جست‌وجوی استاد و پژوهشگر

Antske Fokkensمشاهده پروفایل

استاد

• Computational Linguistics
• Language Technology
• Interpretable AI
• +۳ مورد دیگر

Prof. Antske Fokkens is a Full Professor in Computational Linguistic Methods at Vrije Universiteit Amsterdam, with joint appointments in the Faculty of Humanities and the Network Institute. She directs the Text Mining/Language and AI track in the Linguistics Master's program and serves as Vice Dean of Research. Her research investigates methodological aspects of computational linguistics, focusing on language models, interpretable AI, and digital humanities. She develops tools to extract patterns from large text corpora for applications in social science and history, emphasizing transparency and interdisciplinary collaboration. Current projects include analyzing perspective expression in media and semantic modeling for biographical data. Recent publications examine shortcut learning in text classification, persona-driven content generation, hate speech model alignment, and cross-disciplinary approaches to stance detection. Her work integrates NLP with social science theories to analyze discourse on sustainability, polarization, and media framing.

Ioana Banicescuمشاهده پروفایل

استاد

• Parallel Algorithms
• Scientific Computing
• Performance Modeling
• +۳ مورد دیگر

Ioana Banicescu is a Professor at Mississippi State University's Department of Computer Science and Engineering. She holds a Ph.D. in Computer Science from New York University and previously directed the Center for Cloud and Autonomic Computing. Her research focuses on parallel algorithms, scientific computing, performance modeling, and autonomic systems. Her education includes: Ph.D. Computer Science - New York University M.S. Computer Science - New York University B.S./M.S. Electronics and Telecommunications - Polytechnic University of Bucharest Banicescu's research explores fundamental challenges in high-performance computing, including dynamic load balancing, performance optimization, and autonomic resource management. Her recent work emphasizes explainable AI for cybersecurity systems and robust resource allocation models for heterogeneous computing environments. Her publication record demonstrates consistent focus on performance optimization in parallel systems, with recent expansion into cybersecurity applications using machine learning approaches. She maintains collaborations with national laboratories and international research forums. Banicescu has received multiple National Science Foundation awards for research excellence and serves on numerous program committees for IEEE/ACM conferences. She contributes to the academic community through editorial roles for Cluster Computing and the International Journal on Computational Science and Engineering.

Chengkai Liمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Data Management
• +۹ مورد دیگر

Chengkai Li is a Professor and Associate Chair in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), directing the Innovative Data Intelligence Research (IDIR) Lab and co-directing the Center for Artificial Intelligence and Big Data (CARIDA). He holds adjunct roles in the Multi-Interprofessional Center for Health Informatics (MICHI). His academic journey includes a Ph.D. from UIUC (2007) and faculty positions at UTA since 2007, progressing from Assistant to Full Professor (2019). Education: Ph.D. in Computer Science (UIUC, 2007), M.E. and B.S. from Nanjing University (2000, 1997). Research focuses on AI-driven data systems for social good, including computational fact-checking, knowledge graphs, and graph data usability. Key projects include ClaimBuster (end-to-end fact-checking), FactWatcher (automated fact monitoring), and Maverick (exceptional fact discovery). His work spans 30+ news features and collaborations with organizations like Knight Foundation and Google. Publications (over 100) appear in top venues (SIGMOD, KDD, VLDB), with awards like the 2017 SIGMOD Most Reproducible Paper Award. He advises over 30 students and leads grants totaling millions from NSF, Knight Foundation, and industry partners. Labs/Teams: IDIR Lab (40+ researchers), CARIDA (AI/Big Data initiatives), and partnerships with Duke Tech & Check Cooperative.

Samuel Cartonمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Samuel Carton is an Assistant Professor in the Department of Computer Science at the University of New Hampshire (UNH), within the College of Engineering and Physical Sciences. His research focuses on human-centered natural language processing (NLP), emphasizing the implicit knowledge acquired by NLP models and methods to present this knowledge effectively to human stakeholders for ethical and practical use. He holds a Ph.D. in Information Science/Studies from the University of Michigan and has conducted postdoctoral research with Chenhao Tan at the University of Colorado Boulder and University of Chicago. He teaches courses including Natural Language Processing, Advanced Topics in CS, and Doctoral Research. Education: Ph.D., Information Science/Studies, University of Michigan Research Interests: Human-AI collaboration, model interpretability, ethical AI, and explainable NLP. His work bridges algorithm design and human-subject experimentation to ensure models are both effective and trustworthy. Grants & Advising: No explicit grants listed; actively involved in doctoral research supervision as seen in his course offerings. Labs & Teams: No specific lab affiliations explicitly mentioned, but his research likely involves collaborations with NLP and AI ethics groups.

