جست‌وجوی استاد و پژوهشگر

Pedro Miguel Rodriguesمشاهده پروفایل

استادیار

• Digital Signal Processing
• Artificial Intelligence
• Neurodegenerative Diseases
• +۶ مورد دیگر

Pedro Miguel Rodrigues is an Assistant Professor at the Faculty of Biotechnology, Catholic University of Portugal (UCP), and an integrated member of the Centre for Biotechnology and Fine Chemistry (CBQF-UCP). He co-coordinates the Bioengineering Degree and leads the Data Science in Biotechnology Postgraduate Program. His research focuses on applying digital signal processing, artificial intelligence, and biosensors to diagnose neurodegenerative, cardiac, and speech disorders, alongside developing smart tools for agri-food and biomedical systems. He holds a PhD in biomedical engineering from the University of Porto. Education: BSc, MSc, and PhD in Biomedical Engineering from University of Porto. Research Projects: Includes STF-EDIH (Smart Sustainable Farms), gBiOT (microbot against gut disorders), and HAC4CG (Climate Change initiatives). His work aligns with UN SDGs in sustainable food systems and climate action. Awards: CBQF Annual Incentive (2020) and Best Oral Presentation (2023, shared). Supervision: Advised 19 students in biotechnology and data science programs. Active in projects like avocado ripening prediction and ECG-based disease detection. Labs/Teams: CBQF-UCP, collaborating on interdisciplinary initiatives in biotechnology and digital health.

Matthew LaHayeمشاهده پروفایل

پژوهشگر

• Quantum effects in macroscopic systems
• Fundamental limits of measurement
• Nanoelectromechanical Systems (NEMS) applications
• +۳ مورد دیگر

Matthew LaHaye is affiliated faculty in the Physics department at Syracuse University and holds an affiliated faculty role at the Air Force Research Laboratory (AFRL). His research focuses on quantum phenomena in macroscopic systems, measurement limitations, and nanoelectromechanical systems (NEMS) applications. Dr. LaHaye's work bridges theoretical and applied physics, exploring foundational questions about quantum behavior in engineered systems. Education details: Not explicitly listed in provided text Research interests emphasize experimental and theoretical investigations into quantum measurement, NEMS devices, and macroscopic quantum effects. His work has potential applications in precision sensing and quantum technologies. No publications or awards are listed here, suggesting focus on ongoing research or classified projects. No grants, advising relationships, or laboratory affiliations are specified in the provided information.

• Structural Health Monitoring
• Anomaly Detection
• Modal Analysis
• +۴ مورد دیگر

Yacine Bel-Hadj is a researcher at the Vrije Universiteit Brussel within the Acoustics & Vibrations Research Group under the Faculty of Engineering Sciences. She holds a doctoral degree in Applied Mechanics and is actively involved in structural health monitoring (SHM) research. Her work focuses on anomaly detection, deep learning applications, and vibration-based monitoring for critical infrastructure like transmission towers, wind turbines, and bio-composite bridges. Research Interests: Her expertise includes Structural Health Monitoring (SHM) systems for civil infrastructure Machine learning in anomaly detection and damage localization Modal analysis and vibration-based diagnostics Environmental adaptation in SHM Smart materials and sustainable engineering solutions Publications: Her recent work emphasizes innovative methods such as self-supervised learning frameworks, automated operational modal analysis (OMA), and population-based monitoring strategies. She has published extensively in journals like Structural Health Monitoring and conference proceedings. Awards/Grants: No specific awards mentioned, but her research has been supported through institutional grants and collaborations. She served as an editorial board member for Wind Energy (May 2023–May 2024). Labs/Teams: Primary affiliation with the Acoustics & Vibrations Research Group, collaborating on projects involving fiber-optic sensing, bio-composite materials, and offshore wind turbine monitoring.

Naresh Neupaneمشاهده پروفایل

عضو هیئت علمی

• Climate Change Impacts
• Ecological Forecasting
• Butterfly Conservation
• +۷ مورد دیگر

