جست‌وجوی استاد و پژوهشگر

Matthieu Labeauمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Learning
• Representation Learning
• +۲ مورد دیگر

Matthieu Labeau is a Senior Lecturer at Télécom Paris, affiliated with the Department of Image, Data, Signal (IDS). He joined the institution in 2019 after completing his PhD at the University of Paris-Saclay and a postdoctoral position at the University of Edinburgh. His research primarily centers on Natural Language Processing (NLP), with specialized interests in representation learning, language modeling, and conversational AI. His work spans: Core NLP : Contextual word representations, semantic alignment, and polysemy analysis. Machine Learning : Hierarchical classification, graph prediction, and few-shot learning techniques. Applications : Emotion recognition in dialogues, persuasiveness decoding, and educational NLP tools. Labeau leads research in the Signal, Statistics and Learning (S2A) team at the Information Processing and Communication Laboratory (LTCI). His recent publications demonstrate a strong focus on improving language model interpretability and efficiency, with innovations in tokenization effects and multimodal fusion. Though no awards or grants are mentioned, his consistent output in top-tier venues (e.g., NeurIPS, ACL, AAAI) highlights significant scholarly contributions. He actively collaborates on tools like EZCAT for conversation annotation and mentors researchers in NLP projects. Current work explores LLM capabilities in persuasion assessment and optimal transport methods for graph-based learning.

Willi Menapaceمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• 3D Reconstruction
• +۳ مورد دیگر

Willi Menapace is a Researcher at the University of Trento , affiliated with the Multimedia and Human Understanding Group (MHUG). His work focuses on the intersection of Computer Vision , Deep Learning , and 3D Reconstruction , with applications in Medical Imaging and Interactive Video Generation .

Liucheng Guoمشاهده پروفایل

پژوهشگر ارشد

• AI software-hardware co-design
• embedded AI
• high-performance computing (HPC)
• +۷ مورد دیگر

Dr. Liucheng Guo is a Visiting Senior Research Fellow and RAEng Industrial Fellow at King’s College London, affiliated with the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. His expertise spans AI software-hardware co-design, embedded AI, and high-performance computing (HPC), with a focus on sustainable human-machine interfaces (HMI) for sectors like IoT, Automotive, Healthcare, and XR. He holds a Ph.D. from Imperial College London and an MA from Peking University. Dr. Guo has co-founded the award-winning AI startup TG0, securing seven patent groups and over £20 million in grants from InnovateUK, RAEng, and the European Innovation Council. His research emphasizes smart home, IoT, and healthcare applications, including VR mirror therapy for post-stroke rehabilitation and gesture-based control systems. He is a Senior Member of IEEE and Fellow of IET/BCS/RSA, reflecting his leadership in bridging research and industry. His grants include Smart Grants, KTP awards, and SME Leader initiatives. He actively promotes STEM through roles as RAEng Visiting Professor and public engagement ambassador.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۴ مورد دیگر

Cédric Le Ruyet is a prominent researcher at the Conservatoire National des Arts et Métiers (CNAM) , affiliated with the CEDRIC Laboratory. His work spans Wireless Communications , Signal Processing , and 5G/6G Network Design , with a focus on Filter Bank Multicarrier (FBMC) systems, MIMO , and Cognitive Radio . He collaborates extensively with institutions like CEA-LETI and CNAM . His research interests include Channel Modeling , Resource Allocation , Beamforming , and Machine Learning in wireless contexts. He has contributed to Reconfigurable Intelligent Surfaces (RIS) for beam optimization and SCMA/OFDM codebook design. Recent publications highlight his work on QAM-FBMC systems, Rayleigh Fading Models , and 5G Network Coexistence . His articles often explore Distributed MIMO , Low-Complexity Equalizers , and Indoor Positioning via Channel State Information (CSI) . He has authored numerous IEEE Transactions and conference papers, with technical reports for projects like ANR POSEIDON and NF-PERSEUS . His methodologies integrate Genetic Algorithms , Lattice-Reduction , and Deep Learning for next-gen wireless systems.

