جست‌وجوی استاد و پژوهشگر

Giuliano Grossiمشاهده پروفایل

دانشیار

• Sparse representation models
• Signal processing
• Face recognition
• +۵ مورد دیگر

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

Daniel Patelمشاهده پروفایل

پژوهشگر

• Geological Visualization
• Computer Graphics
• Scientific Visualization
• +۳ مورد دیگر

Daniel Patel is an Adjunct Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for geological and geophysical data, with a particular emphasis on interactive modeling, real-time rendering, and domain-specific tools for scientific communication. He has contributed to projects such as 'Geological Storytelling' and 'Rapid Sketch-Based Modeling,' which aim to bridge the gap between geologists' conceptual ideas and computational modeling. Patel’s research integrates computer graphics innovations with domain-specific challenges in geology and marine science. His research interests include volume rendering, sketch-based modeling, seismic data interpretation, and educational approaches to PhD training. He collaborates with interdisciplinary teams, including the VisGroup at UiB, to develop tools for visualizing complex geological structures and sonar data. Patel holds a PhD in expressive visualization from TU Wien (2009) and has published extensively on topics ranging from chromatic shadows in medical visualization to statistical transfer functions for noisy data. Key contributions include the development of occlusion shading models, instant convolution shadows, and knowledge-assisted visualization frameworks for seismic exploration. His work emphasizes rapid prototyping and user-centric design, enabling domain experts to rapidly communicate and explore geological hypotheses.

• Approximation Theory
• Mathematical Theory of Neural Networks
• Statistics
• +۳ مورد دیگر

Jonathan W. Siegel is an Assistant Professor in the Mathematics Department at Texas A&M University. He holds a PhD in Mathematics from UCLA (2018) under Prof. Russel Caflisch and was a postdoc at Penn State (2018–2022) under Prof. Jinchao Xu. His research focuses on approximation theory, mathematical foundations of neural networks, numerical methods for PDEs, and applications in materials science. He is supported by NSF grants (DMS-2424305, CCF-2205004) and an ONR MURI grant (N00014-20-1-2787). Education: PhD in Mathematics, UCLA, 2018 Postdoctoral Researcher, Penn State University, 2018–2022 Research interests span theoretical machine learning, numerical analysis, and computational materials science. He explores topics such as neural network expressivity, greedy algorithms for PDEs, and optimization techniques for scientific computing. His work emphasizes the development of rigorous mathematical frameworks for deep learning methods and their application to real-world problems like materials property prediction. Grants and Awards: NSF DMS-2424305, NSF CCF-2205004, ONR MURI N00014-20-1-2787. Advising: Supervised 6 students, including collaborations with Jinchao Xu and Stephan Wojtowytsch. Courses taught include advanced graduate-level topics like the Mathematical Theory of Deep Learning and Approximation Methods.

Frank Sottileمشاهده پروفایل

استاد

• Algebraic Geometry
• Discrete Geometry
• Real Algebraic Geometry
• +۴ مورد دیگر

Frank Sottile is a Professor in the Department of Mathematics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on Algebraic Geometry, Discrete Geometry, and their applications. He holds a Ph.D. from the University of Chicago (1994) and has held academic positions at institutions including the University of Massachusetts and the University of Waterloo. Notably, he was elected as a Fellow of SIAM (2025) and AAAS (2024), and an inaugural Fellow of the AMS (2012). Education: Ph.D. in Mathematics (University of Chicago, 1994); CPGS in Mathematical Tripos (Cambridge, with distinction); B.S. in Physics (Michigan State University, summa cum laude). Research Interests: Real and computational algebraic geometry, Schubert calculus, algebraic combinatorics, spectral theory, and applications in discrete mathematics. His work bridges theoretical advances with computational methods, including numerical algebraic geometry. Awards include NSF CAREER Award (2002–2007), Kavli Fellowship (2007), and Clay Research Scholar (2004). He has advised numerous graduate students and led research grants totaling over $4 million in funding. Professional Service: Director of Honors and Undergraduate Research at TAMU; editorial roles at SIAM Journal on Applied Algebra and Geometry, American Mathematical Monthly, and Arnold Mathematical Journal. Organized major conferences like Texas Algebraic Geometry Symposium and SIAM AG17. Teaching: Courses include Real Algebraic Geometry for Applications and Honors advising. Active in outreach, including the TAMU Math Circle and international collaborations through CIMPA.

