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Eric DarveView profile

Professor

• Machine Learning
• Numerical Linear Algebra
• High-Performance Computing
• +5 more

Eric Darve is a Professor of Mechanical Engineering and Director of the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning for science/engineering, numerical linear algebra, high-performance computing, and GPU-accelerated algorithms. He holds a PhD in Applied Mathematics from Pierre et Marie Curie University (2009), and completed postdoctoral work at Stanford/NASA Ames before joining Stanford’s faculty in 2001. Education: PhD, Applied Mathematics, Paris VI University (1999) MS, Applied Mathematics, Paris IX University (1994) BS, Mathematics & Physics, Paris VI University (1993) Research interests include: Machine learning for scientific applications Surrogate/Reduced Order Modeling Anomaly detection in engineering systems Parallel computing and numerical algorithms Physics-informed learning machines Awards include: H. Julian Allen Award (NASA, 2010) Habilitation à Diriger des Recherches (France, 2007) Leslie Fox Prize in Numerical Analysis (2001) James H. Clark Faculty Scholar (2001) Led development of scalable linear solvers and hybrid physics-data approaches. Active in computational engineering education and interdisciplinary research initiatives through ICME.

Tianhai TianView profile

Associate Professor

• Computational Biology
• Computational Statistics
• Multi-scale Modelling
• +4 more

Tianhai Tian is an Associate Professor at the School of Mathematics, Monash University. He holds a PhD in Computational Mathematics from the University of Queensland (2001), and has held prestigious positions including Australian Research Fellow at ARC, Lord Kelvin Fellow at University of Glasgow, and ARC Future Fellow. His research focuses on mathematical modeling of biological systems, with expertise spanning computational biology, statistical inference, and multi-scale modeling of complex networks. Research Interests: Mathematical modeling of genetic regulatory networks and cell signalling pathways Computational approaches to cancer therapy optimization Stochastic modeling of biochemical systems Network inference from high-dimensional biological data Multi-scale modeling approaches for complex biological systems His publication trends demonstrate strong focus on cancer modeling, computational immunology, and development of novel algorithms for biological data analysis. Recent works increasingly integrate machine learning with dynamical systems modeling. Awards and Honors: Australian Research Fellowship, ARC (2006) Lord Kelvin Fellowship (2007) ARC Future Fellowship (2011) Research Grants: Stochastic modelling of telomere length regulation in ageing research (ARC, 2012-2017) Stochastic modelling of genetic regulatory networks with burst process (ARC, 2011-2016) Multiscale stochastic modelling of tumour robustness (ARC, 2010-2016) Spatio-temporal modelling of Ras dependent MAP kinase activation (ARC, 2007-2011)

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +4 more

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

• Complex Networks
• Neuroscience
• Functional Brain Connectivity
• +3 more

Tommaso Gili is an Assistant Professor at the IMT School for Advanced Studies Lucca, specializing in interdisciplinary research at the intersection of network science, neuroscience, and biomedical engineering. His work focuses on analyzing complex systems through network theory and advanced imaging techniques, with emphasis on brain connectivity, functional networks, and their disruptions in neurological disorders. Key research areas include: Functional and structural brain network integration Multi-scale Laplacian analysis in heterogeneous systems Applications of network theory to medicine (e.g., schizophrenia, epilepsy, inflammatory bowel disease) Development of novel methods for network coarse-graining and renormalization His research leverages fMRI, MRI, and computational modeling to study: Cerebral blood flow dynamics Neurological connectivity alterations Biomedical signal processing Complex disease biomarker discovery Notable contributions include innovations in: Functional connectivity analysis of C. elegans Topological symmetry-breaking in brain networks Machine learning approaches for brain digital twins

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +3 more

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

Ming MaView profile

Assistant Professor

• Artificial Intelligence
• Medical Imaging
• Large Language Model
• +3 more

Ming Ma is an Assistant Professor in the Department of Graduate Computer Science and Engineering at the Katz School of Science and Health, Yeshiva University. He leads the Medical Imaging and Artificial Intelligence lab. He holds a Ph.D. in Computer Science from Stony Brook University and completed a postdoctoral fellowship at Stanford University. His research focuses on integrating artificial intelligence with medical imaging, particularly in areas like medical image segmentation, deep learning applications in healthcare, and geometric modeling for biomedical systems. Education: Ph.D. in Computer Science, Stony Brook University, USA Postdoctoral Scholar, Stanford University, USA Research Interests: His work spans Artificial Intelligence , Medical Imaging , Large Language Models , Geometric Modeling , and Computer Vision . He develops novel algorithms for medical image analysis, including techniques for dose prediction in radiation therapy, pulmonary nodule detection, and segmentation of vascular structures. Recent projects emphasize the application of graph convolutional networks and optimal transport methods in low-dose CT imaging. Conference Contributions: He serves on program committees for conferences like AAAI 2022, ICONIP 2024, and CHASE 2025, reflecting his leadership in advancing AI and biomedical engineering research. Labs/Teams: His lab at Yeshiva University focuses on interdisciplinary research at the intersection of AI, medical imaging, and computational anatomy, with a commitment to translating algorithmic innovations into clinical applications.