Elaine Shortمشاهده پروفایل

استادیار

• human-robot interaction
• accessibility
• robotics
• +۴ مورد دیگر

Elaine Short is an Assistant Professor in the Department of Computer Science and a secondary appointment in Mechanical Engineering at Tufts University's School of Engineering. She leads the Assistive Agent Behavior and Learning (AABL) Lab, focusing on human-robot interaction, accessibility, and assistive technology. Education: PhD in Computer Science, University of Southern California (2017) MS in Computer Science, University of Southern California (2012) BS in Computer Science, Yale University (2010) Her research applies human-centered design and disability community values to AI/ML development in robotics, emphasizing robust human-robot interaction in natural environments, group/crowd dynamics, and inclusive design for non-normative users. Recent publications highlight trends in human-robot co-creative collaboration, shared control systems, policy modification through imagined actions, and accessibility challenges in robotics. Awards include NSF Graduate Research Fellowship, Google Anita Borg Scholarship, and multiple teaching/research recognitions. Scientific Awards: National Science Foundation Graduate Research Fellowship USC Provost's Fellowship Google Anita Borg Scholarship Viterbi School of Engineering Merit Award WiSE Merit Award Best Research/Teaching Assistant Awards Saybrook College Mary Casner Prize

Bence Palfiمشاهده پروفایل

مدرس

• Decision-making in human-AI interactions
• Meta-science
• Open Science practices
• +۱ مورد دیگر

Bence Palfi is a Lecturer in the Department of Psychology at Goldsmiths, University of London. His research focuses on human-AI interactions, meta-science, and improving research practices through Open Science initiatives. He actively develops LLM-based tools for preregistration validation via the ResearchCheck collaboration and explores Bayesian statistical methods in psychological research.

Christopher C. Yangمشاهده پروفایل

استاد

• Healthcare Informatics
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Christopher C. Yang is a Professor in Information Science at Drexel University, where he directs the Healthcare Informatics Research Lab and serves as Program Director for the MS in Health Informatics. He has held leadership roles in academic conferences including steering committee chair and general co-chair positions at IEEE International Conference on Healthcare Informatics (ICHI) and IEEE International Conference on Intelligence and Security Informatics. Education: PhD, MSc, and BSc in Computer Engineering from University of Arizona His research spans healthcare informatics , artificial intelligence , and data science , focusing on applications in pharmacovigilance, drug repositioning, sepsis modeling, and health equity. His recent work examines fairness in healthcare AI and large language model applications in medical diagnostics. Grants/Awards: 2022 DoD grant for prostate cancer treatment personalization 2017 NSF BigData grant ($999,993) 2013 PCORI Challenge Award 2025 Artificial Intelligence in Medicine (AIME) Most-cited Paper Award

Zhu Hanمشاهده پروفایل

استاد

• Game Theory
• Wireless Networking
• Security
• +۴ مورد دیگر

Dr. Zhu Han is the John and Rebecca Moores Professor at the University of Houston's Cullen College of Engineering, Department of Electrical and Computer Engineering. His research focuses on game theory, wireless networking, security, data analysis, and smart grid applications. He holds doctoral and master's degrees from the University of Maryland and a bachelor's from Tsinghua University. Research interests span: Next-generation wireless systems (6G/7G) AI/ML integration in communications Reconfigurable intelligent surfaces Quantum machine learning applications Secure and efficient network architectures His recent publications demonstrate strong focus on generative AI integration in wireless systems, quantum networking, semantic communications, and security frameworks for future networks. Awards and honors include: IEEE/ACM/AAAS Fellow status IEEE Kiyo Tomiyasu Award (2021) Highly Cited Researcher since 2017 IEEE Distinguished Lecturer (2015-2018) Dr. Han leads research in wireless communications and networking, with extensive industry collaboration. His lab focuses on developing theoretical foundations and practical implementations for next-generation communication systems.

