جست‌وجوی استاد و پژوهشگر

Manos Papagelisمشاهده پروفایل

دانشیار

• Data Mining
• Graph Mining
• Machine Learning
• +۳ مورد دیگر

Dr. Manos Papagelis is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He serves as the Graduate Program Director for the MSc and MScAI programs. His research focuses on data science and machine learning, particularly in data mining, graph mining, big data analytics, mobility analytics, and knowledge discovery. Education: PhD in Computer Science (University of Toronto), MSc and BSc in Computer Science (University of Crete, Greece). Prior to York, he held postdoctoral and research roles at UC Berkeley, Yahoo! Labs Barcelona, and FORTH, Greece. Research emphasizes trajectory analysis, emotion recognition, and scalable systems. Recent work includes TrajLearn (2025) and Disease Outbreak Detection (2025), highlighting contributions to mobility and health informatics. He has filed three U.S. patents and designed systems like Confious (conference management) and Green2.0 (socio-technical building analysis). Honors include the Lassonde Educator of the Year (2021) and IEEE MDM Best Paper Awards (2020, 2018). His advising spans interdisciplinary teams in AI and data science, with active involvement in grants related to mobility analytics and healthcare technologies. Labs/Teams: Director of the Data Mining Lab, collaborating on projects like trajectory prediction and emotion-aware systems.

Tianyi Liمشاهده پروفایل

استادیار

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +۸ مورد دیگر

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Wenbin Zhangمشاهده پروفایل

استادیار

• Societal Aspects of AI
• Generative AI
• Health Informatics
• +۵ مورد دیگر

Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.

• Artificial Intelligence
• Semantic Web
• Machine Learning
• +۲ مورد دیگر

Eugenio Di Sciascio is a Full Professor and Scientific Coordinator at Politecnico di Bari. His research spans artificial intelligence, semantic web technologies, machine learning, and explainable AI, with applications in recommender systems, healthcare, and cybersecurity. As evidenced by 413+ publications, his work frequently bridges theoretical computer science with practical implementations. Recent research trends focus on trustworthy AI systems, particularly in healthcare diagnostics and recommendation security. His 2024-2025 publications demonstrate strong emphasis on explainable AI methodologies in medical applications (e.g., BRAINEX for brain age prediction, MORIX for mortality inference) and adversarial robustness in recommender systems. Additional work explores neurotechnology interfaces and semantic web foundations.

• Web Engineering
• Web Measurement
• Web Security
• +۳ مورد دیگر

Dr Yanlong Zhang is a Senior Lecturer at Manchester Metropolitan University. He holds a PhD in Software Engineering and a PGCE in Higher Education, complemented by industry experience as an Assistant Engineer, Engineer, and Teaching Assistant. His research focuses on web engineering, including web measurement, security, and games design, with particular attention to software metrics and user interface design. Teaching responsibilities include undergraduate modules on web design and development, computer systems, game design, and human-computer interaction, as well as postgraduate courses in information systems. His work explores GUI generation using GANs, structural similarity in source code, and navigability metrics for websites. Research outputs span topics like melanoma classification via image similarity and quality analysis of software architectures. His publications demonstrate expertise in both theoretical frameworks (e.g., HASARD model) and applied systems (e.g., MEIC design). Office hours are Monday 10-11, 1-2, and Thursday 10-11. Proficient in English, Chinese, and basic Japanese/German, Dr Zhang contributes to education and industry through his roles as a Fellow and teaching-focused academic. His work bridges software engineering principles with practical web development challenges.

Antonio Pellicaniمشاهده پروفایل

پژوهشگر ارشد

• Data Mining
• Bioinformatics
• Social Network Analysis
• +۴ مورد دیگر

Antonio Pellicani is a Research Fellow at the Department of Computer Science, University of Bari Aldo Moro (Italy). He holds a PhD in Computer Science and Mathematics from the same institution, completed in 2024 under the supervision of Prof. Michelangelo Ceci. Research Interests: Bioinformatics Time Series Analysis Social Network Analysis Anomaly Detection Process Mining Data Mining His recent work focuses on hybrid computational approaches for analyzing complex data types, including microRNA expression data for disease biomarker discovery and cryptocurrency time series for anomaly prediction. Publications span high-impact journals like Knowledge-Based Systems and Information Fusion , with applications in cybersecurity and biomedical informatics. Laboratory Affiliation: KDDE lab (Knowledge Discovery and Data Engineering) at the University of Bari. International Collaboration: Visiting student at Jožef Stefan Institute’s Knowledge Technologies Department (2023).

