جست‌وجوی استاد و پژوهشگر

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis and Artificial Intelligence
• +۳ مورد دیگر

Dr. Jungeun (Jenny) Won is an Assistant Professor in the Department of Biomedical Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. Her research focuses on optical imaging, biomedical device instrumentation, medical image analysis with artificial intelligence, and optical coherence tomography (OCT). She leads the Translational Biophotonics Laboratory, advancing non-invasive diagnostic tools for ophthalmology and infectious diseases. Her work combines cutting-edge imaging techniques with clinical applications, such as analyzing retinal pathologies and middle ear biofilms. Dr. Won’s research emphasizes high-resolution OCT advancements for studying age-related macular degeneration (AMD), diabetic retinopathy, and biofilm dynamics. Her innovations include automated motion correction algorithms, variable interscan time analysis (VISTA), and multimodal microscopy platforms. These tools enhance diagnostic precision and enable longitudinal tracking of disease progression. Her articles highlight trends in OCT-based vascular imaging, capillary blood flow quantification, and therapeutic interventions for otitis media. She explores interdisciplinary solutions at the intersection of engineering and medicine, contributing to both fundamental science and clinical translation. Her lab’s work is disseminated through top-tier journals and has implications for improving patient care in ophthalmology and infectious disease management.

• Transportation Modeling
• Applied Operations Research
• Urban Mobility Systems
• +۳ مورد دیگر

Dr. Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (School of Engineering and Applied Sciences). Her research focuses on transportation modeling, applied operations research, and urban mobility systems. She teaches courses including IE 374 (Modeling of Stochastic Systems), IE 550 (Resources Planning & Optimization), and specialized topics in operations research. Dr. Kang holds a PhD and has been actively involved in academic contributions since at least 2006. Her research interests span transportation systems optimization, emergency logistics, and sustainable mobility solutions, particularly involving autonomous vehicles and shared mobility systems. She has advised numerous PhD and MS students, including notable advisees such as Fatemeh Aarabi and Zhiheng Xu. Her work integrates data analytics, network design, and policy analysis to address real-world challenges in urban and disaster-response contexts. Dr. Kang’s publications since 2008 reflect a strong focus on transportation systems, including studies on electric vehicles, hydrogen infrastructure, and evacuation planning. She maintains an active role in academic collaborations, hosting visiting scholars like Alireza Fallah and Dian Sun. Her teaching and research emphasize both theoretical frameworks and practical applications in operations research and transportation engineering.

• Audio Signal Processing
• Music Information Retrieval
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Panagiotis G. Zervas is an Associate Professor at the Department of Electrical and Computer Engineering, University of Peloponnese (since 2020). His expertise spans audio signal processing, music information retrieval, and natural language processing for knowledge extraction. He teaches courses including Signals & Systems, Digital Signal Processing, and Machine Learning. His research focuses on AI-driven applications in sound analysis, music feature extraction, and multimodal information processing. Education: PhD (2007) in Electrical Engineering from the University of Patras, specializing in Greek prosody modeling for text-to-speech systems. Previous roles include Assistant Professorships at Hellenic Mediterranean University (2015–2020) and Technical Educational Institute of Crete (2008–2015). Research Interests: Natural Language Processing (NLP) for text analysis and large language models (LLMs) Audio signal processing, voice analysis, and embedded systems AI applications in job market analytics and skills frameworks Machine learning for music information retrieval Notable Projects: Principal Investigator in EU projects EU-ALMPO (2025–), Train4Blue (2025–), GROWTH4BLUE (2024–), and MICROIDEA (2024–) World Bank consultant (2023–) for AI-driven employment systems in Greece and Pacific Islands Publications in journals like 'Acoustics' and conferences like WAC 2022 and Forum Acusticum 2023 Office: Building K, Office K2.07 | Contact: pzervas@uop.gr

• Big data
• Data science
• Data analytics
• +۱۲ مورد دیگر

Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)

Gao Minمشاهده پروفایل

استاد

• Personalized Recommendation Systems
• Anomaly Detection
• Social Media Mining
• +۷ مورد دیگر

