جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Deep Learning
• Health Data Analysis
• +۳ مورد دیگر

Mattias Ohlsson is a Visiting Professor at the School of Information Technology , Halmstad University . His research focuses on machine learning and deep learning for analyzing diverse health data, particularly patient trajectory modeling with multimodal approaches. Primary affiliation: IT - Computer Department Collaboration areas: Healthcare sector and industry His work emphasizes explainable AI in clinical contexts, including survival analysis, cardiac event prediction, and cross-domain applications in satellite poverty mapping. Recent projects explore temporal healthcare data analysis using transformer architectures and self-supervised learning . Key article trends include: 2025: AI integration with medical expertise for emergency diagnostics 2024: Survival model evaluation, temporal data challenges, and graft failure prediction 2023: Multi-robot routing optimization and fatty liver disease etiology modeling 2022: Heart failure mortality algorithms and anomaly detection systems Current affiliations include the CAISR Health research group, with technical expertise spanning CNNs, transformers, and robust imputation methods.

• Pharmacoepidemiology
• Drug Regulation
• Pharmacovigilance
• +۳ مورد دیگر

Prof. Marie Louise De Bruin is a Professor in Drug Regulatory Science at Utrecht University and Scientific Director of the Utrecht Centre for Pharmaceutical Policy and Regulation. She holds dual expertise as a pharmacist (Utrecht University) and epidemiologist (Erasmus University Rotterdam) and integrates academic research with regulatory practice, having served on the Dutch Medicines Evaluation Board (2008–2016) and the EMA's Pharmacovigilance Risk Assessment Committee (2012–2018). Her research focuses on pharmacoepidemiology , drug safety , and regulatory policy optimization , with emphasis on quantitative methods to evaluate regulatory systems and develop decision-making tools. Key interests include risk minimization strategies, real-world evidence applications, and global pharmaceutical policy frameworks. Recent publications (2022–2025) demonstrate strong themes in regulatory effectiveness studies, vaccine safety, real-world data in oncology, and pediatric drug development. Over 80% of articles address EU regulatory processes, with consistent focus on bridging gaps between policy, clinical practice, and industry innovation. Prof. De Bruin leads public-private research collaborations and oversees the Utrecht Centre for Pharmaceutical Policy and Regulation, driving initiatives to enhance drug regulatory systems globally.

• Cyber-Physical Systems
• Automata Theory
• Machine Learning
• +۳ مورد دیگر

Martin Tappler is a researcher at Vienna University of Technology's Faculty of Electrical Engineering and Information Technology, working in the Cyber-Physical Systems research area (Institute of Computer Engineering). Holding a technical doctorate (Dr.techn.), he serves as a Project Assistant conducting advanced research at the intersection of formal methods and machine learning. His research focuses on automata theory , neural network semantics , and reinforcement learning systems . Tappler's work bridges theoretical computer science with practical applications in cyber-physical systems, particularly exploring how formal models can enhance machine learning approaches. His methodology combines rigorous mathematical frameworks with empirical validation in complex decision-making environments. Tappler's recent publications demonstrate significant contributions to understanding the relationship between neural network architectures and formal language structures, as well as developing novel testing methodologies for reinforcement learning systems. His research shows a clear trajectory toward creating more reliable and interpretable AI systems for safety-critical applications. As a researcher at TU Wien, Tappler collaborates with colleagues across multiple institutions, as evidenced by his co-authored publications with researchers from various academic backgrounds. His work appears in prominent computer science venues focusing on formal methods and machine learning.

