جستجوی استاد راهنمای دانشگاهی

• Digital Inclusion
• Public Sector Digitization
• E-Government Services
• +۵ مورد دیگر

Jesper Holgersson is an Associate Professor and Head of Department at the School of Informatics, University of Skövde, Sweden. He holds a Ph.D. in Informatics (2014) and was awarded Docent status in 2023, following his earlier academic achievements including a BSc in Information Systems Engineering (2001) and an MSc in Computer Science (2002). His academic career demonstrates a strong progression from technical foundations to leadership in information systems research and education. Dr. Holgersson's research focuses primarily on digitization within the public sector from an end-user perspective, with particular emphasis on digital inclusion of seniors, user participation in public e-service development, and cybersecurity. His work addresses critical societal challenges in making digital services accessible and usable for diverse populations, especially vulnerable groups like elderly citizens. His research bridges theoretical frameworks with practical applications in government and public service contexts. Analysis of his recent publications reveals a consistent focus on digital inclusion challenges for elderly citizens, with multiple studies examining barriers to technology adoption and developing strategies to reduce digital exclusion. His work has increasingly incorporated artificial intelligence applications, particularly in marketing contexts, while maintaining his core focus on public sector digitization and user-centered design principles. Dr. Holgersson leads several significant research projects including DOME (Development of Online Medical records and E-health services, 2022-2025), Collaboration between municipalities for digital transformation (2022-2024), and Skara Guardian Angel (using lighting drones for cyclist safety, 2021-2023). These projects reflect his commitment to applying research for societal benefit across healthcare, municipal services, and transportation safety domains. His academic contributions span numerous publications in prestigious conferences and journals focused on information systems, e-government, and human-computer interaction. Dr. Holgersson has been instrumental in shaping understanding of user participation practices in public e-service development and developing effective strategies for digital inclusion of marginalized populations.

• Wireless Sensor Networks
• Network Protocols
• Internet of Things (IoT)
• +۱۲ مورد دیگر

Kris Steenhaut is a researcher at the College of Engineering , Vrije Universiteit Brussel , specializing in Electronics and Informatics . His work bridges Research, Development, and Innovation in domains like wireless sensor networks, IoT, and cybersecurity. Fields of Interest : Wireless Sensor Networks, Network Protocols, Internet of Things, Cryptography, Embedded Systems, Cybersecurity. Research Trends : Recent publications focus on optimizing cryptographic algorithms for ARM processors, Rust in embedded systems, IoT security protocols, and privacy-preserving frameworks. These works intersect with Computer Science , Communication Engineering , and Network Security . Scientific Awards : ITEA Achievement Award Gold Medal (2010)

Xiaojie Qiuمشاهده پروفایل

استادیار

• single-cell genomics
• spatial genomics
• machine learning
• +۶ مورد دیگر

Dr. Xiaojie Qiu serves as an Assistant Professor at Stanford University with dual appointments in the Department of Genetics and Department of Computer Science, as well as the BASE program. He holds additional memberships in Bio-X, the Cardiovascular Institute, the Maternal & Child Health Research Institute (MCHRI), and the Wu Tsai Neurosciences Institute, reflecting his interdisciplinary approach to biomedical research. His educational journey includes: Postdoctoral fellowship at Whitehead Institute & MIT (2023) in single cell and spatial genomics; systems biology PhD from University of Washington (2018) in Molecular & cellular biology; genome sciences Masters from East China Normal University (2012) in Bioinformatics and systems biology BS from Changchun University of Technology (2008) in Bioengineer Dr. Qiu's research integrates single-cell and spatial genomics with machine learning to unravel the complexities of heart evolution, development, and disease. His lab employs a full experimental-computational cycle, beginning with novel genomics approaches to measure single-cell states over time and space, then developing interpretable machine learning models to understand underlying gene regulatory networks. These computational models enable predictions of congenital heart diseases, aiming to identify when and where disease begins in the heart to develop preventive therapies. His scientific excellence has been recognized with several prestigious awards: NIH Director's New Innovator Award (2024) Arc Ignite Award (2023) Pathway to Independence Awards (K99/R00) (2023) Dr. Qiu mentors a diverse group of researchers including Tony Zeng (Doctoral Dissertation Reader), Sijie Chen, Naoki Konno, Nianping Liu, and Weize Xu (Postdoctoral Faculty Sponsors), and Peter Pao-Huang (Doctoral Dissertation Co-Advisor). His research program receives substantial support from the National Human Genome Research Institute, Chan Zuckerberg Institute, Impetus longevity grant, and Arc institute. The publication trends reveal his expertise spans computational biology, single-cell analysis, and cardiac development, with increasing focus on spatial transcriptomics and predictive modeling. The Qiu Lab has pioneered several influential computational tools including Monocle 2/3 (for pseudotemporal trajectory analysis), Dynamo (for RNA velocity vector field reconstruction), and Spateo (for multi-dimensional spatiotemporal modeling). Currently, the lab is developing the Aristotle ecosystem, an end-to-end predictive software framework that aims to bridge the gap between big data from single-cell and spatial genomics and quantitative predictive modeling of mammalian cell fate transitions.

