Find Academic Supervisors

Vincent ItierView profile

Lecturer

• Multimedia Security
• Digital Forensics
• Machine Learning
• +4 more

Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.

Oliver J. Bear Don’t Walk IV, ApsáalookeView profile

Assistant Professor

• Natural language processing
• Machine learning
• Community engagement
• +6 more

Oliver J. Bear Don’t Walk IV, Apsáalooke, serves as Assistant Professor in the Department of Biomedical Informatics and Medical Education at the University of Washington with special responsibilities at the Center for Indigenous Health. His work fundamentally reimagines biomedical informatics through Indigenous knowledge systems and community-driven ethics. His academic foundation includes: PhD in Biomedical Informatics, Columbia University MA, Stanford University BS, Stanford University Dr. Bear Don’t Walk's research pioneers epistemological justice in health AI through three interconnected pillars: technical development of NLP/ML systems for social driver identification, community governance models ensuring Tribal data sovereignty, and theoretical integration of intersectionality into algorithmic design. His projects actively collaborate with Indigenous communities to transform EHR data practices and rectify colonial research legacies. Recent publications (2020-2025) reveal consistent innovation at the ethics-technology nexus, particularly in race/ethnicity annotation frameworks, community-informed social driver extraction, and Tribal data governance protocols. His work demonstrates how technical NLP advancements must be inseparable from decolonial research partnerships. Dr. Bear Don’t Walk currently leads U01-funded research on HIV patient social drivers and mobile-based hallucination studies while teaching BIME 585. He explicitly welcomes new students committed to community-engaged, justice-oriented informatics and maintains active collaborations with Tribal health organizations through formalized research agreements.

• stochastic approximation
• reinforcement learning
• sampling
• +4 more

Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.

• topological data analysis
• machine learning
• mathematical statistics
• +6 more

Maxim Evgenievich Beketov is a Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2020. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, contributing to cutting-edge research in computational methods and artificial intelligence. His educational background includes: Master's degree (2017) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor's degree (2015) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Beketov's research spans multiple interdisciplinary fields with a strong mathematical foundation. His primary interests include topological data analysis, machine learning, mathematical and Bayesian statistics, differential geometry, and computational neuroscience. He applies these methods to problems in dimensionality reduction, variety assessment, and graph neural networks. His work bridges theoretical mathematics with practical applications in artificial intelligence and neuroscience, particularly in understanding cognitive processes through topological approaches. An analysis of his recent publications reveals a strong focus on topological methods in machine learning, with increasing emphasis on applications to neuroscience and cognitive mapping. His work demonstrates a progression from theoretical mathematical foundations toward practical implementations in spiking neural networks, traffic control systems, and music information retrieval. The interdisciplinary nature of his research connects computer science, mathematics, and neuroscience through topological approaches. His scientific achievements include: High Professional Potential Group (HSE Personnel Reserve), Category: New Researchers (2025) Beketov has been actively involved in academic teaching, offering courses including Introduction to Discrete Differential Geometry and Mathematical Analysis. His research is supported through the HSE University Basic Research Program, as acknowledged in his publications. He collaborates with researchers across multiple institutions, as evidenced by his co-authorship on papers with numerous collaborators. He is a core member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where he contributes to projects involving topological data analysis, machine learning, and computational neuroscience. His work in the laboratory focuses on developing advanced mathematical methods for analyzing complex data structures, with applications ranging from cognitive neuroscience to transportation systems.

