Find Academic Supervisors

• Stochastic Modelling
• Health Economics
• Healthcare Analytics
• +4 more

Gizem S. Nemutlu is Assistant Professor of Data Analytics at Brandeis International Business School and a research affiliate at Massachusetts General Hospital’s Institute for Technology Assessment. Trained in stochastic modelling and health economics, she applies operations-research methods to cancer-surveillance policy and immunisation programmes. Education: Ph.D. in Management Sciences, University of Waterloo, Canada B.S. in Statistics, Hacettepe University, Turkey Research Interests: Her work integrates stochastic modelling, data-driven optimisation and health-economic evaluation to inform policy design in immunisation and cancer surveillance. Recent projects quantify cost-effectiveness of risk-stratified hepatocellular-carcinoma surveillance, evaluate HPV-related cancer trends, and forecast global breast-cancer control trajectories toward 2030 SDG targets. Publication Trends: Across 16 peer-reviewed works (2013-2025) she consistently employs Markov and decision-process models to compare surveillance strategies for liver, anal and colorectal cancers, assess biomarker-based screening, and measure population-level impacts of HPV vaccination. Studies appear in high-impact oncology and health-policy venues. Scientific Awards: None disclosed in supplied materials. Advising & Grants: No students or funded-grant details are provided in the source text. Labs & Teams: She collaborates with the Institute for Technology Assessment at Mass General Hospital, indicating ongoing interdisciplinary teamwork in health technology assessment and economic evaluation.

Paul AtandohView profile

Assistant Professor

• statistical analysis of network data
• natural language processing
• variational inference
• +4 more

Dr. Paul Atandoh serves as an Assistant Professor of Statistics within the College of Liberal Arts and Sciences at Mercer University, specializing in statistics and actuarial science applications across health and computational domains. His educational background includes: Ph.D. in Statistics from Western Michigan University M.A. in Actuarial Science from Ball State University M.Sc. in Mathematical Science from the African Institute for Mathematical Sciences B.Sc. in Actuarial Science from the University for Development Studies Dr. Atandoh's research program integrates advanced statistical methodologies with real-world applications, particularly in natural language processing and health analytics. His work on statistical analysis of network data employs variational inference and machine learning techniques to address challenges in document clustering and sentiment analysis. In health research, he investigates critical public health issues including hypertension-depression comorbidity, prenatal mental health disparities, and cancer treatment inequities using large-scale survey data and population registries. Analysis of his 2021-2024 publications reveals a dual research trajectory: computational statistics innovations (deep learning architectures for text analysis, stochastic blockmodels for document clustering) and epidemiological investigations (health disparities by citizenship status, access to care impacts). His technical contributions frequently combine CNNs, BiLSTMs, and word embedding techniques with statistical modeling, while his health studies leverage complex survey methodologies to examine social determinants of health outcomes in US populations. Information regarding Dr. Atandoh's current student advising responsibilities and active grant funding is not specified in available materials.

Joshua SmithView profile

Professor

• Educational Psychology
• Statistics
• Methodology
• +7 more

Joshua Smith is a Professor at Loyola University Maryland's School of Education, where he served as Dean from 2012-2021. He was a Visiting Professor at National Chengchi University in Taiwan from 2021-2023, teaching international higher education and design thinking. Education: Ph.D. in Educational Psychology and Methodology (University at Albany, 2002) M.S. in Educational Psychology and Statistics (University at Albany) B.A. Cum Laude in U.S. History with a minor in Sociology (University at Albany) His research focuses on educational transitions (middle school to high school, high school to college), student engagement, and organizational effectiveness. He collaborates with schools and community organizations to design and evaluate educational programs. Recent scholarship trends highlight his work in post-pandemic higher education innovation (e.g., student agency, design thinking), trauma-informed care in educational settings, international collaboration in education, and computational methods in interdisciplinary fields like renewable energy and environmental policy. No scientific awards or honors were explicitly mentioned in the text. Joshua Smith has advised academic programs and partnerships but no formal advisees or grants are listed in the provided data. His affiliations include Loyola University Maryland and National Chengchi University (visiting role).