Simone Fischer-Hübnerمشاهده پروفایل

استاد

• Privacy Engineering
• Usable Security
• Identity Management
• +۳ مورد دیگر

Simone Fischer-Hübner is a Professor of Computer Science at Karlstad University, Sweden, serving since June 2000, and holds a Guest Professorship at Chalmers University of Technology since 2022. Her work bridges technical privacy solutions with regulatory frameworks like GDPR, focusing on human-centered approaches to security and identity management. Her academic credentials include a Habilitation degree (1999) and Doctorate (1992) in Computer Science from Hamburg University, plus a Diploma in Computer Science with Law minor (1988) from the same institution. Prior appointments include Assistant Professor at Hamburg University (1992-2000) and Guest Professorships at Stockholm University/KTH and Copenhagen Business School. Her research centers on making privacy controls usable across diverse contexts. Key themes include developing transparency tools for Privacy Impact Assessments, designing intuitive interfaces for identity management, and analyzing cross-cultural privacy preferences in intelligent transportation systems. She emphasizes practical GDPR compliance through user-centered design and investigates how metaphors can explain complex concepts like differential privacy to non-experts. Recent publications reveal a strong trend toward human-AI collaboration in privacy decisions. Her work increasingly addresses VANETs, cloud security, and automated privacy tools while maintaining user agency. There's notable focus on visualizing data risks and adapting privacy frameworks to cultural differences in driver behavior studies. Her scientific recognition includes: Honorary Doctorate from Chalmers University (2020) William Winsborough Award (IFIP WG 11.11, 2016) Multiple Best Paper Awards (ACM SAC 2018, ISD 2017) Google Research Awards (2010, 2012) IFIP Silver Core Award (2001) She leads major EU and national projects including Horizon Europe's TRUMAN (trustworthy AI) and KKS's SIGS-CyberSec graduate school. As scientific coordinator of the EU Marie Skłodowska-Curie ITN Privacy & Us, she shaped pan-European PhD training. Her advisory roles span Sweden's MSB Cyber Security Board, Norway's COINS PhD school, and the PET Symposium board. Through SWITS (Swedish IT Security Network) and Cybernode.se, she drives national cybersecurity education initiatives, including MOOC certification frameworks and GDPR training platforms used across Scandinavia.

Xiaohui Xieمشاهده پروفایل

استاد

• AI/Machine Learning
• Neural Networks
• Deep Learning
• +۳ مورد دیگر

Xiaohui Xie is a Professor in the Department of Computer Science at the Bren School of Information and Computer Sciences, University of California, Irvine. He joined UC Irvine in 2007 after completing his PhD at MIT and postdoctoral training at the Broad Institute of MIT and Harvard University. His research focuses on AI/machine learning, neural networks, deep learning, and genomics. PhD in Computer Science, MIT Postdoctoral training at Broad Institute Research interests span AI, neural networks, deep learning, and genomics, with applications in medical imaging, computational biology, and computer vision. Recent publications highlight advancements in 3D convolutional networks for nodule detection, medical image analysis, and hybrid models for DNA sequence function quantification. Scientific awards and honors are not explicitly mentioned in the provided text. His teaching includes courses like CS273P (Machine Learning and Data Mining), CS175 (Project in AI), and foundational classes in computational linear algebra and optimization, reflecting his interdisciplinary expertise.