Hanchen Wangمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence for Science
• Biomedical AI Agents
• Multi-omics Analysis
• +۷ مورد دیگر

Hanchen Wang is a Postdoctoral Research Fellow at Stanford AI Lab and Genentech, working under Jure Leskovec and Aviv Regev. He holds a PhD in Computer Science from Cambridge University completed in 3 years under Joan Lasenby, and a BS in Physics from Nanjing University where he was valedictorian. His research bridges artificial intelligence and biomedical discovery, with appointments spanning both academic and industry settings. Wang's research focuses on AI for Science , particularly developing autonomous agents for biomedical discovery. His work spans multi-omics analysis , spatial transcriptomics , live-cell imaging , and perturbation assays , with applications in cancer therapeutics , autoimmune diseases , and neurological disorders . He has pioneered multiple AI agent frameworks including Biomni (a general-purpose biomedical agent), SpatialAgent, and PerTurboAgent for specialized biological discovery tasks. His publication record demonstrates significant impact across both computer science and biology venues, with first-author papers in Nature , Nature Biotechnology , and NeurIPS . His research has been deployed by Anthropic, Amazon Web Services, and Genentech, and featured in Nature , The Economist , and DeepMind communications. Chan Zuckerberg Initiative Faculty Applicant Bootcamp participant UCSF Gladstone Institute Trainee-to-Tenure Track Program member OpenAI Researcher Access Program recipient Multiple conference travel awards Wang actively mentors early-career researchers including PhD students from institutions like CSHL, MIT, Harvard, and KAIST. He serves as Area Chair for ICLR 2026, organizes workshops on AI for Science at major conferences, and gives invited talks at leading institutions including Harvard, Yale, and the Broad Institute. His research is supported by Genentech internal funding ($500k/year) and OpenAI resources.

Andreas Krauseمشاهده پروفایل

استاد

• Machine Learning
• Adaptive Systems
• Probabilistic Artificial Intelligence
• +۴ مورد دیگر

Andreas Krause is a Professor of Computer Science at ETH Zurich , where he leads the Learning & Adaptive Systems Group . He holds additional roles as Academic Co-Director of the Swiss Data Science Center , Chair of the ETH AI Center , and co-founder of the ETH spin-off LatticeFlow . Previously, he was an Assistant Professor at Caltech . Education : PhD in Computer Science from Carnegie Mellon University (2008) , Diplom in Computer Science and Mathematics from Technical University of Munich (2004) His research focuses on machine learning and adaptive systems , with applications in probabilistic AI , sequential decision-making , and responsible AI . He has contributed to advancements in active learning , reinforcement learning , and large language models , particularly for scientific and real-world challenges. Scientific awards and fellowships include: Rössler Prize ERC Starting and Consolidator Grants German Pattern Recognition Award NSF CAREER Award ETH Golden Owl Teaching Award ACM Fellow, IEEE Fellow, ELLIS Fellow Microsoft Research Faculty Fellow Kavli Frontiers Fellow Test of Time Awards at ACM SIGKDD (2019) and ICML (2020) He has held leadership roles in major AI conferences, serving as Program Co-Chair for ICML 2018 and General Chair for ICML 2023 , and is an Action Editor for the Journal of Machine Learning Research . In 2023–24, he contributed to global AI governance as a member of the United Nations’ High-level Advisory Body on AI .

Michel Audiffrenمشاهده پروفایل

مدرس

• Cognitive Neuroscience
• Executive Functions
• Mental Effort
• +۷ مورد دیگر

Michel Audiffren is a Lecturer at the University of Poitiers, affiliated with the Cognition and Learning Research Center (CeRCA) and Maison des Sciences de l’Homme et de la Société (MSHS). His research focuses on the bidirectional relationship between physical activity and cognitive processes, spanning cognitive neuroscience, executive functions, mental fatigue, and self-control. He employs neuroimaging (NIRS) and psychophysiological methods to study aging, cognitive performance, and exercise-induced adaptations. Role: Lecturer, University of Poitiers Research Affiliation: CeRCA/MSHS Key Collaborations: University of Geneva, University of Queensland, University of Bordeaux Michel Audiffren’s research demonstrates how chronic exercise modulates cognitive aging via neuroplasticity, while acute exercise enhances executive functions. His recent work explores mental fatigue’s impact on athletic performance, brain endurance training, and psychophysiological markers of effort engagement. Collaborative studies address genetic polymorphisms (BDNF, COMT) and physical activity’s role in depression and inhibitory control among older adults. His publications from 2022-2025 emphasize mental fatigue dynamics, cognitive load assessment, and neuroergonomics. Collaborators include Nathalie André, Cédric Albinet, and international institutions. Audiffren investigates the interplay of motor processes, decision-making, and cognitive resource allocation using behavioral and neuroimaging approaches. PhD Supervision: Anne Canivet (2012-2016), Nounagnon Frutueux Agbangla (2013-2016), Thomas Mangin (2017-2020), Marine Grousset (2019-2023) Research Tools: Near-infrared spectroscopy, dual-task paradigms, CRUNCH model