Krishna Narayananمشاهده پروفایل

استاد

• Wireless Communications
• Large Language Models
• Information Theory
• +۳ مورد دیگر

Dr. Krishna Narayanan is Professor and Edgar and Yolanda Sanchez Endowed Chair in Electrical & Computer Engineering at Texas A&M University. He directs research in AI-native wireless communications, large language models for communication systems, and massive multiple access networks. His research integrates information theory with deep learning to develop novel communication paradigms. Current work focuses on Transformers for wireless estimation, LLM-based text compression, and efficient coding for massive IoT connectivity. Narayanan bridges theoretical foundations with practical implementations across wireless, storage, and computing domains. Publications demonstrate consistent innovation in coding theory applications, from compressed sensing for massive access to analog joint source-channel coding. Recent work explores LLM-powered text compression and Transformers for optimal wireless estimation, establishing new intersections between deep learning and communication theory. Honors include IEEE Communications/Information Theory Societies Best Paper Award (2022) and university-level teaching awards. Narayanan serves as Associate Director for Educational Initiatives at Texas A&M Institute for Data Science, developing curriculum integrating communication theory with AI methodologies.

Simon Lacoste-Julienمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Simon Lacoste-Julien is a Full Professor at the Université de Montréal's Department of Computer Science and Operations Research, part of the Faculty of Arts and Sciences. He is affiliated with the Centre de recherches mathématiques (CRM) and Mila – Quebec AI Institute. His research focuses on machine learning, deep learning, optimization, and theoretical foundations of AI. Notable contributions include advancements in constrained optimization algorithms, game-theoretic learning, and sparsity-aware models. Education: Doctorate in Machine Learning (PhD), specialized in optimization and graphical models. Research Interests: His work bridges theory and practice, exploring topics such as: - Optimization for Machine Learning : Developing efficient algorithms for large-scale problems. - Game-Theoretic Approaches : Applying game theory to prediction and mechanism design. - Disentangled Representations : Learning structured latent variable models for better interpretability. - Adaptive Gradient Methods : Improving convergence and stability of training processes. Grants & Projects: Lead researcher on multiple grants including: - Model-based Machine Learning with Guarantees (2025-2031, CRSNG) - Robust and Efficient Structured Prediction (2017-2025) - Projects on few-shot learning and algorithmic fairness funded by MITACS and IVADO. Labs & Teams: Active member of Mila, leading research in optimization and theoretical aspects of deep learning. Collaborates widely with industry (e.g., Samsung Electronics) and contributes to open-source tools like the Cooper library for constrained optimization.

• Machine Learning
• Neural Networks
• High-Dimensional Data
• +۳ مورد دیگر

Dr. Michael Murray is a Lecturer in the Department of Mathematical Sciences at the University of Bath. His research centers on theoretical machine learning, neural network optimization, and high-dimensional data analysis. He holds a PhD qualification as indicated by his professional title and maintains an active research profile in mathematical foundations of deep learning. His key research areas include neural network theory, benign overfitting, loss landscape analysis, and privacy-preserving machine learning. Dr. Murray's work bridges mathematical rigor with practical applications in computer science. Recent publications demonstrate a focus on neural tangent kernel theory (2022-2024), overparameterization effects in ReLU networks (2023), and compressed sensing algorithms (2022). His research consistently addresses fundamental questions in generalization, optimization, and efficient learning from noisy or high-dimensional datasets.

• Signal Processing
• Biomedical Engineering
• Compressive Sampling
• +۳ مورد دیگر

Azime Can is an Assistant Professor at the Swanson School of Engineering, University of Pittsburgh. Their research focuses on signal processing with applications in biomedical engineering, nonstationary signal analysis, compressive sampling, and control systems. Key areas include asynchronous signal processing techniques, sparse signal reconstruction, and event-based control systems. Research interests emphasize interdisciplinary challenges in biomedical signal analysis (e.g., swallowing accelerometry, heart sounds) and industrial fault diagnosis. Publications span theoretical advancements in time-frequency analysis and practical implementations in embedded systems. Recent work highlights contributions to compressive sensing, event-triggered control, and adaptive signal processing algorithms. No scientific awards are listed, but active research in high-impact areas suggests potential for future recognition. Advising and grant details are not provided in available data.