Naresh Neupane is a Research Assistant Professor at Georgetown University, where he has been actively contributing to climate and ecological research since 2015. His work integrates climate science, ecological modeling, and conservation biology, with a strong focus on understanding the impacts of global warming on biological systems, particularly butterfly populations. His research interests center on climate change impacts on species distribution , ecological forecasting , and spatio-temporal modeling . He investigates how global warming affects the population dynamics and migratory patterns of insects, especially monarch butterflies, and explores the broader implications for biodiversity and ecosystem stability. His interdisciplinary approach combines remote sensing, citizen science data, and advanced statistical models to forecast ecological changes under future climate scenarios. Analysis of his recent publications reveals a consistent focus on insect conservation under climate change , with particular emphasis on monarch butterflies, Arctic and boreal species, and disease vectors. His work spans diverse ecosystems across North America and integrates data from satellites, field observations, and community science. He frequently publishes in top-tier journals such as Science , Nature Ecology & Evolution , and PNAS , indicating high scientific impact. Dr. Neupane collaborates extensively with leading ecologists, including Leslie Ries and Elise Zipkin, on large-scale projects related to butterfly monitoring and climate responses. While no formal students are listed, his role suggests active mentorship within research teams. He has also contributed to public health research through modeling Lyme disease dynamics in Virginia. His work is supported by robust computational and modeling frameworks, and he has developed novel methods for predicting forest green-up and classifying insect developmental stages using AI. These innovations highlight his technical expertise and commitment to advancing conservation through technology.

Syed Fawad Hussainمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Multi-View Learning
• +۷ مورد دیگر

Dr. Syed Fawad Hussain is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai campus. He is actively engaged in research and academic leadership, contributing significantly to the fields of artificial intelligence and machine learning. Education: PhD in Computer Science, Grenoble Alpes University, France (2010) MSc in Computer Science, Sorbonne University (Pierre and Marie Curie), Paris, France (2006) BS in Computer Science (2003) Dr. Hussain's research focuses on developing advanced machine learning algorithms for high-dimensional data, with specializations in multi-view learning, co-clustering, and semantic kernels. His work spans interdisciplinary domains including mobile healthcare, social media analysis, and cultural heritage preservation. He applies AI to classify ECG/EEG signals, detect diseases like schizophrenia and NAFLD, restore ancient manuscripts, and improve wireless communication systems. His recent publications reveal a strong trend in applying deep learning and ensemble methods to real-world problems across healthcare, defense, transportation, and digital humanities. He frequently collaborates on projects involving signal processing, image classification, and data fusion. Dr. Hussain has supervised numerous graduate students and co-authored with researchers across multiple disciplines. He has contributed to national-level academic development in Pakistan as co-chair of the Higher Education Commission's curriculum revision committee. Scientific Contributions: Published in high-impact journals such as Scientometrics , Information Fusion , Expert Systems with Applications , and Knowledge-Based Systems . Active in both theoretical and applied AI research. Contributor to national curriculum development in computer science. He is affiliated with the University of Birmingham’s Dubai campus and previously served as a faculty member and dean at GIK Institute, Pakistan. His work continues to bridge theory and application in artificial intelligence.

• Radar Systems
• Signal Processing
• Automatic Target Recognition
• +۷ مورد دیگر

Professor Carmine Clemente is a leading academic in Radar Systems and Signal Processing at the University of Strathclyde, Faculty of Engineering. His research spans defense and civilian applications, with international collaborations (USA, China, Europe) and over 185 research outputs. Key areas include emerging radar techniques, machine learning, and AI applications. PhD in Electronic & Electrical Engineering, University of Strathclyde Senior Member IEEE Chair, Advanced Signal Processing for Radar/EW at EMSIG (UK Radar Society) Research focuses on micro-Doppler signature analysis , MIMO radar , SAR polarimetry , and automatic target recognition . Recent projects include structural health monitoring of bridges and AI applications for space operations. Publications highlight advancements in cognitive radar jamming and SAR vibration analysis. 2nd place Copernicus Masters 2020 Best Paper Awards (Metrology for Aerospace 2020, SSPD Conference) European Satellite Navigation Competition winner 2016 Teaches Digital Signal Processing Principles and Assignment and Professional Studies . Supervises research teams in defense, security, and industrial/biomedical applications. Current projects include Leonardo Lab Phase 0 Activity and AI for Space Operations (£3000+ grants).

• Biophysical Chemistry
• G-quadruplex DNA Structures
• Thermal Liquid Biopsy
• +۷ مورد دیگر

