جستجوی استاد راهنمای دانشگاهی

Malika Korganbekova is an Assistant Professor of Marketing at the University of Chicago Booth School of Business and a Fishman Faculty Fellow. Her research combines causal inference, empirical industrial organization, and machine learning to analyze how technology affects societal issues like privacy, data regulation, and sustainability. PhD in Quantitative Marketing (Kellogg School of Management, Northwestern University, 2024 expected) Master’s in Economics (Penn State, 2018; Central European University, 2015) Bachelor’s in Economics (Lomonosov Moscow State University, 2013) Her work with major e-commerce platforms explores: Privacy restrictions' impact on personalization algorithms Environmental consequences of fast shipping Regulatory challenges in dual-platform B2B markets Dynamic pricing strategies influenced by ranking systems Key methodologies include structural econometrics, deep learning, and field experimentation. Her research has been presented at leading conferences such as INFORMS, KDD Workshop, and Wharton Innovation Doctoral Symposium. Scientific awards include: MSI Alden G. Clayton Best Dissertation Proposal Award (2023) Shankar-Spiegel Best Dissertation Proposal Award (2023) INFORMS Society for Marketing Science Doctoral Fellowship (2022, 2023) She has collaborated with Wayfair’s Machine Learning Science team and previously worked at Google, Morgan Stanley, and KPMG.

• Marketing
• Technology and Society
• Data Privacy
• +۶ مورد دیگر

Manuel Mucientes Molina is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) within the University of Santiago de Compostela . His research focuses on Artificial Intelligence , particularly in Computer Vision and Machine Learning , with applications in object detection, process mining, and healthcare diagnostics. Research Areas : Machine learning, Computer vision, Process mining, Deep learning, AI for healthcare. Projects : Protonterap-IA (2025), AZOR (2024), RAI4P (2021), eXplica-IA (2018), DronePlan (2014), SoftLearn (2012). Publications highlight advancements in few-shot object detection, small object tracking, and AI-driven conformance checking. Notable collaborations include work on X-ray vision systems and medical mask detection in operating rooms. Awards : Best Student Paper Nomination (2015), Runner-up best industry-oriented paper award (2008). Teaching includes courses on Statistical Learning, Deep Learning, and Automata Theory at the M.Sc. and B.Sc. levels.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Silvia Garcia Mendez is a Part-Time Lecturer at the University of Vigo , affiliated with the College of Telecommunication Engineering and Department of Telematics Engineering . She collaborates with the Research Center for Telecommunication Technologies and the TC1 Information Technologies Group . Research Interests : Silvia specializes in Natural Language Processing , Machine Learning , and Human-Computer Interaction , with applications in healthcare , finance , and social network trust systems . Her work focuses on explainable AI and stream-based data analysis . Publication Trends : Her research spans NLP , ML , and AI ethics , with recent emphasis on mental health detection , predictive maintenance , and disinformation analysis . Articles cover both theoretical advancements and real-world implementations. Labs & Teams : Actively involved with the TC1 Information Technologies Group and Research Center for Telecommunication Technologies , driving interdisciplinary projects in telematics engineering and AI applications .

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

David Becerra Alonso is a Full Professor in the Department of Quantitative Methods for Economics and Business at Loyola University Andalusia. With a strong interdisciplinary background spanning physics, computing, and bioinformatics, he bridges quantitative methods with practical applications across multiple domains including social sciences, healthcare, and education. His research expertise encompasses machine learning, artificial intelligence, natural language processing, and dynamical systems. Dr. Becerra Alonso applies these computational methods to address critical social issues, particularly in gender-based violence prevention through intimate partner femicide risk assessment, media bias detection, and educational technology development. His recent publications demonstrate a strong focus on applying AI to social challenges, with multiple 2024-2025 papers addressing media bias detection and intimate partner femicide prediction using advanced computational techniques. His work spans both theoretical machine learning contributions and practical applications in public health, journalism, and education. Principal Investigator for Sapiens project (2021-2023) on autonomous aquatic vehicle networks Researcher in Disorders of Consciousness (DoC) project (2018-2021) with European Commission Researcher in AIPE project (2024-2025) on AI for physical education of vulnerable adolescents Dr. Becerra Alonso supervises doctoral students working on computational approaches to social issues, including femicide analysis, sustainable development goal classification, and mathematics education in distance learning contexts. His leadership in the Quantitative Research Methods and Applications (MICA) research group supports interdisciplinary collaboration across quantitative methods and applied domains.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۵ مورد دیگر

Christian Mata Miquel is a Lecturer in the Department of Systems, Automatic and Industrial Informatics Engineering at the Barcelona East School of Engineering, part of the Polytechnic University of Catalonia (UPC). He is affiliated with the Institute of Research and Innovation in Health and actively contributes to the BIOCOM-SC (Computational Biology and Complex Systems) research group as well as the ANCORA subgroup focused on cardiac rhythm analysis. His research interests center around medical imaging, computer vision, and artificial intelligence with specific applications in biomedical engineering. Dr. Mata Miquel's work spans various subfields including medical image processing, neuroimaging analysis, pediatric imaging applications, and computational biology. His research bridges engineering and healthcare, developing computational tools for clinical applications. Analysis of his recent publications reveals a strong focus on applying deep learning techniques to medical imaging challenges, particularly in neuroimaging and pediatric applications. His work shows consistent progression toward more sophisticated AI methodologies with emphasis on segmentation, enhancement, and diagnostic support systems. The publications span high-impact journals and conferences in medical imaging, computer vision, and biomedical engineering. CaixaResearch Institute Fund for the Innovation Hub Dr. Mata Miquel participates in numerous competitive R&D projects, including the AI-CARdiology Advanced Monitoring cloud-Based platform and the Pediatric Computational Imaging Center at Hospital Sant Joan de Déu. His research collaborations extend across multiple institutions, with significant contributions to wildfire monitoring systems and medical imaging applications. He serves on scientific committees for conferences including the 18th Multi Conference on Computer Science and Information Systems and the 37th IEEE International Symposium on Computer-Based Medical Systems. His work is conducted through the BIOCOM-SC research group and the ANCORA subgroup, focusing on computational approaches to biomedical problems. Current projects include developing AI-enhanced tools for brain morphometry analysis, prostate cancer segmentation, and pediatric neuroimaging applications, demonstrating the integration of engineering expertise with clinical healthcare needs.

• Medical Imaging
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر