جست‌وجوی استاد و پژوهشگر

Jan Arne Telleمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Graph Theory
• +۳ مورد دیگر

Jan Arne Telle is a Professor in the Department of Informatics at the University of Bergen. His research focuses on algorithms, computational complexity, and graph theory, with recent contributions to explainable AI (XAI) and machine teaching. He leads the Norwegian Research Council-funded project 'Machine Teaching for Explainable AI' and has taught courses such as INF339 on Algorithmics of Causality. His work spans graph algorithms, parameterized complexity, and combinatorial optimization, with notable contributions to graph decomposition techniques like mim-width, cliquewidth, and boolean-width. Collaborations include researchers from institutions like the University of Rostock and the University of Montpellier. Key research trends include analyzing time series classification interpretability, optimizing robust simplifications for machine learning models, and solving combinatorial problems in machine teaching. Telle's algorithms for problems like Feedback Vertex Set and Perfect Matching Cut have advanced the field of parameterized complexity. Despite no listed awards, his extensive publication record reflects significant academic impact. He is involved in the Algorithms Research Group at UiB and contributes to computational theory education and outreach, including participation in programming competitions and academic leadership roles.

• Artificial Intelligence in Healthcare
• Health Policy
• Opioid Use Disorder
• +۵ مورد دیگر

Md Mahmudul Hasan is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida (UF), with a joint appointment in the Department of Information Systems and Operations Management. He also serves as Assistant Director and Faculty Lead of the Medical Marijuana Research Repository under the Florida-funded Consortium for Medical Marijuana Clinical Outcomes Research. His affiliations include the UF Center for Drug Evaluation and Safety (CoDES) and Center for Addiction Research and Education (CARE). Appointed through UF’s AI initiative, his research focuses on AI-driven clinical decision support systems and prescriptive models to address substance use disorders, opioid-related adverse events, and chronic disease management. Dr. Hasan holds a PhD from Northeastern University’s Decision Analytics Lab and completed a postdoctoral fellowship at the FDA’s Center for Drug Evaluation and Research (CDER). His work integrates machine learning, statistical modeling, and management science to improve healthcare decision-making, with notable projects funded by the CDC and Massachusetts Department of Public Health. His research has been featured in media outlets including NPR, The Main Street Daily News, and Medical Press. His interdisciplinary studies emphasize opioid use disorder (OUD), predicting treatment discontinuation, and mitigating biases in AI models. He explores telehealth’s role in OUD treatment outcomes and develops frameworks to reduce opioid diversion through secure disposal programs. Dr. Hasan’s methodological contributions include causality-aware machine learning models and fairness-aware algorithms for healthcare applications. Teaching responsibilities include courses on pharmaceutical policy, managerial statistics, and decision analytics. His research has generated 15+ peer-reviewed publications since 2015, spanning journals like Computers in Biology and Medicine , Addiction , and Journal of the American College of Cardiology . Current projects aim to advance AI ethics in healthcare and optimize resource allocation for chronic disease management.

MEKKI Mohamedمشاهده پروفایل

پژوهشگر ارشد

• Networking
• Cloud Computing
• Artificial Intelligence
• +۳ مورد دیگر

MEKKI Mohamed is a Research Fellow at EURECOM's Communication Systems department, focusing on cutting-edge research in networking, cloud computing, and artificial intelligence. His work emphasizes zero-touch network management, AI-driven infrastructure optimization, and edge-cloud continuum systems. Mekki holds a PhD in 'Enabling Zero-Touch Cloud Edge Computing Continuum Management' (2024). His research spans network slicing, microservices architecture, and 6G technologies. He has published extensively in top-tier journals and conferences, addressing challenges in distributed systems, resource management, and trustworthy network automation. Mekki collaborates with industry leaders to develop scalable frameworks for 5G/6G testing and vertical service deployment. Key contributions include XAI integration for network management, proactive lifecycle management for microservices, and performance analysis of cloud-native configurations. His work is supported by collaborations with EURECOM's research teams in Digital Security and Data Science domains.