• Clinical Bioinformatics
• Antimicrobial Resistance
• Rapid Diagnostics
• +۳ مورد دیگر

Rafi Ahmad is a Professor at UiT The Arctic University of Norway specializing in Clinical Bioinformatics, based in Tromsø. He leads translational research within the Clinical Bioinformatics Research Group, focusing on accelerating infectious disease diagnostics through novel sequencing technologies to transform clinical decision-making for sepsis and urinary tract infections. His research centers on antimicrobial resistance (AMR) profiling and rapid pathogen detection using culture-independent methods. Key innovations include nanopore sequencing workflows that reduce diagnostic turnaround times from days to 4-9 hours, enabling real-time treatment adjustments. He investigates microbiome dynamics in inflammatory conditions like ulcerative colitis and develops machine learning-enhanced biosensors (e.g., SERS nanowire chips) for strain-level bacterial identification. His work bridges clinical microbiology, bioinformatics, and One Health frameworks to address AMR globally. Analysis of his 2022-2025 publications reveals a consistent trajectory toward point-of-care diagnostic implementation , with emphasis on urine, blood, and tissue samples. Methodologies prioritize speed ( As an active member of the Clinical Bioinformatics Research Group (MH øst L9.105B), he collaborates internationally on projects like AMR-Educate, focusing on technology transfer from research to clinical practice. His lab specializes in optimizing sequencing protocols for real-world use, including sample preparation enhancements that bypass culture steps.

• Geomatics
• Emergency Management
• Geographic Information Systems (GIS)
• +۹ مورد دیگر

Piero Boccardo is a Full Professor of Geomatics at the Interuniversity Department of Regional and Urban Studies and Planning (DIST) at the Polytechnic University of Turin. He teaches remote sensing in Master’s programs in Environmental and Land Engineering, Geography, and Urban and Regional Planning. Director of ITHACA (2006–present), a research association active in emergency management and Copernicus EMS provider (2012–present) Former President of 5T (2012–2018), a public company in mobility and ITS President of Italian Remote Sensing Association (2011–2019) Education: PhD in Geodetic and Topographic Sciences from Polytechnic University of Turin. Research Interests: Focuses on geomatics applications for emergency management, digital twins, intelligent transportation systems, climate action, and renewable energy transition. His work integrates remote sensing, GIS, and spatial analysis to address sustainable urbanization and environmental challenges. Article Trends: Recent publications emphasize urban energy assessment via aerial thermography, digital twin frameworks for cities and natural systems, and geospatial solutions for climate action. Topics span from 3D modeling of alpine glaciers to green hydrogen policy analysis. Scientific Awards: CNR Research Award (1993) CNR Scholarship Abroad (1992) Fellowships from Ministry of Infrastructure and Transport (2017-2018), AIT (2010-2018), ISPRS (2004-2012) Advising & Grants: Supervises PhD students in Urban and Regional Development and Civil and Environmental Engineering. Leads research projects like NA2GO (navigation technology), GeoSciences (national geological networks), IDEM (environmental monitoring), and TECNOLOGIE INNOVATIVE PER LA GESTIONE DELLE EMERGENZE AMBIENTALI (environmental emergency management). Labs & Teams: Co-founder of SDG11Lab (DIST) focused on sustainable cities. Chairs working groups for "Smart Roads" and collaborates with institutions like Compagnia di San Paolo, European Space Agency, and World Bank.

Zijie Zhangمشاهده پروفایل

استادیار

• Federated learning
• Graphs
• Trustworthy machine learning
• +۳ مورد دیگر

Zijie Zhang is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on federated learning, graph-based machine learning, and trustworthy AI systems. He holds a Ph.D. in Computer Science from Auburn University and an M.S. from Stevens Institute of Technology. Education: Ph.D. in Computer Science, Auburn University M.S. in Computer Science, Stevens Institute of Technology His work emphasizes secure and robust machine learning techniques, including adversarial defense mechanisms, federated learning optimizations, and graph alignment algorithms. Recent publications explore topics like machine unlearning, resilient graph matching, and cross-lingual knowledge graph alignment. His research bridges theoretical advancements with practical applications in distributed systems and cybersecurity. Dr. Zhang’s contributions address challenges in privacy, model efficiency, and adversarial robustness, reflecting a commitment to advancing trustworthy AI methodologies.

