جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Cybersecurity
• Bayesian Networks
• +۵ مورد دیگر

Zhou Zhou is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on machine learning, adversarial robustness, Bayesian networks, and cybersecurity. He explores topics such as test-time adaptation, neural network robustness, and graph-based methods. Zhou has contributed to advancements in stochastic neural networks, feature distribution optimization, and multi-relation extraction for cybersecurity applications. His work bridges theoretical foundations and practical implementations in domains like combat network analysis and structural deformation monitoring. Education details are not explicitly provided in the text, but his research spans interdisciplinary areas including artificial intelligence, data science, and network analysis. His publications address challenges in model generalization, adversarial defense, and optimization algorithms. Zhou's research trends emphasize robustness in machine learning models, with a focus on adapting to real-world data variations. His work on Bayesian networks integrates ensemble methods and edge constraints to improve structure learning. In cybersecurity, he applies ontology-enhanced learning and graph theory to extract multi-relation insights. Recent articles highlight innovations in DAG learning, confidence calibration, and hybrid remote sensing techniques for infrastructure monitoring. He has no listed awards or grants mentioned in the text. His affiliations include Queen Mary's School of Electronic Engineering and Computer Science, where he contributes to research groups focused on AI, cybersecurity, and computational methods.

Nathaniel Johnstonمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Linear Algebra
• Matrix Analysis
• +۳ مورد دیگر

Nathaniel Johnston is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on quantum information theory, linear algebra, matrix analysis, and cellular automata. He is known for contributions to quantum entanglement, operator theory, and the development of the QETLAB toolbox for quantum entanglement research. Education details are not explicitly listed in the provided information. Johnston's research interests span quantum entanglement, matrix analysis, and the mathematical foundations of quantum computing. He has published extensively on topics such as quantum state characterization, tensor analysis, and cellular automata dynamics. His work often bridges abstract mathematical concepts with computational applications, as seen in his development of algorithms for quantum information processing. In his recent publications, he explores advanced techniques in quantum entanglement certification, spectral graph theory, and the application of linear algebra to quantum systems. His contributions include studies on matrix factorizations, operator norms, and the theoretical underpinnings of quantum coherence. No scientific awards are listed in the provided materials. Johnston has advised no students listed here, and no grants are mentioned. His work is primarily theoretical, with a focus on advancing mathematical frameworks in quantum information science. He is associated with the Department of Mathematics and Computer Science at Mount Allison University, contributing to both research and teaching in these fields.

Nidhal C. Bouaynayaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +۵ مورد دیگر

Nidhal C. Bouaynaya is a Professor at Rowan University's Henry M. Rowan College of Engineering, holding dual roles as Associate Vice President for Artificial Intelligence and Director of MAVRC. She leads the Electrical & Computer Engineering department and focuses on AI-driven research in health informatics and engineering systems. Education: Ph.D. & M.S. in Electrical & Computer Engineering (University of Illinois at Chicago), M.S. in Pure Mathematics (UIUC), B.S. in Electrical Engineering (ENSEA, France). Research: Specializes in AI, Machine Learning, Big Data Analytics, and Medical Imaging. Her work includes brain tumor segmentation (BRATS 2016 winner), healthcare AI applications, and aviation safety systems. She co-founded MRIMATH LLC to commercialize oncology AI solutions. Grants & Funding: Over $10M+ secured from NSF, NIH, FAA, USDA, and industry partners. Active projects include aviation safety data analysis, AI-based water resource modeling, and tumor surveillance systems. Awards: Rowan Research Achievement Award (2017), Best Paper Awards (IEEE 2019, GENSIPS 2013), and recognition for entrepreneurial ventures in AI healthcare. Students: Mentors 18+ doctoral, master's, and undergraduate researchers in AI, biomedical systems, and signal processing. Labs & Teams: Leads MAVRC (Multidisciplinary Applied Vision and Reality Computing lab) and collaborates with industry partners like Lockheed Martin and NASA on applied AI solutions.