Gao Min is a Professor and Doctoral Supervisor at the School of Big Data and Software, Chongqing University. He is a member of IEEE, CCF, and CAAI, and has held visiting scholar positions at Arizona State University and Reading University. His research focuses on personalized recommendation systems, anomaly detection, and social media mining, with strong emphasis on security aspects such as shilling attacks and fake news detection. His research interests include: Personalized Recommendation Systems Anomaly and Attack Detection in Recommender Systems Social Media Mining and Fake News Detection Graph-based and Contrastive Learning for Recommendations Domain Adaptation and Meta-Learning Time Series and Behavioral Forecasting The recent articles highlight a consistent trend in adversarial and robust learning for recommender systems and misinformation detection. His team leverages contrastive learning, graph neural networks, and meta-learning to enhance model robustness against poisoning and shilling attacks. There is a growing focus on simulating user behaviors, modeling fine-grained discrepancies, and applying domain adaptation techniques, particularly in detecting fake news and securing recommendation platforms. His scientific awards are primarily reflected through the recognition of his students, including multiple recipients of the National Graduate Scholarship, Huawei Scholarship, and Chongqing Outstanding Master’s Thesis Award. National Graduate Scholarship (awarded to students: Tian Renli, Yu Junliang, Song Yuqi, Zhao Zehua, Zhang Junwei, Wang Jia, Peng Lin, Ma Hao) Huawei Scholarship (awarded to students: Tan Kan, Wang Jia, Huang Yinqiu) Chongqing Outstanding Master's Thesis Award (awarded to students: Yu Junliang, Zhao Zehua, Zhang Junwei) Aerospace Scholarship (awarded to student: Zhang Junwei) Gao Min has secured significant research funding as principal investigator, including two National Natural Science Foundation projects, a sub-project of the National Key R&D Project, two Chongqing Natural Science Foundation projects, and one China Postdoctoral Fund project. He has also contributed as a main researcher in major national programs such as the 973 Program, National Key R&D Program, and National Science and Technology Support Program. He advises a vibrant research group that values autonomy, academic freedom, and practical research, with students regularly publishing in top venues and securing top-tier industry and academic positions. His team has developed key research platforms including QRec (Recommendation Algorithm Experiment Platform), Yue (Music Recommendation), ARLib (Data Pollution Attack Platform), and SDLib (Shill Attack Detection Platform). He serves as a reviewer for major journals and is a PC member of top conferences including CIKM, IJCAI, and AAAI.

• Multimodal Conversation Analysis
• Interpersonal Communication
• Psychological Reactance
• +۱۲ مورد دیگر

Dr. Maximilian Krug is a Postdoctoral Researcher at the Institute of Communication Science, Faculty of Humanities, University of Duisburg-Essen, Germany. He is a core member of the research group Multimodal Communication, Social Interaction & Technology led by Prof. Dr. Karola Pitsch. His work bridges communication studies, conversation analysis, and digital media research, with a focus on conflict, reactance, and multimodal interaction. Research Interests: Maximilian Krug investigates the structural dynamics of conflict in interpersonal communication, analyzing both face-to-face and online interactions. His research spans rejection and resistance in multimodal interactions , incivility and psychological reactance in digital spaces (especially Telegram), and science communication , including the role of generative AI in public understanding of science (AICOS project). He employs multimodal conversation analysis, eye-tracking, and mixed methods to study coordination in complex social settings such as theatre rehearsals, medical emergencies, and authoritarian discourse. Recent Research Trends: His recent publications reveal a strong focus on temporal coordination in joint activities, verbal and nonverbal prompting, and structural compatibility in multiactivity settings. Projects like AICOS and DFG-funded studies on pronoun use in GDR letters highlight his interdisciplinary reach across communication, linguistics, and political discourse. The integration of visual analytics (e.g., VisCoMET) demonstrates his innovative methodological approach. Scientific Awards: No specific awards or fellowships are mentioned in the provided texts. Advising and Grants: While no formal students are listed, Dr. Krug supervises student theses and contributes to academic training. He is involved in multiple externally funded projects, including those supported by the German Research Foundation (DFG) , the Bavarian Research Institute for Digital Transformation (bidt) , and the Federal Office for the Protection of the Constitution . These grants support his habilitation projects on incivility and reactance. Labs and Teams: Dr. Krug is an active member of the Multimodal Interaction & Eye-Tracking Lab at the University of Duisburg-Essen. This lab focuses on empirical studies of social interaction using advanced technologies such as mobile eye-tracking and multimodal data integration. He collaborates with interdisciplinary teams in projects involving medical training, science communication, and historical discourse analysis.