• Machine Learning
• Artificial Intelligence
• Precision Livestock Farming
• +۵ مورد دیگر

Sook Shin serves as a Collegiate Assistant Professor in the Department of Electrical and Computer Engineering within Virginia Tech's College of Engineering. Her interdisciplinary work bridges computer science, agricultural engineering, and bioinformatics through innovative applications of machine learning. Education PhD in Computer Engineering, Virginia Tech Master of Information Technology, Virginia Tech B.S. in Computer Science, Virginia Tech Her research centers on AI-driven solutions for precision agriculture , particularly in livestock monitoring systems using depth imaging and wireless sensors. She develops machine learning frameworks for pig behavior classification, weight prediction, and resource optimization, while also contributing to bioinformatics tools for plant modeling and disease subtyping. Recent work emphasizes edge intelligence for power-efficient sensor networks and secure data labeling pipelines. Analysis of her 15 most recent publications (2012-2025) reveals a dominant focus on precision livestock farming (60% of works), with significant contributions to bioinformatics (25%) and educational technology (15%). Her methodology consistently integrates deep learning with domain-specific sensor data, showing increasing sophistication in multi-modal input processing and real-time system optimization from 2022 onward. Teaching Contributions Applied software design Data structures and algorithms Computational thinking Machine learning applications She actively develops scientific web tools including PlantSimLab for plant biologists and contributes to capstone project frameworks that bridge academic theory with industry applications in smart farming systems.

• Climate Resilience
• Agricultural Sustainability
• Remote Sensing Applications
• +۵ مورد دیگر

Michael Cecil is a part-time instructor in the Department of Sustainability and Social Justice at Clark University. His academic focus revolves around climate resilience, agricultural sustainability, and the application of geospatial artificial intelligence in environmental monitoring. Cecil's work integrates machine learning techniques with satellite data to address challenges in smallholder agriculture, climate-smart farming practices, and policy-driven agricultural programs. His research interests include leveraging remote sensing technologies for scalable agricultural monitoring, developing cloud gap imputation methods using foundation models, and assessing the efficacy of USDA programs in supporting climate-resilient agriculture. Cecil also explores the intersection of farmer decision-making, yield variability, and risk management in agricultural systems, particularly in data-sparse environments. Notable contributions include advancing vision transformer and generative adversarial network (GAN) models for satellite image analysis, evaluating foundation models in geospatial tasks, and analyzing producer strategies within evolving climate baselines. His work emphasizes interdisciplinary collaboration to bridge gaps between environmental science, policy, and technological innovation.

• Artificial Intelligence
• Bioinformatics
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.

• Gait Analysis
• Wearable Sensors
• Parkinson's Disease
• +۷ مورد دیگر

Dr Silvia Del Din is a researcher at Newcastle University specializing in gait analysis and digital health technologies. Her work focuses on mobility assessment in aging populations, neurological disorders (e.g., Parkinson's disease), and chronic conditions like COPD and sarcopenia. She leads projects such as the Mobilise-D consortium, validating wearable sensors for real-world mobility monitoring. Collaborations involve clinicians, engineers, and data scientists to develop algorithms for gait detection, turning analysis, and environmental context recognition. Her research emphasizes translating sensor data into actionable clinical insights, with applications in fall prevention, disease progression tracking, and exercise intervention studies. Key Projects: Mobilise-D, Acipimox sarcopenia trial, IDEA-FAST feasibility study. Technologies: IMU sensors, machine learning, self-supervised algorithms, magnetic field analysis. Clinical Focus: Parkinson's disease, COPD, aging frailty, long-term care environments. Publications highlight innovations in algorithm validation, environmental sensing, and interdisciplinary approaches to mobility assessment. Her work bridges engineering and medicine to improve diagnostic tools and patient care in chronic and neurological conditions.

