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Alperen ErgürView profile

Assistant Professor

• Real algebraic geometry
• Convex geometry
• Randomized numerical algorithms
• +5 more

Alperen Ergür is an Assistant Professor at the University of Texas at San Antonio , affiliated with the College of Sciences and Department of Mathematics. His teaching integrates modern mathematical and computational skills, including courses on probability theory, optimization, algorithmic foundations of data science, reinforcement learning, and computational abstract algebra. Ph.D. in Mathematics from Texas A&M University M.S. in Mathematics from TOBB University of Science and Technology B.S. in Mathematics from Bilkent University Research spans real algebraic geometry , convex geometry , randomized numerical algorithms , and reinforcement learning . Key trends in his publications include: Polynomial system analysis Tropical geometry applications Tensor rank approximation Algorithm complexity in computational geometry Probabilistic methods in optimization Homotopy algorithms for real zeros He organizes the UTSA Algorithms Seminar and co-founded a student-friendly research seminar on Geometry, Probability, and Computing with Grigoris Paouris and Petros Valettas. With Suleyman Tek and Rodrigo Velez, he established a creative/competitive math circle for middle schoolers.

Chris KeylockView profile

Professor

• Fluid Mechanics
• Turbulence
• Nonlinear Dynamics
• +6 more

Professor Chris Keylock is a Professor of Fluid Mechanics at the University of Leicester. His research focuses on non-linear and complex phenomena, particularly in non-equilibrium turbulence dynamics. Key interests include the role of non-local effects in turbulent flows, synthetic data for null hypothesis testing, and multifractal methods. He has contributed to intermittency science, wavelet techniques, and closure models for computational fluid dynamics. Notable work includes studies on canopy turbulence (with Duke, MIT, and Western Australia) and boundary-layer modulation using Hölder exponents (with Southampton and Melbourne). Educations: MA, MSc, PhD Research emphasizes turbulence structure analysis, velocity gradient tensor dynamics, and applications in fluid mechanics, geomorphology, and hydrology. His work bridges statistical and physics-based perspectives, with interdisciplinary applications in environmental and engineering systems. Scientific Awards: 10th Fluid Dynamics Research Prize (2016) Advising and grants: No explicit mentions of advising students or grants in the text. His research groups and collaborations are active in fluid mechanics and turbulence, with contributions to international conferences and publications. Labs/Teams: Engaged with research groups focused on non-equilibrium turbulence, synthetic data generation, and fluid-structure interactions.

• Machine Learning
• Deep Learning
• Biomedical Signal Processing
• +7 more

Yue Zhang is a Professor at Westlake University's School of Engineering with an exceptionally active research program spanning multiple disciplines. With dozens of publications in high-impact venues during 2025-2026 alone, Dr. Zhang demonstrates leadership in interdisciplinary research connecting computer science with practical applications in healthcare, environmental science, and education. Affiliation: Westlake University, School of Engineering Research Areas: Machine Learning, Biomedical Engineering, Computer Vision, Environmental Monitoring Publication Output: 90+ papers indexed in dblp (1996-2026), with remarkable productivity in recent years Dr. Zhang's research interests center on applying advanced machine learning techniques to solve real-world problems across multiple domains. The work demonstrates particular strength in biomedical signal processing (EEG, ECG, HRV analysis), environmental monitoring (mangrove forests, crop yield estimation, nitrogen content), and educational technology (AI integration in classrooms). The research approach typically combines deep learning architectures with domain-specific knowledge to create interpretable and effective solutions. The publication record reveals a strong trend toward multimodal and interdisciplinary approaches, with increasing focus on practical applications of AI in healthcare diagnostics, environmental conservation, and educational transformation. Recent work shows particular innovation in attention mechanisms, tensor-based optimization, and knowledge distillation techniques adapted for specialized domains. Dr. Zhang's collaborative network spans multiple institutions and disciplines, with frequent co-authorship patterns suggesting established research groups in both computer science and application domains. The work appears well-funded given the range of sophisticated applications and high publication output across top venues.