Weng-Keen Wongمشاهده پروفایل

استاد

• Machine Learning
• Anomaly Detection
• Computational Sustainability
• +۴ مورد دیگر

Weng-Keen Wong is a Full Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU), part of the College of Engineering. He serves as the OSU Site Director for the NSF-funded Pervasive Personalized Intelligence Center. His academic journey includes a PhD and MS from Carnegie Mellon University (2004/2001) and a BS from the University of British Columbia (1997). Research focuses on machine learning, with specialties in anomaly detection, explainable AI, computational sustainability, and spatio-temporal data analysis. Notable projects include disease outbreak surveillance algorithms, bird species distribution modeling, and RF device authentication systems. He collaborates across disciplines in public health, ornithology, and environmental sciences. Teaching includes courses like Artificial Intelligence (CS 331), Probabilistic Graphical Models (CS 536), and Ethics in Computer Science (CS 391). He advises a team of 9 current students (4 PhD, 1 MS, 4 undergrad) and has mentored over 25 alumni in roles spanning academia and industry. Labs and initiatives include the Wong Lab focusing on anomaly detection and sustainability projects. He has led NSF grants and industry collaborations, emphasizing practical applications of machine learning in real-world challenges.

• Answer Set Programming
• Artificial Intelligence
• Cybersecurity
• +۱۲ مورد دیگر

Prof. Martin Gebser is a University Professor and Deputy Director at the Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt. His work bridges theoretical advancements in Answer Set Programming (ASP) with practical applications in industrial scheduling, semiconductor manufacturing, and explainable AI systems. Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt His research focuses on Answer Set Programming and its extensions for complex scheduling problems, particularly in semiconductor production. Key areas include: Multi-shot ASP solving for job-shop decomposition Hybrid AI systems integrating reinforcement learning and logic programming Explainable AI for battery health monitoring and semiconductor dispatching Recent publications emphasize temporal planning, constraint learning, and real-world data integration. He has developed customizable simulators and optimization frameworks for industrial applications.

• Artificial Intelligence
• Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Dr. Jaime Pizarroso Gonzalo is an Assistant Professor at the Department of Telematics and Computing within the Higher Technical School of Engineering (ICAI) at Comillas Pontifical University. He joined the university in 2019 and specializes in teaching undergraduate courses on machine learning, natural language processing, time series analysis, and deep learning. His research focuses on data analysis and artificial intelligence applications, particularly in explainable AI (XAI), neural networks, and sensitivity analysis. Education: PhD in Industrial Engineering (2023), Comillas Pontifical University Double Master's in Industrial Engineering and Connected Industry (2019) Industrial Engineering (Electronics specialization, 2017) Research Interests: Jaime's work combines theoretical advancements in AI with practical applications. His current projects include developing XAI techniques using partial derivatives, analyzing neural network sensitivity, and applying machine learning to real-world problems such as energy demand forecasting and satellite image analysis for environmental monitoring. He emphasizes interpretable AI to ensure transparency in algorithmic decision-making. Key Projects (2020–2025): ENDESA: Satellite imagery analysis for cork oak tree identification (2025) Santalucía Chair: Interactive neural network application for education (2024–2025) IDB: Energy demand scenarios during the pandemic (2020) Airbus: Intelligent inertial positioning system (2019–2020) Dissemination & Training: Jaime has delivered invited lectures on XAI, conducted training courses in generative AI and machine learning, and contributed to media discussions on AI's societal impacts. He actively participates in academic conferences, including presenting at the RTGF 2024 Geometry-Physics Meeting.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Mengnan Du is an Assistant Professor in Data Science at New Jersey Institute of Technology (NJIT). Their research focuses on artificial intelligence, machine learning, and natural language processing with a strong emphasis on fairness, explainability, and bias mitigation in AI systems. Key areas include large language models (LLMs), neural network interpretability, and ethical AI. Dr. Du leads the Collaborative Research: III: Medium: Towards Effective Detection and Mitigation for Shortcut Learning project funded by the National Science Foundation (2023-2027), exploring framework development for addressing shortcut learning in deep neural networks. They are also involved in interdisciplinary work combining AI with solar physics and financial systems. Research themes include: 1) XAI (explainable AI) techniques for LLMs, 2) bias detection/mitigation in NLP systems, 3) robustness in neural networks, and 4) AI applications in healthcare/finance. Notable contributions include fairness-aware classifier generalization methods and novel approaches for debiasing knowledge graphs. Recent work has addressed topics like concept depth analysis in LLMs, generative AI ethics in advertising, and neural operator networks for scientific computing. Their publications span top conferences like ACL, COLING, and WWW, with a focus on bridging theory and real-world AI system development.

