جست‌وجوی استاد و پژوهشگر

Michael Blumمشاهده پروفایل

استاد

• Digital Health
• Health Informatics
• Preventive Cardiology
• +۷ مورد دیگر

Michael Blum, MD, is a Professor of Medicine in the Division of Cardiology at the University of California, San Francisco (UCSF), within the School of Medicine and Department of Medicine. He serves as Associate Vice Chancellor for Informatics and Chief Digital Transformation Officer at UCSF, leading strategic digital health initiatives. He is also the Executive Director of the UCSF Center for Digital Health Innovation (CDHI), where he drives innovation in digital health technologies. His research and clinical interests focus on preventive cardiology , digital health , and clinical informatics . Dr. Blum specializes in the care of patients with congestive heart failure, valvular heart disease, and general cardiology, with a strong emphasis on wellness and early detection of heart disease. His work integrates clinical care with digital transformation, including the development of clinical decision support systems and enterprise health data platforms. The available publications highlight his leadership in health informatics, particularly in electronic medical records, clinical documentation, and care transition systems. His work reflects a consistent trend toward leveraging technology to improve clinical workflows, patient safety, and care delivery innovation. AMDIS Award for Excellence and Achievement in Advancement of Clinical Information Systems Dr. Blum has led major digital health initiatives, including the enterprise deployment of Epic EHR and the development of a large-scale data warehouse supporting clinical, research, and educational missions at UCSF. He has secured and led strategic partnerships with industry leaders such as Samsung, Cisco, and GE to advance digital health innovation, including AI in healthcare. He mentors through leadership in CDHI and similar programs, though no formal students are listed. He leads the UCSF Center for Digital Health Innovation (CDHI) , which serves as an incubator for early-stage digital health technologies. The center validates innovations and partners with industry for commercialization. He also co-founded the UCSF-Samsung Digital Health Innovation Laboratory , the UCSF-Cisco Connected Health Interoperability Platform , and the UCSF-GE collaboration on artificial intelligence and deep learning in healthcare .

Udo Fiedlerمشاهده پروفایل

استاد

• Manufacturing Engineering
• High-Speed Machining
• Production Engineering
• +۷ مورد دیگر

Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.

• Robotic Systems
• Intelligent Computing
• Artificial Intelligence
• +۳ مورد دیگر

Nuno Miguel Fonseca Ferreira is a Full Professor at the Instituto Superior de Engenharia de Coimbra (ISEC), part of the Polytechnic of Coimbra, where he currently serves as President of the Scientific Council. His academic career spans over 25 years at ISEC, progressing from Assistant to Professor Coordenador Principal. He has held significant leadership positions including Vice-President of ISEC (2001-2005), Pro-President of the Polytechnic of Coimbra (2009-2010), President of ISEC (2010-2013), and Vice-President of the Polytechnic of Coimbra (2013-2017), where he was responsible for internationalization initiatives. His educational background includes a degree in Electrical Engineering from the University of Porto (1996), a Doctorate in Electrical Engineering from the University of Trás-os-Montes and Alto Douro (2006), and a Habilitation Title (Aggregation) from the same institution (2020). His research focuses on Robotic Systems, with specialization in cooperative robotic systems as evidenced by his Habilitation work. Professor Ferreira's research spans multiple domains of robotics and intelligent systems, with particular emphasis on multi-robot coordination, environmental applications, and medical robotics. His work bridges theoretical control systems with practical applications across diverse fields including forestry, healthcare, manufacturing, and education. He has developed innovative approaches to robotic manipulation, sensor integration, and human-robot interaction, often incorporating advanced techniques from artificial intelligence and machine learning. His recent publications demonstrate a strong trend toward practical applications of robotics in real-world environments, particularly in forestry maintenance, industrial automation, and medical applications. The research shows progression from theoretical control systems to applied robotics in challenging environments, with increasing integration of computer vision, deep learning, and collaborative systems. His work spans both fundamental robotics research and immediate industrial applications, reflecting a balance between academic inquiry and practical implementation. Professor Ferreira has supervised two doctoral theses and participated in numerous research projects with substantial funding. His leadership extends to coordinating 15 of the 33 national and international R&D projects he has participated in, demonstrating significant grant acquisition and management capabilities. His international collaborations through Erasmus+ and other European programs highlight his role in fostering global research partnerships. He is an integrated member of GECAD (Research Group in Engineering and Intelligent Computing for Innovation and Advanced Development), a Portuguese R&D unit classified as Excellent by the Portuguese Science and Technology Foundation. Additionally, he is a member of LASI (Associated Laboratory for Intelligent Systems), the Portuguese laboratory associated with Artificial Intelligence, connecting him to a broader national research ecosystem.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Zachi Attia, Ph.D., M.B.A., is an Associate Professor at the Mayo Clinic College of Medicine and Science, Rochester, Minnesota. His research focuses on applying artificial intelligence (AI) and machine learning to cardiac biosignals, particularly for early disease detection and prediction. He holds primary and joint appointments as Consultant in AI within the Department of Cardiovascular Medicine, collaborating with the Center for Digital Health and the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. Education: Ph.D. in Electrical Engineering from the University of Minnesota, Rochester; BSc and MSc in Electrical Engineering from Ben Gurion University, Israel. Dr. Attia's work centers on developing AI models that analyze multimodal cardiac data (ECGs, echocardiograms, angiograms) to detect silent diseases. His research includes pragmatic clinical trials to validate AI's impact on patient outcomes, explainable AI for biological insights, and integrating AI dashboards into medical records for clinical usability. Recent publications highlight AI applications in detecting atrial fibrillation, hypertrophic cardiomyopathy, and pulmonary hypertension via ECG analysis. Scientific Awards: No explicit awards mentioned in the text. Email: attia.itzhak@mayo.edu