• Mathematical Optimization
• Discrete Mathematics
• Mathematics of Data Science
• +3 more

Alberto Del Pia is a Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin–Madison, where he has been a faculty member since 2014 and received tenure in 2020. His research focuses on mathematical optimization, discrete mathematics, and the mathematics of data science. He holds a Ph.D. in Mathematics from the University of Padova (2009) and has held postdoctoral positions at Otto von Guericke University, ETH Zürich, and IBM Research as a Goldstine Fellow. Educational Background: PhD in Mathematics (University of Padova, 2009); MS and BS in Mathematics (University of Padova, 2005 and 2004). Research Interests: Del Pia's work emphasizes theoretical and computational aspects of optimization, including convex quadratic programming, mixed-integer programming, and polynomial optimization. His contributions span the design of algorithms and polyhedral analysis, with applications in data science and operations research. Notable Awards: He has received the Egon Balas Prize (INFORMS Optimization Society, 2023), the INFORMS Computing Society Prize (2023), and the Young Researchers Prize (INFORMS Optimization Society, 2017). His postdoctoral work was supported by the IBM Herman Goldstine Fellowship (2013) and the INdAM Scholarship (2000). Teaching and Service: Del Pia teaches courses in integer optimization and linear optimization across multiple departments (Industrial & Systems Engineering, Mathematics, Computer Science). He has also organized conferences, such as the 2023 IPCO conference in Madison.

• Mars Seismology
• Seismic Data Analysis
• Tomography
• +3 more

Ross R Maguire is an Assistant Professor in the Department of Earth Science and Environmental Change at the University of Illinois . His research focuses on planetary seismology, particularly Marsquake analysis, seismic data interpretation, and applications of deep learning in geophysical signal processing. He has contributed to studies using data from NASA's InSight mission to Mars, investigating Mars' interior structure, core dynamics, and tectonic activity. His work combines advanced computational methods with traditional seismology to address questions about planetary interiors, magma systems, and seismic event discrimination. Key areas include: Seismic waveform analysis of Marsquakes (e.g., S1222a) Generalization of deep-learning models for earthquake/explosion classification Tomographic imaging of volcanic systems (e.g., Yellowstone, Hawaii) Recent publications explore Mars' upper mantle structure, core-transiting phases, and the application of neural networks to diverse geologic settings. Collaborations span international teams and interdisciplinary projects, such as the InSight mission and the CRESCENT Cascadia velocity modeling initiative.

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

Philippe JawinskiView profile

Researcher

• Molecular Genetics
• Biological Psychiatry
• Machine Learning
• +4 more

Dr. Philippe Jawinski is a researcher at Humboldt-Universität zu Berlin's Department of Psychology, affiliated with the Lebenswissenschaftliche Fakultät. He holds a Dr. rer. nat. (summa cum laude) from the University of Leipzig and has conducted postdoctoral research at Humboldt-Universität since 2018, focusing on Molecular Psychology. His research interests include molecular genetics, biological psychiatry, machine learning applications in neuroimaging, sleep science, and open science practices. He has been a visiting research associate at the Max Planck Institute for Human Cognitive and Brain Sciences and has contributed to projects such as the ENIGMA-EEG Working Group. Education and Career: PhD in Psychiatry and Psychotherapy (2018), University of Leipzig Postdoctoral Research Associate (2018–present), Humboldt-Universität zu Berlin Visiting Research Associate (2020–present), Max Planck Institute for Human Cognitive and Brain Sciences Internship in Cognition and Emotion (2012), University of Western Australia Research Interests: Jawinski's work bridges molecular genetics and neuroimaging, with a focus on aging biomarkers, depression, sleep, and psychiatric genetics. He uses machine learning and EEG/MRI to study brain age, white matter integrity, and genetic influences on mental health. His commitment to open science includes signing research transparency pledges and contributing to collaborative projects like ENIGMA-EEG. Awards and Grants: ISPG Best Oral Presentation Award (2024) DAAD Travel Award (2023) OHBM Merit Abstract Award (2020) Multiple grants from the German Sleep Society and other organizations Teaching and Labs: Jawinski teaches courses on behavioral genetics, computational neuroscience, and sleep at Humboldt-Universität. He collaborates with groups like the Molecular Psychology team and contributes to the ENIGMA-EEG consortium, advancing large-scale neuroimaging genetics research.

• Psychiatry
• Clinical Psychology
• Psychosis Research
• +4 more

Chantal Michel is an Associate Professor and Group Leader at the University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern. She is also the Scientific and Clinical Co-Head of the Early Detection and Intervention Centre for Mental Crisis (FETZ Bern). Her work focuses on early detection and intervention for individuals at clinical high risk (CHR) for psychosis, with expertise in transdiagnostic therapeutic approaches and the application of technologies like ecological momentary assessments and virtual reality. Dr. Michel holds a habilitation in Psychiatry and has led numerous studies on neuroimaging correlates of psychosis risk, cognitive deficits, and mental health service utilization patterns. Her research interests span psychosis spectrum disorders, developmental psychopathology, and the neurobiological underpinnings of mental health issues. Dr. Michel has published extensively on topics such as EEG biomarkers in psychosis, white matter abnormalities in CHR populations, and the implementation of national psychosis prevention networks. She is actively involved in clinical trials and machine learning approaches to personalize treatment decisions for at-risk individuals. Dr. Michel's contributions include pioneering work on age-related effects of psychosis symptoms, the role of coping strategies in psychopathology, and the integration of innovative technologies in mental health monitoring. She has presented her findings at major conferences like the International Early Intervention in Mental Health (IEPA) and the European Society for Child and Adolescent Psychiatry (ESCAP).