James H. NolenView profile

Professor

• Probability
• Partial Differential Equations
• Stochastic Dynamics
• +3 more

James H. Nolen is a Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from the University of Texas, Austin (2006) and a B.S. from Davidson College (2000). His research focuses on probability, partial differential equations, and stochastic dynamics, with applications to biological models and reaction-diffusion systems. Notable awards include the 2025 Bass Chair and an NSF Postdoctoral Fellowship (2006-2008). Leadership: Directed Duke's Mathematics Postdoctoral Training Program (2019-2023) Grants: Co-PI for NSF RTG grant on Analysis and Applications (2021-2026), PI for NSF CAREER award on stochastic dynamics (2014-2020) His work bridges theoretical analysis with applications, including studies on random walks, interacting particle systems, and front propagation in heterogeneous media. He serves as an Associate Editor for SIAM Journal on Mathematical Analysis and SIAM Multiscale Modeling and Simulation.

Marina Iuliana KnightView profile

Professor

• Wavelet Multiscale Methods
• Nonstationary Time Series Analysis
• Long Memory Processes
• +6 more

Marina Iuliana Knight is a Professor of Statistics in the Department of Mathematics at the University of York. She holds a BSc in Mathematics from Bucharest, an MSc in Stochastic Processes and Theoretical Statistics from Bucharest, and a PhD in Statistics from the University of Bristol. Her research focuses on multiscale methods for nonstationary time series, with applications in energy, biology, and neuroscience. She has led projects such as the EPSRC-funded NeST programme grant and collaborates widely across disciplines, including with Prof Seth Davis (Biology), Dr Elena Geangu (Psychology), and Prof Guy Nason (Imperial College London). Current roles include Head of the Statistics Section and membership in the University Impact Leads for Science Departments group. Her work involves developing wavelet-based techniques for complex network data, with R packages like 'GNAR' and 'lpacf' available on CRAN. She has supervised numerous PhD students, including Maddie Shelley (current), Dingjia Cao (2024), and Jonathan Embleton (2021). Her research has addressed topics such as circadian rhythms, EEG/LFP data analysis, and wearable sensor integration for infant studies. She actively participates in international conferences and has authored 33+ peer-reviewed publications. Key achievements include advancing methods for locally stationary time series forecasting and network time series modelling. Her work bridges statistical theory with practical applications in medicine and environmental science, reflecting a commitment to interdisciplinary impact.

Mohammad Nasr EsfahaniView profile

Senior Lecturer

• Sensors
• Advanced Materials
• Micro/Nano-Fabrication
• +2 more

Mohammad Nasr Esfahani is a Senior Lecturer in Mechanical Engineering at the University of York's School of Physics, Engineering and Technology. He serves as Director of Teaching and Learning (Engineering) and leads the Advanced Materials for Functional Safety research group. His research focuses on nanotechnology, advanced materials, and micro/nano-fabrication for applications in biomedical sensors, energy systems, and functional safety. He develops computational models and experimental approaches to improve system reliability and performance. Professor Esfahani is a Fellow of the UK Higher Education Academy and member of IEEE and IMechE. He serves on the editorial board of Frontiers in Lab on a Chip Technologies.

• Topological Data Analysis
• Machine Learning
• Biomedical Engineering
• +3 more

Dhananjay Bhaskar is a Research Fellow in the Department of Genetics at Yale School of Medicine, Yale University. His research integrates topological data analysis, machine learning, and mathematical modeling to address problems in biophysics and biomedical research. He holds a PhD in Biomedical Engineering and Sc.M. in Data Science from Brown University, and degrees in Computer Science and Applied Mathematics from the University of British Columbia. His work spans applications such as immune response analysis, epidermal regeneration, IVF embryo selection, and cancer cell migration. Education: PhD Brown University (2021), ScM Data Science (2021), MSc Applied Mathematics (2017, UBC), BSc Computer Science (2015, UBC). Research interests focus on leveraging interdisciplinary methods to understand complex biological systems. Key areas include topological analysis of single-cell data, machine learning for medical diagnostics, and computational modeling of cellular dynamics. His work on glial scar formation and calcium signaling in epidermis regeneration has advanced understanding of tissue repair mechanisms. Notable recognition includes the 2024 Kavli Postdoctoral Fellowship for his project on neuroenergetic principles in C. elegans neural circuitry. Collaborations include work with Smita Krishnaswamy on immune response prediction and Valentina Greco on epithelial regeneration. Publications highlight contributions across neuroscience, immunology, and cell biology, with recent focus on multimodal AI for psychiatry and geometric scattering methods for molecular analysis.