Kok-Leong Ongمشاهده پروفایل

استاد

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Kok-Leong Ong is a Professor of Business Analytics at RMIT University's College of Business & Law, where he serves as Director of the CoBL Technology Initiative, Director of the Enterprise AI and Data Analytics Hub, and Head of the Department of Information Systems and Business Analytics. With over $1.4 million in research grants, he specializes in translating analytics and machine learning into practical business applications across multiple verticals including e-Commerce, public health, sports, urban studies, marketing, and learning. His research has consistently ranked in the top 25% and 5% of works in their domains according to Altmetric. Professor Ong's research spans Business Analytics, Artificial Intelligence, Machine Learning, and Information Systems, with a focus on making data actionable through analytics-2-business translation, automation, and applications. His work addresses critical challenges in cybersecurity for wearable health devices, ECG-based authentication systems, federated learning privacy, carbon accounting for maritime transport, and AI-driven solutions for vehicle damage detection. He has developed frameworks for operationalizing analytics in business contexts and has made significant contributions to mHealth applications for infant care and breastfeeding support. Among his notable scientific achievements, Professor Ong has received two VC's Teaching Awards and was named one of Australia's Leading Data Academics by CDO Magazine in 2021. He has secured over $1.4 million in research funding and serves on prestigious conferences including KDD and PAKDD. His research has been recognized for its high impact, with many works ranking in the top percentiles of their respective fields. Professor Ong actively supervises numerous research students across diverse topics including human-aligned AI, securing LLMs for financial applications, cyber risks in enterprise AI systems, ECG authentication security, and AI transformation for SMEs. He previously played a key role in establishing Australia's first Business Analytics degree and led La Trobe Business School's analytics program from 2015 to 2019 before joining RMIT in September 2021. Through the Enterprise AI and Data Analytics Hub and his role with RMIT University's Digital3 board, Professor Ong leads initiatives focused on bridging the gap between advanced analytics capabilities and business value creation. His work emphasizes practical implementation of AI and analytics solutions that address real-world challenges across multiple industry sectors.

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +۴ مورد دیگر

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .

• Assistive Technologies
• Health Informatics
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Andreu Català Mallofré is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic and Industrial Informatics Engineering. He serves as the principal investigator at the CETpD - Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma (Technical Research Center for Dependency Care and Autonomous Living) and leads the TOC - Tecnologia Orientada a la Comunitat (Community-Oriented Technology) research group. With an academic career spanning over 30 years, he specializes in assistive technologies, human-computer interaction, and biomedical data analysis. His research interests focus on Wearable health monitoring systems Parkinson's disease management solutions Frailty assessment algorithms Emotion-driven computing Community-embedded technology Gerontechnology applications These areas have produced 367 academic outputs including 59 indexed journal articles, 148 conference papers, and 66 competitive R&D projects. Recent publications demonstrate a clear trend toward multimodal health monitoring combining inertial sensors with biometric data for aging populations . Key application areas include freezing-of-gait detection in Parkinson's patients and distress estimation during assisted mobility. His 2024 work explores agricultural sustainability through data-driven fertilizer optimization. Scientific recognition includes UPC Social Council Award (2013) 20+ years as conference committee member Multiple competitive R&D grants 23391773000 Scopus ID Orcid 0000-0001-8775-1955 As academic advisor, he has mentored 3 PhD candidates including Daniel Manuel Rodriguez-Martin (Human Movement Analysis, 2020) Leonid Ivonin (Physiological Signal Processing, 2014) John Neal Abram Brown (Calm Computing, 2014) His teams operate within Vilanova i la Geltrú's School of Engineering, with strong industry and healthcare sector collaborations.

