جست‌وجوی استاد و پژوهشگر

Guillaume Charpiatمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Guillaume Charpiat is a Research Officer at INRIA Saclay and Head of the Data Science department at the LISN laboratory (Paris-Saclay University). He holds a PhD in computer vision from the École Polytechnique (2006), conducted under Olivier Faugeras and Renaud Keriven. His postdoctoral research at the Max Planck Institute for Biological Cybernetics focused on statistical learning. He joined the TAO/TAU team in 2015, specializing in machine learning and optimization. His research spans deep learning theory, neural networks, and applications in satellite imagery, genetics, fluid dynamics, and medical imaging. Education: PhD in Computer Vision (École Polytechnique, 2006), Postdoc in Statistical Learning (Max Planck Institute). Teaching includes advanced deep learning courses at MVA/CentraleSupélec and AI master's programs. Research interests emphasize neural architecture design, generative models, and interdisciplinary applications. His work bridges machine learning with physics and biology, addressing challenges like dynamical systems modeling and population genetics inference. Key contributions include neural architecture growth strategies, equivariant GNNs for materials science, and deep learning frameworks for genomic data. Advised over 20 students across PhD and master's levels, focusing on topics like neural networks, causal discovery, and generative models. Active in seminars and interdisciplinary collaborations, he co-leads the TAU team's research on machine learning and optimization.

Enrique Ortizمشاهده پروفایل

استاد

• Mathematics Education
• Cognitive Levels in Learning
• Social Justice Mathematics
• +۶ مورد دیگر

Enrique Ortiz is a Professor in the School of Teacher Education at the University of Central Florida (UCF), specializing in mathematics education with a focus on cognitive learning frameworks, social justice integration, and educational technology. His work bridges theoretical research and practical classroom applications across Puerto Rico, Louisiana, and Florida, emphasizing teacher development and innovative pedagogical strategies. Dr. Ortiz's academic credentials include: Ed.D. in Secondary School Mathematics with Statistics from Louisiana State University M.A. in Administration and Supervision of Schools with Statistics from Phoenix University, Puerto Rico Resident Center B.A. in Secondary Education with Mathematics minor from Interamerican University of Puerto Rico His research spans cognitive levels in mathematics learning (concrete, pictorial, abstract, and virtual frameworks), geometric abstraction art integration, optical topography of brain activity during mental tasks, and TeachLive simulation technology for teacher assessment efficacy. He develops problem-solving curricula with emphasis on social justice, fractions, algebra, and virtual/physical instructional tools like the Triangle Puzzle Challenges. His publications include a book chapter in High School Mathematics Lessons to Explore, Understand, and Respond to Social Justice . Publication analysis reveals a dual trajectory: while his core 2005 work on cognitive learning aligns with mathematics education, Google Scholar attributes 14 computer vision publications (2007-2018) covering license plate recognition, vehicle identification, and facial recognition systems. This divergence suggests either interdisciplinary expansion or potential attribution anomalies in technical fields unrelated to his documented expertise. Dr. Ortiz actively contributes to academic service through conference organization, manuscript review, and international presentations. His scholarly impact includes curriculum development, social justice mathematics frameworks, and instructional game design, though specific grants and student mentoring details remain unreported in public sources.

• Quantum Dots
• Mid-infrared Sensing
• Nanocrystalline Materials
• +۴ مورد دیگر

Dr. Dong Ko is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). He specializes in nanomaterials, colloidal quantum dots, and mid-infrared sensing technologies. His research focuses on developing novel optoelectronic devices, photodetectors, and infrared sensing systems through advanced material synthesis and device engineering. Dr. Ko leads or co-leads multiple National Science Foundation (NSF) projects, including initiatives on retinomorphic vision systems, resonant energy transfer in hybrid lasers, and high-performance thin-film transistors for IoT devices. His work integrates nanocrystal engineering, thin-film fabrication, and device optimization to address challenges in infrared detection, energy-efficient optoelectronics, and flexible electronics. His research interests span quantum dot materials, mid-infrared photonics, and energy-efficient sensor technologies. Notable contributions include colloidal Ag 2 Se quantum dots for intraband photodetection, PbSe-based mid-wavelength infrared photoconductors, and low-temperature 2D material growth for flexible electronics. Received 4 NSF grants focusing on infrared sensors, hybrid lasers, and flexible electronics Published 44 peer-reviewed articles with 2,366 citations and an h-index of 23 His lab collaborates internationally on topics like nanocrystal assembly and thermoelectric materials. Current projects aim to reduce power consumption in infrared vision systems and advance scalable quantum dot technologies for widespread applications.

