جست‌وجوی استاد و پژوهشگر

• Schizophrenia
• Psychosis
• Neuroimaging
• +۵ مورد دیگر

Judith Ford, PhD is a Professor in the Department of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a member of the UCSF Weill Institute for Neurosciences. She maintains an active research program with continuous NIH funding as Principal Investigator since 1984, demonstrating her sustained contributions to schizophrenia and psychosis research. Dr. Ford's research focuses on understanding the neural mechanisms underlying schizophrenia through advanced neuroimaging techniques. Her work extensively utilizes fMRI and EEG to investigate functional and structural brain abnormalities, with particular emphasis on auditory processing deficits, neural network instability, and the relationship between brain activity and clinical symptoms. Recent projects examine neural network instability in schizophrenia that may be improved with ketogenic diets, auditory steady-state responses across the psychosis spectrum, and biomarkers of rumination through concurrent EEG and fMRI studies. Analysis of her recent publications (2023-2025) reveals a strong trend toward sophisticated multimodal neuroimaging approaches, with increasing focus on machine learning applications, network connectivity analyses, and metabolic influences on mental health. Her work spans multiple disciplines including computational neuroscience, electrophysiology, and clinical psychiatry, with consistent application to schizophrenia research. Dr. Ford has received continuous NIH funding as Principal Investigator since 1984, with current projects including R21MH127498 (2021-2023) on neural network instability in schizophrenia, R03MH125226 (2021-2022) on auditory steady-state response across the psychosis spectrum, and R03MH121900 (2020-2022) exploring the RDoC construct of agency using fMRI and EEG. Her longest-running project, I01CX000497 'Rescuing the ruminating brain,' has been active from 2011-2022. She actively collaborates with researchers across multiple institutions through large-scale consortia, contributing to major neuroimaging initiatives like the ENIGMA consortium. Her laboratory combines cutting-edge neuroimaging technology with advanced computational approaches to develop biomarkers that could inform diagnosis, prognosis, and treatment development for schizophrenia and related disorders.

Hadi Fanaee Torkمشاهده پروفایل

دانشیار

• Machine Learning
• Tensor Analysis
• Anomaly Detection
• +۳ مورد دیگر

Hadi Fanaee Tork is an Associate Professor (Docent) in Machine Learning and Program Director for the Applied AI bachelor program at Halmstad University's School of Information Technology, Sweden. He earned his PhD in Computer Science with distinction from the University of Porto, Portugal in 2015, followed by postdoctoral positions at the University of Oslo (2017-2020) and INESC TEC research institute in Portugal. His primary research interests include machine learning, tensor decompositions, anomaly detection, and time series analysis. His work spans theoretical foundations to practical applications in healthcare, transportation, environmental monitoring, and industrial systems. He has developed novel methods for tensor-based event detection, multi-aspect data analysis, and space-time clustering, with applications ranging from disease outbreak detection to energy efficiency in maritime transport. His recent publication trend shows a strong focus on tensor analysis applications across multiple domains, with increasing emphasis on practical implementations in transportation efficiency and healthcare analytics. His work bridges theoretical machine learning with real-world problem solving, particularly through tensor decomposition techniques applied to complex, high-dimensional data streams. ERCIM Cor Baayen Young Researcher Award Finalist (2017) Publons Top Reviewer Award (2017) PhD Scholarship, MAP-i doctoral program (2011) Best PHP Class Award (2012) Professor Fanaee Tork has advised numerous PhD and Master's students across multiple institutions including Halmstad University, University of Oslo, and international collaborations with institutions in Iran, Portugal, and Germany. His research has been supported through European FP7 Project "MAESTRA" and various academic collaborations. He maintains active research groups focused on tensor analysis and natural learning approaches, with strong industry connections particularly in maritime technology and healthcare analytics.