Robert Buscaglia is an Associate Professor in the Department of Mathematics and Statistics at Northern Arizona University, where he contributes to interdisciplinary research at the intersection of mathematics, biophysics, and biomedical sciences. His work integrates statistical modeling, thermodynamics, and machine learning to address challenges in genomics, cancer diagnostics, and environmental remote sensing. Research Interests: Dr. Buscaglia's research focuses on the biophysical properties of G-quadruplex DNA structures, particularly in telomeres and oncogene promoters. He employs techniques such as differential scanning calorimetry to study DNA stability and ligand interactions. His work extends to clinical applications, including thermal liquid biopsy for detecting myocardial injury and melanoma progression through plasma thermogram analysis. Additionally, he applies eigenfeature-enhanced deep learning models to classify tree species in mixed conifer forests using LiDAR data, demonstrating strong expertise in computational and environmental data science. Recent Research Trends: His recent publications (2023–2025) reflect a dual focus: (1) biomedical applications involving angiotensinogen, cardiovascular biomarkers, and cancer survival prediction using thermodynamic profiling; and (2) ecological modeling using advanced machine learning on remote sensing data. These works highlight his ability to bridge mathematical theory with real-world biological and environmental problems. Scientific Contributions: While specific awards are not listed, his h-index of 20 and 1284 citations indicate significant scholarly impact. His fingerprint in research includes high relevance in G-quadruplex, telomere biology, promoter regions, and thermal profiling. Collaborations and Advising: Dr. Buscaglia collaborates extensively with medical and ecological researchers, including K. C. F. Lidani, N. C. Garbett, and A. J. Sánchez Meador. No formal advisees are listed in the provided text. He is involved in large-scale studies such as the Multi-Ethnic Study of Atherosclerosis (MESA), suggesting participation in federally funded or multi-institutional grants. Laboratories and Research Groups: While no specific lab name is mentioned, his work in thermal liquid biopsy and DNA biophysics suggests affiliation with a biophysical chemistry or computational biology laboratory at NAU, likely collaborating with health and environmental science departments.

Mu Muمشاهده پروفایل

استاد

• Human-Centred Artificial Intelligence
• Immersive Media
• Virtual Reality
• +۵ مورد دیگر

Dr. Mu Mu is a Professor of Human-Centred Data Intelligence at the University of Northampton's Faculty of Arts, Science and Technology (FAST). He leads the university's Artificial Intelligence and Data Science programme and founded the Centre for Intelligent Digital Infrastructure (CIDI), focusing on immersive media and machine learning research. His teaching portfolio includes Media Technology, Artificial Intelligence, and Interaction Design. His research integrates human-centred designs , intelligent networks , and immersive media , with applied projects in digital health (e.g., VR therapy for phobia/trauma) and data-driven service optimization. He collaborates extensively with industry partners and SMEs on innovation projects funded by UKRI and the EU. Recent publications emphasize VR-based mental health interventions , AI-driven diagnostics (e.g., cancer prediction, cataract detection), and metaverse navigation systems . His work shows a consistent trend toward clinically applicable AI and ethically informed immersive technologies. Awards & Fellowships: Best Paper Award, IEEE/ACM IWQoS 2015 He supervises 9+ PhD students in projects spanning applied AI, VR therapy, and medical imaging. His grants include: Data analysis for Flow Neuroscience/NHS tDCS depression treatment (PI) Commercialization of VR interventions for phobia (PI) Social XR for mental healthcare (Co-PI) At CIDI, he oversees labs for volumetric capture, motion tracking, and metaverse development, partnering with healthcare and industrial stakeholders.

Farhad Kosariمشاهده پروفایل

دانشیار

• Molecular Medicine
• Genomics
• Prostate Cancer
• +۹ مورد دیگر

Farhad Kosari, Ph.D., is an Associate Professor in the Department of Molecular Medicine at Mayo Clinic, Rochester, Minnesota. He also holds the academic rank of Assistant Professor of Biochemistry and Molecular Biology. His research is centered on molecular oncology, with a strong emphasis on genomics, cancer biomarkers, and translational diagnostics in prostate cancer, lung adenocarcinoma, and mesothelioma. Dr. Kosari earned his Ph.D. in Biochemistry and Molecular Biophysics from the University of Pennsylvania, complemented by advanced degrees in Electrical Engineering (M.S. from Virginia Tech, B.S. from Texas Tech University), reflecting his interdisciplinary approach to biomedical research. His research interests span genomic alterations in cancer , chromosomal rearrangements , cell-free DNA analysis , immunotherapy response biomarkers , and early detection assays . He leverages deep sequencing, nanopore technology, and bioinformatics to uncover molecular drivers of malignancy and to develop non-invasive diagnostic tools. The trends in his recent publications (2018–2025) highlight a focus on targeted cancer therapies , PROTAC-based degradation , immune monitoring in mesothelioma , and molecular diagnostics in non-neoplastic tissues . His work integrates genomics, transcriptomics, and clinical data to advance precision oncology. Scientific Awards and Recognition: No specific awards are listed in the provided text. Dr. Kosari has been actively involved in grant-funded research, serving as Principal Investigator on projects funded by the Congressionally Directed Medical Research Programs and as Co-PI on a National Cancer Institute grant. His work supports postdoctoral fellowships and collaborative training programs. He is embedded in key research centers including the Mayo Clinic Comprehensive Cancer Center, the Epidemiology and Genetics of Lung Cancer Research Program, and the David F. and Margaret T. Grohne Cancer Immunology and Immunotherapeutics Program. His laboratory collaborates extensively with clinicians and scientists in oncology, pathology, and immunology to translate molecular findings into clinical applications.