Mimma NardelliView profile

Assistant Professor

• Biomedical Signal Processing
• Wearable Monitoring
• Nonlinear Physiological Dynamics

Mimma Nardelli is an Assistant Professor at the University of Pisa's Department of Information Engineering and Research Center 'E. Piaggio'. Her research focuses on biomedical signal processing, wearable monitoring, and nonlinear analysis of physiological dynamics in clinical and healthy populations. She develops methods for assessing autonomic function, emotional states, and clinical conditions through multimodal biosignal analysis. She coordinates the PRIN project MOONSHINE and collaborates on EU projects including POTION and NEVERMIND. Her teaching includes Biometrics Systems, Affective Computing, and AI in Medicine. Awards include the 2017 GNB Award for her PhD thesis on HRV analysis for mood assessment. Research spans: wearable photoplethysmography validation, autonomic state characterization using multiscale entropy, cardiovascular coupling analysis, and physiological responses to affective stimuli. Applications include neonatal care, mental health monitoring, and human-robot interaction.

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +3 more

Romit Maulik is an Assistant Professor at the College of Information Sciences and Technology, Pennsylvania State University, and holds a joint appointment as a faculty member at Argonne National Laboratory. His research focuses on integrating machine learning with computational physics, particularly in turbulence modeling, fluid dynamics, and high-performance computing. He leads projects such as the Interdisciplinary Scientific Computing Laboratory (ISCL), which develops AI-driven algorithms for applications like weather modeling and fusion energy. Education: Ph.D. from Oklahoma State University under Dr. Omer San, specializing in computational fluid dynamics. Postdoctoral work at Argonne National Laboratory with Prasanna Balaprakash and Bethany Lusch on scalable machine learning for scientific computing. Research interests include data-driven closure models, reduced-order modeling, and interpretable AI for fluid flow reconstruction. He is associated with the DeepHyper project for neural architecture search and has contributed to open-source tools like TensorFlowFoam and PythonFOAM. Recent achievements include awards from the NERSC AI4Science program for GPU hours and recognition for students like Xuyang Li and Dibyajyoti Chakraborty. His work bridges advanced computing, physics, and AI to solve complex multiscale problems.

Bin ZhangView profile

Associate Professor

• Genome Structure
• Computational Biology
• Biophysics
• +4 more

Bin Zhang is an Associate Professor of Chemistry at the Massachusetts Institute of Technology (MIT), leading the Zhang Lab focused on understanding genome structure and function through theoretical and computational approaches. His research integrates statistical mechanics, machine learning, and experimental data to model genome organization and dynamics at multiple scales. Key areas include predictive modeling of 3D genome structure using sequence information, multiscale modeling of chromatin fiber mechanics, and coupling genome dynamics with gene regulation. His work addresses challenges in non-equilibrium processes, epigenetic regulation, and the interplay between chromatin structure and cellular differentiation. Supported by funding from NIH, NSF, and private foundations, the lab develops open-source tools like OpenNucleome and OpenABC for structural modeling. Collaborations involve experimental groups to validate computational predictions, emphasizing translational applications in synthetic biology and disease mechanisms.

Arthur Matsuo Yamashita Rios De SousaView profile

Assistant Professor

• Applied Mathematics
• Statistical Physics
• Probability Theory
• +7 more

Arthur Matsuo Yamashita Rios De Sousa is an Assistant Professor in the School of Computing at the Tokyo Institute of Technology, where he conducts interdisciplinary research bridging applied mathematics, statistical physics, and data science. His work focuses on the modeling and analysis of complex systems through stochastic processes, time series analysis, and network science. His primary research interests include: Applied Mathematics and Probability Theory Statistical Physics and Econophysics Complex Systems and Network Science Time Series Analysis and Forecasting Symbolic Dynamics and Entropy-Based Methods Power-Law and Heavy-Tailed Distributions His recent publications demonstrate a consistent focus on developing quantitative methods for analyzing financial time series, sales data, and other real-world complex systems. The articles span topics such as volatility modeling, network-based market analysis, multiscale entropy, and symbolic dynamics, reflecting a strong integration of theoretical physics and practical data science applications. Notable trends in his research include the use of entropy measures for regime detection, modeling of heavy-tailed phenomena in economics, and the application of complex networks to multivariate systems. These efforts contribute to both fundamental understanding and practical tools in econophysics and data-driven science. There are currently no listed scientific awards or honors in the provided text. Dr. Yamashita advises students in computational and quantitative research, particularly in areas related to data analysis of complex systems. While specific grant funding is not mentioned, his sustained publication record suggests active research support. He likely contributes to collaborative projects involving financial data modeling, nonlinear dynamics, and interdisciplinary applications of statistical physics. He is involved in academic events such as the Econophysics Colloquium and workshops at the Complexity Science Hub, indicating participation in international research networks focused on complex systems science.