Max Ehrlichمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Computational Imaging
• Video Compression
• +۸ مورد دیگر

Max Ehrlich is an Adjunct Assistant Professor at the University of Maryland in the Department of Computer Science and a research scientist at NVIDIA. His work spans machine learning, computational imaging, and compression technologies. Adjunct Assistant Professor, University of Maryland Research Scientist, NVIDIA Research Interests: Combines machine learning with computational imaging to solve real-world problems. Key areas include video/image compression, land cover segmentation, and explainable AI. Research focuses on first-principles understanding rather than black-box models. Recent Publication Trends: 2025 work on implicit neural representations for video compression, 2024 studies on metadata-driven video enhancement, 2023 contributions to adaptive networks, and earlier work on JPEG artifacts, multi-task learning, and remote sensing. Scientific Awards: 3rd place in 2018 CVPR DeepGlobe challenge Teaching: Instructed CMSC421 Intro to Artificial Intelligence (Spring 2024) and CMSC422 Intro to Machine Learning (Spring 2022). Also served as a mentor for high school students.

Toh Kim Chuanمشاهده پروفایل

استاد

• Optimization Algorithms
• Semidefinite Programming
• Machine Learning
• +۴ مورد دیگر

Professor Toh Kim Chuan is the Leo Tan Professor in Science and serves as Research Director (Technical) at the Institute of Operations Research and Analytics (IORA) within the National University of Singapore (NUS). His primary affiliation is with the Department of Mathematics in the Faculty of Science. His research focuses on matrix optimization problems, large-scale semidefinite programming, fast algorithms for statistical and machine learning tasks, and iterative methods for solving large linear systems in optimization contexts. His recent work emphasizes contributions to nonsmooth nonconvex optimization, distributionally robust optimization, and scalable algorithms for sparse and low-rank optimization problems. Notable methodologies include Bregman proximal algorithms, adaptive sieving techniques, and inexact augmented Lagrangian methods. Professor Toh is also actively involved in developing computational tools like CDOpt for Riemannian optimization and frameworks for low-bit communication in distributed model training. While no specific awards are listed, his extensive publication record (over 150 papers from 2020-2025) demonstrates significant contributions to optimization theory and applications. His research bridges theoretical foundations with practical implementations, addressing challenges in large-scale systems, machine learning, and operations research.

Victoria Kostinaمشاهده پروفایل

استاد

• Information Theory
• Coding Theory
• Wireless Communications
• +۴ مورد دیگر

Victoria Kostina is a Professor of Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Her research bridges information theory , coding theory , and wireless communications , with a focus on the nonasymptotic regime to understand fundamental limits of delay-constrained systems. Bachelor's, Moscow Institute of Physics and Technology Master's, University of Ottawa PhD, Princeton University Her work explores the intersection of random processes , concentration of measure , and control theory to develop theoretical frameworks for modern communication systems. She has pioneered techniques for finite blocklength information theory and feedback communication , analyzing stability and performance in stochastic environments. Victoria's publications span channel coding , source-channel coding , and control with communication constraints , with a focus on practical applications in real-time systems . Her research has advanced the understanding of data compression , error exponents , and quantization in nonasymptotic scenarios. Notable scientific recognitions include: Princeton Electrical Engineering Best Dissertation Award (2013) NSF CAREER Award (2017) J. K. Wolf ISIT Student Paper Award Finalist (2021) She actively supervises students in the Computing and Mathematical Sciences (CMS), Control and Dynamical Systems (CDS), and Electrical Engineering (EE) PhD programs and leads the development of the SPECTRE Toolbox for finite blocklength information theory.