Prashant Srivastavaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Image Analysis
• +۵ مورد دیگر

Prashant Srivastava is a Researcher at the Department of Systems Biology and Bioinformatics under the Faculty of Computer Science and Electrical Engineering at the University of Rostock, Germany. His work bridges machine learning with bioinformatics and financial risk modeling. Education: MSc Data Science and Analytics (2018-2020) from Royal Holloway, University of London Integrated B.S.-M.S. in Physics (2013-2018) from Indian Institute of Science Education and Research, Kolkata Master's Thesis in Computer Science (2020) on statistical risk methods in financial markets Master's Thesis in Physics (2017-2020) on extraordinary transmission in silicon-based micro-meter wire grid polarizers Research Focus: Prashant specializes in machine learning and deep learning applications. His work spans bioinformatics (bitter peptide prediction), data science (ConvGeN algorithm for imbalanced datasets), and financial risk modeling using VaR and Expected Shortfall frameworks. Recent Publications: His research trends include graph neural networks for peptide analysis, convex space learning for generative oversampling, and self-attention mechanisms for molecular interaction extraction from biological texts. Technical Expertise: Proficient in Python (numpy, scipy, tensorflow), R, C++, MATLAB, cloud computing (Azure), and database systems (SQL, NoSQL). He develops libraries like mgarch for GARCH modeling and applies advanced ML techniques (CNN, RNN, GAN, NLP) across domains. Labs & Teams: Active member of the Systems Biology and Bioinformatics group at the University of Rostock, contributing to interdisciplinary research at the intersection of computer science and life sciences.

• Advanced High-performance Construction Materials
• Multiscale Experimental Testing
• Physical and Chemical Analyses
• +۶ مورد دیگر

Weina Meng is an Associate Professor in the Department of Civil, Environmental and Ocean Engineering at Stevens Institute of Technology. She holds appointments since 2018, leading research in advanced construction materials and sustainable infrastructure. Her work focuses on ultra-high-performance concrete (UHPC), carbon sequestration, AI-driven material design, and waste utilization. Educational Background PhD in Civil Engineering, Missouri University of Science and Technology (2017) Postdoctoral Research at Missouri S&T under Prof. Kamal H. Khayat (2013–2018) Research Interests Meng’s research emphasizes: Development of low-carbon UHPC and geopolymer composites Integration of AI for material optimization and characterization Utilization of industrial byproducts (fly ash, slag) in construction Microplastic detection and environmental impact mitigation Awards & Recognition 2020 Educator of the Year, ASCE New Jersey Section First Place, 2nd International UHPC Symposium (2019) Women’s Transportation Seminar Helene M. Overly Memorial Scholarship (2016) Grants & Collaborations Active projects include: NSF CAREER Grant: Consecutive Assembly-and-Mineralization Processed C-S-H Nacre (2021–2026) NJDOT-funded NJ UHPC Development (2019–2023) National Academies Early-Career Fellowship in Offshore Energy Safety (2024–2026) Labs & Teams She directs the Advanced Concrete Technology (ACT) Lab, focusing on UHPC innovation and sustainable materials. Collaborations span academia and industry partners like ceEntek and CCDC.

• Software Engineering
• IT Security
• Cloud Systems Engineering
• +۳ مورد دیگر

Diomidis Spinellis is a Professor at the Department of Management Science and Technology, Athens University of Economics and Business. He is a leading researcher in software engineering, IT security, and cloud systems engineering, with over 300 publications and 10,000 citations. He has authored award-winning books including Code Reading , Code Quality: The Open Source Perspective , and Effective Debugging: 66 Specific Ways to Debug Software and Systems (2016). As a Senior Member of ACM and IEEE, he served as Editor-in-Chief of IEEE Software (2015–2018) and contributed to open-source tools like CScout, UMLGraph, and dgsh. Award-winning author in software engineering Developer of critical open-source tools Editorial leadership in IEEE Software Contributor to macOS and BSD Unix His research spans code quality, software evolution, security, and developer productivity. Articles highlight Unix modernization, AI-assisted coding, dependency analysis, and incident management. He has served on the IEEE Computer Society Board of Governors and holds degrees from Imperial College London (MEng, PhD). Scientific contributions include open-source datasets (e.g., VulinOSS, Alexandria3k) and innovative tools for software analysis. His work bridges academic research and industrial practice, with case studies on Eclipse, Android APIs, and ING’s incident management.

