUEROR' for concurrent graph query processing. His academic contributions span influential publications in top-tier conferences like SIGMOD, KDD, and ICML, with notable works on graph algorithms, fraud detection, and knowledge graph robustness. He advises Ph.D. students and has supervised over a dozen researchers, including current students Xiao Hanhua, Wang Sha, and Ye Chang. His awards include the Lee Kong Chian Fellowship and the Best Demo Paper Award at CIKM 2021. Li’s research bridges theoretical foundations and practical applications, addressing challenges in graph processing, social media analysis, and fintech. His team develops systems like 'Dupin' for fraud detection and 'ThunderRW' for in-memory graph processing, showcasing expertise in GPU acceleration and scalable algorithms.

Sean ClearyView profile

Professor

• Geometric Group Theory
• Combinatorial Group Theory
• Spaces of Trees
• +4 more

Roles and Affiliations: Sean Cleary is a Professor of Mathematics at The City College of New York (CCNY) and a Graduate Faculty member at the CUNY Graduate Center. He serves as Assistant Chair and Major Advisor in the Department of Mathematics. His office is located in Marshak 301C, and he can be reached at scleary@ccny.cuny.edu . Education: Ph.D., Mathematics, University of California, Los Angeles A.B., Mathematics and Physics, Cornell University Research Interests: Cleary's research focuses on geometric and combinatorial group theory, spaces of trees, and algorithms for trees. He explores Thompson's groups and their properties, including rotation distances between trees and metric structures on tree spaces. His work often intersects computational mathematics and phylogenetics, with applications to algorithm design and geometric dynamics. Teaching: Recent courses include Calculus III with Vector Analysis (Math 213), Differential Geometry (Math 461/A6100), and advanced topics in group theory. He emphasizes rigorous mathematical training and pedagogical innovation, including the use of computational tools like Mathematica. Professional Contributions: Cleary is a member of the New York Group Theory Cooperative and contributes to the calculus.org editorial board. His research has been supported by collaborative efforts within the CUNY system, and he actively participates in academic service roles at CCNY.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +5 more

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Cristiano PalegoView profile

Senior Lecturer

• Microwave Engineering
• Biosensors
• Machine Learning Applications in Biology
• +6 more

Dr. Cristiano Palego is a Senior Lecturer in Microwave Instrumentation at the School of Computer Science and Engineering, Bangor University. His research bridges microwave engineering, biosensor development, and environmental monitoring, with a focus on insect telemetry and cancer cell analysis. Key affiliations: SUMCASTEC (EU-funded), KESS II (PhD projects), and collaborations with S&A Fresh Produce Ltd and Cardiff University. Research Interests span microwave biosensors for cellular analysis, machine learning in insect behavior classification, and electromagnetic interactions with cancer stem cells. His work addresses UN Sustainable Development Goals in environmental conservation and health innovation. Scientific Contributions include advancements in: Microwave-based bee-tracking systems Glioblastoma organoid exposure studies MEMS phase shifters for biomedical applications Dielectrophoretic cell discrimination Recent Publications (2022–2024) highlight trends in integrating machine learning with microwave and radar systems for: Honeybee behavior classification Automated pollination monitoring High-frequency cancer cell analysis Awards & Recognition include contributing to Bangor University's 2023 Queen's Anniversary Prize-winning research on societal impact. Supervision : Open to advising PhD students in microwave biosensing, entomology, and biomedical device development.

Chunxu LiView profile

Senior Lecturer

• Motion planning
• Human-robot interaction
• Cybernetics
• +3 more

Dr. Chunxu Li is a Senior Lecturer in Mechanical Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a PhD from Swansea University (2019) and has extensive experience in robotics, including expertise in ROS, C++, Python, and MATLAB. He has published over 40 academic papers, with 33 indexed in SCI/EI, and has been recognized with awards such as the Best Student Paper Award at IEEE conferences. His research focuses on motion planning, human-robot interaction, cybernetics, and soft robotics. Dr. Li's research interests include advanced control systems for robots, fault-tolerant manipulation, and the development of intuitive human-machine interfaces. His work integrates machine learning, neural networks, and sensor fusion to enhance robotic autonomy and precision. Notable projects include the Blue Hand soft robotic hand and studies on trajectory capture for KUKA iiwa robots. He has secured significant funding, including a £1.013 million grant for intergenerational technology co-creation. His teaching includes modules on electromechanical control and systems engineering. Awards include being an Associate Fellow of the Higher Education Academy and membership in IEEE. Dr. Li supervises PhD students in areas like neural network control for unmanned aerial vehicles and human-machine interaction systems. He collaborates on projects such as wearable rehabilitation robots and sensor fusion platforms for localization, demonstrating a strong commitment to interdisciplinary robotics research.

• Physical Chemistry
• Theoretical Chemistry
• Condensed Matter Physics
• +3 more

John Weeks is a Distinguished University Professor at the University of Maryland, affiliated with the Institute for Physical Science & Technology. His research focuses on theoretical and computational studies of solvation phenomena, interfacial chemistry, and non-equilibrium systems. Key areas include molecular field theory, ion solvation dynamics, hydrophobic effects, and surface electromigration. He investigates long-range forces in aqueous systems, the structural and thermodynamic properties of liquids at interfaces, and the development of advanced simulation methods for complex fluids. His work bridges statistical mechanics, condensed matter physics, and chemical theory to explain molecular-scale phenomena in both equilibrium and non-equilibrium settings. Recent contributions address crystal nucleation mechanisms, the role of distant boundaries in charged particle solvation, and the application of local molecular field theory to ionic systems. His research has implications for understanding biological solvation, materials science, and environmental chemistry. Notable trends in his publications include: (1) advancing multiscale models for long-range interactions in confined systems, (2) analyzing hydrophobic effects through molecular-scale structural changes in water, and (3) developing efficient computational frameworks for simulating Coulombic systems. While no specific awards or grants are listed here, his extensive publication record reflects sustained leadership in theoretical chemistry and materials science. He has advised numerous researchers through collaborative projects at the Institute for Physical Science & Technology.

Niko SuenderhaufView profile

Professor

• Robotic Vision
• Computer Vision
• Machine Learning
• +7 more

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.