• Efficient sampling methods
• Compressed sensing
• Medical imaging: ultrasound, MRI, CT
• +۱۲ مورد دیگر

Prof. Yonina Eldar is a Professor of Electrical Engineering at the Faculty of Mathematics and Computer Science , Weizmann Institute of Science. She holds the Dorothy and Patrick Gorman Professorial Chair and serves as Head of the Manya Igel Center for Biomedical Engineering and Signal Processing . Her research bridges classical signal processing with modern deep learning techniques. Develops model-based deep learning frameworks combining domain knowledge with data-driven approaches Focuses on sub-Nyquist sampling for efficient data acquisition in radar, ultrasound, and communications Created hardware prototypes for time-encoding machines and modulo-ADC systems Innovates in joint radar-communication systems for autonomous vehicles Her work emphasizes algorithm unrolling to create interpretable neural networks with reduced training requirements. Publications demonstrate applications in: Medical imaging (ultrasound, ECG monitoring) Autonomous systems (automotive radar) Communication technologies (DFRC systems) Active in theoretical foundations of model-based deep learning, with recent work establishing mathematical guarantees for unfolded networks. Collaborations include Tsinghua University and industry partners for hardware validation.

Kaiquan Wuمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Fiber-Optic Communication Systems
• Error Correction Codes
• +۳ مورد دیگر

Kaiquan Wu is a doctoral candidate and postdoc researcher at Eindhoven University of Technology (TU/e), Netherlands, specializing in digital signal processing for fiber-optic communication systems. His research focuses on error correction codes, coded modulation, and channel modeling. PhD Candidate in Electrical Engineering (Signal Processing Systems) Postdoc in Electrical Engineering (ICT Lab) Member of the ICONIC , BIT-FREE , and DIGI-OPT research projects Research interests include combating signal impairments in optical systems through advanced DSP techniques, such as decision feedback equalizers (DFE), geometric shaping, and low-complexity detection algorithms. He has published extensively on FSO systems, IM-DD links, and energy dispersion analysis. Collaborations involve developing simplified FSO channel models, low-complexity architectures for data center applications, and patent innovations in amplitude shaping methods. His work contributes to increasing the capacity of optical communication systems while reducing complexity and error rates.

Frank R. Kschischangمشاهده پروفایل

استاد

• Digital Communications
• Coding Theory
• Information Theory
• +۴ مورد دیگر

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.

• Machine Learning
• Computational Biology
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Daniel Levine is a Research Fellow in the Department of Internal Medicine at the Yale School of Medicine. His work focuses on integrating large language models (LLMs) with biological and biomedical data, advancing computational methods for single-cell analysis, neuroscience, and numerical analysis. Recent research includes applications of causal language models to discrete diffusion processes and Volterra flow matching. He also contributes to foundational mathematics, particularly in algebraic geometry and sheaf theory. Levine’s research interests span computational biology, artificial intelligence in healthcare, and interdisciplinary approaches merging machine learning with traditional scientific domains. His 2025 studies highlight advancements in applying LLMs to single-cell datasets and causal inference frameworks. Earlier work in algebraic geometry (2020) explores sheaf cohomology and moduli spaces on Del Pezzo surfaces. No awards or grants are explicitly listed in the provided information. He is affiliated with Yale’s Internal Medicine department and can be reached at daniel.levine@yale.edu .

Anouar Yatribiمشاهده پروفایل

پژوهشگر ارشد

• Coding and Information Theory
• Error Correcting Codes
• Wireless Communication Systems
• +۵ مورد دیگر