Francesc Serratosa Casanellesمشاهده پروفایل

استاد

• Artificial Intelligence
• Biotechnology
• Aquaculture
• +۶ مورد دیگر

Francesc Serratosa Casanelles is a Full Professor of Computer Science at the Universitat Rovira i Virgili in Tarragona, Spain. He leads the ASCLEPIUS research group (AGAUR 2021SGR-00111), focusing on Smart Technology for Smart Healthcare. He coordinates European projects like SbD4Nano (H2020-NMBP-TO-IND-2019-862195) and NanoInformaTIX (H2020-NMBP-14-2018-814426). His research spans Artificial Intelligence applied to drug discovery, biotechnology, aquaculture, and nanotechnology. He teaches Biometrics, Computer Forensics, and Entrepreneurship at the Master's Degree in Computer Security Engineering and Artificial Intelligence (MESIIA). Education: PhD in Computer Science (Universitat Politècnica de Catalunya, 2000); Engineering Degree (UPC, 1993) Affiliations: Department of Computer Engineering and Mathematics, Southern Catalonia International Excellence Campus Research Interests: Focus on AI-driven solutions for healthcare (drug discovery, nanotoxicity prediction), graph-based machine learning, and molecular modeling. His work integrates computational methods with biomedical applications to address challenges in precision medicine and toxicology. Publications & Grants: Authored ~170 papers, 4 patents, and oversees projects like NextPandemics (PID2022-138327OB-I00). He is ranked in the top 2% of global researchers (Plos Biology, 2020). Labs/Teams: ASCLEPIUS Group, Tarragona Graph Database Initiative

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Abbas Akkasi is a Research Fellow at the School of Computer Science, Carleton University. His research focuses on advancing artificial intelligence, machine learning, natural language processing (NLP), and accessibility technologies. He has contributed to projects like TactileNet, which uses AI to create tactile graphics for visually impaired individuals, and has explored biomedical informatics applications in clinical text analysis and symptom recognition. His work bridges multiple disciplines, including multimodal learning, semantic analysis, and generative AI. Notable contributions include developing methods for job description parsing, reference-free summarization evaluation, and improving chemical named entity recognition through undersampling techniques. He has also engaged with interdisciplinary efforts in telecommunications (e.g., LTE-D2D for connected cars) and grid computing optimization. Key research themes include leveraging large language models (LLMs) for biomedical NLP tasks, improving accessibility through assistive technologies, and enhancing machine learning algorithms for imbalanced datasets. His publications reflect a commitment to both theoretical advancements and practical applications of AI across healthcare, education, and industry. Abbas has collaborated with institutions such as the TakeLab (as indicated in his SemEval-2018 participation) and has authored over 20 peer-reviewed articles since 2015. His work emphasizes innovation in NLP, computer vision, and interdisciplinary problem-solving.

• Parallel and distributed processing
• Massively parallel systems
• Computer architecture
• +۴ مورد دیگر

K. Wendy Tang is an Associate Professor and Associate Chair in the Department of Electrical and Computer Engineering at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. She also serves as the Associate Provost for Online Education. Her research focuses on parallel and distributed processing, computer architecture, neural networks, and wireless sensor networks. Her academic career includes advising PhD students such as Mahdin Mahboob Divith A. Babu and former advisees like Dongsoo Kim (Microsoft) and Jaewook Yu (Intel Labs). She has published extensively on graph theory applications in communication networks, including works on Borel Cayley Graph topologies and routing protocols. Notable contributions include the 'Best Paper Award' at the 1997 IEEE International Performance, Computing, and Communications Conference. Key Research Themes: Distributed systems, network topology design, and algorithm optimization Collaborations: Multidisciplinary work in healthcare (e.g., speech recognition for disabilities education) and robotics. Her recent articles emphasize scalable communication topologies for multi-agent systems and energy-efficient wireless sensor networks. She has pioneered graph-based resizing algorithms for ultrafast information distribution.