• Causal Discovery
• Computational Argumentation
• Explainable AI
• +۴ مورد دیگر

Dr. Fabrizio Russo is a Research Associate in the Computational Logic and Argumentation group within the Department of Computing at Imperial College London's Faculty of Engineering. He obtained his PhD in Safe and Trusted AI from Imperial College in 2025, funded by the UKRI Centre for Doctoral Training (STAI). Previously, he spent six years in industry as Head of Data Science at 4most Europe, specializing in credit risk modeling. His research focuses on augmenting human decision-making through Causal Discovery , Computational Argumentation , and Explainable AI . He develops transparent techniques that integrate causality with machine learning to create contestable AI systems for high-stakes domains. Key interests include human-AI debate frameworks, knowledge injection in neural networks, and Shapley-based causal structure learning. Publications demonstrate sustained contributions to knowledge-driven AI , with recent works exploring argumentative causal discovery (2024), contestable neural networks (2023), and classifier explanation methods (2023). Research consistently combines formal reasoning with practical applications in trustworthy AI. Awards & Recognition: AISTATS 2023 Top-Reviewer Award Teaching & Service: Served as Graduate Teaching Assistant for Introduction to Machine Learning (2021-2022). Co-organizes the Explainable AI Seminars @ Imperial and contributes as reviewer for top conferences (ICLR, NeurIPS, ICML). Assistant Chair for XAI-FIN workshops at ICAIF (2022-2024).

Antonio Vergariمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Modeling
• +۵ مورد دیگر

Antonio Vergari serves as a Reader (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, affiliated with the Institute for Adaptive and Neural Computation (ANC). His research develops probabilistic machine learning systems that are provably reliable in real-world applications through the integration of complex reasoning, efficient inference, and neuro-symbolic learning paradigms. His primary research domains include Artificial Intelligence, Machine Learning, Probabilistic Modeling, and Neuro-symbolic AI, with specific expertise in probabilistic circuits, tensor networks, and constraint-satisfying architectures. Current investigations focus on unifying tensor factorization theory with circuit representations to overcome expressiveness limitations while maintaining computational tractability for complex reasoning tasks. Analysis of his publication trajectory reveals consistent advancement in reliable probabilistic modeling, particularly through circuit-based approaches that guarantee logical consistency in neural predictions. His work bridges theoretical foundations (e.g., expressiveness hierarchies) with practical implementations (e.g., Cirkit library), addressing critical gaps in benchmark complexity and scalable constraint satisfaction. Scientific recognition includes: ICLR 2024 Spotlight presentation (top 5% acceptance rate) NeurIPS 2023 Oral presentation (top 0.6% acceptance rate) NeurIPS 2021 Oral presentation (top 0.6% acceptance rate) Vergari leads the APRIL Lab, which actively contributes to open-source probabilistic modeling tools like Cirkit while organizing community initiatives such as the CoLoRAI workshop at AAAI-25. The lab maintains strong industry and academic collaborations focused on developing theoretically-grounded, deployable probabilistic systems.

Gannon Whiteمشاهده پروفایل

استادیار

• Biomechanics
• Exercise Science
• Electromyography (EMG)
• +۷ مورد دیگر

Gannon White is an Assistant Professor of Kinesiology at Colorado Mesa University, where he teaches exercise science and biomechanics. He holds a PhD in Exercise Science from the University of Northern Colorado with a minor in applied statistics, an MEd in Education/Kinesiology from Stephen F. Austin State University, a BGS in General Studies from Lamar University, and an AS in Computer Science from Allen County Community College. PhD, Exercise Science: University of Northern Colorado MEd, Education/Kinesiology: Stephen F. Austin State University BGS, General Studies: Lamar University AS, Computer Science: Allen County Community College His research focuses on the biomechanical and statistical analysis of human movement, particularly in electromyography (EMG), muscle synergies, economy of motion, and perturbed motor tasks such as slacklining. He applies quantitative methods to understand motor control and neuromuscular coordination, with applications in neurorehabilitation and prosthetics. His work often involves collaboration with engineers and neuroscientists to model and classify movement patterns. The recent publications highlight a strong trend in using muscle synergies for understanding motor behavior, identifying locomotion modes, and improving myoelectric control systems. His research spans biomechanics, motor control, signal processing, and rehabilitation engineering, with a focus on both theoretical frameworks and practical applications in assistive technologies. Dr. White previously served as a faculty member and graduate coordinator of the Exercise Science Program at the University of Arkansas at Little Rock for nine years before joining Colorado Mesa University. He emphasizes interactive, hands-on learning and equips students with analytical tools for assessing human mechanics. There are no listed scientific awards or formal advisees in the provided text. He integrates engineering and statistical approaches into kinesiology, fostering interdisciplinary research in motor behavior and rehabilitation. His lab work appears to involve EMG signal processing, dynamic movement analysis, and modeling of neuromuscular control strategies, often in collaboration with researchers in neural systems and biomedical engineering.

