جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.

Brian Fisherمشاهده پروفایل

استاد

• Cognitive Science
• Visual Analytics
• Human-Computer Interaction
• +۲ مورد دیگر

Brian Fisher is a Professor at Simon Fraser University's School of Interactive Arts and Technology, where he leads the Interaction Science Research Group. He holds affiliate positions at the University of British Columbia and serves as Associate Director for the Media And Graphics Interdisciplinary Centre (MAGIC). His research investigates human reasoning in responsive environments through cognitive science approaches, developing visualization systems that support individual and distributed cognition. Fisher's work spans personalized health, aircraft safety analysis, space object classification, and emergency management. His teaching integrates perceptual, cognitive, and social science knowledge into design education. He actively participates in science policy through organizations including NSERC, Defence R&D Canada, EUREKA Eurostars, and IEEE committees. Fisher has been recognized as a Fellow of the Psychonomics Society and chaired major conferences including IEEE VIS 2019. His research is characterized by strong interdisciplinary collaboration and real-world applications in high-stakes decision domains.

H. David Jeongمشاهده پروفایل

استاد

• Construction Management
• Artificial Intelligence in Civil Engineering
• Data Analytics for Infrastructure
• +۵ مورد دیگر

H. David Jeong is the Associate Department Head and Professor in the Department of Construction Science at Texas A&M University's College of Architecture. His work focuses on advancing construction management through data analytics, artificial intelligence, and innovative technologies. Key areas of research include risk analysis, cost estimation, transportation infrastructure optimization, and collaboration strategies in project delivery. He leads initiatives such as the Serious Games and Simulations for the Built Environment (SeGSiBE) and contributes to the Data Analytics and Artificial Intelligence for Construction Management program. Jeong’s research integrates machine learning, NLP, and GIS technologies to address challenges in construction scheduling, asset management, and project performance evaluation. His projects emphasize practical applications, such as developing predictive models for gravel loss, contingency estimation frameworks, and chatbot-driven workflow tools. He has published extensively on topics like highway construction contract times, AI-based project prioritization, and lifecycle data management in transportation assets. Education : While specific academic credentials are not detailed, his roles imply advanced training in construction engineering and management. Labs/Teams : Leads SeGSiBE lab and collaborates on the HCCI (Highway Construction Cost Index) tool development. Grants/Advising : No student advisees listed, but his research projects likely involve graduate students and industry partnerships.

Tan Cher Hengمشاهده پروفایل

دانشیار

• Radiology
• Artificial Intelligence in Healthcare
• Medical Imaging
• +۳ مورد دیگر

Associate Professor Tan Cher Heng is an Associate Professor at the Lee Kong Chian School of Medicine (Nanyang Technological University Singapore) and Assistant Dean (Clinical Research). He serves as a Senior Consultant at Tan Tock Seng Hospital's Department of Diagnostic Radiology and holds leadership roles in the National Healthcare Group (NHG) as Group Chief Research and Innovation Officer and Director of the Academic Partnerships Office. His expertise spans radiology, artificial intelligence (AI) in healthcare, and clinical research innovation. He completed an MBBS (Singapore), FRCR (UK), and an Executive MBA from Insead. Education: MBBS, Singapore FRCR (UK) Abdominal Imaging Fellowship at MD Anderson Cancer Center Executive MBA from Insead Research Interests: His work focuses on advancing AI-driven diagnostics in radiology, optimizing imaging techniques for liver and prostate cancers, and translating clinical research into healthcare practice. He has pioneered initiatives like the national imaging AI platform (AimSG) and contributed to guidelines for imaging appropriateness and technology adoption. His research bridges clinical practice, innovation, and population health. Articles Summary: Recent publications emphasize AI applications in pneumonia diagnosis, radiology education, and liver/prostate cancer imaging. Themes include AI ethics, imaging cost-effectiveness, and guideline adaptation for diseases like tuberculosis and COVID-19. His work highlights leveraging technology to improve diagnostic accuracy and healthcare accessibility. Labs/Teams: He leads the Academic Partnerships Office and collaborates with global networks like the Asian Oceanian Society of Radiology. His roles in NHG and LKCMedicine drive cross-disciplinary projects in AI, clinical research, and medical innovation.