• Graph Query
• Data Mining
• Pattern Recognition
• +۲ مورد دیگر

Dr. Taotao Cai is a Senior Lecturer in Computing at the University of Southern Queensland (UniSQ) and Honorary Lecturer at Macquarie University. He holds a BEng from Jinggangshan University, MCmpScTch from Shenzhen University, and PhD from Deakin University. His research focuses on advancing graph query technology and developing novel computational methods. His research interests span graph-based data analysis, intelligent systems for knowledge discovery, and applications in remote sensing and IoT. Key areas include developing algorithms for efficient data processing and pattern recognition in complex datasets. Dr. Cai has received the School Research Encouragement Award and secured significant funding including an AEA Ignite Grant ($429,720) and ECR Seed Grant ($10,000) for projects on intelligent energy systems and pattern recognition. He mentors graduate students in projects spanning computer vision, road extraction from remote sensing, and knowledge-driven traffic safety models. His team won the 2024 UniSQ Learning and Teaching Award for curriculum innovation.

Junsong Yuanمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Video Analytics
• +۶ مورد دیگر

Junsong Yuan is a Professor and Director of the Visual Computing Lab at the Department of Computer Science and Engineering, University at Buffalo (SUNY). He previously served as an Associate Professor and Nanyang Assistant Professor at Nanyang Technological University (NTU), Singapore. His research focuses on computer vision, pattern recognition, video analytics, and large-scale visual search. He holds a Ph.D. from Northwestern University, M.Eng. from National University of Singapore, and B.Eng. from Huazhong University of Science and Technology. Education : Ph.D., Electrical and Computer Engineering, Northwestern University, 2009 M.Eng., Electrical and Computer Engineering, National University of Singapore, 2005 B.Eng., Communication Engineering, Huazhong University of Science and Technology, 2002 Research Interests : Dr. Yuan's work spans computer vision, video analytics, gesture/action analysis, and multimodal learning. He emphasizes scalable visual search, 3D reconstruction, and federated learning for medical imaging. His lab develops algorithms for autonomous systems, generative media processing, and human-AI interaction. Awards : IEEE Fellow (2021) IAPR Fellow (2018) SUNY Chancellor's Award for Excellence in Scholarship (2022) Advising & Grants : He has advised numerous PhD students, including Sudhir Yarram, Nan Xi, and Tianyu Luan. His research is supported by grants focusing on video generation, medical image analysis, and federated learning. He serves as Editor-in-Chief of JVCI and on editorial boards of T-PAMI, T-IP, and T-CSVT. Labs & Teams : Director of the Visual Computing Lab, leading projects in generative models, 3D scene understanding, and healthcare AI. Collaborates with industry partners like Meta, Amazon, and Accenture on applied vision systems.

Gopal Guptaمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Parallel and Distributed Processing
• +۴ مورد دیگر

Dr. Gopal Gupta is the Erik Jonsson Chair Professor of Computer Science at The University of Texas at Dallas (UT Dallas), leading the Department of Computer Science since 2009. He holds a Ph.D. from the University of North Carolina at Chapel Hill (1991) and has held academic roles since 1992, including Department Head and Associate Head at UT Dallas. His research focuses on Programming Languages, Software Engineering, Parallel Processing, Human-Computer Interaction, and Artificial Intelligence , with notable contributions in logic programming, constraint programming, and neuro-symbolic AI. He has developed influential systems such as the NEXUS parser , DAWN , and VoxBoox , and co-founded Interoperate , a startup translating legacy code into modern formats. His honors include the Junior Faculty Enhancement Award (1992) and multiple best paper awards. Gupta’s work bridges theory and application, emphasizing explainable AI, commonsense reasoning, and system assurance. Key Projects : Leader of the Condicutve Logic Programming initiative Co-developer of the E-plan system for chemical hazard response Pioneer in voice-based accessibility tools like VoxBoox Advocate for entrepreneurial ventures in academia His publications span logic-based AI, parallel computing, and software verification, with recent work in trustworthy AI, causal counterfactuals, and neuro-symbolic frameworks.