• Machine Learning
• Robotics
• Natural Language Processing
• +۱ مورد دیگر

Mahmoud Rahat is a Senior Lecturer at the Department of Information Technology , Halmstad University , Sweden. He is affiliated with the Center for Applied Intelligent Systems Research (CAISR) since 2019, focusing on data-driven solutions for industrial applications. Research Expertise: Machine Learning, Predictive Maintenance, Robotics, Natural Language Processing Industrial Collaboration: Volvo Group Truck Technologies, Northern Illinois University (2015-2016) His work bridges academia and industry through projects in intelligent mobility and automated idea detection . He has developed innovative approaches in survival analysis and XAI (Explainable AI) for predictive maintenance systems. Current trends in his publications include: Explainable AI methods for industrial data (Shapley values, Integrated Gradients) Survival analysis in neural networks Domain adaptation for automotive sensor data Federated learning frameworks Virtual sensors via tensor completion Mahmoud supervises academic projects including: Lecturer: Machine Learning for Predictive Maintenance, Big Data Parallel Programming Course Responsible: Applied Data Mining Examiner: Degree Project in Computer Science and Engineering

Caterina Rosanoمشاهده پروفایل

استاد

• Population Neuroscience
• Aging
• Neuroepidemiology
• +۷ مورد دیگر

Caterina Rosano, MD, MPH, is a Professor of Epidemiology at the University of Pittsburgh School of Public Health, serving as Director of the MD/PhD Program in Epidemiology and Associate Director for Clinical Translation at the Aging Institute. Her research focuses on Population Neuroscience of Aging, investigating neurobiological drivers of resilience in healthy aging through advanced neuroimaging and epidemiological methods. Education: BS, Scientific Lycee B. Croce, Palermo, Italy (1989) MD, University of Palermo Medical School, Palermo, Italy (1995) Internship in Internal Medicine & Emergency Medicine, Policlinico, University of Palermo (1996) Mini Residency in Neurology, Alzheimer's Disease Research Center, UPMC (2002) MPH in Epidemiology, University of Pittsburgh School of Public Health (2003) Trainee, Epidemiology of Aging Training Program (2004) Trainee, Mentor Training Program (2010) Research Focus: Dr. Rosano pioneers the integration of neuroscience and population science to understand why some individuals age better than others. Her work reveals that microstructural brain integrity, small vessel health, and dopaminergic signaling are critical resilience factors. She demonstrates that modifiable elements like stable blood pressure and low inflammation preserve brain function, with implications for interventions targeting accelerated brain aging in conditions like diabetes. Her eBRAIN research group applies advanced MRI techniques to virtually dissect brain networks in diverse aging populations. Publication Trends: Her 2021-2022 research spans neuroimaging of aging, mobility, and health disparities. Key themes include genetic influences on gait (COMT polymorphism), vascular contributions to Parkinsonism, olfactory system changes in aging, and Alzheimer's disease disparities in minority women. Her work consistently bridges clinical neuroscience with population health, emphasizing modifiable resilience factors. Mentorship & Leadership: Dr. Rosano actively mentors trainees including postdoctoral scholar Meredith Phillips (who advocated for NIH funding at Capitol Hill 2025), PhD candidate Kailyn Witonsky, and conference-presenting students Alex Reese and Alejandra Ramirez-Tirado. She leads the eBRAIN group coordinating neuroepidemiological studies on brain resilience, recently participating in the Vanscoy Winter Academy 2025 to discuss healthy aging strategies.