Elaine Chenمشاهده پروفایل

مدرس ارشد

• Business Analytics
• Artificial Intelligence
• Generative AI
• +۴ مورد دیگر

Dr. Elaine Chen serves as Senior Lecturer in Business Analytics and Course Leader for the MSc Business Analytics and Artificial Intelligence at Nottingham Business School, Nottingham Trent University. Her teaching emphasizes practical applications of data and AI technologies for business decision-making, with dedicated focus on accessibility for diverse student backgrounds across technical and strategic domains. Her academic credentials include: PhD in Computing Science MSc in Business Information Technology Postgraduate Certificate in Academic Practice BTech (Hons) in Business Information Systems Chen's research bridges educational and business contexts through data-AI integration: Generative AI adoption in higher education, particularly for neurodivergent/disabled students Human-AI collaboration frameworks in organizational settings SME applications for AI-driven efficiency and competitiveness Workforce analytics and talent management systems Her work consistently connects technical AI capabilities with real-world implementation challenges. Publication analysis (2023-2025) reveals accelerating focus on generative AI's educational impact and business strategy integration, evolving from her foundational work in social recommender systems (2014-2020) which established methodologies now applied to contemporary AI challenges in business contexts. Her professional recognition includes: Senior Fellow of the Higher Education Academy (HEA) Chen actively supervises PhD candidates in AI education, human-AI collaboration, and workforce analytics domains. Her pedagogy leadership includes designing accredited business analytics curricula and securing teaching innovation projects with documented outcomes in student engagement metrics. Prior industry experience as an automation engineer at Intel informs her practical approach to AI implementation. Current initiatives focus on generative AI ethics frameworks and longitudinal SME adoption studies, extending her established research trajectory into emerging business technology challenges.

Ji-young Shinمشاهده پروفایل

استادیار

• Language assessment
• Corpus linguistics
• Educational technology
• +۴ مورد دیگر

Ji-young Shin serves as an Assistant Professor in the Teaching Stream within Education Studies at the University of Toronto Mississauga's Department of Language Studies. Her position integrates teaching, research, and practical application in language education with emphasis on assessment innovation and technology integration. Her academic credentials include: PhD in English / Second Language Studies & ESL from Purdue University, USA MS in Educational Psychology & Research Methodology from Purdue University, USA MA in Teaching English to Speakers of Other Languages (TESOL) from Hankuk University of Foreign Studies, South Korea BA in English Education (Japanese minor) from Hankuk University of Foreign Studies, South Korea Shin's research program focuses on language assessment, corpus linguistics, and technology-mediated pedagogy. She employs mixed methods with quantitative emphasis, utilizing corpus-informed computational analysis and latent-trait modeling complemented by qualitative discourse analysis. Her work addresses critical gaps in second language acquisition, educational measurement, and digital learning environments, particularly regarding AI applications and test validation across diverse language contexts. Her publication trajectory from 2017-2025 demonstrates consistent innovation in language assessment methodologies and technology integration. Key themes include AI-powered chatbots for L2 Korean instruction, online vocabulary testing for Spanish, and corpus-based writing pedagogy. Her work shows increasing focus on artificial intelligence applications and cross-linguistic validation studies, reflecting evolving priorities in language education research. Shin has secured competitive research funding including: Global Classrooms Grant (2025–2026) for VR-enhanced intercultural competence Black, Indigenous, and/or Racialized Scholar Grant (2025–2026) for cross-border AI language learning IRCC Engage3 Project (2022–2024) as Co-PI on VR/AI educational advancement Advancement Fund Plus (2022) for generative AI in teacher education British Council Award (2019) for technology-mediated assessment development She teaches advanced courses including 'The Future of Ed Tech: Active Learning Classrooms and Artificial Intelligence' (EDS285) and supervises community-based experiential learning (EDS388). Her grant portfolio indicates active supervision of research assistants and collaboration with international teams focused on AI implementation and assessment validation. Current projects explore generative AI for language learning across diverse contexts and virtual reality applications in teacher education. While specific laboratory infrastructure isn't documented, her collaborative publications with researchers across Korea, Spain, and the United States suggest participation in international research networks focused on language assessment technology and corpus applications.