• Urban Mobility
• Traffic Simulation
• Smart City Technologies
• +۲ مورد دیگر

Maria Paz Linares Herreros is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC) in the Department of Statistics and Operations Research within the Faculty of Mathematics and Statistics (FME). She is affiliated with the IMP - Information Modeling and Processing research group and inLab FIB. Her educational background includes a Licenciada en Matemáticas, a Doctorate from UPC, and a Master's in Logistics, Transportation, and Mobility. Licenciada en Matemáticas Doctorat from Universitat Politècnica de Catalunya Máster en Logística, Transporte y Movilidad Her research focuses on urban mobility , traffic simulation , and smart city technologies . She develops data-driven models for transportation systems and parking management, integrating deep learning techniques for real-time predictions. Her work addresses environmental impact assessment of traffic policies and inclusive mobility solutions . Recent publications analyze urban mobility trends through macroscopic and microscopic traffic models , deep learning applications for parking predictions, and dynamic ride-sharing systems . She explores IoT interoperability and data integration in transportation. Scientific awards include the IV International Award on Transport Infrastructure Management Research (2014). She participates in competitive R+D+I projects like ALGORAE and Virtual Mobility Lab, focusing on transportation innovation and smart city policy evaluation . She contributes to multimodal transport simulation frameworks such as CitScale and Barcelona Virtual Mobility Lab, which evaluate emerging mobility concepts and city policies using integrated modeling approaches .

Alfredo Bensoمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +۱۶ مورد دیگر

Alfredo Benso is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. He has been continuously involved in the Doctoral College for Computer and Systems Engineering since 2008, guiding PhD education across numerous cycles. His academic roles span teaching and research leadership in bioinformatics, systems biology, and computer engineering. Research interests: Bioinformatics and computational biology Systems biology and gene regulatory networks Artificial intelligence and machine learning in biomedicine Molecular modeling and multiscale simulation Biological database systems and data integration Health informatics and public health modeling His recent publications reflect a strong trend in applying AI and machine learning to biological and medical challenges, including protein function prediction, Alzheimer’s and Multiple Sclerosis modeling, food fraud detection, and viral genome analysis. These works span journals and conferences in bioinformatics, computational biology, and biomedical engineering, demonstrating interdisciplinary innovation. Scientific Awards: BIOINFORMATICS 2014 BEST PAPER AWARD (INSTICC, United Kingdom) Advising and Grants: He supervises PhD students, including Sofia Ostellino, and leads major research initiatives such as the BIGMECH (2023–2025) and FISHUB (2016–2018) projects. He has served as Scientific Director for collaborative agreements with public administrations and as Principal Investigator on EU, national (PRIN), and regional research grants focused on bioinformatics, space systems, and reliable digital technologies. Labs and Teams: He is a key member of the SBG - System Biology Group (DAUIN) and contributes to PolitoBIOMed Lab, fostering collaborative research in biomedical engineering and computational biology.

• Neurodegenerative Diseases
• Alzheimer's Disease
• Biomarkers
• +۵ مورد دیگر

Alexa Pichet Binette is an Associate Professor and researcher at the Université de Montréal's Faculty of Medicine, Department of Pharmacology and Physiology, and the Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM). She holds a PhD in Neuroscience from McGill University and completed postdoctoral training at Lund University, Sweden. Her research focuses on understanding neurodegenerative processes in aging and Alzheimer's disease, utilizing multimodal biomarker approaches such as neuroimaging (PET/ MRI), blood-based markers, and proteomics across international cohorts. Her work is funded by the Fonds de recherche du Québec and the IUGM Foundation. Affiliations: CRIUGM, Institut de Génie Biomédical (IGB), CIUSSS Centre-Sud-de-l’Île-de-Montréal Recruitment: Actively seeking master’s/PhD students and postdocs Key research interests include Alzheimer's pathophysiology, biomarker development, and early detection strategies. Her lab analyzes longitudinal data to track disease progression and identify therapeutic targets. Recent studies emphasize tau and amyloid pathology interactions, vascular influences, and plasma biomarker validation. Awards and grants are not explicitly listed, but her work is supported by major research funds. Publications highlight advancements in tau PET imaging, plasma biomarkers, and machine learning applications in neurodegenerative disease prediction. Ongoing collaborations aim to translate findings into clinical practice, emphasizing prevention and early intervention strategies.