Gabriel E. Hineمشاهده پروفایل

پژوهشگر

• Biometrics
• Signal Processing
• Information Security
• +۱۳ مورد دیگر

Gabriel E. Hine is a Postdoc researcher at the Biometric Systems and Multimedia Forensics Lab within the Department of Engineering at Roma Tre University, Italy. He is actively engaged in research at the intersection of signal processing, biometrics, and information security, and contributes to teaching in advanced signal processing and biometric systems. Bachelor’s Degree in Electronic Engineering, Roma Tre University (2013, cum laude) Master’s Degree in Information and Communication Technology Engineering, Roma Tre University (2015, cum laude) PhD in Applied Electronics, Roma Tre University (2018) Research collaboration at Telefonica I+D, Barcelona (2016) Research involvement in EU H2020 ENCASE and PRIN 2011/2012 projects His research focuses on biometric cryptosystems, template protection, and privacy-preserving biometric recognition, with significant contributions to fingerprint, palmprint, EEG, and signature-based systems. He has also conducted influential work on online social media dynamics, particularly analyzing 4chan’s /pol/ forum. The analysis of his recent publications reveals a strong trend in developing secure and unlinkable biometric templates using deep learning, sparse signal representations, and zero-leakage frameworks. His work spans both theoretical formulation and experimental validation on real-world data, particularly in finger-vein and palm-vein recognition. He also explores novel biometric modalities such as in-air 3D signatures and resting-state EEG. Best Paper Runner-Up, ICWSM-17 (2017) Selected as 'Best of Arxiv' paper Widely covered in Nature , MIT Technology Review , The Independent , Vice , and Italian media Hine has served as a lecturer and teaching assistant for courses including 'Biometric Systems' and 'Signal Theory' at Roma Tre University. He has led a didactic laboratory on biometric systems, guiding students in implementing and analyzing biometric recognition solutions. His research is supported by major grants such as EU H2020 ENCASE and PRIN 2011/2012. He collaborates extensively with Prof. Patrizio Campisi and other researchers in the BioMedia4n6 lab. He is a core member of the BioMedia4n6 research group, which specializes in biometrics, data hiding, and blind deconvolution, applying advanced signal processing techniques to multimedia forensics and security challenges.

Delphine Demangeمشاهده پروفایل

دانشیار

• Programming Languages Implementation
• Compiler Verification
• Formal Semantics
• +۷ مورد دیگر

Delphine Demange is an Associate Professor in Computer Science at the University of Rennes, affiliated with Inria, CNRS, and IRISA, where she conducts research in the Epicure group. Her work focuses on programming languages, formal semantics, compiler verification, and program verification using interactive theorem provers. Research Interests: Programming Languages Implementation Compiler Verification Formal Semantics Program Verification with Interactive Theorem Provers Static Analysis and Language-Based Security Her recent publications demonstrate a strong trend in mechanized semantics, verified compilation, and correctness of intermediate representations such as SSA forms and dataflow circuits. She frequently employs Coq for formal verification and contributes to foundational aspects of compiler correctness. Scientific Awards: EAPLS Best PhD Dissertation Award 2012 Gilles Kahn PhD Thesis Award 2013 Delphine Demange has held significant service roles, including Program Co-Chair for CC 2021 and General Co-Chair for JFLA 2023 and 2024. She has served on numerous program committees for top conferences such as POPL, PLDI, CPP, ESOP, and OOPSLA, reflecting her active engagement in the programming languages community. She teaches courses in programming, algorithmics, compilation, semantics, and software security at both undergraduate and master's levels. She is part of the Epicure research team at IRISA, focusing on verified systems and programming language foundations.