Julien Vrydaghمشاهده پروفایل

پژوهشگر ارشد

• Deliberative democracy
• Citizen participation
• Democracy theory
• +۳ مورد دیگر

Julien Vrydagh is a Research Fellow at the Institute of Social Sciences, Department of Political Theory and Empirical Democracy Research, focusing on deliberative democracy and citizen participation. He is affiliated with Vrije Universiteit Brussel . Education Bachelor of Political Sciences, Université Libre de Bruxelles (2010–2013) Master of European Studies, Ghent University (2013–2015) Master of International Relations, Ghent University (2015–2016) PhD, Vrije Universiteit Brussel & UCLouvain (2017–2023) Research Focus Deliberative minipublics Citizens' assemblies Policy impact of democratic innovations Collaborative governance Youth political engagement Citizens' meeting legitimacy Recent Publications (2023–2025) His work spans democratic innovations, analyzing the integration of deliberative minipublics into mainstream politics, their policy impacts using frameworks like the Sequential Impact Matrix, and challenges in linking minipublics to broader governance systems. Key themes include citizen participation, climate change governance, and institutional ethics. Projects Postdoctoral Researcher, "Designing Democracy on Mars and Earth" (2023–2024) Teaching Assistant, Vrije Universiteit Brussel (2017–2023)

Jeffrey W. Ohlmannمشاهده پروفایل

دانشیار

• Operations Research
• Logistics
• Sports Analytics
• +۳ مورد دیگر

Jeffrey W. Ohlmann is an Associate Professor and Director of Graduate Studies in Business Analytics at the Tippie College of Business, University of Iowa. He holds a PhD in Industrial and Operations Engineering from the University of Michigan and a BS in Mathematics from the University of Nebraska-Lincoln. Research focuses on sequential decision making under uncertainty, logistics optimization, and sports analytics 2022 Master of Business Analytics Instructor of the Year recipient 2010 Strategic Innovation Faculty of the Year award winner His recent publications address stochastic vehicle routing, crowdshipping dynamics, and social media applications in sports recruitment. Ohlmann has received significant recognition for teaching excellence and has presented at international conferences including INFORMS and Metaheuristics International Conference. Key research areas: Operations Research, Logistics, Sports Analytics, Stochastic Programming, Dynamic Programming Applications in college football recruiting, delivery systems, and lean production

Anastasia Stuart-Edwardsمشاهده پروفایل

دانشیار

• Workplace Mindfulness
• Occupational Health
• Mental Health at Work
• +۲ مورد دیگر

Anastasia Stuart-Edwards is an Associate Professor at the Dhillon School of Business, University of Lethbridge, specializing in Human Resource Management & Labour Relations. Her research focuses on workplace mindfulness, occupational health, and mental health at work. Her recent work explores team mindfulness, reciprocal relationships in workplace mental health, multijobbing dynamics, and flow theory advancements. She employs interdisciplinary approaches spanning psychology, organizational science, and behavioral economics. Education: BA, PhD from the University of Manitoba (2019) Contact: a.stuartedwards@uleth.ca , (403) 317-5034

Lingzhong Guoمشاهده پروفایل

مدرس

• Signal Processing
• Biomedical Engineering
• Nonlinear Systems Identification
• +۳ مورد دیگر