• Applied Linguistics
• Language Assessment
• Computational Linguistics
• +۳ مورد دیگر

Zhi Li is a faculty member at the University of Saskatchewan in the Department of Linguistics . With expertise in applied linguistics and technology-assisted language learning , their work bridges language assessment and computational linguistics . Primary research: language testing , CALL , corpus-based analysis Key methodologies: NLP , text mining , multimodal analysis Research trends include: Automated Writing Evaluation (AWE): Systematic impact analysis on grammatical accuracy and ESL pedagogy . Assessment Validity: Argument-based approaches to placement tests and response time metrics . Technological Integration: Mobile-assisted learning, multimodal lecture analysis , and digital discourse patterns .

• Human-Computer Interaction
• Quality of Experience (QoE)
• User Experience (UX)
• +۷ مورد دیگر

Lieven De Marez is a Professor at Ghent University, affiliated with the Faculty of Engineering and Architecture and the Department of Information Technology. His research spans human-computer interaction, quality of experience (QoE), smart homes, digital education, and social media behavior, reflecting a strong interdisciplinary approach combining technical and social sciences. His research focuses on understanding user interactions with emerging technologies, particularly in educational, healthcare, and domestic environments. Key interests include learning analytics, privacy in social media, VR/AR experiences, and the impact of digital technologies on youth and vulnerable populations. He frequently investigates how users perceive and adapt to new systems, emphasizing user-centered design and ethical implications. The most recent publications highlight a trend toward digital well-being, algorithmic transparency in education, and the integration of AI in health and urban contexts. His work often involves mixed-method studies, longitudinal data analysis, and experimental designs to assess user acceptance, behavior, and experience. Recipient of multiple collaborative research grants (inferred from sustained publication output). Active advisor and collaborator with PhD and postdoctoral researchers (inferred from co-authorship patterns). He has contributed to major projects on mobile TV, smart cities, and digital inclusion, often in collaboration with Flemish and European institutions. His work appears in top journals such as Telematics Informatics , New Media & Society , and IEEE Transactions . Lieven De Marez leads or contributes to interdisciplinary research teams focused on interactive technologies, including work on VR prototyping, personal data stores (Solid), and ambient intelligence in public spaces. His recent involvement in projects like ExperienceDNA and Art and Science Interaction Lab underscores his leadership in experimental and user-centric research infrastructures.

• Data Protection Law
• Cyber Security Regulation
• AI Regulation
• +۳ مورد دیگر

Bjørn Aslak Juliussen is a University Lecturer at the Faculty of Law , UiT The Arctic University of Norway. His work focuses on Data Protection Law , Cyber Security Regulation , and AI Regulation , with a particular emphasis on privacy implications in digital surveillance and compliance frameworks. Current Affiliation : Faculty of Law, UiT The Arctic University of Norway Research Groups : Member of Menneskerettigheter og folkerett , Crime Control and Security Law , and Cyber Security Group (CSG) Projects : Privacy and Proportionality in Artificial Intelligence (AI) and Compliance and Technology His publications span topics like: GDPR and AI Regulation (2025) Legal Person Rights under ECHR Article 8 (2025) Compliance Engineering (2024) Third Country Data Transfer Challenges (2023) Machine Unlearning and Right to Erasure (2023) Marine Surveillance and AI Applications (2022) He actively participates in legal discourse through contributions to platforms like Rett24.no and nrkbeta.no , addressing issues such as: Governmental surveillance Corporate privacy rights AI governance Data protection in healthcare