جست‌وجوی استاد و پژوهشگر

Julfikar Haiderمشاهده پروفایل

مدرس ارشد

• Composite Materials
• Surface Engineering
• Tribology
• +۵ مورد دیگر

Dr. Julfikar Haider is a Senior Lecturer in Mechanical Engineering at Manchester Metropolitan University (MMU), UK. He holds a PhD in Surface Engineering from Dublin City University and is a Chartered Engineer (CEng) and Fellow of the Higher Education Academy (FHEA). His research focuses on composite materials, surface engineering, tribology, manufacturing for Industry 4.0, and AI-driven solutions in engineering. Education: BSc in Mechanical Engineering (Bangladesh University of Engineering and Technology, 1999); PhD in Surface Engineering (Dublin City University, 2005). Additional qualifications include an MA in Higher Education (MMU, 2020) and a Postgraduate Certificate in Academic Practice (2014). Research interests span advanced manufacturing techniques, material characterization, and sustainable technologies. He has secured over £1.5M in Innovate UK grants for Knowledge Transfer Partnership projects and supervised 17 research associates and students. His work has led to 170+ peer-reviewed papers and 15 book chapters. Key achievements include the IMechE Young Mechanical Engineer of the Year (2017) and a Silver Medal from the AMPT conference. He serves on editorial boards for journals like Advances in Materials Processing Technologies and Scientific Reports , and is a member of the EPSRC Peer Review College. Teaching includes modules on engineering design, materials, sustainability, and Industry 4.0 systems at both undergraduate and postgraduate levels. Current supervision involves PhD and MRes projects on dental materials and sustainable ventilation systems.

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

Daniele Meli-Dep is a Temporary Assistant Professor at the University of Verona's Department of Computer Science, where he contributes to the ISLa - Intelligent Systems Lab. His roles include membership in the Computer Science Teaching Committee, Information Engineering Teaching Committee, and the Department Council. He specializes in AI applications for robotics, autonomous systems, and explainable machine learning. Research Interests: - Intelligent Agents: Developing autonomous entities for uncertain environments using reinforcement learning and automated planning. - AI & Robotics: Enhancing robotic autonomy via algorithms adaptable to real-world constraints and human-robot interaction. - Explainable AI (XAI): Focusing on causal analysis, logical models, and interpretable machine learning frameworks. - Neurosymbolic Planning: Combining reinforcement learning with symbolic logic to improve explainability and generalization. Key Projects: - BEHAVE (2024): Safe human-robot cooperation learning. - SINERGHY (2024): Sustainable energy systems for green hydrogen. - ARS (2017): Autonomous robotic surgery advancements. Teaching & Leadership: - Instructor for courses like AI and Robotics, Explainable AI, and Mobile Robotics. - Serves on academic committees overseeing teaching quality and department governance. Labs & Affiliations: - ISLa Lab: Focuses on AI, statistical learning, and intelligent systems applications.

Klaus-Robert Müllerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Brain-Computer Interfaces
• +۸ مورد دیگر

Prof. Dr. Klaus-Robert Müller is a Full Professor and Chair of Machine Learning at Technical University of Berlin, leading the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds joint appointments at Korea University (Department of Artificial Intelligence) and collaborates with institutions like Google Research and the Max Planck Society. With a PhD in theoretical computer science from the University of Karlsruhe, Müller has pioneered work in statistical learning theory, brain-computer interfaces, and explainable AI. His research spans computational neuroscience, molecular dynamics, and medical imaging applications such as digital pathology and neuroimaging analysis. He has directed major initiatives like the Berlin Center for Machine Learning (BZML) and the Berlin Big Data Center (BBDC), now integrated into BIFOLD. Müller is a member of prestigious academies, including the Leopoldina and Berlin-Brandenburg Academy of Sciences, and has received awards like the Vodafone Innovations Award (2017) and Berlin Science Prize (2014). Education: PhD in Theoretical Computer Science (1992), University of Karlsruhe Diploma in Mathematical Physics (1989), University of Karlsruhe Research Interests: Müller’s work integrates machine learning with interdisciplinary challenges, emphasizing explainability and ethical AI. Key areas include neural network interpretability, non-stationary data analysis, and applications in healthcare (e.g., tumor classification, genomic data analysis) and materials science (e.g., quantum chemistry simulations). His contributions to brain-computer interfaces and neuroimaging have advanced understanding of neural oscillations and brain connectivity. Awards & Recognition: Member, German National Academy of Sciences Leopoldina (2012) Member, Berlin-Brandenburg Academy of Sciences (2017) Science Prize of Berlin (2014) Vodafone Innovations Award (2017) Labs & Leadership: Directs BIFOLD and co-leads initiatives like the TEA Challenge in molecular dynamics. Supervises interdisciplinary teams focused on AI in pathology, computational chemistry, and neurotechnology. His work bridges theory and practice, with impactful contributions to open datasets (e.g., QCML) and scalable machine learning frameworks.