Giomara Lárraga Maldonadoمشاهده پروفایل

پژوهشگر

• Computer Science
• Interactive Multiobjective Optimization
• Evolutionary Computation
• +۳ مورد دیگر

Giomara Lárraga Maldonado is a Postdoctoral Researcher at the Faculty of Information Technology within the University of Jyväskylä , Finland. She contributes to the Multiobjective Optimization Group and is affiliated with the Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) thematic research area. Research Focus: Interactive Multiobjective Optimization, Evolutionary Computation, Explainable AI Key Areas: Preference integration, Decomposition-based methods, Human-Computer Interaction for decision support Her recent work explores explainability frameworks (e.g., LIME integration), phase-specific algorithm configuration, and semantic distance studies for visualization. She collaborates with researchers like Kaisa Miettinen and Giovanni Misitano. She has contributed to conferences such as GECCO, PPSN, and AAMAS, with publications emphasizing open-access availability. The R-XIMO framework (2022) highlights her work on explainable systems.

Mislav Balkovićمشاهده پروفایل

دانشیار

• Data Science
• Education Policy
• Labor Market Analysis
• +۷ مورد دیگر

Mislav Balković is an Associate Professor at Algebra University of Applied Sciences, where he has served as Director and Dean since 2000 and 2009, respectively. He drives strategic development for the Algebra Group and contributes to national education policy through roles in expert bodies like the National Council for Science, Higher Education and Technology, and the Accreditation Council of the Agency for Science and Higher Education. Education: PhD (2016) from Faculty of Electrical Engineering and Computing, University of Zagreb Bachelor’s and Master’s degrees from Faculty of Electrical Engineering and Computing, University of Zagreb Research Interests: Mislav’s work bridges Data Science with applications in labor market dynamics , education policy , qualification frameworks , and energy systems . His projects focus on smart grid optimization, digital identity protection, and AI-driven educational reforms. Publications Trends: His recent articles emphasize cross-disciplinary applications , including brain-computer interfaces for image generation, EU labor market analysis , and smart grid resilience . Earlier works explore blockchain-based academic credentials and data analytics in auditing . Grants and Projects: He has led ~50 EU and domestic projects , spanning smart tourist management , lean methodologies for screen creators , and ICT services for alternative communication . His policy work includes drafting laws on adult education and higher education reform. Leadership: Vice President of the Croatian Employers' Association in Education (HUP-UPO) and past President of the Sectoral Council for Electrical Engineering and Computing.

• Lifelong Learning
• Digitalization in Education
• Teacher Shortage
• +۶ مورد دیگر

Marcia Håkansson Lindqvist is a researcher at Mid Sweden University's Department of Education (UTV), with expertise in digitalization in education, teacher shortage, and school leadership. She contributes to projects like European Education Policy Network and HIMUV (Sustainability in Mid Sweden University Everyday Life) , focusing on organizational development and lifelong learning. Her research spans Technology-enhanced learning environments Professional development frameworks Artificial intelligence applications in education Comparative studies on teacher shortages in Nordic countries She has published extensively in journals like Journal of Educational Administration and British Journal of Educational Technology . Recent work explores AI integration in school leadership, hybrid work-integrated learning models, and cross-sector collaboration for special needs education. She collaborates with researchers across Scandinavia and contributes to edited volumes on leadership identity and digital competence.

Louis Collinsمشاهده پروفایل

استاد

• Neuroimaging
• Medical Image Processing
• Magnetic Resonance Imaging (MRI)
• +۷ مورد دیگر

Louis Collins is a Professor in the Department of Biomedical Engineering and Department of Neurology and Neurosurgery at McGill University, with associate membership at the Center for Intelligent Machines. His work focuses on advanced medical imaging techniques for neurological applications. Key Expertise: Non-linear image registration, model-based segmentation, neuroimaging, MRI analysis Applications: Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, schizophrenia Methodology: Development of computer vision algorithms for image-guided neurosurgery (IGNS), automated atlasing, and biomarker quantification Collins' research combines computational neuroanatomy with clinical translation, particularly in: Quantifying brain atrophy and anatomical variability across populations Optimizing MRI templates for improved diagnostic accuracy Developing tools like SEEGAtlas for surgical electrode classification Exploring neurophysiological fingerprints of neurodegenerative diseases His lab (NIST-Lab) actively pursues CIHR-funded projects on ultrasound-based image-guided neurosurgery and machine learning applications in clinical trials.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

Novi Quadriantoمشاهده پروفایل

استاد

• Machine Learning
• Responsible AI
• Algorithmic Fairness
• +۷ مورد دیگر

Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.