Yanbing Wangمشاهده پروفایل

استادیار

• Transportation Cyber-Physical Systems
• Connected and Automated Vehicles
• Dynamical Systems
• +۱ مورد دیگر

Dr. Yanbing Wang is a tenure-track Assistant Professor at Arizona State University's School of Sustainable Engineering and the Built Environment (SSEBE), where she leads applied research initiatives with government and industry partners on intelligent transportation systems and mobility analytics. Before joining ASU in 2025, she conducted postdoctoral research at Argonne National Laboratory's Transportation and Power Systems division and earned her PhD in Civil and Environmental Engineering from Vanderbilt University in 2023. PhD, Civil and Environmental Engineering, Vanderbilt University (2023) BS, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign (2018) Dr. Wang specializes in advancing transportation cyber-physical systems (CPS) through computationally-efficient tools for large-scale empirical data generation and analysis. Her work bridges theory and practice via real-world experimentation with Tennessee's I-24 MOTION testbed and collaboration with public agencies. Key research themes include connected vehicle dynamics, adaptive cruise control personalization, and real-time traffic monitoring frameworks. Her 15 most recent publications (2024-2022) demonstrate interdisciplinary trends spanning transportation engineering, computer vision, control theory, and environmental data science. Articles focus on trajectory reconstruction, multi-object tracking algorithms, digital twin infrastructure, and machine learning applications for traffic stability, with notable emphasis on heterogeneous traffic estimation, parameter calibration, and 3D vehicle tracking benchmarks. Cyber-Physical Systems Rising Star (University of Virginia, 2023) Dwight D. Eisenhower Transportation Fellowship (FHWA, 2018-2023) Sidney P. Colowick Graduate Scholarship (Vanderbilt, 2021) Best Student Paper Award at SMC 2020 Dr. Wang advises MS students like Raswanth Prasath, whose work on multi-object tracking was spotlighted at ASU's Graduate Poster Symposium. Her research has been supported by Department of Energy funding at Argonne and a USDOT grant through the Eisenhower Fellowship. She collaborates with institutions including Toyota InfoTech Labs, Mitsubishi Electric Research Laboratories, and UCLA's IPAM, while leading projects like the I-24 MOTION data infrastructure and CIRCLES congestion reduction initiative.

• Numerical Fourier Analysis
• Wavelet Theory
• Signal Processing
• +۳ مورد دیگر

Gerlind Plonka-Hoch is a Professor of Applied Mathematics and Deputy Director of the Institute for Numerical and Applied Mathematics (NAM) at the University of Göttingen. She leads research in numerical analysis and signal processing, with a focus on mathematical methods for image reconstruction and analysis. Her work bridges theoretical mathematics with practical applications in medical imaging and data science. Professor Plonka-Hoch's research spans several key areas in applied mathematics: Numerical Fourier analysis and fast algorithms Wavelet theory and sparse signal representation Regularization methods and nonlinear diffusion Applications in signal and image processing, particularly medical imaging Phase retrieval and parameter estimation problems Her recent publications demonstrate a strong focus on medical imaging applications, particularly using Optical Coherence Tomography (OCT) data. She has developed innovative approaches combining wavelet analysis, deep learning, and sparse representation techniques for image reconstruction and classification. Her work on ESPIRA (Estimation of Signal Parameters by Iterative Rational Approximation) has provided new methods for reconstructing exponential sums from limited data, with applications across multiple scientific domains. Professor Plonka-Hoch leads an active research group at the University of Göttingen, mentoring several doctoral students including Dr. Yurii Kolomoitsev, M.Sc. Benjamin Kocurov, M.Sc. Anahita Riahi, M.Sc. Yannick Nicola Riebe, and M.Sc. Janina Schmidt. Her working group has produced numerous publications on numerical methods and their applications, with a strong emphasis on both theoretical foundations and practical implementation.

• Tensor Methods
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Kim Batselier is an Associate Professor at Delft University of Technology, affiliated with the Delft Center for Systems and Control. He earned his Master’s and Ph.D. in Electrical Engineering from KU Leuven, Belgium, under the supervision of Prof. dr. ir. Bart De Moor. Prior to his current role, he worked at BioRICS (2005–2009) on real-time monitoring algorithms for AC Milan football players and was a Post-doctoral Research Fellow at the University of Hong Kong (2013–2018). Education: Master’s and Ph.D. in Electrical Engineering, KU Leuven Previous Affiliations: BioRICS, University of Hong Kong His research focuses on innovative tensor methods for high-dimensional data problems in signal processing, machine learning, system identification, and control. Notable contributions include a tensor network-based MIMO Volterra model estimator (achieving 10 million coefficient estimation in 1 second with 5% relative error), Tensor Network Kalman filters, polynomial classifiers for image processing, and tensor decomposition algorithms for nonlinear system identification. All implementations are open-source. Kim is also passionate about education and science popularization , emphasizing accessible dissemination of engineering concepts.