Hoda Faresمشاهده پروفایل

استادیار

• Intelligent Neuroprosthetics
• Artificial Sensory-Motor Restoration
• Tactile Sensing & Electronic Skin
• +۳ مورد دیگر

Hoda Fares is an Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuroprosthetics and human-machine interfacing. Her research focuses on developing brain-inspired neural interfaces (BI-BCIs) that integrate neuromorphic hardware and flexible materials to restore sensorimotor function in individuals with disabilities such as stroke or amputation. She works on closed-loop systems combining bioinspired spiking neural networks (SNN) with low-power neuromorphic hardware. Her expertise includes intelligent neuroprosthetics, tactile sensing technologies, and electrotactile stimulation for bionic limbs. Current projects involve wearable neuromorphic interfaces for bidirectional control of prostheses and enhancing rehabilitation systems through artificial sensory-motor restoration. Fares' work bridges artificial intelligence with neuroengineering to create adaptive, miniaturized neural implants. Selected publications highlight advancements in functional electrical stimulation for stroke recovery, electronic skin validation, and multi-channel tactile feedback systems. Her research emphasizes clinical translation of technologies for real-world prosthetic applications, leveraging flexible/stretchable materials and distributed sensing networks. Despite no listed academic awards, her contributions to tactile sensing and prosthetic control systems demonstrate significant innovation in biomedical engineering. Ongoing work includes collaborations on neuromorphic hardware design and validation of virtual prosthetic systems with tactile feedback interfaces.

Fabien LEURENTمشاهده پروفایل

استاد

• Transport Systems Modeling
• Urban Development
• Environmental Economics
• +۴ مورد دیگر

Fabien Leurent is a Professor and Research Director at the International Center for Research on Environment and Development (CIRED), with an HDR in Operational Research from Université Paris-Dauphine (2006). He leads the ENPC-IDFM Chair on Territorial Mobility and has directed over 20 PhD theses. His research focuses on systemic modeling of transport networks, urban mobility systems, and sustainable infrastructure. Key areas include transport demand modeling, shared mobility economics, and data-driven analysis using trajectory and smartcard data. Education: HDR in Operational Research (2006), Université Paris-Dauphine. Extensive involvement in collaborative projects with Renault, RATP, SNCF, and the French Ministry of Transport. His work bridges academic research and policy, emphasizing techno-economic analysis and environmental impact assessments. Research interests span urban transport networks, parking system modeling, and sustainable mobility policies. Notable contributions include the DREAM urban equilibrium model and studies on electric vehicle deployment. His recent work explores shared mobility services, autonomous vehicles, and the integration of digital tools in mobility systems. Awards and recognitions: While no specific awards listed, his leadership in CIRED and ENPC-IDFM underscores academic and professional acclaim. Over 100 publications, including books on eco-design of infrastructure and transportation systems.

• Signal Processing
• Audio and Speech Processing
• Pattern Recognition
• +۳ مورد دیگر

Kris Demuynck is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, Department of Electronics and Information Systems (EA06), and leads the Internet Technology and Data Science Lab. With expertise in audio/speech processing, pattern recognition, and biomedical NLP, he supervises 14 doctoral researchers and contributes to speech recognition for healthcare and low-resource languages. Current projects: Core speech technology, NeLF (Flemish speech recognition), BioLORD-2023 (biomedical semantic models) Past grants: Special Research Fund, FWO, bilateral agreements Research Interests : Speech recognition architectures combining linguistic features with neural networks Biomedical semantic representation learning using LLMs and knowledge graphs Robust speaker verification and diarization in multi-speaker environments Selected Publications (2024-2025) demonstrate advancements in audio hashing, clinical NLP, and stress biomarker detection. His 15 most recent works span speech enhancement, speaker separation, and multilingual model adaptation. Supervision : Mentored doctoral projects on time series analytics, speech intelligibility, and pathological voice analysis. Active collaborations include FWO-funded precision health initiatives and industrial research fund projects on child linguistic analysis.

