جست‌وجوی استاد و پژوهشگر

Laurenz Wiskottمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Neural Networks
• +۴ مورد دیگر

Laurenz Wiskott is a Professor of Computer Science at the Ruhr-Universität Bochum (RUB), leading the Theory of Neural Systems group at the Institut für Neuroinformatik. His research focuses on machine learning, computational neuroscience, and neuro-inspired AI. He holds affiliations with multiple departments including the Department of Physics and Astronomy, Research Department of Neuroscience, and the International Graduate School of Neuroscience. Wiskott earned his PhD in Physics from RUB in 1995, followed by postdoctoral research at the Salk Institute and Humboldt University Berlin. He has authored over 100 publications, including influential work on Slow Feature Analysis (SFA) and its applications in vision, memory, and reinforcement learning. Notable awards include the 'Best Paper Award' at Machine Learning conferences and recognition for his educational tools like the student advising dashboard. His teaching spans courses on machine learning, computational neuroscience, and AI fundamentals. Current projects include explainable AI, curriculum analytics, and neuro-inspired RL efficiency. Education: PhD in Physics (1995), Ruhr-Universität Bochum Diploma in Physics (1990), University of Osnabrück Studies in Physics (1985–1989), University of Göttingen Research Interests: Slow Feature Analysis, generative models of episodic memory, reinforcement learning, human-centered AI ethics, curriculum analytics, and neuro-inspired machine learning architectures. Grants & Projects: Leads the HUMAINE (Human-Centered AI) initiative and contributed to EU-funded projects like NET-humAIn. Active in educational tech through KI:edu.nrw, developing dashboards for student advising. Labs/Teams: Directs the Theory of Neural Systems lab at INI, collaborating with the Center for Mind and Cognition and the Machine Learning & AI group at RUB.

Yue Wangمشاهده پروفایل

استادیار

• Text Data Mining
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Yue (Ray) Wang is an Assistant Professor at the School of Information and Library Science, University of North Carolina at Chapel Hill. He holds a PhD in Computer Science and Engineering from the University of Michigan, and prior degrees from Shanghai Jiao Tong University and Georgia Institute of Technology. His research focuses on text data mining, machine learning, and health informatics, with an emphasis on developing interactive and interpretable algorithms to reduce human effort in data analysis. He has contributed to clinical NLP, high-recall information retrieval, and computer-assisted qualitative analysis, winning awards such as the WSDM Best Paper Award (2016) and the Deborah Barreau Award for Teaching Excellence (2021). Dr. Wang’s work bridges computational methods with real-world applications, including environmental policymaking through extreme systematic reviews and NeuroBridge—a platform for neuroimaging data discovery. He teaches courses like INLS 509 (Information Retrieval) and INLS 690-270 (Data Mining). His recent research explores user-centered explainability in AI, leveraging crowdsourced experiments and in-lab studies to understand how users interact with machine predictions. He collaborates with organizations like the EPA on machine-assisted literature screening for environmental policy. Education: BS/BA in Information Security & English (Shanghai Jiao Tong University) MS in Computer Applied Technology (Shanghai Jiao Tong University) MS in Electrical and Computer Engineering (Georgia Tech) PhD in Computer Science and Engineering (University of Michigan) Awards: UNC Junior Faculty Development Award (2022) Outstanding Reviewer Award (WSDM 2022) Deborah Barreau Teaching Excellence Award (2021) WSDM Best Paper Award & Outstanding Reviewer (2016) Dow Distinguished Award (2015) Key Research Themes: User-centric AI interpretability Health informatics applications Large-scale data screening for policy Explainable retrieval models

Heleen Muijlwijkمشاهده پروفایل

پژوهشگر

• Human-Technology Interaction
• Explainable AI (XAI)
• Health Informatics
• +۳ مورد دیگر