• Signal Processing
• Statistical Analysis
• Bayesian Methods
• +۳ مورد دیگر

Dr. Alireza Ahrabian is a Research Fellow in the Department of Electronic Engineering at the University of Surrey. He holds a PhD in Signal Processing and an MEng in Electrical Engineering from Imperial College London (2014 and 2010, respectively). His research focuses on developing algorithms for detecting changes in time-series data, with emphasis on Bayesian approaches and statistical signal processing techniques. He has contributed to IoT data stream analysis, sensor data web services, and multi-sensory accelerometer segmentation. Education: PhD in Signal Processing, Imperial College London, 2014 MEng in Electrical Engineering, Imperial College London, 2010 His work bridges theoretical advancements with practical applications, including bone loading exercise classification using machine learning and parameter labelling in Bayesian change detection models. While no awards are explicitly listed, his publications demonstrate impactful contributions to data analysis methodologies. Dr. Ahrabian's research emphasizes: Sequential and batch processing algorithms IoT-enabled sensor data interpretation Bayesian statistical models for dynamic systems His publications span domains like smart cities, health monitoring, and activity recognition, reflecting a multidisciplinary approach to time-series challenges.

Ravi Shekharمشاهده پروفایل

مدرس

• Natural Language Processing
• Conversational AI
• Cross-Lingual Representation
• +۳ مورد دیگر

Ravi Shekhar is a Lecturer (Teaching & Research) at the University of Essex’s School of Computer Science and Electronic Engineering (CSEE). Previously, he was a Post-Doctoral Researcher at Queen Mary University of London (2019–2023), working on projects like EMBEDDIA and SoDeStream. He holds a Ph.D. from the University of Trento (2015–2019), focusing on language and vision models, and an M.S. from IIIT Hyderabad. His research spans Conversational AI, Cross-Lingual NLP, and Social Media Analysis, with a focus on ethical AI and low-resource language challenges. Education Ph.D., Language, Interaction and Computation Laboratory, University of Trento (2015–2019) M.S. (Research), Computer Science, IIIT Hyderabad (2010–2013) B.Sc. (Hons) Computer Science, Delhi University (2001–2005) Research Interests : Dr. Shekhar’s work addresses challenges in NLP and multi-modal systems, including abusive language detection, cross-lingual models, and conversational AI. He emphasizes practical applications in social media moderation and ethical AI, with contributions to datasets like CoRAL and LEDA. Key Contributions : His 2024 work on ‘Power and Vulnerability in Organisational Communication’ explores language dynamics in hierarchies, while ‘Denoising Labeled Data for Comment Moderation’ advances active learning techniques. He leads grants from Innovate UK and the EU, focusing on dialogue systems for safety-critical applications. Awards Outstanding Reviewer (EMNLP 2019) Graduate Research Assistantship (University of Trento) Institute Fellowship (IIIT Hyderabad) Advising & Grants : Supervised 8 graduate students (Master’s/PhD) and secured grants totaling £1.2M, including Innovate UK projects on commodities trading AI and harm mitigation in data systems. Active in mentoring underrepresented groups and promoting collaborative research networks. Labs & Teams : Leads the NLP research group at Essex, collaborating with institutions like UvA Amsterdam and Queen Mary. Engages in open-source projects and dataset development, emphasizing reproducibility and code sharing.

• Machine Learning Security
• Adversarial Attacks
• Data Mining
• +۳ مورد دیگر

Pan Li is a Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on cybersecurity vulnerabilities in machine learning systems, particularly adversarial attacks and defense mechanisms. Key research areas include: Security analysis of machine learning models Poisoning attack detection and mitigation Knowledge transfer regularisation Multi-view data analysis Few-shot learning robustness Recent work demonstrates expertise in cross-domain learning security, network embedding for fraud detection, and spectral clustering-based defense mechanisms. Publications from 2018-2024 reveal consistent contributions to academic journals and conferences in computer science and cybersecurity. Email contact: pan.li@qmul.ac.uk