• Medical Imaging
• Diffusion Tensor Imaging
• Cardiac MRI
• +۵ مورد دیگر

Yuemin Zhu is a Permanent Professor at INSA-Lyon and CNRS Permanent Researcher Director at CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé), where he directs the MYRIAD work group focused on Modeling & analysis for medical imaging and diagnosis. He also serves as China Affairs Coordinator at INSA-Lyon, facilitating international academic collaboration between France and China. His research spans multiple areas of medical imaging with particular expertise in diffusion tensor imaging (DTI), cardiac MRI, and advanced image reconstruction techniques. His work integrates sophisticated mathematical approaches with clinical applications, focusing on improving imaging quality, developing novel reconstruction algorithms, and applying machine learning to medical image analysis. His research has significant implications for cardiac diagnostics, tumor characterization, and materials identification in medical imaging contexts. The publication trends show a clear evolution from fundamental image reconstruction techniques to increasingly sophisticated deep learning approaches. His recent work heavily features self-supervised learning methods, transformer architectures, and multi-modal fusion techniques applied to challenging medical imaging problems. The research spans cardiac imaging, oncology applications, and materials science, demonstrating remarkable versatility while maintaining focus on core imaging methodology. Professor Zhu has mentored numerous researchers through collaborative projects, as evidenced by his extensive publication record with varied co-authors. His work has been supported by various research grants enabling the development of advanced imaging techniques and their clinical translation. He leads the MYRIAD research group at CREATIS, which focuses on developing innovative mathematical and computational approaches for medical imaging analysis and diagnosis, with particular emphasis on cardiac applications and diffusion imaging techniques.

Nadav Cohenمشاهده پروفایل

دانشیار

• Theoretical Machine Learning
• Deep Learning
• Neural Networks
• +۴ مورد دیگر

Nadav Cohen is an Associate Professor of Computer Science & AI at Tel Aviv University and co-founder of Imubit, where he serves as CTO and President. His research focuses on the theoretical and algorithmic foundations of deep learning, particularly analyzing expressiveness, optimization, and generalization in neural networks through mathematical frameworks like tensor analysis. B.Sc. in Electrical Engineering & Mathematics, Technion (summa cum laude) Ph.D. in Computer Science, Hebrew University of Jerusalem (direct track) Postdoctoral Fellow, School of Mathematics, Institute for Advanced Study (Princeton) His work explores implicit regularization, gradient descent dynamics, and the role of depth in neural networks, often intersecting with tensor methods. Awards include the ERC Starting Grant, Google Research Scholar Award, and multiple fellowships. Advised notable students like Noam Razin, recipient of the Israel Council for Higher Education Fellowship Founded a research group at Tel Aviv University (2019–present) with current and alumni students Developed lecture notes on linear neural networks and hosted industry leaders like OpenAI's CEO

Miguel A. Morales Silvaمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Condensed Matter Physics
• Computational Physics
• +۳ مورد دیگر