Tahir Hameedمشاهده پروفایل

استادیار

• Information Technology Standardization
• Healthcare Analytics
• Consumer Health Informatics
• +۳ مورد دیگر

Tahir Hameed is an Assistant Professor of Management at the Girard School of Business, Merrimack College. His research focuses on Information Technology Standardization, Healthcare Analytics, and Technology Innovation Management, with particular emphasis on socio-technical systems and their impact on healthcare and technology commercialization. He holds a Ph.D. in Information Technology Management from KAIST (Korea Advanced Institute of Science and Technology), an M.S. in Computer Science from Lahore University of Management Sciences, and a B.E. in Industrial Electronics Engineering from NED University of Engineering and Technology. Key research interests include AI-driven healthcare solutions, trustworthy biomedical content systems, and innovation policies in technology-intensive sectors. His work often explores the interplay between technical systems and human behavior in knowledge-intensive environments. Recent publications highlight contributions to AI audit frameworks for health information, decentralized biomedical content systems, and predictive models for hospital readmissions. His scholarship bridges healthcare informatics, technology management, and socio-cognitive innovation theories. Dr. Hameed's research has addressed challenges in South Korea's technology commercialization processes, fog computing for healthcare IoT, and semantic interoperability in biomedical data. He has contributed to both technical and policy-oriented solutions in eHealth standardization, information overload in health searches, and the social impacts of digital technologies.

• Robot Vision
• Machine Learning
• Autonomous Systems
• +۹ مورد دیگر

Prof. Dr. Sebastian Houben is a Professor of Robot Vision and Machine Learning at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS). He holds roles as International Affairs Representative and Head of the Master's Examination Board for Autonomous Systems. His research focuses on AI-driven solutions for autonomous systems, environmental monitoring, and energy efficiency. Key projects include: INNERVATE : Enhancing driver maneuver evaluation via AI for automotive safety. DKZ.2R : Promoting data literacy and high-performance computing across disciplines. DigitalTwin-4-Multiphysics Lab : Urban and industrial digital twins for efficiency optimization. GARRULUS : AI-driven reforestation using drones and precision seeding. He leads the Institute for AI and Autonomous Systems (A2S) and collaborates with industry partners like GKN Driveline. His research spans robotics, computer vision, and sustainable energy systems. Prof. Houben is a member of the Promotionskolleg Nordrhein-Westfalen (Doctoral College North Rhine-Westphalia). His work emphasizes explainable AI (XAI), safety-critical systems, and interdisciplinary applications.

• Behavioral Economics
• Computational Economics
• Artificial Intelligence
• +۷ مورد دیگر

Ernesto D'Avanzo is an Associate Professor at the University of Trento's Department of Economics and Management. He holds a PhD in Information and Communication Technology from the University of Trento and a Laurea Degree in Philosophy from the University of Salerno. His academic career includes roles as an Assistant Professor at the University of Salerno and research positions at institutions like the High Performance Computing and Networking Institute (ICAR) - CNR and the University of Haifa. He is an associate researcher in multiple groups including the Economic Development and Social Sustainability (EDASS) research group at the University of A Coruña. His research focuses on decision models leveraging artificial intelligence (e.g., machine learning, natural language processing) and multivariate analysis (e.g., factor analysis, Bayesian models). Key areas include data-driven decision making in business strategies, sustainable management, and ethical governance. He has contributed to projects like Y Media Analytica, an AI-driven analytics startup. His articles explore topics like ethical decision-making frameworks, AI applications in healthcare and marketing, and structural equation modeling for corruption analysis. He has received the 2018 Emerald Literati Award and holds editorial roles in journals like Sustainability and International Journal on Semantic Web and Information Systems .