Fabio Galassoمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Fabio Galasso is a Researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. He holds a PhD from the University of Cambridge, where his research focused on 'shape-from-texture' under Dr. Joan Lasenby. His postdoctoral work at the Machine Intelligence Laboratory (supervised by Prof. Roberto Cipolla) explored motion-based video segmentation and spatio-temporal clustering. Prior to his PhD, he worked at Ericsson Laboratories and Telecom Italia as a Researcher and Project Engineer, respectively. Education: Bachelor's/Master's in Electrical Engineering and Signal Processing at RomaTre University, Rome. PhD at the University of Cambridge (Engineering and St. John's College). His research interests center on advancing computer vision techniques, with emphasis on video analysis and segmentation. Notable projects include Spectral Graph Reduction and the VSB100 Unified Video Segmentation Benchmark. He has contributed to teaching High-Level Computer Vision courses at the institute. Though his institutional webpage is outdated (last updated 2014), further details can be found on his personal site http://fabiogalasso.org/ .

• Computer Science
• Bioinformatics
• Remote Sensing
• +۷ مورد دیگر

Ying Liang is a multidisciplinary researcher with significant contributions across computer science, bioinformatics, and remote sensing. Their work spans AI-driven medical diagnostics, environmental monitoring, and computational biology, with recent focus on precision nutrition, skin lesion recognition, and urban heat island analysis. Research Interests : Machine learning for biomedical applications (e.g., skin cancer detection, pancreatic segmentation), remote sensing (landslide mapping, urban structure dynamics), and computational biology (RNA-protein interactions, microRNA regulation). Notable Article Trends : 2025 publications highlight self-supervised remote sensing classification, fractal reservoir modeling, and biomedical AI. 2024 works emphasize cross-domain gait recognition, panoptic segmentation, and health informatics via Wi-Fi sensing. Grants & Collaborations : Co-authored studies with institutions like Open University and Chinese universities, focusing on thermal cycling reliability, multi-omics data fusion, and wireless health monitoring. Labs & Teams : Collaborated on interdisciplinary projects involving neural network architectures, hybrid AI models for environmental and biomedical applications, and biomedical signal processing systems.

• High Performance Computing
• Parallel Programming
• OpenMP
• +۲ مورد دیگر

Matthias S. Müller is affiliated with RWTH Aachen University's IT Center, with additional associations to TU Dresden's Center for Information Services and High Performance Computing and the University of Stuttgart's High Performance Computing Center. His research focuses on parallel computing paradigms, OpenMP optimizations, and energy-efficient high-performance computing. Recent publications demonstrate specialization in parallel pattern compilers, OpenMP runtime optimizations, and energy-aware computing benchmarks. His team develops tools for performance analysis and optimization in heterogeneous computing environments, with applications in computer vision and scientific computing.

• Computer Vision
• Deep Learning
• Neural Network Acceleration
• +۵ مورد دیگر

Kaisheng Ma is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With an extensive publication record spanning from 2013 to 2025, he has established himself as a leading researcher in computer vision, deep learning, and computer architecture. His work bridges theoretical advances with practical applications in neural network acceleration and hardware-software co-design. Professor Ma's research interests focus on advancing the frontiers of computer vision and deep learning systems. His work in knowledge distillation has significantly improved the efficiency of neural networks, while his contributions to hardware acceleration have enabled more powerful AI systems. His recent research demonstrates a growing emphasis on 3D vision, embodied AI, and the intersection of language and vision systems. He has pioneered techniques in frequency domain analysis for image processing and developed novel approaches for neural network acceleration through hardware-software co-design. Analysis of Professor Ma's recent publications (2023-2025) reveals a strong research trajectory with increasing focus on multimodal learning, 3D scene understanding, and efficient neural network deployment. His work spans both theoretical advances in machine learning (particularly knowledge distillation and continual learning) and practical hardware implementations for AI acceleration. The publications show consistent collaboration with a core group of researchers while expanding into new areas like brain-computer interfaces and language-grounded spatial reasoning. Professor Ma has successfully mentored numerous PhD students who have become active contributors to the field, including Linfeng Zhang, Runpei Dong, and Zekun Qi. His research has been supported by significant grants that enable cutting-edge work in neural network acceleration and 3D vision systems. His students have published in top-tier conferences including CVPR, NeurIPS, and ICCV, demonstrating the strength of his research group. Professor Ma leads a research group focused on the intersection of computer vision, deep learning, and computer architecture. His lab investigates efficient neural network deployment, hardware acceleration techniques, and novel computer vision algorithms. The team maintains strong collaborations with industry partners working on AI hardware and has developed several innovative approaches to neural network acceleration and efficient model deployment.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Huan Zhou is a prolific researcher affiliated with multiple institutions including the University of Oldenburg , China Three Gorges University , and Wuhan University . His work spans Computer Science , Mathematics , and Engineering disciplines. Key affiliations: Signal Processing Group (Oldenburg), Computer and Information Technology College (China Three Gorges University), Geodesy and Geomatics (Wuhan University) Research interests include: Graph Theory : Resistance spectra, planar graph properties, and spectral analysis Optimization Algorithms : Manta ray foraging, vortex search, and swarm intelligence applications Computer Vision : Road segmentation, stereo matching, and remote sensing image analysis Telecommunications : RIS-assisted wireless systems and beamforming techniques Machine Learning : Anomaly detection, continual learning, and transformer architectures Recent publications demonstrate cross-domain impact in bioinformatics (TriGCN for medicine), network systems (Plover distributed logging), and computational pathology (glioma diagnosis with transformers).

