جستجوی استاد راهنمای دانشگاهی

Demba Baمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Model-Based Deep Learning
• Signal Processing
• +۳ مورد دیگر

Demba Ba is an Associate Professor of Electrical Engineering and Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He serves as the Dean of Undergraduate Studies for Bioengineering since 2020 and joined SEAS in 2015 after a postdoctoral fellowship at MIT (2007-2014). His research bridges computational neuroscience and artificial intelligence, focusing on sparse signal representations, interpretable AI, and neural network theory. Fluent in Wolof, Fulani, French, Spanish, English, and Arabic, he also contributes to signal processing, statistical learning, and dynamic systems. PhD in EECS from MIT (2011) MS in EECS from MIT (2006) BS in Electrical Engineering from University of Maryland (2004) His work explores connections between sparse coding and neural networks through publications in top venues like NeurIPS, ICML, and IEEE Transactions. Articles emphasize convolutional dictionary learning, Bayesian frameworks, and applications to neural data analysis. He has received the 2016 Sloan Fellowship in Neuroscience and 2021 Roslyn Abramson Award for undergraduate teaching excellence. 2021: Gaussian process convolutional dictionary learning (Submitted) 2020: Deep residual auto-encoders for dictionary learning 2018: Multitaper time-frequency analysis for neuroscience Demba Ba leads the CRISP research group and holds advisory roles at Harvard. His awards include: 2021 Roslyn Abramson Award 2016 Alfred P. Sloan Foundation Fellow 2010 ICME Best Student Paper Award He teaches courses like ES 201 (Decision Theory) and ES 157 (Biomedical Signal Processing), while maintaining interdisciplinary collaborations in neuroscience, machine learning, and signal processing.

Nurdal Watsujiمشاهده پروفایل

استادیار

• Electrical-Electronics Engineering
• Image Processing
• Signal Processing
• +۱ مورد دیگر

Nurdal Watsuji serves as Assistant Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, specializing in Electrical-Electronics Engineering with research emphases on Image Processing, Signal Processing, and Digital Design. His academic foundation includes: Doctorate in Electrical and Electronic Engineering from Imperial College London (1987-1991) Degree in Electrical-Electronic Engineering from Middle East Technical University, Gaziantep (1982-1985) Licence in Electrical Engineering from Middle East Technical University, Gaziantep (1975-1981) Watsuji's research integrates theoretical signal processing with practical engineering applications, particularly in LED/OLED lighting systems and image analysis. His work demonstrates consistent innovation in optical efficiency optimization, compressed sensing algorithms, and wavelet-based texture classification, bridging electrical engineering principles with real-world implementation challenges in energy systems and computer vision. Publication analysis from 2003-2016 reveals evolving expertise from foundational wavelet techniques to advanced optical efficiency modeling in solid-state lighting. The recurring theme involves mathematical optimization of signal/image processing methods for specific engineering constraints, with significant contributions to nonconvex compressed sensing and LED reflector design. His graduate mentoring spans eight students: PhD candidates: İpek Atık (2016) on LED reflector optimization and Taner Ince (2012) on compressed sensing Master's students: Ugur Polat (2022) on energy analyzers, Gulden Akay Ozbay (2008) on hand recognition, Taner Ince (2006) on medical image processing, Recep Oz (2004) on 3D rendering, Omer Cengiz Celebi (2003) on volume rendering, and Bayram Kara (2003) on wavelet texture analysis This diverse portfolio reflects sustained engagement with computational methods across lighting systems, biomedical imaging, and computer graphics applications.

Cédric HERZETمشاهده پروفایل

پژوهشگر ارشد

• Statistical Learning Theory
• Optimization
• Operational Research
• +۳ مورد دیگر

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

Behtash Babadiمشاهده پروفایل

دانشیار

• Statistical and Adaptive Signal Processing
• Neural Signal Processing
• Systems Neuroscience
• +۳ مورد دیگر

Behtash Babadi is an Associate Professor in the Department of Electrical & Computer Engineering and a faculty member at the Institute for Systems Research and the Brain and Behavior Institute at the University of Maryland, College Park. He also holds affiliate appointments in the Program in Neuroscience & Cognitive Science and the Applied Mathematics & Statistics program. Education: Ph.D. in Engineering Sciences, Harvard University (2011) M.Sc. in Engineering Sciences, Harvard University (2008) B.Sc. in Electrical Engineering, Sharif University of Technology (2006) Research Interests: Dr. Babadi’s work focuses on statistical and adaptive signal processing frameworks for understanding neural systems. Key areas include: Neural signal processing and systems neuroscience Granger causality and functional connectivity analysis Dynamic modeling of neuronal assemblies Applications to auditory processing and cognitive recovery Scientific Contributions: His recent publications address cortical network dynamics, MEG source analysis, and robust causal inference. Notable methods include Network Localized Granger Causality (NLGC) for direct connectivity estimation and multitaper spectral analysis for neuronal spiking data. Awards: NSF CAREER Award (2016) E. Robert Kent Teaching Award (2019) GSAS Merit Fellowship (Harvard, 2010) Collaborations: Dr. Babadi collaborates with institutions like MIT, Harvard, and Massachusetts General Hospital, and participates in interdisciplinary initiatives such as the Brain and Behavior Initiative (BBI) and NIH BRAIN grants.