• Requirements Engineering
• Human-Computer Interaction
• Digital Creativity Support
• +۱۲ مورد دیگر

N. A. M. Maiden is a leading academic at City, University of London, with a prolific research output in human-centred artificial intelligence, digital creativity support, and requirements engineering. His work focuses on designing and evaluating digital tools that augment human creativity in professional domains such as journalism, elite sports coaching, and dementia care. His research interests are highly interdisciplinary, bridging computer science, human-computer interaction, and social applications. Key areas include co-creative AI systems, computational creativity, natural language processing, rule-based reasoning, and user-centred design. He investigates how technology can empower professionals by enhancing their creative thinking processes while maintaining high levels of human control. The trends in his recent publications demonstrate a consistent focus on developing and evaluating practical digital tools like 'Sport Sparks' for coaches and 'JECT.AI' for journalists. His work employs design science methodology, emphasizing co-creation with practitioners and empirical evaluation in real-world settings. The research spans from technical system architecture to human factors and societal impact, particularly in journalism innovation and healthcare technology. Automating science journalism tasks Designing digital tools for creative thinking in sports coaching Towards AI-enabled support for business model creativity Using computational tools to support journalists' creativity Creativity Triggers for requirements elicitation Dr. Maiden has made significant contributions to the field through numerous publications in top journals and conferences. His research often involves collaborative projects with industry partners and other academic institutions, securing funding for innovative work in human-computer collaboration. He mentors a research group focused on digital creativity and has been instrumental in developing novel methodologies for requirements engineering and creative problem solving. His work includes the development of serious games for forensic mental health settings (e.g., 'StreetWise') and digital solutions for improving dementia care. These projects highlight his commitment to applying technology for social good and improving quality of life through innovative information systems.

• Distributed Systems
• Cloud Computing
• Data-Intensive Workflow Management
• +۵ مورد دیگر

Dr. Zhiming Zhao is an Associate Professor and Chair of the Multiscale Networked Systems (MNS) research group at the Informatics Institute (IvI), University of Amsterdam (UvA). He serves as the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure for ecology and biodiversity science. Zhao holds an IEEE Senior Member designation and is the Managing Editor of the Journal of Cloud Computing . He earned his Ph.D. in Computer Science from UvA in 2004. His research focuses on quality-critical distributed computing, data-intensive workflows, virtual research environments, and digital twins. He leads projects such as LTER-LIFE (Dutch research infrastructure for digital twins) and coordinates UvA contributions to EU initiatives like ENVRI-HUB Next , EVERSE , and BlueCloud-2026 . Zhao’s work spans technical development in EU projects (e.g., ENVRI-FAIR , ARTICONF , CLARIFY ) and leadership roles in international workshops and conferences. His team develops frameworks like NaaVRE (Jupyter-based collaborative environments) and CloudsStorm (dynamic infrastructure planning). Current research emphasizes trustworthy AI in cloud systems, federated learning, and edge-cloud resource optimization. Key achievements include over 150 peer-reviewed publications, supervision of numerous PhD students, and contributions to open science initiatives. His lab actively explores interdisciplinary applications in environmental science, medical imaging, and blockchain-based decentralized systems.

Antoine Geissbuhlerمشاهده پروفایل

استاد

• Medical Informatics
• eHealth
• Telemedicine
• +۷ مورد دیگر

Antoine Geissbuhler is a Full Professor at the University of Geneva's Faculty of Medicine, Department of Radiology and Medical Informatics. He serves as Dean of the Faculty of Medicine since July 2023, following his role as Vice-Rector for Digital Strategy and Innovation. Additional leadership positions include Director of the HUG Innovation Center and Chief Physician of the eHealth and Telemedicine Department at Geneva University Hospitals (HUG), recognized as a WHO collaborating center. He also presides over BioAlps, Western Switzerland's life sciences cluster. Geissbuhler's research focuses on designing and implementing IT tools in healthcare, with emphasis on telemedicine networks and digital health strategies. His work spans global contexts including Africa, Asia, and Latin America through the RAFT project connecting hundreds of healthcare professionals across 20 countries. Key domains include maternal health monitoring, shared medication plans, AI chatbots for patient education, and digital solutions for non-communicable diseases. His publications reveal strong trends in implementation science, particularly frameworks for reporting digital health interventions (iCHECK-DH) and learning from implementation failures. WHO Collaborating Center designation for eHealth department President of BioAlps life sciences cluster (since 2022) Architect of Swiss eHealth strategy implementation Developer of RAFT telemedicine network As an educator, Geissbuhler has pioneered telemedicine training across resource-limited settings through the RAFT network. His leadership extends to digital health policy, including contributions to global standards for health information systems and ethical frameworks for health data reuse. Current initiatives emphasize patient-centered digital tools and strengthening health system resilience through technology.