Heleen Muijlwijk is a Postdoctoral Researcher at Eindhoven University of Technology , affiliated with the Human Technology Interaction department under the Industrial Engineering and Innovation Sciences school. Her work focuses on human-AI collaboration, explainable AI, and health coaching systems. With a dual background in Psychology (BSc) and Mathematics (MSc), she bridges qualitative and quantitative research methods to understand how technology can be designed for human needs. Education & Roles: Bachelor's in Psychology Master's in Mathematics Postdoctoral Researcher at TU/e Former Statistical Consultant at CQM High School Mathematics Teacher Research Interests: Exploring transparency in AI systems, mental models of technology, and applications in healthcare. She emphasizes human-centered design to ensure effective collaboration between health professionals and AI tools, particularly in self-tracking data analysis. Grants & Labs: Involved with EAISI Health and Eindhoven MedTech Innovation Center . Her work is supported by interdisciplinary collaborations with Philips Electronics. Volunteer Work: Guest lecturer at primary schools promoting critical thinking about AI, especially encouraging girls' participation in technology 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

Mehrdad Asadiمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Robotics
• Intelligent Transportation Systems
• +۷ مورد دیگر

Mehrdad Asadi is a Research Fellow at the Vrije Universiteit Brussel (VUB), affiliated with the Department of AI and Robotics within the School of Informatics and Applied Informatics. His research focuses on advancing AI and robotics with an emphasis on safety, explainability, and real-world applications in transportation systems. He holds post-doctoral scholarships at Federated Labs AI and Robotics and the Informatics and Applied Informatics unit. Research Interests: - Development of trustworthy AI systems resistant to adversarial attacks - Optimization algorithms for multi-agent robotics and transportation networks - Explainable AI methodologies for ethical and transparent decision-making - Heuristic approaches for real-time system coordination - Integration of human preferences in automated systems Collaborations: - Active participation in international conferences like GECCO and eXplainable AI workshops - Co-organized events including 'Interactive eXplainable AI - Theory and Practice' (2025) - Cross-disciplinary projects with institutions like IEEE and academic consortia Key Contributions: - Developed the SPATIAL architecture for monitoring AI inference capabilities - Pioneered heuristic methods for intelligent transportation systems - Published 9 peer-reviewed works between 2021-2025 with a focus on safety-critical applications

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.

• Computer Science
• Programming Languages
• Software Engineering
• +۳ مورد دیگر

Xinyu Wang is a Research Fellow at the University of Michigan's Department of Physics, affiliated with the School of Literature, Science, and the Arts (LSA). Their primary office is located in the Randall Laboratory, and they are also associated with the Homer A. Neal Lab. Wang's work focuses on advancing program synthesis techniques, web automation systems, and data-driven methodologies to enhance software development and user interaction. Their research addresses challenges in SQL query verification, cloud computing simplification, and integrating human feedback into automated processes. While specific educational background details are not provided in the text, Wang's research demonstrates expertise in computer science, programming languages, and software engineering. Their projects include developing tools like VeriEQL for SQL equivalence verification, Miwa for user-controlled web automation, and SlabCity for query optimization. These efforts highlight a trend toward creating systems that blend formal methods with practical usability. Key Research Themes: Program synthesis, compiler design, database systems, human-computer interaction, neurosymbolic AI, and automated workflows Wang’s contributions include innovations in data structure refinement, cloudless computing architectures, and tensor transformation tools. Their work often emphasizes reliability, user confidence, and scalable solutions. No formal advisees or grants are explicitly listed in the provided texts. However, their involvement in advising resources like 'Advising Career Resources' suggests a role in supporting academic and professional development within the department. Wang collaborates with labs and teams focused on physics and computational sciences, contributing to interdisciplinary projects. Their contact information includes the email wongxy@umich.edu and office phone numbers 734-764-4437 and 734-763-9694.