• Human-Computer Interaction
• Social Robotics
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Kaisa Väänänen is a Professor at Tampere University, specializing in Human-Computer Interaction (HCI), Social Robotics, and Artificial Intelligence Ethics. Her research focuses on societal participation, user experience (UX) in technology design, and ethical integration of AI systems. She has contributed extensively to understanding how social robots can foster civic engagement and how AI can collaborate effectively with humans in knowledge work and smart environments. Her work bridges theoretical frameworks with practical applications, including studies on robot-mediated youth engagement, sustainable HCI practices, and the design of ethical AI systems. She has led projects exploring AI collaboration in manufacturing, healthcare, and education, emphasizing human-centric approaches. Väänänen has also edited major conference proceedings, such as the 30th International Conference on Intelligent User Interfaces (IUI 2025), showcasing her leadership in academic discourse. Key research interests include: Social robotics for youth empowerment AI ethics and human-AI collaboration UX design for smart cities Tangible interfaces for explainable AI Her recent articles highlight trends in AI-driven societal participation, sustainability in technology design, and the role of large language models in transforming research and collaboration methods. She advocates for frameworks that ensure technology aligns with human values and societal needs.

• Cybersecurity
• Critical Infrastructure Defense
• Security Governance Modeling
• +۷ مورد دیگر

Prof. Marco Angelini is an Associate Professor in Computer Engineering at Link Campus University, Rome. He holds a PhD in Computer Engineering from Sapienza University of Rome (Department of Computer, Control and Management Engineering) with a thesis on Predictive Visual Analytics. His research focuses on cybersecurity for critical infrastructures, AI governance, and human-AI interaction. Education: PhD in Computer Engineering (Sapienza University of Rome, Level A - Excellent) Master's in Computer Engineering (Sapienza University of Rome) Research & Projects: Member of CINI Cybersecurity National Laboratory Co-author of Italy's National Cybersecurity Framework (2019) Coordinator of CyberReadiness.IT project EU H2020 ECHO Project consultant Lead Link Campus unit for FARADAI (frugal AI) and RESILIENT-TRUST (SME cybersecurity) Professional Roles: Member of Italy’s AI Commission (Presidency of Council of Ministers) Past researcher at Sapienza’s Cyber-Intelligence & Security Center Publications: Over 72 peer-reviewed papers in international journals/conferences.

Peter Vamplewمشاهده پروفایل

استاد

• Multi-Objective Reinforcement Learning (MORL)
• Human-Aligned Artificial Intelligence
• AI Safety
• +۴ مورد دیگر

Professor Peter Vamplew is a leading academic in Artificial Intelligence at Federation University’s Mt Helen campus. He holds a B.A., B.Sc. (Hons) from Flinders University and a PhD from the University of Tasmania. As a former Associate Dean (Research) and leader in multiple research initiatives, he has pioneered multi-objective reinforcement learning (MORL) since 2008, focusing on ethical AI alignment and safety. His roles include co-founding the Australian Responsible Autonomous Agents Collective (ARAAC) and membership in the Future of Life Institute’s AI Safety Research Community. His teaching spans computer games design, programming, AI, and industry engagement through professional experience courses. Research interests include MORL applications in human-aligned AI, explainable systems, and cybersecurity. He has supervised over 20 PhD/Masters students on topics like AI ethics, malware analysis, and game classification. Key collaborations include the Centre for Smart Analytics (CSA) and Health Innovation and Transformation Centre (HITC). Research funding exceeds $4.2M, including projects on adversarial environments, autonomous systems safety, and fraud detection. Notable publications address MORL algorithm development, AI apology frameworks, and ethical decision-making. His work bridges theoretical AI advancements with real-world deployment challenges in autonomous systems.

• Contract Law
• Licensing
• Digital Platforms
• +۳ مورد دیگر

Jude Mbonu is a Lecturer in Law at Newman University's Faculty of Arts, Society and Professional Studies. Previously, he served as an Associate Lecturer at Queen Mary University of London and a Visiting Lecturer at the University of Kent. A qualified Barrister and Solicitor in Nigeria since 2014, he practiced corporate law at Banwo & Ighodalo and has appeared in Nigerian courts. His academic credentials include an LLB and LLM from Bangor University, a PhD in Intellectual Property Law from Queen Mary, and interdisciplinary studies in Philosophy (B.Phil from Urban University, Rome; MA from Nnamdi Azikiwe University). Education: PhD (Queen Mary), LLM (Bangor), LLB (Bangor), MA Philosophy (Nnamdi Azikiwe), B.Phil (Urban University) Research focuses on Contract Law, Digital Platforms, Human Rights, EU Law, and Intellectual Property challenges in digital environments. His recent work addresses audiovisual performers' rights under international treaties and AI governance transparency. He is an Advance HE Associate Fellow and contributes to institutional roles like the Data Protection Task Group. Professional memberships include the Nigerian Bar Association. Collaborative efforts include submitting UK Parliament evidence on AI governance with Dr. Guan H. Tang.

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.