Pamela Abshireمشاهده پروفایل

استاد

• VLSI Circuit Design
• Bioengineering
• Information Theory
• +۷ مورد دیگر

Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. She holds the rank of Fischell Institute Fellow and is affiliated with the Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. Her work bridges VLSI circuit design and bioengineering, focusing on performance-resource tradeoffs in natural/engineered systems. Education: B.S. Physics (Caltech, 1992), M.S. and Ph.D. in Electrical Engineering (Johns Hopkins University, 1997 and 2001). Pre-UMD career included R&D roles at Medtronic (1992-1995). Research focuses on CMOS biosensors, low-power microsystems, and bio-inspired designs for applications like cell-based sensing, robotics, and medical devices. Notable projects include nose-on-a-chip odor detection systems, ant-like microrobots, and lab-on-CMOS platforms for real-time cell monitoring. Awards include IEEE Fellow (2018), NSF CAREER Award (2003), and 2021 University Distinguished Scholar-Teacher honor. Active in academic leadership roles including ADVANCE Professor (2020-2021) and editorial work for IEEE Transactions on Circuits and Systems. Grants include NSF funding for olfactory sensing, AFOSR bio-inspired flight tech, and DARPA CogniSense initiatives. Her Integrated Biomorphic Information Systems Lab collaborates on semiconductor innovation through partnerships like the Mid-Atlantic Semiconductor Collaborative. Labs/Teams: Leads the Integrated Biomorphic Information Systems Lab and contributes to Microelectronics at Maryland group. Co-develops biohybrid systems integrating CMOS, MEMS, and biological components.

Palina Salanevichمشاهده پروفایل

استادیار

• Signal Processing
• Data Analysis
• Graphs
• +۹ مورد دیگر

Dr. Palina Salanevich is an Assistant Professor at Utrecht University's Mathematical Institute, specializing in Mathematical Modeling. Her work bridges harmonic and time-frequency analysis, geometric functional analysis, signal and image processing, and high dimensional probability theory. Her research focuses on: Geometric properties of random frames and matrices, particularly Gabor frames Phase retrieval problems and their applications in ptychography and speech recognition Machine learning with randomization, especially Random Vector Functional Link networks Data processing using non-negative matrix factorization for audio and EEG signals Harmonic and time-frequency analysis on graphs Dr. Salanevich's recent publications show a strong focus on phase retrieval with Gabor frames, random matrix theory applications in signal processing, and randomized approaches to machine learning. Her work consistently bridges theoretical mathematics with practical signal processing applications, particularly in developing stable and efficient algorithms for complex inverse problems. Her notable scientific achievements include: NWO Veni grant (2021) for "Time-frequency structured measurements in phase retrieval: stability and reconstruction" Board member of the NDNS+ research cluster (2024) Dr. Salanevich actively supervises students, including PhD student Olov Schavemaker (since December 2021), and offers various projects for Bachelor and Master students in areas related to mathematical modeling and signal processing. She has been involved in several collaborative research projects with institutions like CWI and researchers including Deanna Needell, Goetz E. Pfander, and others. She is organizing the "Phase retrieval in mathematics and applications" workshop at the Lorentz Center in August 2024, demonstrating her leadership in her research community.

Konstantin Fackeldeyمشاهده پروفایل

پژوهشگر

• Drug Discovery
• Computational Biology
• High-Performance Computing
• +۷ مورد دیگر

Konstantin Fackeldey is a researcher at the Technical University of Berlin and heads the Efficient Large Scale Computing group at the Zuse Institute Berlin (ZIB) . His roles include Deputy Director of the Central Institute SETUB and leadership in the Activity Group Mathematics of Data Science at MATH+. He also serves as a high school mathematics teacher and contributes to the Q-Master program. His research spans drug discovery using high-performance computing and machine learning , with a focus on Protein-Ligand Docking (VirtualFlow Project) and Markov State Models . Collaborative projects include Quantum Skills in Teacher Education and co-organizing the Workshop on Tensor Methods for Quantum Simulation . He holds a habilitation in Applied Mathematics from TU Berlin (2015). Key contributions include developing algorithms for adaptive virtual screening and non-equilibrium molecular dynamics . His work bridges mathematical theory with practical drug design , involving interdisciplinary collaborations across institutions.