Miguel A. Morales Silva is a Research Scientist at the Center for Computational Quantum Physics (CCQ) within the Flatiron Institute . His research focuses on developing and applying quantum Monte Carlo (QMC) methods to solve quantum many-body problems, aiming to create predictive, parameter-free first-principles techniques for strongly correlated materials and large-scale high-performance computing applications. Education : B.A. in Physics and Mathematics (2004), University of Puerto Rico at Mayaguez; Ph.D. in Physics (2009), University of Illinois at Urbana-Champaign His research spans computational quantum physics, condensed matter theory, and materials science. Key areas include: Quantum Monte Carlo methodologies Electronic structure of strongly correlated systems Phase transitions in quantum materials High-performance computing for quantum simulations Downfolding ab initio Hamiltonians His recent publications emphasize quantum Monte Carlo studies of 2D materials, electron gases, and cavity-QED systems, with software development contributions to the QMCPACK package. Notable awards include: NNSA Defense Programs Award of Excellence LLNL Early and Mid-Career Recognition Program MOSI Early Career Hispanic Scientist of the Year Presidential Early Career Award for Scientists and Engineers (PECASE)

Miles Stoudenmireمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Computational Physics
• Machine Learning
• +۳ مورد دیگر

Miles Stoudenmire is a Researcher at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) , joining in 2017. He holds a Ph.D. in Physics from the University of California Santa Barbara (2010) and a B.S. in Physics and Math from the Georgia Institute of Technology (2005). His work focuses on tensor networks and their applications in quantum mechanics and machine learning. Education: Ph.D. in Physics, University of California Santa Barbara (2010) B.S. in Physics and Math, Georgia Institute of Technology (2005) Stoudenmire specializes in enhancing tensor network methods to model realistic quantum systems, including finite temperature effects and chemically accurate basis sets. He has developed the ITensor software library, which enables efficient transcription of tensor network diagrams into code for algorithm prototyping. His research bridges quantum physics and computational techniques, with applications in condensed matter physics and quantum computing. Key publication trends include advancements in density matrix renormalization group (DMRG) methods, quantum-inspired machine learning algorithms, and topological quantum simulations. Affiliations: Flatiron Institute (2017–present) University of California Irvine (postdoctoral) Perimeter Institute for Theoretical Physics (research scientist)

Alex Cayco Gajicمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Cerebellum
• Motor Control
• +۷ مورد دیگر

Alex Cayco Gajic is a Junior Professor and Principal Investigator in the Department of Cognitive Studies at École Normale Supérieure (ENS), part of PSL University in Paris. She leads a research team within the Group for Neural Theory (GNT), which is embedded in the Laboratoire de Neurosciences Cognitives et Computationelles. Her work sits at the intersection of machine learning, mathematical modeling, and systems neuroscience, with a particular focus on understanding how neural populations represent behavior and change over learning. Dr. Cayco Gajic received her Ph.D. in Applied Mathematics from the University of Washington in 2015, where she worked under Eric Shea-Brown, followed by a postdoctoral fellowship in Angus Silver's lab at University College London. She established her independent laboratory at ENS in 2019, integrating her mathematical training with experimental neuroscience to study cerebellar function and neural dynamics during learning. Her research interests span computational neuroscience, cerebellar function, neural coding, dimensionality reduction techniques, reinforcement learning, and dynamical systems. Dr. Cayco Gajic aims to identify fundamental principles of how task-relevant neural dynamics emerge over learning, particularly focusing on the cerebellum but also exploring its interactions with the motor cortex and basal ganglia during motor and cognitive learning. Her recent publications reveal a strong trend toward developing novel dimensionality reduction methods for analyzing large-scale neural recordings, with particular emphasis on understanding how neural representations evolve during learning. Her work bridges theoretical modeling with experimental neuroscience, often collaborating closely with experimental teams to validate computational approaches with real neural data, as evidenced by her development of slice tensor component analysis (sliceTCA) for identifying multiple classes of covariability in neural data. Dr. Cayco Gajic currently supervises several PhD students in her lab, including Helen Todd (working on cerebellar interneuron synchronization), Leonardo Agueci, Mattia Della Vecchia, and Hugo Ninou. Her team is actively working on understanding how interacting circuits in the brain control behavior, with particular focus on the cerebellum's role in both motor and cognitive functions. She maintains affiliations with multiple research centers including the ENS Quantitative Biology Centre and the Paris Artificial Intelligence Research Institute, reflecting the interdisciplinary nature of her work that spans neuroscience, mathematics, and artificial intelligence.