• Artificial Intelligence in Healthcare
• Oncology
• Machine Learning
• +۷ مورد دیگر

Le Minh Thao Doan is a PhD candidate and part-time Lecturer in Computer Science at the School of Computing, Engineering & Digital Technologies, Teesside University. Her research focuses on AI applications in healthcare and oncology, particularly leveraging machine learning and deep learning for cancer diagnosis and prognosis. She emphasizes Explainable Artificial Intelligence (XAI) to enhance early cancer detection and improve patient outcomes. Previously, she worked as a financial auditor and data analyst at multinational firms like EY and AB InBev. Later, she contributed to SME digitalization as a data analytics developer at Teesside University. Her expertise spans big data, business intelligence, and data analytics for solving complex business challenges. Key research areas include AI-driven healthcare solutions, cybersecurity (intrusion detection systems, payment fraud prevention), and digitalization in healthcare supply chains using smart wearables. She has published extensively on topics like machine learning in oncology, cybersecurity, and healthcare technology innovation. Her work bridges technical and clinical domains, aiming to translate AI advancements into practical tools for healthcare and enterprise sectors. Collaborations include projects with clinical partners to evaluate real-time respiratory monitoring in neonatal care.

• Climate Science
• Ecohydrology
• Machine Learning
• +۳ مورد دیگر

Dr. Shijie Jiang is a Project Leader at the Max Planck Institute for Biogeochemistry , leading the Machine Learning for Hydrological and Earth Systems group within the Department of Biogeochemical Integration. His research focuses on integrating hybrid AI methods with domain knowledge to study climate-water-ecosystem interactions under anthropogenic and climatic pressures. Key areas include ecohydrological processes, explainable machine learning applications in earth systems, and climate extremes predictability. Education: Ph.D. in Environmental Science (2017–2021), National University of Singapore M.Sc. in Environmental Science (2013–2016), Beijing Normal University B.Sc. in Environmental Engineering (2009–2013), Wuhan University Research Interests: Dr. Jiang explores interactions between climate, water, and ecosystems using AI-driven frameworks. Specific foci include: Physics-AI hybrid models for ecohydrology and climate processes Machine learning interpretability in earth system science Predictability of climate extremes and their impacts on water cycles Global hydrological drought mechanisms and compounding climatic drivers Labs/Teams: His work is anchored in the Biogeochemical Integration Department, collaborating with the ELLIS Unit Jena. Research outputs emphasize bridging data-driven discovery with traditional geophysical modeling. Grants/Advising: No specific grants or advisees listed in current records. Activities focus on leading interdisciplinary projects and postdoctoral mentorship within his group.

• Wireless Sensor Networks
• Machine Learning
• Deep Learning Methods
• +۵ مورد دیگر

Shagufta Henna is a Lecturer in Computer Science at the Department of Computing, Atlantic Technological University. Her research focuses on machine learning applications in wireless sensor networks, IoT systems, cybersecurity, and environmental monitoring. She contributes to UN Sustainable Development Goals through projects addressing environmental sustainability and health. Her expertise includes federated learning for malware detection in healthcare IoT, deep learning optimization for sensor networks, explainable AI for water quality prediction, and ensemble methods for human activity recognition. Recent work highlights include: Developing graph neural networks for IoT-based healthcare cybersecurity Accelerating environmental monitoring via deep learning in uncontrolled sensor networks Deploying SHAP-based XAI for water quality prediction interpretability No scientific awards explicitly listed. Research output includes 45 publications across journals and conferences, with recent contributions in Results in Engineering and IEEE proceedings. Active collaborations span international institutions focusing on AI-driven environmental and healthcare solutions.