Michael L. Austinمشاهده پروفایل

مدرس ارشد

• Sound and music in interactive media
• Ludomusicology
• Sound studies
• +۷ مورد دیگر

Dr. Michael L. Austin is a Senior Lecturer in Music and Sound at Edge Hill University, serving as Programme Leader in Music Production since January 2023. He previously held academic roles at Louisiana Tech University, Howard University, and others. His research focuses on sound and music in interactive media, including video games, social media, and smart devices. He is Executive Director of the Society for the Study of Sound and Music in Games and a Research Associate at the Library of Congress's Radio Preservation Task Force. Education: PhD in Humanities (Aesthetic Studies) from the University of Texas at Dallas (2011), MA in Music Theory from UT Austin (2007), and BA in Music Composition from UT San Antonio (2005). Research interests include ludomusicology, cultural representation in media, sound design for UX, and algorithmic bias in emerging technologies. His 2024 monograph Audiovisual Alterity explores identity representation in music videos, while earlier works like Music Video Games: Performance, Politics, and Play (2016) analyze music's role in digital games. Publications span video game music analysis, algorithmic bias, and sound art. His talks include topics like 'Sounding Drag Identities in Video Games' and 'Algorithmic Bias in Emerging Media.' As a sound artist, his work has been exhibited at the Smithsonian and Dallas Museum of Art.

Francesca Dalia Faraciمشاهده پروفایل

مدرس ارشد

• Biomedical Signal Processing
• Medical Data Analysis
• Wearable Technology
• +۵ مورد دیگر

Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.

Zheng Zhangمشاهده پروفایل

پژوهشگر

• Human-AI Interaction
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.

• Patient Safety
• Behavioral Medicine
• Healthcare Simulation
• +۱۷ مورد دیگر

Ambrose Wong, MD, MSEd, MHS, is an Associate Professor Adjunct in the Department of Emergency Medicine at Yale School of Medicine. He is a prominent researcher in patient safety, behavioral health, and healthcare simulation, with a focus on improving care for agitated patients and preventing workplace violence in emergency settings. University: Yale School of Medicine School: School of Medicine Department: Department of Emergency Medicine Email: ambrose.wong@yale.edu Dr. Wong’s research spans qualitative and mixed-methods health services research, with a strong emphasis on teamwork, technology-based interventions, and behavioral health. He applies healthcare simulation to develop and test innovative solutions such as agitation code teams and peer support-enhanced crisis response systems. His work addresses critical issues including clinician burnout, health equity in restraint use, and the impact of social determinants on emergency care. His recent publications highlight a trajectory toward leveraging artificial intelligence for agitation prediction, conducting large pragmatic trials, and investigating disparities in behavioral emergency care. This reflects a growing integration of data science, implementation science, and equity-focused research in emergency medicine. Dr. Wong has received significant recognition and funding for his work, including: NIMH R01 grant for system dynamics modeling to improve agitation management PCORI funding for a pragmatic trial on peer support in crisis response NIMH K23 and NIH KL2 career development awards Society for Simulation in Healthcare Research Award He actively mentors and collaborates, with frequent co-authorship with researchers such as Bidisha Nath, Leigh Evans, James Dziura, and Kimberly Yonkers. He serves on editorial boards for Annals of Emergency Medicine and Academic Emergency Medicine Education & Training , and is a member of the Society for Academic Emergency Medicine Simulation Academy. He also contributes to institutional leadership as a member of the Dean's Advisory Council for LGBTQI Affairs at Yale. His laboratory and research team focus on interdisciplinary collaboration, integrating emergency medicine, psychiatry, data science, and public health to develop scalable, evidence-based interventions that enhance safety and equity in emergency care.

Daniel Fürstenauمشاهده پروفایل

استاد

• Digital Transformation
• IT Infrastructures
• Digital Platforms
• +۱۲ مورد دیگر

Daniel Fürstenau is a Professor of Business Administration with a focus on IT Management and Digital Transformation at Freie Universität Berlin’s School of Business & Economics, and is affiliated with the Einstein Center Digital Future (ECDF). He also holds positions as Researcher at Charité – Universitätsmedizin Berlin and previously served as Associate Professor at IT University Copenhagen and Assistant Professor at Copenhagen Business School. He earned his doctorate from Freie Universität Berlin in 2014 and completed postdoctoral research with affiliations at UC San Diego, Copenhagen Business School, and the University of British Columbia. His academic journey is complemented by professional experience as a consultant at BOC Information Technology Consulting, where he guided digital transformation in banking and public sectors. Fürstenau’s research centers on digital transformation, IT infrastructures, and digital platforms, with a strong emphasis on healthcare applications. He investigates how digital systems influence organizational structures, innovation, and patient care, using methods such as network analysis and simulation. His work spans AI in nursing, digital therapeutics, health data governance, and low-code development. He leads interdisciplinary projects involving Charité and ECDF, focusing on digital health ecosystems and fall prevention using AI. His recent publications reflect a trend toward interdisciplinary digital health research, with a focus on AI, data platforms, governance, and sustainability. He frequently publishes in top-tier journals such as Information Systems Research , Journal of Management Information Systems , and Business & Information Systems Engineering . 2020 Best Paper Award, Academy of Management (OCIS) Best Associate Editor, International Conference on Information Systems (Healthcare track) Spokesperson, Division Digital Health, Gesellschaft für Informatik Cluster Spokesperson (since 2024) Member, Association for Information Systems (AIS) Fürstenau leads a research team of assistants based at FU Berlin and Charité, and is involved in major projects like DIPA-SCALE, CCA, and SoKIP. He is committed to socially relevant research with impact on medical care and financial systems. His leadership at ECDF underscores his role in shaping digital future research in Berlin.