• Developmental Neuroscience
• Psychiatric Disorders
• Neuroimaging
• +3 more

Ravi Bansal, PhD, serves as a Research Professor in the Departments of Psychiatry and Behavioral Sciences and Pediatrics at the University of Southern California (USC), with primary affiliation to the Keck School of Medicine. His research program integrates advanced neuroimaging with developmental psychiatry to investigate brain development from prenatal stages through adulthood, focusing on environmental and biological determinants of neurocognitive outcomes. Dr. Bansal's research interests center on Developmental Neuroscience and Psychiatric Disorders , utilizing Neuroimaging to examine how prenatal exposures—including maternal nutrition (zinc, iron), immune activation, air pollution, and socioeconomic factors—shape infant brain structure, metabolism, and connectivity. His work identifies early biomarkers for conditions like autism spectrum disorder and depression, employing multimodal MRI techniques to dissect tissue microstructure and functional networks. This research bridges pediatric neurology and psychiatry, emphasizing translational applications for early intervention. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) longitudinal studies tracking brain development in preterm/term infants exposed to prenatal risks; (2) neurotoxic effects of pediatric cancer treatments (e.g., methotrexate in leukemia); and (3) advanced statistical frameworks for parsing heterogeneity in psychiatric conditions. His methodology increasingly combines diffusion tensor imaging, spectroscopy, and machine learning to correlate imaging phenotypes with clinical outcomes across depression, autism, and schizophrenia spectra.

• Congenital Heart Disease
• Neurodevelopmental Outcomes
• Fetal Imaging
• +5 more

Jodie K. Votava-Smith, MD, is a Clinical Associate Professor in the Department of Clinical Pediatrics at the University of Southern California , with clinical and research affiliations to Children's Hospital Los Angeles (CHLA) . Her work focuses on the neurodevelopmental and hemodynamic consequences of congenital heart disease in neonates and infants. Research Themes : Her publications highlight interdisciplinary research at the intersection of Pediatric Cardiology Neurovascular Interaction Fetal Brain Development Placental Hemodynamics Developmental Outcomes in Congenital Heart Disease Sociodemographic Influences on Cardiac Anomalies Scientific Contributions : Through over 20 peer-reviewed articles since 2005, she has advanced understanding of cerebral autoregulation , brain dysmaturation , and neurovascular coupling in cardiac infants, employing advanced imaging modalities like MRI and near-infrared spectroscopy.

Stefanos Aldo PapanicolopulosView profile

Senior Lecturer

• Computational Mechanics
• Micromechanics of Materials
• Mechanics of Generalized Continua
• +4 more

Dr. Stefanos Aldo Papanicolopulos is a Senior Lecturer in the School of Engineering at the University of Edinburgh, specializing in Civil and Environmental Engineering . His research focuses on computational mechanics, micromechanics of materials, and mechanics of generalized continua, with applications to granular materials and strain-gradient elasticity. Education: MEng in Civil Engineering (2002), National Technical University of Athens MSc in Structural Engineering (2004), National Technical University of Athens PhD in Mechanics (2008), National Technical University of Athens Research Interests: Mechanics of generalized continua, including Cosserat and micropolar theories Micromechanical modeling of granular materials using DEM and FEM Strain-gradient elasticity and computational methods Development of efficient finite element and cubature rules Experimental and numerical studies of particle breakage and flow Scientific Awards: Fellow of the Higher Education Academy Grant Collaborations: Active EU-funded projects like TUSAIL (Training In Upscaling Particle Systems) and grants from the Carnegie Trust for Scottish Universities. Key collaborations include the VELaSSCo platform for DEM data visualization.

