جست‌وجوی استاد و پژوهشگر

• AI and Robotics
• Human-Robot Interaction (HRI)
• Autonomous Agents
• +۷ مورد دیگر

Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +۱۳ مورد دیگر

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

Jatinder Singhمشاهده پروفایل

استاد

• Accountability in AI Systems
• Responsible AI Development
• Data Governance and Stewardship
• +۷ مورد دیگر

Jatinder Singh is a Professor at the RC Trust and Principal Research Associate (equivalent to Research Professor) at the Department of Computer Science & Technology, University of Cambridge. He is primarily affiliated with the University of Duisburg-Essen, Germany, where he leads the Compliant and Accountable Systems research group within the Law department. His work operates at the critical intersection of computer science, legal frameworks, and societal impact, focusing on practical implementations that align technology with regulatory requirements while addressing user and community concerns. Research interests center on accountability mechanisms for AI systems, responsible development practices, data governance, and privacy/security in emerging technologies. He examines governance, agency, trustworthiness, and transparency gaps in algorithmic systems through interdisciplinary socio-technical lenses. Current work addresses bias in LLMs, stakeholder participation frameworks, and human rights implications in domains like healthcare, maritime enforcement, and consumer IoT, emphasizing contextual awareness and real-world applicability. His 15 most recent publications (2025-2024) reveal dominant trends in AI transparency, fairness proxy development, and legal-compliance engineering. Key focus areas include stakeholder involvement in AI governance, bias mitigation in language models, data justice applications for vulnerable populations, and operationalizing human-centered AI in clinical settings. The work consistently bridges technical implementation with regulatory frameworks like the EU Cyber Resilience Act and GDPR. Scientific Awards: No awards or fellowships were mentioned in the provided text. Advising and Grants: The text does not specify PhD/Master's students or grant details. As leader of an active research group publishing high-impact work on EU regulations and human rights, he likely directs funded projects and mentors early-career researchers, though concrete evidence is absent in the source material. His position suggests involvement in interdisciplinary grant consortia addressing socio-technical challenges. Labs and Teams: Singh leads the Compliant and Accountable Systems research group at University of Duisburg-Essen, which collaborates across university-wide clusters including Artificial Intelligence and Society, Human-AI Interaction, Trustworthy Human Language Technologies, and Verification of Machine Learning. The group develops frameworks for legal compliance in AI, focusing on demonstrable accountability through tools for transparency, bias auditing, and stakeholder engagement in real-world deployments.

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +۳ مورد دیگر

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

• Machine Learning
• Bioinformatics
• Medical Engineering
• +۴ مورد دیگر

Susanne Schaller is an Assistant Professor at FH Hagenberg's Research Center Hagenberg, specializing in Medical Engineering and Bioinformatics. She leads interdisciplinary projects focusing on AI-driven solutions in healthcare, including applications in oncology, immunology, and geriatric care. Her work integrates machine learning with genomic data analysis to address challenges in disease diagnosis and treatment. Her research interests include machine learning techniques for gene expression prediction, bioinformatics tool development, and AI applications in chronic disease management. Notable projects include the SmartBeetle initiative (AI-based pest control) and TrainYourBrain Optimizer (Alzheimer’s training software). Projects: 8 active/completed projects (e.g., BF-SigSim, ImmuneProfiler) Collaborations: Interreg Austria-Czech Republic, MAS Demenzservicestellen Key Focus Areas: T-cell signaling, mutation identification in leukemia, and AI-driven healthcare systems Her work bridges computational methods with clinical needs, exemplified by her role in developing IMPI—a tool for low-frequency mutation analysis in chronic myeloid leukemia. She has authored 66+ research outputs, including peer-reviewed articles and commissioned reports on healthcare innovation.

• Service-Oriented Architectures
• Data Management
• Cloud Computing
• +۵ مورد دیگر

Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.

Giancarlo Manziمشاهده پروفایل

دانشیار

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +۵ مورد دیگر

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

Arsalan Heydarianمشاهده پروفایل

دانشیار

• Intelligent Infrastructure Design
• Smart Transportation Systems
• Mixed Reality (VR/AR) in Construction
• +۲ مورد دیگر

Arsalan Heydarian is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Virginia (UVA) , with affiliations to the UVA Link Lab and Omni Reality and Cognition Lab (ORCL Lab) . His work focuses on user-centered intelligent infrastructure through virtual/augmented reality and data-driven design . Education: Ph.D. in Civil Engineering (USC) M.Sc. in Systems Engineering (USC) M.Sc. in Civil Engineering (Virginia Tech) B.Sc. in Civil Engineering (Virginia Tech) Research Streams: Intelligent built environments Mobility infrastructure design User-centered autonomous vehicles Data-driven mixed reality Construction automation Recent Article Trends: Spanning smart buildings , VR/AR applications , and transportation systems , his publications emphasize human-environment interaction , adaptive infrastructure , and immersive technology in civil engineering. Awards: NSF $1M Grant for inclusive AI education initiatives Labs & Teams: Co-founder of the Omni Reality and Cognition Lab (ORCL) , a leading facility for VR/AR-based infrastructure research and human behavior studies in built environments.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +۴ مورد دیگر

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .

• Machine Learning
• Case-Based Reasoning
• Natural Language Processing
• +۳ مورد دیگر

Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .

Gyunam Parkمشاهده پروفایل

پژوهشگر

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +۱۲ مورد دیگر

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Anurag Purwarمشاهده پروفایل

دانشیار

• Robotics Design
• Kinematic Analysis
• Machine Learning in Engineering
• +۳ مورد دیگر

Dr. Anurag Purwar is an Associate Professor of Mechanical Engineering at Stony Brook University, directing the Computer-Aided Design and Innovation Lab and serving as PI for NSF I-Corps. He holds a Ph.D. from Stony Brook (2005) and a B.Tech. from IIT Kanpur (1995). His research merges rigid body kinematics with machine learning for mechanism and robot design, yielding 107+ peer-reviewed publications and patents licensed to industry. Key projects include the sit-to-stand walker assistive device (SAE Top 100 Award) and MotionGen software tools. Education: Ph.D., Stony Brook University (2005); B.Tech., IIT Kanpur (1995) Awards: A.T. Yang Award (2017), SAE Top 100 Award (2016), ASEE Distinguished Teaching Award (2021) Leadership: Program Chair (ASME 2014), Conference Co-Chair (IDETC 2016), NAI Senior Member Research focuses on machine learning-driven design of mechanisms, robotics, and assistive technologies. His labs develop algorithms for path synthesis, generative design, and kinematic analysis. He actively commercializes innovations through industry partnerships and has spun off startups like MotionGen.io.

• Computer Security
• Program Analysis
• Machine Learning
• +۴ مورد دیگر

Mu Zhang is an Assistant Professor at the Kahlert School of Computing, University of Utah. His research focuses on computer security, particularly developing tools using program analysis, machine learning, and data mining to address security issues in Web3, cyber-physical systems, and mobile systems. Previously, he was a Postdoc at Cornell University and a Research Staff Member at NEC Labs America. Education and Background: Postdoc, Cornell University Research Staff Member, NEC Labs America Research Interests: His work emphasizes automated detection and mitigation of security vulnerabilities, including smart contract flaws, industrial control system threats, and malware analysis. He explores innovative solutions like ICSTracker for intrusion backtracking and DEEPVMUNPROTECT for VM-protected Android malware recovery. Recent efforts span blockchain formal verification, Wasm runtime testing, and cross-domain threat inference. Recent Contributions: Notable publications include papers on provenance analysis for industrial control systems (DSN'25), storage collision mitigation in smart contracts (USENIX Sec'25), and automated generation of security-centric smart contract descriptions (ISSTA'23, distinguished paper). Funded projects include NSF grants for AI security education and Cisco Research for smart contract semantics recovery. Awards: His team received the ACM SIGSOFT Distinguished Paper Award (2023) and CCS 2022 Best Paper Honorable Mention. Advising and Grants: Supervises PhD/MSc students like Yu, Wanjing, and Taiji. Active in proposal funding (e.g., Idaho National Lab for industrial control systems, Stellar Foundation for smart contract analysis). Served on TPCs for NDSS, CCS, and ISSTA. Labs/Teams: Leads a research group at University of Utah focusing on secure software systems and automated security tool development.

• 5G/6G mobile networks
• Radio access networks
• Automation systems and autonomy
• +۱۹ مورد دیگر

Bruno Dzogovic is an Assistant Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, specifically within the Department of Computer Science. His research focuses on 5G/6G networks, cybersecurity, cloud computing, and software engineering. 5G/6G mobile networks Cloud computing and virtualization Cyber defense systems Wireless communication Industrial networks Biomedical Wireless Sensor Networks His recent work explores secure network slicing, container technologies, and applications of 5G in healthcare and elderly care systems. He is an active member of the Autonomous Systems and Networks (ASN) research group, contributing to advancements in digital forensics and vulnerability assessment for modern communication systems.