جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• IoT
• Robotics
• +۷ مورد دیگر

Talal Shaikh is an Associate Professor and Director of Undergraduate Studies at the School of Mathematical & Computer Sciences, Heriot-Watt University Dubai. With over a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. His roles include Programme Director for BSc Computer Science and BSc CS (AI). Research interests focus on IoT, ubiquitous computing, robotics, and machine learning applications in healthcare and finance. He pioneered touchless biometric authentication, emotion recognition systems, and transfer learning in robotics. Educational contributions include innovative teaching methods recognized through multiple awards. Awards: Teaching Excellence Awards (2018) Learning and Teaching Oscar - Graduates Award (2017) Switched On Awards (2016-2018) Supervised numerous student dissertations achieving distinctions, though specific advisee names are not listed. Research emphasizes healthcare applications via WiFi sensing and financial machine learning for predictive analytics.

Simone Borsciمشاهده پروفایل

دانشیار

• Human Factors and Ergonomics
• Artificial Intelligence
• Medical Device Development
• +۵ مورد دیگر

Simone Borsci is an Associate Professor at the University of Twente's Digital Society Institute, specializing in Human Factors, AI, and Medical Device Development. He leads major EU projects like PROTECT-CHILD (pediatric genomics), ERJU Transport (rail human factors), and SepsPic (diagnostics). His work bridges technical innovation with societal impact, focusing on user-centered design for healthcare systems, transport automation, and inclusive technologies. Research interests include Human-AI collaboration, usability evaluation of conversational agents, and resilience engineering in complex systems. Notable contributions include the Chatbot Usability Scale (BUS-11) and frameworks for clinical pathway mapping. He co-leads initiatives like the Lean and Agile Multi-dimensional Process (LAMP) for healthcare tech development and the Human Factors Co-Creation Methodology for user requirements engineering. Recent publications highlight advancements in mixed reality UX assessment, ethical AI dialogue systems, and scoping reviews on rail automation challenges. Active in interdisciplinary collaboration, he chairs international conferences (IS-EUD) and serves on expert boards for resilience research and transport innovation. His work aligns with UN SDGs 3 (Health), 9 (Industry), 11 (Sustainable Cities), and 17 (Partnerships). Key grants include Horizon Europe funding for pediatric transplant research (PROTECT-CHILD, €6M) and rail safety innovation (ERJU, €5M). Advises on digital policy for public administrations and co-develops tools like eGLU-Box for web usability testing. Current projects explore AI explainability (XAI), human-robot collaboration in industry, and sepsis diagnostics through photonics.

Casey Overby Taylorمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Biomedical Data Science
• Decision Support Systems
• +۴ مورد دیگر

Casey Overby Taylor, PhD, is an Associate Professor of Medicine-General Internal Medicine and Biomedical Engineering at Johns Hopkins University, with joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. She leads the Translational Informatics Research and Innovation (TIRI) group, focusing on integrating digital health technologies into clinical care and precision medicine. Her work spans biomedical informatics, genomics, and decision support systems, with a focus on improving health equity and clinical adoption of evidence-based practices. Education: PhD in Biomedical Informatics, University of Washington (2011) MS in Biotechnology, University of Pennsylvania (2006) BS in Bioinformatics, University of Michigan (2004) Research Interests: Dr. Taylor’s research combines clinical informatics, data science, and translational research to address challenges in precision medicine, genomic integration, and wearable health technologies. Key areas include: Designing clinical decision support systems for genomic medicine Optimizing postpartum care and chronic disease management Evaluating implementation outcomes for health technologies Recent Publications Trends: Her recent work emphasizes AI applications in healthcare, including opioid use prediction, wearable sensor analytics, and synthetic data generation for ethical AI training. She also explores equity in telemedicine access and genomic testing adoption. Awards: 2025 Presidential Early Career Award for Scientists and Engineers (PECASE) 2023 Johns Hopkins Catalyst Award 2020-2025 NHGRI Genomic Innovator Award 2020-2022 Microsoft Digital Investigator Fellowship Advising & Grants: Leads interdisciplinary teams across multiple institutions, with funding from NIH, Microsoft, and the Malone Center. Current projects include developing AI tools for clinical decision-making and genomic data integration. Labs/Teams: Director of the TIRI Lab, collaborating with the Institute for Computational Medicine and the GIM Biomedical Informatics Data Science Section.