April PudseyView profile

Academic

• Roman History
• Ancient Demography
• Children's Cultures
• +7 more

Dr April Pudsey is a Reader in Roman History and Deputy Director at Manchester Metropolitan University's interdisciplinary Manchester Centre for Youth Studies . Her work focuses on children's and women's lives in Graeco-Roman Egypt through papyrology and material culture analysis, connecting ancient childcare practices to broader demographic and environmental patterns. Key research areas: Roman Egypt, child slavery, breastfeeding practices, ancient demography Leadership roles: Founding Chair of Post92classics, Co-Chair of Women's Classical Committee Major grants: €280,000 Marie-Sklodowska Curie Fellowship (2021-23), AHRC £380,000 project co-Investigator Her Children of Oxyrhynchos book (Palgrave MacMillan, 2025) synthesizes research on youth cultures from thousands of papyri. She also served as Historical Consultant for Ladybird Books and Hachette Children's Group publications. Recent publications span topics including: 2023 : Global history of youth 2022 : Enslaved children's experiences 2021 : 3D-replicated Roman musical instruments 2017 : Disability studies in antiquity Scientific awards include: Leverhulme Early Career Fellowship (2009-11) AHRB Studentships (2001-2006, 2003-06) External examiner roles at Exeter University and Birkbeck College

• Data Science
• Natural Language Processing
• Machine Learning Applications
• +3 more

Chief Assistant Professor Gloria Hristova, PhD candidate in Data Science, holds a dual academic role at Sofia University's Department of Statistics and Econometrics within the Faculty of Economics and Business Administration. Her research focuses on applying machine learning and NLP techniques to business analytics, digital transformation assessment, and public sentiment analysis. She teaches courses on text analytics, machine learning for finance, and quantitative management methods. Active in open-source contributions, her GitHub repositories showcase projects in data science competitions and Python-based analytical tools development. Education: PhD Studies in Data Science (2019–present), Department of Statistics and Econometrics, Sofia University. Professional memberships include the Data Science Society. Research Interests: Business Applications of Machine Learning Sentiment Analysis in Public Services Automated Analytics Tools Development Text Mining for Customer Insights Published works analyze digital transformation in SMEs, public opinion on e-government services, and banking chat data analytics. Her recent projects employ transformer-based language models and BERTopic for topic modeling in crisis contexts like pandemic education shifts. Professional Affiliations: Representative of FEBA in the Assistant's Club at Sofia University. Collaborates with academic and industry partners on data-driven solutions for enterprise digitalization challenges.

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +4 more

Dr. Ying Zhou is a Lecturer in the School of Computer Science at The University of Sydney. She holds a BSc and MEng from Nanjing University (1997) and a PhD from the School of Computing at the National University of Singapore (2003). Her research focuses on human-centred data management, including large-scale data storage, user behavior analysis, and improving image query systems. Current projects include mining socially tagged images and accountability mechanisms for multitenant cloud platforms. Teaching responsibilities include courses on Advanced Data Models (COMP5338), e-Commerce Technology (COMP5347), and Cloud Computing (COMP5349). She collaborates with industry partners like Amazon and IBM, and advises four research students. She is a member of the Sydney Southeast Asia Centre. Her publication record spans 20 years, with recent work emphasizing machine learning applications, big data systems, and cloud security. Research trends include optimizing distributed computing frameworks (Hadoop/Spark/Flink), adversarial machine learning, and trustworthy database systems. Earlier work focused on social network analysis, web communities, and blogosphere dynamics. Current research projects address both technical challenges (e.g., efficient image search, cloud platform accountability) and applied solutions for leveraging social media data. Her lab activities involve interdisciplinary collaboration between computer science and information systems domains.

• Medical Data Science
• Anomaly Detection
• Generative AI
• +3 more

Dr. Thomas Sutter is a Researcher affiliated with the Department of Computer Science at ETH Zurich , specifically part of the Professorship for Medical Data Science. His work focuses on advanced machine learning techniques applied to medical data, including anomaly detection, generative AI, and multimodal learning. He contributes to healthcare innovation through projects like improving radiology diagnostics via Vision-Language Models (RadVLM) and developing denoising techniques for physiological signals in cardiology. Research Interests: Medical Data Science, Anomaly Detection in Healthcare, Generative AI for Biomedical Signals, Multimodal Representation Learning, and Cardiac Function Prediction using Echocardiograms. His methods often combine deep learning with domain-specific medical challenges. Key Publications Trends: Recent work emphasizes medical imaging analysis (e.g., MIMIC-CXR studies), generative models for signal denoising, and contrastive learning for anomaly detection. His research bridges computer science theory with clinical applications, particularly in cardiology and radiology. Awards & Grants: No specific awards or grants mentioned in the provided texts. Advising & Teams: While no advisees are listed, he collaborates within the Medical Data Science research group at ETH Zurich, contributing to interdisciplinary projects in healthcare AI.