Friederike Käte Evelyn Marie-Luise Lüpkeمشاهده پروفایل

استاد

• African Languages
• Multilingualism
• Language Documentation
• +۴ مورد دیگر

Friederike Käte Evelyn Marie-Luise Lüpke is a Professor of African Studies at the University of Helsinki , affiliated with the Faculty of Arts and Department of Languages . She leads the Afristadi research forum and contributes to the Helsinki Institute of Sustainability Science (HELSUS) and Helsinki Inequality Initiative (INEQ) . Research focuses on West African languages (Mandé, Atlantic families) and small-scale multilingualism Develops LILIEMA (Language-independent literacies for inclusive education) Key areas: verbal argument structure, nominal classification, sociohistorical linguistics Selected Publications: Grammar of Jalonke argument structure, Oxford Guide to Atlantic languages, and work on Baïnounk Gujaher. Recent articles explore situational languaging models, multilingual education, and decolonial approaches. Projects include the LILIEMA literacy program (2019-2023), Crossroads on Senegambian multilingualism (2014-2018), and DoBeS documentation initiatives. Current 2023-2024 work at the Helsinki Collegium for Advanced Studies examines multilingualism's role in decolonization research.

• Neuropathology
• Neuro-oncology
• Brain Tumor Classification
• +۴ مورد دیگر

Snuderl M. is a prominent researcher at the German Cancer Research Center (DKFZ) in Heidelberg, Germany, specializing in neuropathology and neuro-oncology. As a key contributor to the cIMPACT-NOW consortium, they have co-authored multiple influential updates (9, 10, and 11) that shape the World Health Organization's classification of central nervous system tumors. Their research primarily focuses on molecular diagnostics, DNA methylation profiling, and the genomic characterization of brain tumors including meningiomas, astrocytomas, and medulloblastomas. Snuderl M.'s work bridges laboratory research with clinical applications, developing diagnostic tools that improve tumor classification and patient outcomes. They have made significant contributions to understanding TERT promoter mutations in meningioma prognosis, BAP1-altered meningioma biology, and the molecular signatures of rare CNS tumors like pleomorphic xanthoastrocytoma and intraparenchymal schwannomas. Their research has been published in leading neuro-oncology journals including Neuro-Oncology, Acta Neuropathologica, and The Lancet Oncology. Their publication record demonstrates expertise in translating complex molecular data into clinically actionable diagnostic criteria. Recent work has focused on refining classification systems for IDH-mutant astrocytomas, H3 G34-mutant diffuse hemispheric gliomas, and NTRK fusion-driven CNS tumors. The research shows consistent emphasis on identifying molecular biomarkers that predict clinical behavior and therapeutic response. cIMPACT-NOW Consortium Member (key contributor to WHO CNS tumor classification updates) Developer of molecular diagnostic approaches for CNS tumors Expert in DNA methylation profiling applications for neuro-oncology Snuderl M. collaborates extensively with international neuro-oncology centers, participating in multi-institutional cohort studies that analyze hundreds of tumor samples. Their work supports the integration of molecular diagnostics into routine neuropathology practice, improving precision in tumor classification and patient management. Current research directions include refining risk stratification systems for pediatric CNS tumors and developing proteomic approaches to identify therapy-resistant tumor subgroups.

• Software Security
• Complex Systems
• Artificial Intelligence
• +۶ مورد دیگر

Pierre Parrend is a researcher at INSA de Lyon's College of Engineering, focusing on software security, complex systems, and artificial intelligence applications. He has published extensively on topics including OSGi platform security, graph-based attack detection, and biologically inspired computing models. His research bridges cybersecurity with machine learning, emphasizing explainable AI for threat detection. Recent work includes Agentic AI frameworks for offensive security Graph processing methodologies for ML Multilayer architectures in cyber-physical systems Key collaborations include Pierre Collet, Stéphane Frénot, and Julio Navarro. While no formal education details appear in the data, his work spans 2006-2025 with significant contributions to zero-day attack detection and security architecture.