Enric Plaza Cerveraمشاهده پروفایل

استاد پژوهشی

• Case-Based Reasoning
• Multiagent Learning
• Conceptual Blending
• +۴ مورد دیگر

Enric Plaza Cervera is a Research Professor at the Learning Systems Department of the Artificial Intelligence Research Institute (IIIA-CSIC) in Catalonia, Spain. His work focuses on foundational aspects of artificial intelligence, particularly in conceptual blending, case-based reasoning (CBR), and multiagent systems. He explores how cognitive principles like image schemas and conceptual blending can be applied to computational models of creativity, semantic understanding, and diagrammatic reasoning. His research also addresses ethical challenges in AI governance and value alignment through agent-based simulations and formal models. Key research areas include: Formalizing cognitive processes in AI for diagram interpretation and creativity Developing ethical frameworks for autonomous systems using AGV (Autonomy-Governance-Value) models Advancing CBR theory through amalgamation techniques for integrating multiple solutions Modeling embodied cognition in understanding abstract concepts through spatial metaphors Recent publications (2020-2024) emphasize interdisciplinary approaches combining cognitive science, formal logic, and multiagent systems to tackle challenges in AI transparency, value alignment, and creative problem-solving. No scientific awards are explicitly mentioned in the provided materials. His work contributes to both theoretical advancements and applied AI systems through the IIIA's research initiatives.

Siamak Layeghyمشاهده پروفایل

مدرس

• Cyber Security
• Artificial Intelligence (AI)/Machine Learning (ML)
• Network Security
• +۴ مورد دیگر

Dr. Siamak Layeghy is a Lecturer in the School of Electrical Engineering and Computer Science at The University of Queensland. He holds a PhD in Information Technology and Electrical Engineering from UQ (2018). His research focuses on AI/ML-driven cybersecurity solutions, particularly network intrusion detection systems (NIDS), IoT security, edge learning, and software-defined networking (SDN). Education : PhD in Information Technology and Electrical Engineering, The University of Queensland (2018) Research Interests : Application of ML techniques (Transformers, GNNs, GANs) for network and IoT security Edge computing and hardware acceleration (e.g., Edge TPU) Domain-invariant NIDS and cross-platform security solutions Federated learning and blockchain for collaborative intrusion detection His work emphasizes explainability, generalization, and real-world deployability of AI models in cybersecurity. Publications : Over 50 peer-reviewed articles, including high-impact journals like Expert Systems with Applications and IEEE Transactions . Key themes include sensor-based anomaly detection, SDN security, and framework development (e.g., FlowTransformer). Labs/Teams : Active contributor to the development of open-source frameworks like FlowTransformer and datasets such as NF-CSE-CIC-IDS2018-v3. Collaborates with industry partners on IoT security and edge computing.

Timo Freieslebenمشاهده پروفایل

پژوهشگر

• Explainable AI
• Interpretable Machine Learning
• Philosophy of Science
• +۶ مورد دیگر

Timo Freiesleben is a Postdoctoral Researcher at the Machine Learning in Science Cluster (University of Tübingen) and an external member of the Munich Center for Mathematical Philosophy (LMU Munich). His work bridges philosophy with machine learning, focusing on interpretable/explainable AI and algorithmic fairness. Education Mathematics Philosophy of Science Computer Science Neuroscience Research Interests Freiesleben explores the philosophical foundations of machine learning, emphasizing causality , ethical constraints , and scientific inference from opaque models. His work addresses the epistemological validity of recourse explanations, robustness in AI, and the interplay between human concepts and neural representations. Recent Publications His 2021-2025 work spans counterfactual explanations , model robustness , and XAI critique , with methodological innovations like adversarial random forests and improvement-focused causal recourse. Themes include ethical AI deployment in healthcare, feature importance analysis, and philosophical implications of black-box models. Collaborations & Labs He collaborates with Dr. Thomas Grote (Tübingen) and was supervised by Prof. Stephan Hartmann (Munich). Affiliated with the Machine Learning in Science Cluster and the Munich Center for Mathematical Philosophy .