Carey Lumengمشاهده پروفایل

استاد

• Obesity
• Adipose Tissue Inflammation
• Immunometabolism
• +۵ مورد دیگر

Carey Lumeng, M.D., Ph.D., is a Professor in the Department of Pediatrics at the University of Michigan Medical School , where he works as a Pediatric Pulmonologist at Mott Children's Hospital. His research focuses on the mechanisms by which obesity negatively impacts health , particularly through adipose tissue inflammation and immune cell dysfunction . Primary Affiliation: University of Michigan Medical School Department: Pediatrics Email: clumeng@umich.edu Research Interests include: Adipose tissue macrophages as antigen-presenting cells Chemokine-mediated monocyte trafficking in obesity Single-cell RNA sequencing of adipose tissue in disease Obesity's role in T cell exhaustion Environmental obesogens and metabolic disruption Advising : Mentored past students like Cara Porsche (Ph.D.) and Brian Zamarron (Ph.D.).

Dimosthenis Kifokerisمشاهده پروفایل

دانشیار

• Construction Management
• Construction Informatics
• Machine Learning
• +۷ مورد دیگر

Dimosthenis Kifokeris is an Associate Professor and Docent in Construction Production and Management of Construction Projects at Chalmers University of Technology's Department of Architecture and Civil Engineering, where he also serves as the Coordinator for Artificial Intelligence. His research bridges construction engineering with digital technologies and organizational theory, focusing on how emerging technologies can transform traditional construction practices. His research interests span construction informatics (particularly machine learning, blockchain/DLT, IoT, and BIM), lean construction, constructability, and sustainable, circular, and regenerative production processes. He approaches these topics through interdisciplinary lenses including labor process theory, labor theory of value, organizational theory, and data science. His PhD thesis utilized supervised and unsupervised machine learning to appraise project constructability through risk analysis. Dr. Kifokeris has led or participated in Swedish and international research projects totaling over 57 million SEK, funded by Formas, SBUF, Vinnova, CMB, and Horizon Europe. His work demonstrates how digital technologies can address longstanding challenges in construction information management, safety analysis, and production processes. He holds significant leadership positions in the international construction research community, serving as Secretary of the Board of the European Council on Computing in Construction (EC3), a Board member of Construction Researchers on Economics and Organisation in the Nordic Region (CREON), and leader of the thematic group Byggprocess och Förvaltning in Sveriges Bygguniversitet (SBU). He chaired the EC3 2022 PhD Summer School in Rhodes and will chair the Thesis-in-Three competition at the EC3 2025 Conference in Porto. His recent publications reveal a strong trajectory toward integrating artificial intelligence with construction management, particularly focusing on how large language models can transform accident analysis and how blockchain can create more transparent and trustworthy information environments throughout the built asset lifecycle. His work represents a unique synthesis of technical innovation and critical organizational perspectives.

Peter George Martinمشاهده پروفایل

پژوهشگر ارشد

• Radiation Detection
• Nuclear Threat Reduction
• Nuclear Forensics
• +۵ مورد دیگر

Dr. Peter George Martin is a Royal Academy of Engineering Research Fellow in the School of Physics at the University of Bristol, specializing in radiation detection systems and nuclear threat reduction. His work develops responsive, high-resolution platforms for monitoring radioactive materials and enhancing nuclear security infrastructure. He earned his BSc and PhD from the University of Bristol, with doctoral research analyzing the 2011 Fukushima Daiichi Nuclear Power Plant accident across 10 orders of magnitude. His educational foundation combines physics with nuclear engineering expertise. Dr. Martin's research spans critical nuclear domains: Radiation Detection: Innovating gamma spectroscopy and sensor networks for real-time monitoring Nuclear Threat Reduction: Creating systems to prevent illicit radioactive material trafficking Nuclear Forensics: Investigating accident scenarios and radiological release incidents AI Integration: Developing machine learning algorithms for radiation data analysis Decommissioning Technologies: Pioneering robotics for nuclear site characterization His recent publications demonstrate converging trends in autonomous radiation mapping, AI-driven spectral analysis, and fundamental nuclear materials research, particularly focused on Fukushima and Chernobyl applications. Scientific recognition includes: Faculty of Science Thesis Commendation (2019) Innovus Nuclear Institute Technology Supply Chain Academic Abstract Prize (2016) Roy G. Post Scholarship (2016) Dr. Martin leads significant grant-funded initiatives including the RAIN at Chernobyl project and international collaborations with Japan, Ukraine, and the USA. His research partnerships span UK Atomic Weapons Establishment, Sellafield, IAEA, and global academic institutions, with emphasis on fieldwork at Fukushima and Chernobyl sites. He directs a research group developing open-source radiation analysis tools and autonomous mapping systems, maintaining active field deployments at nuclear facilities worldwide while advancing nuclear waste characterization methodologies.