Rachida Dssouliمشاهده پروفایل

استاد

• Software Engineering
• Quality Assurance
• Service Engineering
• +۷ مورد دیگر

Rachida Dssouli is a Professor at the Concordia Institute for Information Systems Engineering (Concordia University). Her research focuses on advanced software engineering methodologies, quality assurance systems, and distributed computing frameworks. She specializes in model-based testing, federated learning optimization, and big data quality management. Her work integrates formal verification techniques with modern machine learning approaches to address challenges in edge computing, IoT, and safety-critical systems. Key research areas include: Development of hybrid swarm intelligence algorithms for optimizing large language model deployment in edge-cloud environments Design of reinforcement learning frameworks for robotics motion planning and IoT device scheduling Creation of interpretable machine learning tools for fault detection in software systems Establishment of holistic big data quality frameworks for continuous monitoring and unstructured data analysis Formal verification methods for avionics systems using multi-agent models Her recent work demonstrates trends toward AI-driven solutions for testing methodologies (e.g., SHAP-Driven fault detection) and edge-cloud integration (e.g., MIMO-based computation offloading optimization). The 2025 publications highlight advancements in federated learning and trust-aware IoT scheduling. Earlier works (2018-2020) emphasize foundational contributions to cloud trust models, big data quality metrics, and safety-critical system testing. Her research also addresses emerging technologies for developing countries through frameworks like neurodegenerative disease monitoring systems and mobile application requirements engineering. She has contributed to service-oriented architectures for healthcare systems and cloud-based resource orchestration strategies.

Gareth Jonesمشاهده پروفایل

استاد

• Information Retrieval
• Multimedia Search
• Cross-Language Retrieval
• +۲ مورد دیگر

Professor Gareth Jones leads research in information retrieval at DCU's School of Computing. His work develops novel methods for accessing multimedia, multilingual and personal content through search technologies. Key innovations include spoken document retrieval, cross-language search, and context-aware mobile systems. Research areas: Multilingual and multimedia information access Affective computing in search interfaces Personalized retrieval systems Evaluation methodologies Recipient of 2021 President's Research Award. Co-founded MediaEval multimedia benchmarking initiative and chaired ACM SIGIR 2013. Publications demonstrate consistent focus on improving search effectiveness across diverse content types and languages.

Patrik Rydénمشاهده پروفایل

استاد

• Mathematical Statistics
• Data Analysis
• Ambulance Care Optimization
• +۳ مورد دیگر

Patrik Rydén is a Professor of Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, where he applies statistical methods to complex datasets. He also directs the Industrial Doctoral School for Research and Innovation , fostering academia-industry collaboration. His research spans healthcare (ambulance logistics, cancer diagnostics), industrial optimization (automotive quality control), and bioinformatics. Research Focus Rydén develops data-driven methodologies for: Healthcare : Optimizing ambulance deployment, predicting patient care times, and modeling disease spread. Industry : Statistical process control for manufacturing and quality improvement. Bioinformatics : Cancer subtype identification and vaccine efficacy modeling. Publication Trends His recent work emphasizes applied statistics in healthcare (40%), industrial machine learning (30%), and genomic analysis (30%), with strong themes in predictive modeling, spatio-temporal analysis, and high-dimensional data interpretation. Leadership & Projects As director of the Industrial Doctoral School, he bridges academic research with industry needs. Key projects include: Predicting post-anesthesia care time using ML (2022–2027) Data-driven prehospital care optimization (2018–2028) Statistical learning for industrial defect detection (2017–2023)

Ricard Gavaldàمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Healthcare Analytics
• +۳ مورد دیگر

Ricard Gavaldà is a Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the LARCA research group. Since February 2020, he has been on academic leave to work full-time at Amalfi Analytics, focusing on healthcare data analytics. His research interests include Machine Learning, Data Mining, and their applications to healthcare and social good. He has supervised numerous PhD students and contributed to impactful projects in clinical decision support, traffic prediction, and energy-efficient systems. Education details are not explicitly mentioned in the provided texts. His work spans data stream mining, algorithmic frameworks (e.g., MOA), and healthcare informatics. He has co-founded Amalfi Analytics to develop AI-driven solutions for healthcare management. Notable recent activities include co-chairing the Nectar Track at ECML PKDD 2025 and organizing workshops on Data Science for Social Good. Key technical contributions include methods for adaptive learning, probabilistic modeling, and interpretable AI systems. His research bridges theoretical foundations and real-world applications, with a focus on improving healthcare outcomes and sustainable practices.