Cristina Conatiمشاهده پروفایل

استاد

• Artificial Intelligence
• Intelligent Tutoring Systems
• Explainable AI (XAI)
• +۶ مورد دیگر

Cristina Conati is a Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. She leads the Human-AI Interaction Lab, focusing on advancing AI systems that adapt to human needs, particularly in healthcare and education. Her work emphasizes explainable AI (XAI), adaptive interfaces, and intelligent tutoring systems. Conati holds significant awards including the AAAI Fellow distinction and the Killam Research Prize in Science 2022. Her research bridges cognitive science and computer science, addressing challenges in user modeling, multimodal interaction, and personalized learning technologies. She actively contributes to NSF-funded projects and has authored over 200 peer-reviewed publications. Education: Not explicitly listed in the provided text Research Focus: XAI, adaptive systems, human-AI collaboration, and AI-driven healthcare solutions Recent work explores AI applications in clinical assessment (e.g., Alzheimer’s detection via eye-tracking), ethical AI design, and personalized explanations for educational systems. She teaches advanced courses on human-centered AI and artificial intelligence fundamentals. Her lab’s innovations include gaze-driven adaptive visualizations and AI tools for genetic syndrome identification. Conati’s contributions to AI ethics and transparency have positioned her as a leader in responsible AI development.

Niklas Kühlمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Computer Interaction
• Information Systems
• +۶ مورد دیگر

Niklas Kühl is a Full Professor and Chair for Information Systems and Human-Centric Artificial Intelligence at the University of Bayreuth. He holds leadership roles at the WI institute of Fraunhofer FIT, is a member of the core competence center for finance and information management, and serves as a senior expert in artificial intelligence at IBM. His interdisciplinary research bridges technical machine learning methods with human-centered design, focusing on socio-technical systems, human-AI collaboration, fairness in AI decision-making, and sustainability applications. Affiliations: University of Bayreuth (2023–present) Karlsruhe Institute of Technology (2017–2023) IBM (2020–2023) Education: PhD (summa cum laude) in Information Systems, KIT (2017) Diplom (MSc) in Industrial Engineering, KIT Research Interests include: Human-AI collaboration dynamics Fairness and trust in algorithmic systems Privacy-preserving AI Scalable AI product development AI for sustainability His recent publications analyze fairness perception alignment, data marketplace optimization, and explainable AI applications across domains like healthcare and energy. Scientific Contributions Over 100 peer-reviewed articles Best Paper Awards at top AI conferences Leadership in Applied AI in Services Lab (2017–2023) Community Engagement Co-founder of Education for Refugees e.V. (2015–2022) Active in ACM, AIS, and German Data Science Society

• Multi-agent systems
• Real-Time Systems
• Explainable AI (XAI)
• +۵ مورد دیگر

Davide Calvaresi is an Associate Professor at HES-SO Valais-Wallis (University of Applied Sciences and Arts Western Switzerland) in the Engineering and IT school within the Computer Science and Communication Systems department. His research focuses on multi-agent systems , real-time systems , explainable AI (XAI) , and blockchain for trustworthy applications in healthcare and safety-critical domains. Current projects include EXPECTATION (Personalized Explainable AI for decentralized agents) and past projects like Axe Santé 2020 (interdisciplinary health research) and Simulator of multi-agent systems (timing reliability analysis). Collaborations include researchers from University of Bologna, University of Luxembourg, and Ozyegin University. Recent publications focus on swarm intelligence in IoT , ethical AI for aging populations , and explainable health coaching systems Technical contributions include DEXiRE (rule extraction from neural networks), SCoDES (blockchain-based consent management), and HAExA (parsimonious XAI architecture)