• Manufacturing Engineering
• Product Lifecycle Management (PLM)
• Production Technology
• +۷ مورد دیگر

Franco Lombardi is a Full Professor at the Department of Management and Production Engineering (DIGEP) , Polytechnic of Turin , with affiliations to the J-Tech@PoliTO Interdepartmental Center . He has been a PhD college member for the Production Systems & Industrial Design program since the 19th cycle (2003/2004) and served as a course lecturer in Production Systems (Management Engineering) and Graphic Communication and Mechanical Manufacturing (Industrial Production Engineering) since 2019/2020. Research Focus: Manufacturing technologies, product lifecycle management (PLM), Industry 4.0, sustainable production systems, joining processes, and human-centric knowledge management frameworks. Article Trends: Recent work spans friction stir welding of industrial alloys (2025), sustainability evaluation of AI in product lifecycles (2024), integration of Industry 4.0 in agriculture (2024), and additive manufacturing eco-design (2024). Earlier projects include reconfigurable production systems (2009) and real-time production planning (2019). Grants and Projects: Scientific manager for PNRR-funded initiatives like AGRITECH Spoke 6 (2022-2025) and MANAGE5.0 (2022-2024), alongside regional projects such as TECNOPROTEO (2024-2027) and DynLab4JMat (2018-2021). Collaborated on national patents for gear machining processes. PhD Advisees: Temur Turgunboev (Ingegneria Gestionale), Nicolo Grasso (Gestione, Produzione E Design), Giovanni Marongiu (Gestione, Produzione E Design), Benedetta Fasciolo (Gestione, Produzione E Design), Luigi Panza (Gestione, Produzione E Design), Alberto Faveto (Gestione, Produzione E Design).

Kevin G. Yagerمشاهده پروفایل

پژوهشگر

• Materials Science
• Nanotechnology
• Artificial Intelligence
• +۴ مورد دیگر

Kevin G. Yager serves as the Interim Director for the Center for Functional Nanomaterials (CFN) at Brookhaven National Laboratory, a U.S. Department of Energy scientific user facility supporting over 700 researchers annually from universities, industry, and national laboratories worldwide. He also leads the AI-Accelerated Nanoscience group where he pioneers the integration of artificial intelligence with materials science research. Institution: Brookhaven National Laboratory Center: Center for Functional Nanomaterials Group: AI-Accelerated Nanoscience Position: Interim Director & Group Leader Dr. Yager earned his Ph.D. in Physical Chemistry from McGill University in 2006 following a B.Sc. in Chemistry with a minor in Computer Science. After postdoctoral research at NIST, he joined Brookhaven National Laboratory in 2010, becoming Group Leader of the Electronic Nanomaterials Group in 2016. His academic journey uniquely combines chemistry, physics, and computer science, providing the foundation for his interdisciplinary research approach. Dr. Yager's research focuses on the intersection of artificial intelligence and materials science. He is currently developing AI agents for scientific discovery, conceptualized as a "science exocortex"—an AI expansion to a researcher's cognition and volition. His longstanding interests include non-equilibrium self-assembly of block copolymers, where he has demonstrated techniques to induce these materials to form nanostructures beyond equilibrium configurations. He has made significant contributions to x-ray scattering techniques, particularly in leveraging machine learning for autonomous scientific experiments and data analysis. His publication record shows a clear progression toward increasingly sophisticated AI applications in materials science. The most recent articles demonstrate how AI is being integrated throughout the scientific process, from autonomous experimentation systems to domain-specific knowledge management tools. His work spans computer science, materials science, and physics, reflecting his interdisciplinary approach to accelerating scientific discovery. Major Research Themes: AI/ML for Scientific Discovery Autonomous Experimentation Systems Non-equilibrium Self-Assembly Processes Block Copolymer Nanomaterials Engineering Advanced X-ray Scattering Techniques Science Workflow Automation Dr. Yager manages the CFN x-ray scattering user program and collaborates extensively with the National Synchrotron Light Source II (NSLS-II) on synchrotron beamline research. His leadership of a major DOE user facility positions him at the forefront of national efforts to integrate artificial intelligence with traditional scientific methodologies, potentially transforming how scientific discovery is conducted across multiple disciplines.

Jie Dingمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Statistics
• Scientific Computing
• +۶ مورد دیگر

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.