Alejandro F FrangiView profile

Professor

• Medical Image Analysis
• Computational Physiology
• Machine Learning
• +2 more

Alejandro F. Frangi is the Bicentenary Turing Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Manchester, with joint appointments in the School of Computer Science and School of Health Sciences. He serves as Director of the Christabel Pankhurst Institute and leads the NIHR Manchester Biomedical Research Centre's Digital Infrastructure theme. His education includes a PhD in Medicine from Utrecht University (2001) and an undergraduate degree in Telecommunications Engineering from Universitat Politècnica de Catalunya (1996). He holds honorary positions at KU Leuven and is an Alan Turing Institute Fellow. Professor Frangi's research bridges medical image analysis and computational modeling, with emphases on: Machine learning for population imaging In silico clinical trials for medical devices Computational physiology in cardiovascular and neurosciences Statistical methods for image-based biomarkers His recent publications demonstrate strong focus on AI-driven medical image reconstruction, computational modeling of vascular diseases, and virtual clinical trial methodologies. Research trends show consistent innovation in deep learning architectures for 3D medical image processing. Awards and honors include: IEEE Engineering in Medicine and Biology Technical Achievement Award (2021) Fellow of Royal Academy of Engineering (2023) ERC Advanced Grant recipient President's International Initiative Award from Chinese Academy of Science He has supervised 28+ PhD students to completion and currently leads multiple major grants including a £2.7m Royal Academy of Engineering Chair award. His laboratory develops open-source platforms (GIMIAS, MULTI-X) and has spun off three companies (GalgoMedical, adSilico). As Director of the Christabel Pankhurst Institute, he oversees interdisciplinary teams working on health technology innovation. He founded the InSilicoUK Innovation Network to advance regulatory science for in silico methods.

Hongxu ChenView profile

Research Fellow

• Machine Learning
• Data Mining
• Recommender Systems
• +3 more

Dr. Hongxu Chen serves as an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. His research focuses on machine learning, data mining, and network analysis, particularly in the context of recommendation systems and graph-based models. He has contributed to advancements in graph neural networks, network representation learning, and social network analysis through collaborative projects with international researchers. Dr. Chen's work spans theoretical and applied domains, including social-boosted recommendation systems, graph convolutional networks for centrality analysis, and scalable tensor factorization models. His publications address challenges in link prediction, user behavior modeling, and mobility demand forecasting using graph-based and probabilistic approaches. While no formal awards are listed, his contributions reflect significant engagement with high-impact conferences like NeurIPS, ICDE, and KDD. No advising or grant details are provided in the text. His research activities are centered within the School of Electrical Engineering and Computer Science, though specific lab affiliations are not mentioned.

David ShattuckView profile

Adjunct Professor

• Neuroimaging
• Computational Biology
• Brain Imaging
• +2 more

David Shattuck is an Adjunct Professor in the Department of Neurology at the University of California Los Angeles (UCLA) School of Medicine. He specializes in neuroimaging and computational neuroscience , with a focus on developing advanced software tools for brain analysis. Academic Affiliation: University of California Los Angeles Department: Neurology Research Focus: Neuroimaging, Brain Segmentation, Cortical Thickness Analysis, Computational Biology Research Contributions: Dr. Shattuck is the principal investigator for NIH R01NS074980 , developing the BrainSuite software suite for multimodal brain imaging analysis and visualization. His work spans human connectome mapping , mouse brain atlases , and neurodegenerative disease imaging . Scientific Trends: His recent publications emphasize deep learning in neuroimaging (e.g., SwinUNETR for mouse brain segmentation), structure tensor analysis for brain tissue orientation, and VR environments for neuroimaging visualization. Keywords across his work include Neuroscience , Medical Informatics , Genetic Disorders , and Biomedical Imaging . Collaborations: Dr. Shattuck collaborates extensively with researchers in neuroimaging genetics , clinical neuroscience , and computational biology , including Arthur Toga, Paul Thompson, and Richard Leahy.

Todd MartinezView profile

Professor

• Theoretical Chemistry
• Photochemistry
• Mechanochemistry
• +3 more

Todd Martinez is the David Mulvane Ehrsam and Edward Curtis Franklin Professor of Chemistry and Professor of Photon Science at Stanford University, affiliated with SLAC National Accelerator Laboratory. His research focuses on theoretical and computational chemistry, with emphasis on photochemistry, mechanochemistry, and quantum mechanical methods. He earned his PhD in Chemistry from UCLA in 1994, followed by postdoctoral work at UCLA and the Hebrew University in Jerusalem before joining Stanford in 2009. His group develops novel simulation tools, including GPU-accelerated quantum chemistry software (e.g., TeraChem) and virtual reality interfaces (InteraChem and MolAR) for interactive molecular modeling. Key interests include predicting molecular behavior under light and mechanical force, designing self-healing materials, and advancing reaction discovery through first-principles methods. Awards include the MacArthur Fellowship and recognition as a Fellow of the American Academy of Arts and Sciences. Education: PhD in Chemistry (UCLA, 1994); Postdoctoral Studies at UCLA and Hebrew University (Jerusalem) Research Themes: Molecular modeling, excited-state dynamics, mechanochemical reactions, and computational tools Publications span ultrafast spectroscopy, quantum mechanical modeling, and algorithm development for large-scale simulations. The Martinez Group’s work bridges theory and application, aiming to make molecular design predictive and accessible through interdisciplinary collaborations.