• Technological services
• Service innovation
• Digital humanities
• +4 more

Professor Cai Ruihuang is a distinguished faculty member in the Department of Information Management at National Chengchi University's College of Business. With over 30 years of academic service, he currently serves as Director of the Computer Center and has held various administrative positions including Deputy Director of Research and Development and Director of the Innovation Incubation Center. His extensive academic career spans teaching and research in information systems, neural networks, and digital innovation. PhD in Industrial Engineering and Operations Research, University of California, Berkeley MS in Industrial Engineering, University of California, Berkeley BS in Nuclear Engineering, National Tsing Hua University Professor Cai's research focuses on technological services, service innovation, digital humanities, neural network systems, and enterprise process analysis. His work bridges traditional business domains with cutting-edge technology applications, particularly in cultural institutions like the National Palace Museum. His research demonstrates a consistent trajectory from foundational neural network theory to practical applications in financial analytics, museum technology, and digital transformation. His extensive publication record shows a progression from theoretical neural network research in the 1990s to applied AI and digital innovation studies in recent years. Notably, he has pioneered applications of AI in cultural heritage institutions and developed innovative approaches to financial fraud detection using neural networks. His work spans computer science, business management, and digital humanities, reflecting an interdisciplinary approach to information systems research. 資深優良教師(30年) - Senior Excellent Teacher Award 特聘教授 - Distinguished Professor 國科會獎勵特殊優秀人才 - MOST Special Outstanding Talent Award 學術研究成果國際化優等研究獎 - International Academic Research Excellence Award 教學特優教師 - Outstanding Teaching Award Professor Cai has successfully led numerous research projects totaling millions in funding from both government agencies like MOST and private sector partners including CTBC Financial Holding and National Palace Museum. His current research focuses on AI applications in finance, digital transformation of cultural institutions, and cybersecurity education initiatives. He leads the Financial AI Cloud Ecosystem research project and serves as principal investigator for multiple cybersecurity training programs funded by Taiwan's Ministry of Education. His work connects academic research with practical applications through the Financial AI Cloud Research Center and various industry collaboration initiatives. Professor Cai's research group actively engages with cultural institutions, financial organizations, and technology companies to develop innovative solutions for real-world challenges in digital transformation.

• Speech Prosody
• Phonetics
• Linguistics
• +3 more

Dr Hae-Sung Jeon is a Lecturer in Psychology at the University of Central Lancashire's School of Psychology and Humanities. Her academic profile demonstrates a strong commitment to research in speech communication with particular expertise in speech prosody—the melody, timing, and tonal qualities of voice—as well as phonetic variation in multimodal communication. Dr Jeon's research interests center on linguistic prosody, phonetic variation, and speech perception, with a special focus on Korean language varieties. Her work examines how pitch, timing, and intonation patterns function in communication across different languages and contexts. She has made significant contributions to understanding speech rhythm, intonation patterns in Korean dialects, and cross-linguistic prosodic perception. Analysis of her publication record reveals a consistent research trajectory focused on speech prosody with increasing emphasis on Korean language varieties. Her recent work shows growing international collaboration, particularly with researchers in phonetics and linguistics across Europe. The publications demonstrate methodological diversity, incorporating experimental approaches, corpus analysis, and cross-linguistic comparisons to investigate fundamental questions about speech perception and production. Dr Jeon has published extensively in high-impact journals including Speech Communication, The Journal of the Acoustical Society of America, and Laboratory Phonology. Her research bridges cognitive psychology, linguistics, and phonetics, contributing to our understanding of how humans process speech across different languages and contexts.

• Mathematics Education
• Educational Technologies
• Computer Algebra Systems
• +3 more

PENKA IVANOVA IVANOVA is a Lecturer at the Technical University - Gabrovo working in the Department of Language and specialized training. She has maintained an active academic career at the institution since at least 2017. Her research focuses on Mathematics Education with specialization in teaching methodologies for engineering students. Dr. Ivanova has developed expertise in applying computer algebra systems like Wolfram Mathematica and Maple to enhance understanding of complex mathematical concepts. Her work spans calculus instruction, particularly the Mean Value Theorems and extremum problems, as well as broader educational technology applications including web accessibility and discrete structures. Dr. Ivanova's publication pattern shows consistent development from foundational work on multiple solution approaches (2018) to specialized computational teaching methods (2019-2022), culminating in comprehensive nonlinear teaching frameworks for core calculus concepts (2024). A recurring theme throughout her research is the practical application of mathematical methods for engineering education. She has participated in six significant research projects at TU Gabrovo: Modern engineering solutions for environmental management and protection (Stages I-III, 2022-2024) Multidimensional pyramidal and simplicial finite elements (2021) Innovative educational technologies, discrete structures and web-accessibility (2019) Modern educational technologies, cultural heritage and WEB accessibility (2018) Working as a participant in teams of 12-17 researchers, she has contributed to the university's research initiatives while maintaining her teaching responsibilities. Her involvement in the CompMath Competition highlights her commitment to promoting computational problem-solving approaches in mathematics education, bridging theoretical concepts with practical engineering applications.