Suresh Kumaar Jayaramanمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۴ مورد دیگر

Suresh Kumaar Jayaraman is a Postdoctoral Fellow at the Robotics Institute , Carnegie Mellon University . His research focuses on Human-Robot Interaction , Explainable AI , and Trust Modeling in autonomous systems, particularly in automated driving applications. Robotics Institute, Carnegie Mellon University Research Fellow His work emphasizes behavior-aware control systems , pedestrian-AV interaction modeling , and machine teaching frameworks. Key themes include trust calibration , context-adaptive decision-making , and replicability standards in HRI studies. Recent publications highlight real-time trust estimation for automated vehicles, multimodal pedestrian behavior modeling , and user-centric explainable AI systems. He employs inverse reinforcement learning and hybrid automaton models to enhance human-AV interaction safety. He is affiliated with the TBD Lab at Carnegie Mellon University, focusing on robotics and autonomous systems development.

Pragathi Praveenaمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Human-Robot Interaction (HRI)
• Computer Vision
• +۳ مورد دیگر

Pragathi Praveena is a Research Fellow at the Robotics Institute , Carnegie Mellon University. Her work focuses on advancing human-robot interaction, teleoperation systems, and robotics applications in collaborative environments. She leads the Reliable Autonomous Systems Lab , exploring topics such as Petri Net-based robotic system design, generative AI in robotics, and socially engaging robot interfaces. Her research spans autonomous systems , computer vision , and medical robotics applications . Notable contributions include Periscope (a robotic camera system for remote collaboration) and Rex (user-centered repair frameworks for robots). She investigates challenges in telemanipulation control systems, motion generation algorithms, and optimizing collaborative workflows between humans and robots. Key technical trends in her work include formal methods for system design, integration of AI/ML in robotics, and human-centered approaches to interface development. Her publications address both foundational theory (e.g., Petri Nets) and applied solutions (e.g., cinematic automation). Current lab projects emphasize robustness in robotic groupware and cross-modal interaction systems. As part of the Robotics Institute, her work intersects with computer science, engineering, and design disciplines. Her lab collaborates on projects involving drone motion optimization, real-time respiration monitoring via video analysis, and enhancing remote collaboration through robotic systems.

Randall Davisمشاهده پروفایل

استاد

• Human-Computer Interaction
• Artificial Intelligence
• Medical Technology
• +۳ مورد دیگر

Randall Davis is a Full Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), holding an Esther and Harold Edgerton Endowed Chair (1979-1981). He has served in leadership roles including Associate Director of MIT's Artificial Intelligence Laboratory (1993-1998), Research Director of CSAIL (2003-2007), and Associate Director of CSAIL (2012-2014). His research focuses on human-computer interaction, knowledge-based systems, and medical technology applications. He co-authored Knowledge-Based Systems in AI and pioneered systems like DCTClock , recognized as one of Time's Best Inventions (2021). Education: B.S. (summa cum laude, Phi Beta Kappa) from Dartmouth (1970), Ph.D. in Artificial Intelligence from Stanford (1976). Key Awards: Founding Fellow of AAAI (1990), USAF Decoration for Exceptional Civilian Service (1998), AAAI President (1995) Intellectual Property Contributions: Expert in Computer Associates v. Altai case (1990), led National Academy of Sciences study The Digital Dilemma (2000) His work integrates multimodal interaction (speech, gesture, sketch) to create intuitive human-computer interfaces, with recent focus on cognitive assessment tools for early Alzheimer’s detection. He leads MIT's Human-Computer Interaction research group within CSAIL.