Jichun Xieمشاهده پروفایل

دانشیار

• Genomics
• Bioinformatics
• Computational Biology
• +۷ مورد دیگر

Jichun Xie is an Associate Professor in both the Department of Biostatistics & Bioinformatics and the Department of Computer Science at Duke University . He is also affiliated with the Duke Cancer Institute and the Duke Center for Statistical Genetics and Genomics . His research spans computational biology, genomics, and statistical methods for single-cell data and rare variant analysis. Education: Ph.D. in Biostatistics, University of Pennsylvania (2011) Research Focus: Dr. Xie develops computational frameworks for single-cell RNA sequencing analysis, rare variant association studies, and integrative genomics. His work bridges statistical inference with biological discovery, particularly in cancer immunology, aging, and immune disorders. Publication Trends: Recent papers emphasize single-cell data analysis (e.g., SifiNet, B-Lightning), rare variant mapping (DYNATE), and immune-genomic interactions in chronic GVHD and cancer. Methodologically, he pioneers hierarchical multiple testing and co-expression graph topologies. Grants: Duke Program of Training in Pulmonary Research (PROSPER), NIH/NHLBI (2022-2026) New computational methods for rare variant subregion detection, NIH/NHGRI (2022-2026) Training Program in Bioinformatics at the Intersection of Cancer Immunology and Microbiome, NIH/NCI (2020-2026) Labs and Teams: Dr. Xie leads the Xie Lab , which actively trains students and researchers in computational methods. The lab collaborates with immunologists and oncologists, producing tools like DART and TEAM for biomedical analysis.

• Machine Learning
• Deep Learning
• Applied Mathematics
• +۶ مورد دیگر

Gitta Astrid Hildegard Kutyniok is a Professor in the Department of Physics and Technology at UiT The Arctic University of Norway. Her research spans machine learning, applied mathematics, and signal processing, with a focus on theoretical foundations and practical applications.

• Low vision rehabilitation
• Depth perception
• Binocular vision
• +۵ مورد دیگر

Dr. Vincent Nguyen serves as a Senior Lecturer in Orthoptics within the Graduate School of Health at the University of Technology Sydney. With a distinguished career spanning clinical practice, research, and academia, he brings extensive expertise in visual science and rehabilitation. His academic journey began with Honours in Orthoptics (1993) followed by a Master of Applied Science (1996), culminating in a PhD from the University of Sydney (2003) focused on binocular rivalry in visual psychophysics. Dr. Nguyen's research interests focus on low vision rehabilitation, depth perception, binocular vision, and the application of virtual reality and spatial audio technologies for assistive applications. His work bridges fundamental visual neuroscience with practical rehabilitation solutions, particularly for people with visual impairments. He has pioneered research in acoustic touch interfaces, virtual reality rehabilitation environments, and spatial audio navigation systems. His publication record demonstrates a clear trajectory from fundamental visual neuroscience to applied rehabilitation technologies. Recent work emphasizes immersive virtual reality applications for communication and physical rehabilitation, as well as innovative spatial audio systems that enhance navigation for people who are blind. His research integrates principles from neuroscience, engineering, and clinical practice to develop practical assistive solutions. Dr. Nguyen has secured significant research funding including NHMRC Ideas Grants (2023-2027) for 'Fluent Mobility for the Blind Individual Using Multimodal Auditory Sensory Augmentation' and CRC-P projects like 'ARIA - Bionic Visual-Spatial Medical Device for the Blind' (2022-2024). Current projects include 'EyeBot: AI-Powered Triage for Ophthalmology Referrals' (2025) and 'Next-Generation Extended Reality, Wearable Biosensors, and Metaverse Technologies for Medical Applications' (2024). As an educator, Dr. Nguyen teaches courses in Therapy and Rehabilitation (96037), Clinical Management of Refractive Error (96031), and Eye and Visual Systems (96027). His clinical background includes appointments as a Clinical Electrophysiologist with NSW Health (2007) and work with Vision Australia assisting people with low vision (2012). His postdoctoral work at York University's Centre for Vision Research with Professor Ian Howard focused on human depth perception, building on his foundational expertise in visual neuroscience.