Daniel A. WerningView profile

Researcher

• Ancient Egyptian Language
• Historical Linguistics
• Diachronic Language Typology
• +7 more

Dr. Daniel A. Werning is the Head of Department for the Research Project 'Structures and Transformations of the Vocabulary of the Egyptian Language' at the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW). His primary affiliation is with the BBAW's Department of Egyptian Language Vocabulary, located at Unter den Linden 8, Berlin. He leads multidisciplinary research initiatives focusing on Ancient Egyptian linguistics and digital humanities. Research Focus: Grammar of Hieroglyphic Egyptian, Morpho-syntax, Script Systems, Language History, Spatial Linguistics, and Digital Text Encoding (TEI/EpiDoc). Key Projects: Leads the Thesaurus Linguae Aegyptiae corpus and studies New Kingdom Netherworld Texts and Theology. Contact: +49-30-20370-447 | daniel.werning@bbaw.de His work bridges traditional philology with computational methods, contributing to both historical linguistics and digital scholarship frameworks. Current research emphasizes diagrammatology in religious texts and diachronic language structure analysis.

David M. BleiView profile

Professor

• Machine Learning
• Bayesian Statistics
• Probabilistic Modeling
• +5 more

David M. Blei is the William B. Ransford Professor of Statistics and Computer Science at Columbia University. He is a leading researcher in machine learning, with a focus on probabilistic modeling and Bayesian statistics. His work bridges theoretical foundations with practical applications across various domains. Professor Blei's research spans several key areas in modern machine learning: Development and analysis of probabilistic models for complex data Bayesian inference methods, particularly variational inference Topic modeling and mixed-membership models Causal inference and model criticism Deep generative models and representation learning Applications in natural language processing and recommendation systems His recent publications demonstrate continued advancement in variational inference theory while exploring applications in deep learning and causality. Blei's work on posterior collapse in variational autoencoders, black box variational inference, and scalable recommendation systems has been particularly influential in the machine learning community. Professor Blei mentors PhD students and postdoctoral researchers, fostering the next generation of machine learning researchers. He actively teaches graduate courses on probabilistic models, machine learning, and causal inference at Columbia University, including STCS 6701: Probabilistic Models and Machine Learning (scheduled for Fall 2025). He leads a research group focused on probabilistic modeling, contributing significantly to both theoretical advancements and practical applications of statistical methods in artificial intelligence. The group is part of Columbia's thriving machine learning community, which spans multiple departments and research centers.

Junxi ZhangView profile

Assistant Professor

• Bayesian nonparametric models
• Fairness in machine learning
• Reinforcement learning
• +4 more

Junxi Zhang is an Assistant Professor in the Department of Mathematics & Statistics at Concordia University (Montreal, Canada). His research focuses on Bayesian nonparametric models, fairness in machine learning, and reinforcement learning. He holds a PhD in Statistics from the University of Alberta and has previously served as a postdoctoral fellow at the same institution. Education: PhD (Statistics, University of Alberta), MS (Mathematics, University of Kansas), BS (Statistics, Huazhong University of Science and Technology) Research interests include: Bayesian nonparametric models: asymptotic analysis, random measures, hierarchical models Fairness in machine learning: fair prediction models, bias measurement, health science applications Reinforcement learning: optimal control problems, temporal resolution trade-offs Additional interests: foundation models, time series analysis, survival analysis His recent publications address fairness in precision medicine, social bias in labor market text generation, and mathematical foundations of Bayesian nonparametric models. He is actively seeking graduate students in Mathematics and Statistics at the MA, MSc, and PhD levels for collaborative research.

Henrik MadsenView profile

Professor

• Energy Systems
• Stochastic Processes
• Demand Response
• +7 more

Henrik Madsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His work focuses on energy systems, stochastic processes, and mathematical modeling. He leads research in areas such as demand response in district heating, probabilistic forecasting, and integration of renewable energy sources. Madsen has supervised multiple PhD students and contributes to projects like the IEA DHC Annex TS9 and SEEDS initiative. His expertise includes statistical analysis, time series modeling, and data-driven approaches for energy systems. He actively engages in collaborative research on smart grids, thermal energy storage, and sustainable energy solutions. Education: Academic qualifications from DTU, though specific details are not provided in the text. Research Interests: Mathematical modeling of energy systems Probabilistic forecasting methodologies Integration of wind and solar power Dynamic modeling for district heating Data assimilation in hydrological systems Optimization of energy flexibility Publications & Trends: Recent works emphasize demand response strategies, district heating optimization, and machine learning tools like the nabqr Python package and evalprob4cast R package. His research bridges theoretical stochastic methods with practical applications in energy infrastructure. Grants & Projects: IEA DHC Annex TS9: Digitalization of district heating SEEDS: RES-integrated electrified heating systems Data-Driven Methods for Demand-Side Flexibility Labs/Teams: Collaborates with DTU's Dynamical Systems group and participates in interdisciplinary initiatives like the Frigg 2.0 energy system analysis framework.