Julian Shunمشاهده پروفایل

دانشیار

• Parallel Computing
• High-Performance Computing
• Graph Analytics
• +۷ مورد دیگر

Julian Shun is an Associate Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Previously, he was a Miller Research Fellow at UC Berkeley and earned his Ph.D. from Carnegie Mellon University under Guy Blelloch. His research focuses on parallel and high-performance computing, with emphasis on graph analytics, spatial/graph clustering, and dynamic algorithms. He designs algorithms with theoretical guarantees and empirical efficiency, along with high-level programming frameworks to simplify parallel code development. His work spans cache-oblivious, external-memory, and streaming graph algorithms, addressing scalability and performance across diverse computational architectures. Julien's recent publications highlight advancements in parallel graph traversal, dynamic connectivity, and approximation algorithms for centrality metrics. His research also explores domain-specific languages like GraphIt for graph analytics and frameworks such as Julienne for work-efficient bucketing. Scientific Awards: Miller Research Fellow at UC Berkeley He has taught graduate-level courses at MIT, including 6.506 (Algorithm Engineering) and 6.886 (Graph Analytics), emphasizing theoretical foundations, experimental analysis, and open-ended research projects.

• Media Pedagogy
• Media Didactics
• Adaptive Learning Systems
• +۳ مورد دیگر

Luisa Wellert is a Research Associate at the Innovative Educational Technologies Department within the Tübingen Center for Digital Education (TüCeDE) at the University of Tübingen since September 2023. She is also a PhD student at TüCeDE since October 2022. Education: Master of Arts in Intermedia and General Educational Science (2020-2022) from University of Cologne Bachelor of Arts in Media Studies and Educational Science (2016-2019) from University of Tübingen Research Focus: Her work centers on media pedagogy , media didactics , and adaptive learning systems , with particular emphasis on AI integration in educational contexts. Recent projects examine automated qualitative coding of AI tutoring dialogues using large language models and effectiveness of self-developed adaptive systems in schools. Publications: Recent work analyzes assessment methodologies in adaptive learning systems, compares performance-based and cognitive load-based evaluation approaches, and explores implementation challenges of DIY adaptive technologies. She actively contributes to open-source AI tutoring initiatives through the OSATI project. Presentations: Luisa presents her research at major conferences including EARLI, LEAD Research Meeting, and GEBF Conference, focusing on practical implementations of AI-based educational tools and their cognitive impacts. Professional Activity: Prior to her current role, she served as Project Manager for Educational Research at TüCeDE (2022-2023) and worked as a Research Assistant at mmb Institut GmbH (2021-2022) and mecodia GmbH (2018-2020). She also has experience in media education and empirical media research.

Nikos Aletrasمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +۲ مورد دیگر

Nikos Aletras is a Professor of Natural Language Processing at the University of Sheffield's School of Computer Science, where he serves as Head of the Natural Language Processing research group and is co-affiliated with the Machine Learning group. His academic journey began with a Bachelor's degree in Computer Science from the University of Crete, followed by a PhD in Natural Language Processing at the University of Sheffield. Prior to his current position, he worked as a research scientist at Amazon (Core ML and Alexa) and as a research associate at UCL's Department of Computer Science. Aletras' research spans multiple domains within AI, with particular emphasis on Natural Language Processing applications across social science, legal contexts, and data science. His work demonstrates a consistent focus on practical implementations of NLP techniques to solve real-world problems, especially in computational social science and legal technology. He has developed innovative text analysis methods that bridge traditional disciplinary boundaries, creating tools applicable across multiple scientific domains. His recent publications reveal a strong trend toward efficient and responsible AI, with significant work on model compression, hallucination mitigation in language models, and ethical considerations in computational social science research. The publications also show deep engagement with multilingual NLP challenges, explainable AI, and applications of NLP to social media analysis and legal contexts. Area Chair Award: Society and NLP (2023) Aletras has secured substantial research funding as both Principal Investigator and Co-Principal Investigator, including grants from EPSRC, ESRC, Leverhulme, EC Horizon 2020, and industrial partners like Amazon. His current projects focus on efficient deployment of large language models, addressing socio-technical limitations of LLMs for medical and social computing, and developing speech and language technologies. He actively supervises PhD students and collaborates with researchers across multiple disciplines. He leads the Natural Language Processing research group at Sheffield, which focuses on advancing NLP methodologies while applying them to diverse domains including computational social science, legal informatics, and healthcare technologies. The group maintains strong industry connections, particularly with technology companies working on language technologies, and collaborates with legal scholars and social scientists on interdisciplinary projects.