• Political Theory
• Public Policy
• Regulation and Governance
• +۴ مورد دیگر

John Wright is an Associate Professor at the Institute for Public Policy and Governance at the University of Technology Sydney (UTS), where he conducts research on political theory, public policy, regulation, and governance. His academic career spans multiple prestigious institutions including the London School of Economics and Political Science, London School of Hygiene and Tropical Medicine, and Australian National University. Wright's research focuses on political theory , public policy change , regulation and governance , and public health policy . His work examines critical issues such as the Australian Housing Crisis and British relations with the European Union, analyzing how policy regimes establish, consolidate, erode, and terminate meaning within policy objectives. He employs post-structuralist approaches to understand policy change as the transformation of meaning within discursive configurations. His recent publications reveal a strong focus on policy change analysis , particularly regarding European Union membership and Australian housing policy. The publications demonstrate interdisciplinary work spanning political science, health policy, regulatory studies, and economic evaluation. His research shows particular strength in analyzing how policy regimes function, how meaning is invested in policy objectives, and how these processes lead to policy continuity or change. Wright has secured research funding including the 2023 grant for Life Cycle Assessment of Hydrogen Storage Technologies for Circular and Sustainable Energy Systems . His scholarly impact is evident through numerous citations across his publications in political science, health policy, and regulatory studies. He actively engages with policy communities through external research impact activities related to the Australian Housing Crisis and British-EU relations.

Dominik Stögerمشاهده پروفایل

استادیار

• Mathematical Foundations of Data Science
• Non-convex Optimization
• Machine Learning Theory
• +۴ مورد دیگر

Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.

Tadashi Wadayamaمشاهده پروفایل

استاد

• Coding Theory
• Signal Processing
• Wireless Communications
• +۷ مورد دیگر

Professor Tadashi Wadayama serves in the Department of Computer Science within the Faculty of Engineering at Nagoya Institute of Technology. He holds a full professorship position and leads research initiatives in coding theory, signal processing, and deep learning applications for next-generation communication systems. Professor Wadayama received his B.E., M.E., and D.E. degrees from Kyoto Institute of Technology in 1991, 1993, and 1997 respectively. He began his academic career at Okayama Prefectural University in 1995 as a research associate and spent 1999-2000 as a visiting researcher at Essen University in Germany. He joined Nagoya Institute of Technology as an associate professor in 2004 and was promoted to full professor in 2010. He maintains active memberships in IEEE and the Institute of Electronics, Information and Communication Engineers (IEICE). His research spans multiple interconnected domains with primary focus on Coding Theory , Signal Processing for Wireless Communications , and Deep Learning applications . Professor Wadayama has made significant contributions to LDPC codes, MIMO signal detection, and the emerging field of deep unfolding techniques that bridge neural networks with traditional signal processing algorithms. His work increasingly incorporates physics-aware modeling of communication channels governed by partial differential equations. Recent research demonstrates strong interdisciplinary integration between information theory, machine learning, and communication engineering principles. Analysis of his recent publications reveals a clear trajectory toward developing foundational technologies for post-Shannon communication architectures. His work emphasizes ultra-large-scale coding, goal-oriented communication, digital homeostasis mechanisms, physics-embedded signal processing, and dual-process learning systems that combine fast reactive processing with deliberative meta-learning using LLM orchestrators. Fundamentals Review Best Author Award, IEICE, 2022 SRC 2010 Paper Award, Storage Research Promotion Organization, 2011 Professor Wadayama has successfully led multiple competitive research grants including JSPS Grant-in-Aid projects. He currently serves as Principal Investigator for the JST CRONOS project "Digital Cybernetics: Towards Next-Generation Communication Architecture" (2025-2031), which aims to develop foundational technologies supporting autonomous, adaptive, and robust large-scale AI systems. As IEEE Information Theory Workshop General Co-chair (2020-2021) and former chair of IEICE's Information Theory Research Committee (2020-2022), he maintains active leadership roles in the academic community. He leads the Wadayama Group at Nagoya Institute of Technology, which focuses on digital cybernetics and communication physics. The group develops physics-aware signal processing implementations, dual-process learning systems, digital homeostasis mechanisms, and system integration for next-generation communication architectures. His team collaborates with researchers from Kyoto University, Hiroshima University, Institute of Science Tokyo, and Tokyo University of Science, creating a robust research ecosystem focused on post-Shannon communication frameworks.