• Neurosciences
• Artificial intelligence
• Machine learning
• +4 more

Professor Janaina Mourao-Miranda is a Professorial Research Associate in the Department of Computer Science at University College London's Faculty of Engineering Sciences. She is affiliated with the Centre for Medical Image Computing (CMIC) and leads the Machine Learning and Neuroimaging Lab (MLNL), accessible at http://www.mlnl.cs.ucl.ac.uk. Her research program focuses on developing and applying machine-learning models to investigate complex relationships between neuroimaging data and multidimensional descriptions of mental health disorders. Specifically, she explores whether we can learn about underlying brain mechanisms of mental disorders from these relationships, better stratify patient groups based on these relationships, and combine information from clinical assessments with different neuroimaging modalities to build better diagnostic and prognostic models of mental health disorders. Professor Mourao-Miranda's work spans multiple disciplines including Neurosciences, Artificial Intelligence, Machine Learning, Computational Neuroscience, Biological Psychology, Clinical and Health Psychology, and Pattern Recognition. Her recent publications demonstrate a strong emphasis on multivariate analysis of brain imaging data, advanced techniques for handling incomplete datasets, and developing interpretable machine learning models specifically designed for clinical applications in mental healthcare. Her key scientific contributions include: Developing novel machine learning approaches specifically tailored for neuroimaging analysis Investigating fundamental brain-behavior relationships in mental health disorders Creating innovative methods for patient stratification based on multimodal data integration Advancing statistical techniques for handling incomplete clinical datasets Pioneering interpretable AI models that bridge the gap between computational methods and clinical practice Professor Mourao-Miranda teaches a specialized module on Applied Artificial Intelligence for the MSc programs in AI for Biomedicine and Healthcare and AI for Sustainable Development at UCL, where she shares her expertise in applying artificial intelligence to solve real-world healthcare challenges and contribute to sustainable development goals.

Zin AraiView profile

Professor

• Dynamical Systems
• Computational Topology
• Hénon Maps
• +3 more

Zin Arai is a Professor in the Department of Mathematical and Computing Science at the School of Computing, Institute of Science Tokyo (formerly Tokyo Institute of Technology). He serves as Research Supervisor for JST PRESTO's 'Mathematical Sciences for the Future' initiative. His research centers on dynamical systems using topological and computational methods, with applications spanning chemistry, biology, and engineering. Key research areas include: Dynamical systems theory (Hénon maps, hyperbolic dynamics) Computational topology (Conley index, validated numerics) Interdisciplinary applications (primate social behavior, chemical reaction dynamics) His 15 most recent publications (2007–2025) demonstrate consistent focus on rigorous computational methods, bifurcation analysis, and topological approaches to nonlinear systems. Notable tools developed include programs for parameter space analysis of Hénon maps and tangency verification. As JST PRESTO supervisor, he oversees projects advancing mathematical sciences. No explicit lab affiliation or awards are documented, but his computational work supports broad scientific collaboration.

Manel Peiró PosadasView profile

Professor

• Healthcare Management
• Public Health Management
• Health Systems
• +2 more

Manel Peiró Posadas serves as Full Professor in the Department of People Management and Organization at ESADE Business School, Ramon Llull University, where he directs the Institute for Healthcare Management. His institutional affiliations span academic, clinical, and policy domains including GLIGP Research Group (AGAUR SGR 1556), Sant Andreu Health Foundation (President since 2007), Puigvert Foundation (Vice-president since 2013), and Cemcat Foundation (Vice-president since 2015). His academic credentials include a Doctorate in Business Administration and Management from ESADE and a Medical Degree from Autonomous University of Barcelona. This dual foundation informs his research on healthcare system innovation, strategic leadership, and organizational change in public health settings. Peiró Posadas' research examines critical intersections of healthcare management and clinical operations, with emphasis on strategic planning frameworks, innovation implementation in resource-constrained environments, and leadership models for professional engagement. His work addresses Catalonia's unique healthcare governance challenges through the GLIGP research group, generating practical tools for health system administrators while advancing public management theory. Analysis of his 2005-2024 publications reveals consistent focus on healthcare system transformation, with recent work (2020-2024) prioritizing oncology care pathways, agile innovation methodologies, and shared decision-making frameworks. His scholarship demonstrates strong alignment between theoretical contributions in public management journals and practical applications in Spain's National Health System. Through ESADE's Executive Master in Management of Health Organizations, he trains healthcare leaders while directing research projects funded through GLIGP's AGAUR grant. His foundation leadership roles provide direct access to operational health system challenges, creating feedback loops between academic research and real-world management practice. The Institute for Healthcare Management functions as his primary research hub, focusing on health system functionality analysis and management support for public/private health institutions. His foundation affiliations extend this work into specialized clinical domains including urology (Puigvert), neuroimmunology (Cemcat), and cancer survivorship care.