Carsten Lanquillonمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Prof. Carsten Lanquillon is a Research Professor at Heilbronn University, specializing in Language Technologies and Cognitive Assistance Systems within the Department of Business Informatics. He leads the Center for Industrial AI (iAI), a Carl Zeiss Foundation-funded initiative addressing AI implementation challenges for medium-sized enterprises. His expertise spans Business Intelligence, Data Science, Machine Learning, and Conversational AI with a focus on applying AI in industrial production processes. Key research areas include cognitive assistance systems, anomaly detection, and ethical AI frameworks. His work bridges academia and industry through collaborative projects like the iAI Center, which promotes sustainable AI adoption in regional manufacturing. Research emphasizes practical solutions for AI integration, including secure large language model adaptation, generative AI applications, and hybrid intelligence systems combining human and machine capabilities. Recent efforts explore explainable AI, digital twin integration, and user transparency in industrial contexts. Lanquillon's contributions include process models for data science projects and frameworks for knowledge-grounded NLP systems. He actively publishes on topics ranging from energy-efficient deep learning to interactive quality analysis tools in automotive industries. His research often combines technical innovation with user-centric design principles to ensure practical applicability.

Elisabeth Lexمشاهده پروفایل

دانشیار

• Recommender Systems
• User Modeling
• Information Retrieval
• +۳ مورد دیگر

Assoc. Prof. Dr. Elisabeth Lex is a tenured Associate Professor at Graz University of Technology (TUG) and PI of the Recommender Systems and Social Computing Lab. She serves as Dean of Study for the master's program in Computational Social Systems. With a habilitation in Applied Computer Science, her research focuses on psychology-informed recommender systems, user modeling, and computational social science. She has published over 100 articles in venues like WebConf, HT, and RecSys, and led major projects like the trans-university 'Polarization in Public Opinion.' Education : PhD in Computer Science (2011), TU Graz Postdoc at Universidad Nacional de San Luis (Argentina) and RWTH Aachen (Germany) Research Interests : Her work integrates cognitive science principles into AI systems, addressing fairness, privacy, and transparency in recommendations. She explores how human behavior and social dynamics influence algorithmic systems, with applications in music recommendation, misinformation detection, and polarization analysis. Article Trends : Recent work emphasizes trustworthy AI, including privacy-aware recommendation systems (e.g., differential privacy), bias mitigation in music recommendations, and computational framing analysis for political narratives. She bridges technical innovation with societal impacts, such as analyzing polarization in public health policies via social media. Scientific Awards & Grants : Outstanding Paper Award (ISSI 2015) EU H2020 grants (TRIPLE, AI4EU, OpenUP) Transuniversity Research Project on Polarization (€325k funding) Advising & Labs : Supervised over 20 PhD/Master students on topics like conspiracy narratives, privacy in recommendations, and job recommendation systems. Her lab develops open-source frameworks (e.g., TagRec, AFEL-REC) and collaborates with industry partners through the Know-Center. Labs/Teams : Leads the Recommender Systems and Social Computing Lab at TUG, part of the interdisciplinary Social Computing area at the Know-Center. Collaborates with global initiatives like OpenAire and Open Knowledge Maps.

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.

• Decentralised AI
• Internet of Things (IoT)
• Social Internet of Things (SIoT)
• +۵ مورد دیگر

Safina Showkat Ara is a Lecturer in Computer Science at the University of Sunderland's School of Computer Science within the Faculty of Technology. She teaches modules including Advanced Programming, Agile Development, Immersive Application Development, and Foundations of Computer Science. Her research focuses on Decentralised AI, IoT, Social IoT, Explainable AI, Deep Learning, Semantic Web, and Blockchain technologies. Prior to her current role, she held academic positions at Teesside University and conducted PhD research at National University of Ireland, Galway, alongside a research assistant role at Hankuk University of Foreign Studies. She supervises students in AI, Data Analysis, and Blockchain domains. Her academic journey includes interdisciplinary work bridging distributed systems, AI, and IoT, with a particular emphasis on decentralised architectures and semantic technologies. Recent research outputs explore blockchain-based coordination mechanisms, reputation management in crowdsourcing, and semantic service composition in IoT environments. Her teaching portfolio reflects practical industry-relevant skills development in modern software practices. Notable technical contributions include methodologies for enhancing security and efficiency in distributed systems, as well as semantic similarity computation for resource recommendation. Her work aligns with contemporary challenges in scalable, user-centric internet technologies and AI explainability.