• Behavioral Science
• Management
• Judgement and Decision Making
• +3 more

Gaël Le Mens is a Full Professor at Universitat Pompeu Fabra, specializing in Behavioral Science, Management, and Judgment and Decision Making. His research focuses on organizational theory, social influence, and the psychological mechanisms underlying decision-making processes. He holds a PhD from Stanford University and has published extensively in top journals such as Psychological Review and Journal of Personality and Social Psychology . His work explores topics like information sampling, categorization heuristics, and the dynamics of collective beliefs. Key research themes include the 'simple averaging heuristic' in category evaluation, the impact of social influence on decision-making, and the application of machine learning to analyze political texts and concepts. His recent work extends to studying politician-citizen interactions via social media and the semantic analysis of cultural concepts using AI tools like GPT-4.

Tianyang HuView profile

Assistant Professor

• Statistical Machine Learning
• Representation Learning
• Deep Generative Modeling
• +7 more

Tianyang Hu is a postdoctoral researcher at the National University of Singapore, set to join the Chinese University of Hong Kong, Shenzhen as an Assistant Professor in Fall 2025. His academic journey includes a PhD in Statistics from Purdue University under Prof. Guang Cheng, an M.S. in Statistics from the University of Chicago, and a B.S. in Mathematics from Tsinghua University. PhD in Statistics, Purdue University M.S. in Statistics, University of Chicago B.S. in Mathematics, Tsinghua University His research lies at the intersection of statistics and artificial intelligence, focusing on statistical machine learning, representation learning, and deep generative modeling . He is particularly interested in the theoretical foundations of AI, including diffusion models, large language models (LLMs), and overparametrized neural networks. His recent work explores the duality between prompting and fine-tuning, contrastive learning mechanisms, and efficient sampling in generative models. The trends in his recent publications show a strong emphasis on diffusion models , especially in conditional generation, sampling efficiency, and integration with language models. His work spans both theoretical analysis and practical algorithm design, with applications in image generation, 3D modeling, and multimodal reasoning. He has contributed to understanding the behavior of in-context learning in transformers and the role of regularization in deep networks. His scientific achievements include: Spotlight presentation at NeurIPS 2023 Spotlight presentation at NeurIPS 2022 Oral presentation at ICML Workshop 2025 Tianyang Hu is actively building his research group and is seeking PhD/MPhil students and research assistants with strong theoretical and practical AI skills. He has held research positions at Huawei Noah’s Ark Lab and is currently mentored by Prof. Kenji Kawaguchi at NUS. His work has been supported by collaborations across academia and industry, though specific grants are not listed in the text. While no formal lab name is mentioned, his future group at CUHK-Shenzhen is expected to focus on theoretical AI and generative models , with research directions in LLMs, diffusion models, and representation learning.

Shan DuView profile

Assistant Professor

• Computer Vision/Graphics
• Machine/Deep Learning
• Image/Video Processing
• +3 more

Dr. Shan Du is an Assistant Professor in the Department of Computer Science, Math, Physics & Statistics at the University of British Columbia Okanagan Campus. She holds a PhD in Electrical and Computer Engineering from UBC (2009) and has over 15 years of experience in image/video processing, computer vision, and machine learning. Previously, she worked as an Assistant Professor at Lakehead University and as a Research Scientist at IntelliView Technologies. Her research focuses on computer vision, deep learning, and biometrics, with applications in video surveillance systems and environmental monitoring. She has secured grants including NSERC Discovery, CFI JELF, and Alberta Innovates. Dr. Du is a Senior Member of IEEE and serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and the IEEE Canadian Journal of Electrical and Computer Engineering. Teaching interests include Image Processing, Computer Graphics, and Software Engineering. She advises graduate students and has published extensively on topics like gas leak detection, 3D face modeling, and medical image fusion.

• Mathematical Physics
• Differential Geometry
• Topology
• +4 more

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.