• Chemometrics
• Analytical Chemistry
• Food Science
• +۴ مورد دیگر

Paul-Albert Anselm Schneide is a Research Fellow in the Department of Food Science within the Faculty of Science at the University of Copenhagen, actively contributing to the Design and Consumer Behavior section. His research integrates advanced chemometric methodologies with analytical chemistry to address complex data challenges in food science applications. His primary research interests center on developing innovative algorithms for multilinear data analysis, particularly shift-invariant models that resolve peak misalignments and shape variations in chromatography-mass spectrometry datasets. This work spans theoretical chemometrics framework development, practical signal processing workflows for suspect screening, and applications in sensory-driven food quality prediction. Key focus areas include non-negative tensor decomposition, gas/liquid chromatography data optimization, and crowd-sourced sensory data integration for predictive modeling in food systems. Analysis of his recent publications reveals a cohesive research trajectory advancing chemometric techniques for analytical chemistry data, with increasing emphasis on real-world food science applications. His 2023-2025 work demonstrates progression from foundational shift-invariant tri-linearity models to sophisticated frameworks handling "in-between" data structures, culminating in practical implementations for wine quality prediction and complex sample analysis. This trend highlights his dual expertise in mathematical methodology development and domain-specific food science problem-solving.

Sanjaye Ramgoolamمشاهده پروفایل

استاد

• String theory
• Quantum field theory
• Representation theory
• +۳ مورد دیگر

Dr. Sanjaye Ramgoolam is a Reader in Theoretical Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. His research focuses on string theory, quantum field theory, representation theory, and combinatorics, with particular emphasis on gauge-string duality (AdS/CFT correspondence) and permutation invariant matrix models. He has pioneered mathematical frameworks using representation theory and combinatorics to explore the holographic map between quantum field theories and string theory. His work also extends to applications in financial matrix models and computational linguistics via matrix statistics. Teaching roles include advanced courses such as Mathematical Techniques 4 and Advanced Quantum Field Theory. He has supervised numerous PhD students, including Costis Papageorgakis and Tom Brown, and collaborates with researchers like Andreas Brandhuber and Rodolfo Russo on grants like 'Amplitudes, Strings and Duality' funded by STFC. Recent research highlights include Gaussian permutation invariant matrix models, quantum thermodynamics in large N systems, and combinatorial topological string theories. His work bridges fundamental physics with mathematical structures, offering insights into quantum gravity and dualities. Key grants include the Royal Society-funded 'Combinatorics and algorithms for quantum states in holography' (2025-2026) and STFC's 'Amplitudes, Strings and Duality' (2023-2026). His 15 most recent articles span topics like eigenvalue systems for multi-matrix invariants, permutation symmetry in quantum thermodynamics, and Kronecker coefficients from ribbon graphs. Though no explicit awards are listed, his extensive publications and grants reflect significant contributions to theoretical physics.

Christian Rohlfingمشاهده پروفایل

پژوهشگر

• Signal Processing
• Data Compression
• Audio Source Separation
• +۳ مورد دیگر

Dr.-Ing. Christian Rohlfing is a Researcher at the Institute of Communications Engineering , RWTH Aachen University . His work spans genomic data compression , audio source separation , and video coding . Rohlfing’s research focuses on efficient genomic sequence compression using entropy coding, context-based methods, and standardization within MPEG-G . He also advances point cloud compression via graph transforms and adaptive entropy coding, and audio/video coding with non-negative matrix factorization (NMF), dictionary learning, and convolutional neural networks (CNNs). His publications (2011–2023) reflect expertise in signal processing for multimedia and bioinformatics , with applications in spatial audio coding , informed source separation , and gene variation compression . Rohlfing contributes to international standards like ITU-T and ISO/IEC MPEG , particularly in Versatile Video Coding (VVC) and MPEG-G for genomic data.