Simon Musallمشاهده پروفایل

استادیار

• Neural Network Function
• Decision-making
• Multisensory Processing
• +۳ مورد دیگر

Prof. Simon Musall is an Assistant Professor of Neuromodulation at RWTH Aachen University and Head of the in-vivo Neurophysiology Lab at Forschungszentrum Jülich. His research focuses on understanding how multisensory information is integrated in neural networks to guide behavior, particularly in mice performing cognitive tasks. He employs advanced techniques like high-density electrophysiology and functional imaging to study cortical networks, pathway-specific information transfer, and neuromodulation effects. Key research interests include neural network function, decision-making mechanisms, and neuromodulation's role in information processing. His lab develops cutting-edge neurotechnology, such as flexible neural probes and organic neurohybrid systems, to advance in vivo recordings and closed-loop control. Collaborative projects integrate computational neuroscience with experimental approaches to dissect cortical circuit dynamics and their behavioral correlates. Recent work highlights contributions to spike sorting algorithms (UnitRefine), network dynamics modeling (Riemannian multi-scale decomposition), and enzyme-mediated neurohybrid interfaces. His interdisciplinary approach bridges basic neuroscience with translational tools for studying neurodevelopmental and psychiatric conditions.

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۳ مورد دیگر

Reza Haffari is a Professor in the Department of Data Science & AI at Monash University, within the Faculty of Information Technology. His research focuses on Artificial Intelligence, particularly in low-level perception and high-level reasoning under uncertainty from text and other modalities like vision and speech. Key areas include natural language understanding, semi-supervised learning, and model explainability for critical applications such as healthcare. Education: Doctor of Philosophy in Computer Science, Simon Fraser University (2010) Master of Science in Artificial Intelligence, Sharif University of Technology (2002) Bachelor of Science in Software Engineering, Sharif University of Technology (2000) Teaching Commitments: Chief Examiner for courses: FIT1045, FIT1053, FIT2004, FIT5201 Lecturer for units: FIT1045, FIT2004, FIT3080, FIT4005/FIT5185, FIT4009, FIT5201 Research Projects (2023–2027): Principal Investigator for Aligning Large Language Models with Human Intention Chief Investigator in Trustworthy Generative AI , Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems , and CSIRO Mental Health AI Research Trends in Articles: Recent work emphasizes continual learning , multimodal models , and ethical AI alignment . Key themes include adapting models to dynamic environments, ensuring safety in large language systems, and integrating causal reasoning with machine learning. Labs & Teams: Active in the Data Science & AI Group at Monash, collaborating on projects involving generative AI, neuro-symbolic systems, and healthcare applications.

Srigokul Upadhyayulaمشاهده پروفایل

استادیار

• Optical Imaging
• Biomedical Engineering
• Data Analysis
• +۳ مورد دیگر

Srigokul Upadhyayula is an Assistant Professor in Residence in the Department of Molecular and Cell Biology at the University of California, Berkeley. He serves as the Scientific Director of the Advanced Bioimaging Center (ABC) and is a Chan Zuckerberg Initiative Imaging Scientist. His work bridges applied engineering with basic science, focusing on developing cutting-edge imaging technologies like the Adaptive Optical Multi-functional Lattice Light-Sheet Microscope. The ABC specializes in handling tera- to petabyte-scale imaging data, integrating artificial intelligence to analyze complex datasets and advance both fundamental and translational research. Academic Role: Assistant Professor in Residence Directorships: Advanced Bioimaging Center, CZI Imaging Scientist Research interests center on optical imaging systems, high-resolution microscopy, and computational workflows for biological data analysis. Key projects include multi-scale imaging across millimeter-scale specimens and long-term imaging sessions, emphasizing the synergy between advanced microscopy and AI-driven data interpretation. Publications highlight breakthroughs in lattice light-sheet microscopy, clathrin-mediated endocytosis, and mitochondrial dynamics. His work has been featured on Science and eLife covers, underscoring contributions to cellular and developmental biology. Advising and grants involve leading interdisciplinary teams to develop imaging infrastructure and computational tools. The ABC aims to democratize access to advanced imaging through open-source workflows. Laboratory initiatives include the ABC’s global imaging initiative, combining hardware innovation with AI to solve complex biological questions.

• Computer Architecture
• High Performance Computing
• GPU Programming
• +۵ مورد دیگر

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

Bo Zhuمشاهده پروفایل

استادیار

• Computer Graphics
• Computational Physics
• Scientific Machine Learning
• +۳ مورد دیگر

Bo Zhu is an Assistant Professor at Georgia Tech's School of Interactive Computing. His research focuses on computer graphics, computational physics, and scientific machine learning, particularly in fluid dynamics, vortical structures, and computational design. He holds a Ph.D. in Computer Science from Stanford University and completed postdoctoral training at MIT CSAIL. Prior to Georgia Tech, he served as an Assistant Professor at Dartmouth College. His work is supported by grants such as the NSF CAREER Award (2022). Education: Ph.D. in Computer Science, Stanford University Postdoctoral Training: MIT CSAIL Dr. Zhu's research bridges physics simulation and machine learning, addressing challenges in fluid dynamics and material design. His group develops high-performance algorithms for fluid-structure interaction, topology optimization, and biomedical modeling. Recent projects include real-time fluid simulation techniques and neural network-based approaches for cardiac cell analysis. His publications span topics like fluid reconstruction from videos, particle-based methods, and biomimetic engineering. The NSF CAREER Award recognizes his innovative contributions to computational science and engineering. Advising and Grants: Zhu's grants support interdisciplinary projects in fluid dynamics and machine learning. He advises students in computational physics and design, though specific advisee names are not listed. His lab collaborates with industry and academic partners to advance simulation-driven engineering solutions.

• Large Scale Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +۳ مورد دیگر

Jemima M. Tabeart is an Assistant Professor in the Computational Science group at TU Eindhoven, within the Centre for Analysis, Scientific Computing and Applications. Her research focuses on large-scale numerical linear algebra, data assimilation, and covariance matrices. She also engages in scientific outreach and crafting. Prior roles include a Hooke Fellowship at the University of Oxford and a PDRA position at the University of Edinburgh. Education: PhD in Mathematics from the University of Reading (2019), MRes in Mathematics of Planet Earth from Imperial College London and Reading (2016), MMath from the University of Bath (2015) with a study year at Université Grenoble Alpes. Research interests span numerical linear algebra applications, preconditioning techniques, and variational data assimilation. She co-organizes the 2025 CWI research semester and the 4TU.AMI initiative bridging numerical analysis and machine learning. Her recent work includes advancements in saddle point preconditioners and model reduction techniques for Bayesian inference. Teaching responsibilities at TU Eindhoven include courses on linear algebra and differential equations. She actively supervises Bachelor/Master projects and has one PhD student. Awards and grants include funding from the INI Network Support for Mathematical Sciences. Professional activities include blogging about sustainable travel and co-organizing the Communications in Numerical Linear Algebra series, with talks available on YouTube.

Alexander Petrovمشاهده پروفایل

دانشیار

• Relational Reasoning
• Visual Cognition
• Philosophy of Perception
• +۵ مورد دیگر

Dr. Alexander Petrov is an Associate Professor in the Department of Psychology at Ohio State University with a courtesy affiliation in the Department of Philosophy (since 2019). He directs the Visual Cognition Lab and holds a PhD in Cognitive Science from New Bulgarian University (1998) and a BS/MS in Computer Science from Sofia University (1995). His post-doctoral training included work with John R. Anderson at Carnegie Mellon University and Barbara A. Dosher at UC Irvine. Research Focus: Petrov's interdisciplinary work bridges cognitive psychology, vision science, and philosophy. His lab investigates: (1) High-level relational reasoning and analogy-making mechanisms, (2) Visual extraction of spatial relationships in dynamic scenes, (3) Neural implementations of cognitive processes, and (4) Philosophical foundations of perception. Methodologies combine behavioral experiments, computational modeling (ACT-R, Leabra), eye-tracking, and pupillometry. Publication Trends: His 15 most recent articles demonstrate consistent focus on visual perception dynamics (shape processing, stereoscopy), perceptual learning mechanisms, and cognitive modeling. Recent work increasingly incorporates neurophysiological validation and methodological innovations in measurement techniques. Awards and Honors: Fred Brown Research Award (2023, 2017) Clifford T. Morgan Best Article Award (2022, 2016) Extraordinary Ability status, U.S. Immigration (2002) Grigor Parlichev Award for Outstanding Achievements (1997) Academic Leadership: Actively advises PhD students (recent graduates: Yuhui Du, 2023; Ying Yu, 2020). Leads the Visual Cognition Lab, recruiting students from psychology, computer science, and philosophy. Organized the interdisciplinary workshop Philosophy and Psychology of Visual Space (2019).