Karsten Liberمشاهده پروفایل

استاد

• Aquatic Ecotoxicology
• Water Quality Assessment
• Sediment Contamination
• +۴ مورد دیگر

Karsten Liber is the Executive Director of the School of Environment and Sustainability (SENS) at the University of Saskatchewan, holding the title of University of Saskatchewan Distinguished Professor. His research focuses on aquatic ecotoxicology, water quality assessment, and sediment contamination impacts on ecosystems. He has extensive expertise in mining impacts, pesticide ecotoxicology, and the application of autonomous sensor systems for environmental monitoring. Dr. Liber earned a PhD in Environmental Biology (Aquatic Ecotoxicology) and a Bachelor of Science in Ecology, both from the University of Guelph. His work integrates field studies, lab experiments, and advanced modeling to address environmental challenges in industrial regions like the Alberta oil sands and northern Saskatchewan uranium operations. His research interests include: Metals bioavailability and toxicity in aquatic environments Derivation of water quality guidelines using sensor technologies Effects of pesticides on freshwater organisms Selenium dynamics in food webs Recent studies highlight his work on Chironomus dilutus toxicity thresholds, oil sands pit lake reclamation, and machine learning applications in environmental risk assessment. His 2025 review on neonicotinoid contamination underscores global concerns over pesticide impacts on aquatic invertebrates. Key award: University of Saskatchewan Distinguished Professor (202X) Grants: Multiple NSERC and industry-funded projects on ecotoxicological risk assessment Liber leads SENS's interdisciplinary research teams, focusing on climate change resilience and sustainable water management through initiatives like the Prairie Water project. His lab develops innovative sensor technologies for real-time environmental monitoring in industrially impacted regions.

Shereen Fouadمشاهده پروفایل

مدرس ارشد

• Medical Imaging Analysis
• Explainable AI
• Ethics in AI Healthcare
• +۲ مورد دیگر

Shereen Fouad is a Senior Lecturer in the School of Computer Science and Digital Technologies at Aston University. Her research focuses on explainable AI, medical image analysis, and ethical AI applications in healthcare. She developed AI-enabled diagnostic tools for respiratory diseases evaluated by NHS professionals. Awards include Best Paper Award at the 12th International Conference on Trust Management (2018). She leads the MSc AI for Health program and teaches courses on Artificial Intelligence and Team Projects. Current research projects include developing responsible AI decision support systems for pediatric healthcare and creating AI-enhanced communication tools for medical education using large language models. Funding includes UKRI IAA Impact Builder Award for responsible AI education in NHS organizations and EPSRC Impact Builder funding for interdisciplinary healthcare AI initiatives.

Terence Broadمشاهده پروفایل

مدرس ارشد

• Generative AI
• Computational Creativity
• Artistic Applications of Machine Learning
• +۵ مورد دیگر

Terence Broad is a Senior Lecturer at the Creative Computing Institute, University of the Arts London, and Acting Course Leader of the MSc Applied Machine Learning for Creatives. He is currently completing a PhD at Goldsmiths, University of London. His work bridges art and technology, focusing on generative AI, computational creativity, and the use of machine learning as artistic materials. His research has been exhibited globally at venues like The Whitney Museum of American Art and SIGGRAPH conferences. Education: MSci Creative Computing from Goldsmiths, University of London (2012–2016). Research interests include expressive manipulation of deep generative models, exploring the latent possibilities of 'black-box' systems, and ethical considerations in AI art. He has pioneered methods like 'Network Bending' for creative model customization. Key achievements include winning the ICCV Computer Vision Art Gallery Grand Prize (2019) and serving on the SIGGRAPH jury (2021). His work is part of Geneva's contemporary art collection and has been featured in media like The Independent and Vox. Professional activities include organizing conferences like IGGI 2020 and reviewing for journals like Leonardo and ACM Transactions on Image Processing. He actively participates in exhibitions, artist talks, and academic workshops.