Carolina Alcantar NieblasView profile

Professor

• Educational Technology
• Social Entrepreneurship Competencies
• Open Educational Resources
• +4 more

Carolina Alcantar Nieblas is a Professor at Tecnológico de Monterrey's Institute for the Future of Education, specializing in educational innovation and digital transformation. Her research spans social entrepreneurship competencies, open educational resources, and psychometric instrument development using structural equation modeling. Her research focuses on critical intersections of technology and education, particularly examining how digital transformation reshapes higher education through open educational resources and social entrepreneurship frameworks. She investigates barriers to personalized AI-driven learning while developing validated instruments for measuring complex constructs like bystander intervention in cyberbullying and social innovation profiles. Her methodology expertise in structural equation modeling enables rigorous analysis of multidimensional educational phenomena. Mexican Researcher Certification - Candidate (National System of Researchers) As an active academic leader, she coordinates interdisciplinary research projects emphasizing strategic management and collaborative goal achievement. Her work bridges theoretical development with practical applications through workshops for educators and pre-service teachers. She contributes significantly to academic discourse as a journal reviewer and member of the Mexican Council of Educational Research, focusing on translating research into actionable educational strategies.

• Demography
• Migration Studies
• Health Disparities
• +7 more

Dr. Matt Wallace is an Associate Professor and Reader in Social Inequality at the University of Salford 's School of Health & Society , where he leads research at the Centre for Research on Inclusive Society (CRIS) . He is concurrently a Visiting Researcher at Stockholm University's Demography Unit (SUDA) in Sweden. Wallace secured a prestigious €1.3 million ERC Starting Grant as Principal Investigator for the PARA-MOR project (2024-2029), examining migrant health longevity disparities across Europe. His prior work includes NIH-funded research at France's INED and Swedish Research Council-funded projects on intergenerational mortality. His interdisciplinary research integrates: Core themes : Migrant health disparities, mortality paradoxes, COVID-19 inequities, and social determinants Methodologies : Advanced quantitative analysis of multinational registry data, cohort studies, and demographic modeling Policy domains : Integration frameworks, healthcare access, and equity-focused public health interventions Recent publications demonstrate strong focus on mortality differentials among migrant populations, with 73% of last 15 articles (2021-2025) examining migrant health and 40% analyzing COVID-19 impacts. Longitudinal registry studies dominate (67%), primarily using Scandinavian data. Emerging themes include second-generation health disadvantages and methodological critiques of mortality metrics. Honors & Funding : European Demographer of the Year Award (Population Europe/Allianz) ERC Starting Grant (€1.3M, Horizon Europe) Swedish Research Council Grant (£360,000) He leads doctoral training in health inequity research and migrant studies, supervising PhD candidates through CRIS and Salford's Health & Society programs. His lab employs spatial epidemiology and demographic decomposition techniques using multinational administrative datasets.

Sijia HuangView profile

Assistant Professor

• Latent Variable Modeling
• Item Response Theory
• Psychometrics
• +5 more

Sijia Huang is an Assistant Professor in the Department of Applied Psychology in Education and Research Methodology at the School of Education, Indiana University Bloomington. She holds a Ph.D. in Education from the University of California, Los Angeles and teaches EDUC-Y 520: Strategies for Educational Inquiry. Her research expertise spans: Development and application of latent variable models Advanced item response theory (IRT) for complex data structures Statistical modeling of multidimensional, multilevel, and longitudinal datasets Interdisciplinary collaboration in educational and psychological measurement Dr. Huang actively partners with researchers across disciplines to implement statistical models for empirical data analysis in educational contexts. Her methodological work directly supports evidence-based practices in psychological and educational sciences through rigorous quantitative approaches.