Cem Direkoğluمشاهده پروفایل

استادیار

• Computer Vision
• Pattern Recognition
• Image and Video Analysis
• +۷ مورد دیگر

Cem Direkoğlu is a faculty member in the Department of Electrical and Electronics Engineering at Middle East Technical University - Northern Cyprus Campus (METU NCC), within the College of Engineering. His research is centered on computer vision and image analysis, with a strong emphasis on feature extraction, human behavior modeling, and video understanding. Research Interests: His primary research areas include computer vision, pattern recognition, image and video analysis, and signal processing. He focuses on human (individual and group) behavior analysis in sports and surveillance contexts, video event detection, object detection and localization, motion analysis and tracking, and feature/shape/skeleton extraction and segmentation. His ongoing projects involve crowd behavior analysis in surveillance videos, team behavior analysis in sports, and player detection and classification. Publication Trends: His recent work (2004–2014) shows a consistent focus on applying physical analogies (e.g., heat flow) and mathematical models to shape and feature extraction. Later publications shift toward higher-level video understanding, particularly in sports and surveillance, using motion trajectories, team activity modeling, and information retrieval methods for event detection. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available regarding students supervised or research grants received. However, his involvement in projects like SAVASA (TRECVid) suggests participation in collaborative research initiatives. Labs and Research Teams: While no specific lab is named, his work appears to be conducted within the research ecosystem of the Electrical and Electronics Engineering department at METU NCC, likely involving collaboration with researchers in multimedia and computer vision, as evidenced by co-authorship with researchers from institutions like Dublin City University.

• Aeroacoustics
• Noise Control Engineering
• Signal Processing
• +۳ مورد دیگر

Elias Zea Marcano is an Assistant Professor of Engineering Acoustics at the Marcus Wallenberg Laboratory for Sound and Vibration Research (MWL) at KTH Royal Institute of Technology. His research focuses on noise source separation, aeroacoustics, room acoustics, sparse signal processing, and data-driven methods. He reviews for prominent journals like the Journal of the Acoustical Society of America and serves as a Guest Editor for the Journal of Theoretical and Computational Acoustics. His work is supported by the Swedish Research Council and the European Commission. He teaches courses such as Room Acoustics and Spatial Audio and has supervised numerous graduate students and postdocs in areas like sustainable aviation noise reduction and acoustic material characterization. He actively participates in international conferences and publishes cutting-edge research on topics such as fan noise measurement and data-driven speech enhancement.

Kaie Kubjasمشاهده پروفایل

دانشیار

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +۵ مورد دیگر

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

Brent De Weerdtمشاهده پروفایل

پژوهشگر

• Interpretable Deep Learning
• Distributed Source Coding
• Generative AI
• +۳ مورد دیگر

Brent De Weerdt is a doctoral student and researcher at the Vrije Universiteit Brussel, affiliated with the Faculty of Engineering and the Electronics and Informatics department. His research focuses on interpretable deep learning, distributed source coding, generative AI, and AI explainability. He holds a Master of Science in Electrical Engineering from the same institution (2021). Current projects include the FWOSB140 DUST project, exploring interpretable and efficient deep unfolding sparse transformers for multimodal image processing and generation. He has published in top-tier venues like IEEE Transactions on Signal Processing and IEEE conferences, with contributions to sparse recovery, trajectory anomaly detection, and layered coding. De Weerdt actively engages in peer-review activities for journals such as IEEE Transactions on Multimedia and conferences like the European Signal Processing Conference. His work bridges theoretical signal processing with practical AI applications, emphasizing interpretability and efficiency in neural architectures.

Thomas Felمشاهده پروفایل

پژوهشگر ارشد

• Explainable AI
• Computational Science
• Mathematics
• +۳ مورد دیگر

Thomas Fel is a Research Fellow at the Kempner Institute, Harvard University , specializing in Explainable AI . His work integrates computational science , mathematics , and neuroscience principles. Previously, he completed a PhD at Brown University under Thomas Serre and contributed to the DEEL project at Toulouse University with SNCF support. He has also interned at Google and GoPro. Research Interests focus on advancing model interpretability through interdisciplinary approaches. Key projects include developing novel sparse autoencoder architectures (MP-SAE, RA-SAE, USAE) and creating benchmarks like Visual Anagrams to evaluate holistic shape processing in vision models. His work addresses generative model limitations, feature visualization stagnation, and conceptual blindspots in AI systems. Scientific Recognition : 2025 AFIA National PhD Thesis Award 2025 'Signal, Image, Vision' Best PhD Thesis Award 2025 ICML Top Reviewer Thomas has developed critical tools like LENS Project for feature visualization and Xplique Toolbox for attribution methods. His recent work at ICML 2025 explores hierarchical concept extraction and cross-modal representation alignment.