Peter Stuckeyمشاهده پروفایل

استاد

• Constraint Programming
• Discrete Optimization
• Programming Languages
• +۳ مورد دیگر

Peter Stuckey is a Professor in the Faculty of Information Technology at Monash University and project leader at Data61 CSIRO laboratory. His pioneering research focuses on constraint programming and discrete optimization. He holds B.Sc and Ph.D degrees in Computer Science from Monash University. Research interests include discrete optimization, programming languages, constraint solving algorithms, bioinformatics, and constraint-based graphics. His work extends to applications in databases, timetabling, and system security, with industry collaborations including Oracle and Rio Tinto. Recent publications focus on optimization techniques for transport routing, explainable AI, multi-agent systems, and signal processing. Awards include the Google Australia Eureka Prize and Woodward Medal.

Lena A. Jägerمشاهده پروفایل

دانشیار

• Computational Linguistics
• Eye Tracking
• Machine Learning
• +۵ مورد دیگر

Lena A. Jäger is an Associate Professor in Digital Linguistics at the School for Transdisciplinary Studies, University of Potsdam. Her research focuses on computational linguistics, eye tracking, and machine learning applications in language processing. She leads the Digital Linguistics group, supervising 11 PhD students and contributing to interdisciplinary projects like the MultiplEYE corpus initiative. Her work bridges cognitive science and AI, with key contributions to eye movement analysis, biometric identification, and LLM interpretability. Her research interests span eye tracking in reading, linguistic predictability modeling, and the alignment of language models with human cognition. Notable achievements include developing the pymovements toolkit for eye movement data processing and pioneering studies on detecting alcohol inebriation via gaze analysis. Awards include best paper honors at ETRA 2023 and 2024. Recent work explores generative models (ScanDL) for synthetic eye movement generation, cross-lingual reading behavior analysis, and fairness in biometric systems. She advises on AI ethics through the DSI initiative and collaborates internationally on projects like the MECO corpus.

• Human-Centered AI
• Psychology and Technology
• Machine Learning
• +۵ مورد دیگر

Chao Zhang is an Assistant Professor in Human-Centered AI at Eindhoven University of Technology (TU/e), specializing in the intersection of psychology and technology. He holds an MSc and PhD from TU/e, with a focus on using data-driven methods and psychological theories to design AI systems that positively influence human behavior. His research spans behavior change interventions, AI-driven health persuasion, and the social implications of AI systems. He coordinates the HUMAN-AI Alliance program and co-organized workshops on human-centered AI. Education: MSc (Human-Technology Interaction, TU/e), PhD (Intelligent Systems for Lifestyle Behavior Change, TU/e). Research interests include machine learning applications in psychology, computational modeling for behavior change, and ethical AI design. Awards include a 2021 PhD Thesis Award nomination. Key collaborations include work with Utrecht University and projects like ENFIELD (European Lighthouse for Trustworthy AI). He teaches courses on behavioral research methods and human-AI interaction. His lab explores topics like trust in XAI systems and human-robot bonding mechanisms.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• mHealth
• IoT and Wearables
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Drishty Sobnath is an Assistant Professor in Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. Previously, she held roles as Senior Lecturer and Programme Leader at Solent University, overseeing BSc Computer Science, BSc Software Engineering, and MSc Computer Engineering programs. She earned her PhD in Software Engineering (mHealth) from Kingston University, London. Her research focuses on mHealth, IoT, and AI-driven solutions for healthcare management, including projects like the EU-funded WELCOME initiative for COPD patients and the CANAdvice cancer treatment app. She is also a co-investigator on the Office for Students' AI/Data Science scholarship project addressing workforce digital skills gaps. Research interests span AI in healthcare diagnostics, smart city technologies, and inclusive education strategies. She teaches Data Science and AI at undergraduate/postgraduate levels, contributing to UN Sustainable Development Goals related to health and education equity. Notable achievements include Best Paper Awards (2018, 2019) and a 2021 teaching award nomination. Her work integrates interdisciplinary approaches, combining technical innovation with societal impact through projects like the DAP framework for data-driven daily living and AI-driven PPE monitoring systems. Grants and collaborations include EU FP7 funding, industry partnerships on smart city solutions, and initiatives promoting diversity in STEM education. She advises on postgraduate conversion course design and has authored over 20 peer-reviewed articles, focusing on healthcare tech, computer vision applications, and learning analytics.