Anouar Yatribi is a Postdoctoral Research Associate at Imperial College London's Department of Electrical and Electronic Engineering within the Information Processing and Communications Lab. He holds a PhD from ENSIAS, Mohammed V University (Morocco, 2021), and prior degrees in Physics (BSc, 2011) and Electrical Engineering (MSc, 2013) from Mohammed V University. His research focuses on Coding Theory, Error Correcting Codes, and their applications in wireless communications and DNA data storage, alongside discrete mathematics and optimization algorithms. His academic contributions include pioneering work on iterative decoding techniques, majority-logic decodable codes, and hybrid ARQ protocols. He has authored/co-authored over a dozen peer-reviewed publications in IEEE journals and conferences, with notable work on DNA storage error correction and LDPC code optimization. Yatribi serves as a reviewer for IEEE Access and the American Journal of Networks and Communications. Research trends in his publications emphasize improving code performance through algorithmic innovations (e.g., genetic algorithms for code construction) and exploring novel applications like non-binary codes and finite geometry-based coding. His work bridges theoretical coding advancements with practical implementations in real-world communication systems. Yatribi's professional activities include lab leadership within the Information Processing group and active participation in international conferences (e.g., WINCOM, WCCCS). His lab focuses on advancing communication systems through interdisciplinary approaches combining coding theory, optimization, and machine learning.

• Information Theory
• Coding Theory
• Statistical Learning Theory
• +۳ مورد دیگر

Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.

Narayanan Rengaswamyمشاهده پروفایل

استادیار

• Classical Error Correction
• Quantum Error Correction
• Quantum Computing
• +۳ مورد دیگر

Narayanan Rengaswamy is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona. He is also a member of the NSF Engineering Research Center for Quantum Networks (CQN) and the DoE-funded Superconducting Quantum Materials and Systems (SQMS) Center. His educational background includes: PhD in Electrical and Computer Engineering from Duke University (2020) MS in Electrical Engineering from Texas A&M University (2015) BTech in Electronics and Communication Engineering from Amrita University, India (2013) Rengaswamy's research focuses on the intersection of classical and quantum information theory, with particular expertise in error correction techniques for quantum systems. His work spans quantum computing, quantum networking, and quantum communications, developing novel approaches to fault-tolerant quantum information processing. He has made significant contributions to quantum LDPC codes, stabilizer codes, and the Clifford hierarchy, bridging classical coding theory with quantum applications. His research has practical implications for building scalable quantum computers and quantum networks. His publication record reveals a strong focus on quantum error correction, with particular attention to LDPC codes, stabilizer codes, and fault-tolerant architectures. The research trajectory shows progression from foundational work on classical-quantum connections to increasingly sophisticated quantum error correction schemes, with growing emphasis on practical implementations for quantum hardware. Awards and recognitions include: Selected for a talk at the 2020 Conference on Quantum Information Processing (QIP) Chosen finalist for the NSF ERC Perfect Pitch Competition Rengaswamy serves as a reviewer for several journals and conferences, and was on the Posters Program Committee of the 2022 International Conference on Quantum Computing and Engineering (QCE22). He is actively involved in quantum research initiatives through the NSF-ERC Center for Quantum Networks and the DoE-funded SQMS Center. His work combines theoretical depth with practical applications for emerging quantum technologies. He is a member of the IEEE and the Information Theory Society, contributing to the quantum information community through research, service, and academic leadership.

• Digital Communications
• Neural Networks
• Medical Engineering
• +۳ مورد دیگر

Matthieu ARZEL is an Associate Professor in the Department of Mathematical and Electrical Engineering at IMT Atlantique. He holds an HDR (2021), PhD (2006), and Engineer degree (2002) from Telecom Bretagne/ENST. His research focuses on iterative processing for digital communications, low-power integrated circuits, high-speed digital circuits, and FPGA implementations in domains like neural networks, medical engineering, and communication systems. Key research interests include neuromorphic hardware, neural network pruning, federated learning compression, and energy-efficient signal processing. He has supervised 18 PhD students and contributed to projects like Ouessant coprocessor architectures and clique-based neural network circuits. His work bridges algorithm-architecture interactions, emphasizing low-power and embedded system applications. Recent publications highlight innovations in FPGA-based deep learning deployment and efficient neural network compression techniques. Publications span topics from real-time semantic segmentation on FPGA to collusion-resistant watermarking. His contributions address challenges in hardware-software co-design, iterative decoders for MIMO systems, and biomedical signal processing.