• Artificial Intelligence
• Computational Argumentation
• Uncertainty Representation
• +۳ مورد دیگر

Pietro Baroni is a Full Professor of Computer Science at the Department of Information Engineering, University of Brescia. His research focuses on artificial intelligence, computational argumentation, uncertainty representation, decision support systems, automated diagnosis, and multi-agent systems. He has authored over 110 scientific papers and holds a patent in his field. Key research interests include formal argumentation frameworks, causal reasoning, and their applications in healthcare robotics, energy systems, and decision-making. His work bridges theoretical foundations (e.g., abstract argumentation semantics) with practical implementations in domains like medical automation and sustainable engineering. Notable contributions include advancing automated diagnosis methods for power grids, developing collaborative robotics for personalized medicine, and integrating user preferences into decision support systems. His recent publications emphasize explainable AI, energy efficiency optimization, and interdisciplinary applications of argumentation theory. Baroni’s research has been applied to real-world challenges such as wastewater treatment optimization and environmental footprint assessment. He actively contributes to academic communities through editorial roles and conference organization, including the Logic and Argumentation (CLAR) conference series.

Collin McMillanمشاهده پروفایل

دانشیار

• Software Engineering
• Natural Language Processing
• Program Comprehension
• +۳ مورد دیگر

Collin McMillan, Ph.D., is an Associate Professor of Computer Science at the University of Notre Dame's College of Engineering. He earned his Ph.D. from William & Mary under Denys Poshyvanyk. His research focuses on software engineering and natural language processing, particularly in automated documentation generation, program comprehension, and human-AI collaboration in code analysis. Education: Ph.D. in Computer Science (William & Mary), advised by Denys Poshyvanyk. His work integrates eye-tracking studies, neural networks, and graph-based models to improve code summarization and developer tooling. Research interests include source code summarization, developer behavior analysis, privacy behavior detection in Android apps, and tools for program comprehension. His lab, the Automatic Program Comprehension Lab (APCL), develops datasets and tools like CodeGRITS, available via Huggingface and GitHub. Publications span 2007–2025, emphasizing developer-centric AI models, code summarization techniques, and empirical studies on programmer attention. Key themes include context-aware models, eye-tracking integration, and LLM applications in software engineering. Awards: ASEE Teaching Award (2020), NSF CAREER Award (2015), Distinguished Paper Award (2021). Grants: NSF funding for AI/SE integration, totaling over $2.5M since 2015. Service: Associate Editor for TSE and EMSE, program committee roles at ICSE, FSE, and ASE. Current students include Maria Dhakal (2023), Robert Wallace (2023), and Chia-Yi Su (2022). Graduates have moved to faculty positions (e.g., Clemson University) and industry roles at Google, Qualtrics, and Ascend Health.

• 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.

Robin Burkeمشاهده پروفایل

استاد

• Recommender Systems
• Fairness-Aware Recommendation
• Multistakeholder Systems
• +۴ مورد دیگر

Robin Burke is a Professor and Department Chair in the Department of Information Science at the University of Colorado Boulder. Previously, he held positions at DePaul University, where he co-directed the Web Intelligence Lab and contributed to interdisciplinary initiatives. He earned his PhD in Computer Science from Northwestern University (1993), MPhil from Yale (1990), and BS from Harvey Mudd College (1986). His research focuses on recommender systems, emphasizing fairness, multistakeholder governance, and ethical AI. He has secured grants from NSF, NEH, MacArthur, and others, and actively develops tools like librec-auto for reproducible research. He leads the Recommender Systems Lab and has been involved with ACM RecSys conferences. Teaching includes courses on network science, AI ethics, and data science methods.