Christoph Ortnerمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Applied Analysis
• +۱۲ مورد دیگر

Christoph Ortner is a Professor in the Department of Mathematics at the Faculty of Science, University of British Columbia. His research bridges rigorous mathematical analysis, numerical methods, and practical applications in molecular simulation and materials science. His primary research interests include: Numerical Analysis & Scientific Computing Applied Analysis Multi-scale Modelling and Coarse-graining Molecular Simulation Scientific Machine Learning, particularly for applications in multi-scale modelling Professor Ortner's work focuses on several interconnected areas including Hybrid Mechanistic & Data-driven Modelling of atomistic systems, Material Defects analysis, QM/MM Multiscale Methods, and Atomistic/Continuum Coupling techniques. His research group develops both theoretical foundations and practical algorithms for molecular simulation, with emphasis on mathematical rigor combined with computational efficiency. Recent work increasingly integrates machine learning techniques with traditional numerical methods, particularly through the Atomic Cluster Expansion framework. His publications demonstrate consistent contributions to the field of computational mathematics and materials science, with recent focus on machine learning applications in molecular simulation and the development of mathematically sound interatomic potentials. Professor Ortner is affiliated with the Institute of Applied Mathematics at UBC and is available for collaborations, including interdisciplinary research projects and supervision of graduate students in Mathematics programs.

• Dynamical Low-Rank Approximation
• Numerical Analysis
• Scientific Computing
• +۳ مورد دیگر

Gianluca Ceruti is a researcher at the Mathematical Institute of Eberhard Karls University of Tübingen. His work focuses on numerical analysis and scientific computing, particularly in dynamical low-rank approximation and tensor networks. He is involved in projects related to wave phenomena and quantum system dynamics. Research Interests: Dynamical low-rank approximation, tensor networks, uncertainty quantification, and numerical methods for differential equations. Key Collaborations: Jonas Kusch, Christian Lubich, Lukas Einkemmer, Martin Frank. His recent publications address stability analysis, rank-adaptive integrators, and time integration of tree tensor networks. Contact: ceruti@na.uni-tuebingen.de .

David V Andersonمشاهده پروفایل

استاد

• Audio and Psycho-Acoustics
• Machine Learning
• Digital Signal Processing
• +۷ مورد دیگر

David V. Anderson is a Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology's College of Engineering. He holds appointments in the Bioengineering, Computer Engineering, and Machine Learning research focus areas, with technical interests spanning Digital Signal Processing, Bio-Devices, and Low-Power Integrated Circuits. Dr. Anderson earned his B.S. and M.S. from Brigham Young University and his Ph.D. from Georgia Tech in 1993, 1994, and 1999 respectively. His research focuses on audio and psycho-acoustics, machine learning for human auditory characteristics, and real-time signal processing applications. Key projects include the development of digital hearing aid algorithms that became commercial products, ultra-low power signal processing systems, and bio-inspired signal processing architectures. His recent publications (2022-2024) demonstrate a strong focus on tensor methods for robust data analysis, lightweight neural networks for audio classification, and real-time audio processing systems. Research trends show increasing integration of machine learning with traditional signal processing techniques, particularly for hearing assistance applications and hyperspectral data analysis. National Science Foundation CAREER Award (2004) Presidential Early Career Award for Scientists and Engineers (2004) As a Senior Member of IEEE and member of the Acoustical Society of America and Tau Beta Pi, Dr. Anderson has secured significant research funding resulting in over 150 technical publications and 8 patents/patents pending. His work bridges theoretical signal processing with practical applications in hearing assistance and low-power systems. He has been actively involved in computer-enhanced education initiatives and development of real-time audio processing frameworks like Audiosockets.