Gianmarco Mengaldoمشاهده پروفایل

استادیار

• Computational Science and Engineering
• Data Science
• Machine Learning
• +۷ مورد دیگر

Dr. Gianmarco Mengaldo is an Assistant Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), affiliated with the College of Design and Engineering. He holds an Honorary Research Fellowship at Imperial College London (UK). His academic journey includes a PhD in Aeronautical Engineering from Imperial College London and degrees from Politecnico di Milano (BSc, MSc in Aerospace Engineering). His research integrates computational science, applied mathematics, and machine learning to address complex systems in engineering, climate science, healthcare, finance, and solid earth physics. Key focuses include data-driven pattern identification, high-fidelity multi-physics simulations, and AI-driven predictive modeling. Recent work explores financial explainable AI, climate impact modeling with small language models, and robust ESG analysis. His methodologies span neurosymbolic AI, hybrid deep learning systems, and stochastic-chaotic modeling for extreme weather prediction.

Raul Zurita-Millaمشاهده پروفایل

استاد

• Geospatial Analytics
• Machine Learning
• Environmental Science
• +۴ مورد دیگر

Raul Zurita-Milla is a Full Professor at the University of Twente, holding dual appointments in the Department of Geo-information Processing and the Digital Society Institute. His research focuses on spatiotemporal analytics, geospatial machine learning, and environmental modeling, with applications in public health, climate change, and sustainability. Key areas include wastewater-based epidemiology, phenological analysis, tick dynamics modeling, and open science practices. He co-founded the Open Science Community Twente in 2020, promoting reproducible research and collaborative frameworks. His expertise spans geoinformatics, machine learning, and environmental science, contributing to UN Sustainable Development Goals related to health, climate action, and biodiversity. Notable achievements include the 1st place TU Delft Water for Impact Best Paper Award (2024) and leadership in developing open-source tools like the CGC Python package for geodata clustering. His work integrates interdisciplinary approaches, blending computational methods with real-world environmental and societal challenges. Zurita-Milla’s research outputs include over 155 peer-reviewed publications and 16 datasets, emphasizing reproducibility and open science. Collaborations span global institutions, addressing topics such as false spring detection, agent-based disease modeling, and sustainable wastewater surveillance systems.

Keeley Crockettمشاهده پروفایل

استاد

• Artificial Intelligence Ethics
• Fuzzy Systems
• Psychological Profiling with AI
• +۵ مورد دیگر

Professor Keeley Crockett is a Professor in Computational Intelligence at Manchester Metropolitan University (MMU), leading the Machine Intelligence and Data & AI Ethics themes. She chairs the IEEE Technical Committee SHIELD, focusing on ethical, legal, and social dimensions of AI. Her research spans AI ethics, fuzzy systems, and conversational agents, with a focus on public engagement and SME readiness for ethical AI adoption. Education: BSc (Hons) Computation (UMIST, 1993), PhD (Fuzzy Rule Induction, MMU, 1998). Current roles include EPSRC Grant PI for PEAs in Pods and Manchester City Council’s People’s Panel for AI. She advises on national AI policy, including the UKRI’s AI & Robotics Strategic Advisory Team and All-Party Parliamentary Group on Data Analytics. Research interests include ethical AI frameworks, psychological profiling via AI, and multilingual semantic similarity. Notable contributions: parliamentary inquiries on data and AI governance, co-creation of community-based AI engagement models, and leadership in IEEE committees. Scientific awards include Senior Fellow of the Higher Education Academy (SFHEA) and Senior Member of IEEE (SMIEEE). Awards also include the Disney Spirit Award. Over 130 peer-reviewed publications and 3 textbooks in database systems. Advising: Supervised 28 PhDs, external examiner for multiple UK and international universities. Grants include EU Horizon 2020, Turing Institute, and Innovate UK KTPs. Collaborations with ICO, Turing Institute, and GM businesses. Labs/Teams: Co-lead for AI at MMU’s Centre for Digital Innovation, part of GM Artificial Intelligence Foundry.