Lingzhong Guo is a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield , affiliated with the Insigneo Institute for in silico Medicine and Neuroscience Institute . His research bridges signal processing, nonlinear systems identification, and biomedical applications. PhD from Bristol Robotics Laboratory (UWE Bristol) Specializes in spatio-temporal system identification, PDE control, and machine learning for medical imaging Recent work focuses on deep learning applications for muscle segmentation in MR images and EIS analysis for oral disorder detection. Collaborations with Zilico Ltd. and participation in Horizon2020 projects highlight his translational research. Scientific Recognition SANO Centre grant (H2020, £2.5M) for computational diagnostics SPINe: Numerical and Experimental Repair strategies (H2020 European program, €1.58M) Innovate UK KTP for EIS-based medical diagnosis (£190K) His publications span nonlinear dynamics , multiscale modeling , and biomedical signal processing , with methodological contributions to Volterra series and coupled PDE-ODE systems.

• Pattern Mining
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dominique LI is a Professor in the Department of Computer Science at the University of Tours, affiliated with the Fundamental and Applied Computer Science Laboratory (LIFAT) and the Polytechnic School of the University of Tours (EPU). His research focuses on pattern mining, skyline computation, and data mining applications in domains like lithium-ion battery analysis and fluid mechanics. His recent publications highlight advancements in sequential pattern sampling, dynamic texture classification, and skyline maintenance for data streams. Keywords span Computer Science Machine Learning Database Systems Fluid Dynamics Text Classification Algorithm Design .

• Bayesian time series analysis
• forecasting
• finance
• +۴ مورد دیگر

Dr Kostas Triantafyllopoulos serves as Senior Lecturer in Statistics and MSc Statistics Course Director at the University of Sheffield's School of Mathematical and Physical Sciences. He joined Sheffield in February 2005 after academic positions at Newcastle University (2002-2004) and a post-doctoral appointment at Bristol University (2001-2002). His educational background includes: BSc in Mathematics from Aristotle University of Thessaloniki (1996) MSc in Quality Management from Napier University of Edinburgh (1998) PhD in Statistics from the University of Warwick (2002) Triantafyllopoulos specializes in Bayesian time series analysis and forecasting , with significant applications across finance, bioinformatics, medicine, signal processing, and quality control. His methodological innovations include multivariate stochastic volatility modeling using Wishart processes, real-time covariance estimation for state-space models, and particle filter applications for count time series. Recent work (2019-2022) demonstrates strong focus on medical decision making through flexible survival models and pandemic impact analysis using hybrid Bayesian-neural network approaches for shipping economics. Publication trends reveal a strategic evolution from foundational Bayesian state-space work (2006-2012) toward high-impact interdisciplinary applications. His 30+ journal articles show consistent emphasis on solving autocorrelation challenges in control charts, developing non-parametric monitoring systems for environmental data, and creating flexible parametric frameworks for survival data extrapolation in healthcare contexts. Research funding includes Nuffield Foundation support as Principal Investigator for "The multivariate general steady model" and EPSRC collaboration as Co-investigator on "Simulation Tools for Automated and Robust Manufacturing". He actively contributes to the Mathematical and Statistical Modelling research group, with current work bridging theoretical statistics and real-world industry/healthcare challenges.

Ismini Lourentzouمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Vision-Language Models
• Interactive AI Agents
• +۶ مورد دیگر

Ismini Lourentzou is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, leading the Perception and LANguage (PLAN) Lab. Her research focuses on multimodal machine learning, vision-language models, and human-centric embodied AI systems. School of Information Sciences, University of Illinois Urbana-Champaign Former Assistant Professor, Virginia Tech Department of Computer Science (2021-2023) Research Interests: Multimodal learning with limited supervision Interactive AI agents and human-agent communication Vision-language navigation and task-driven conversational systems Self-supervised learning and grounding vision-language models Applications in healthcare, misinformation detection, and manufacturing Scientific Contributions: Recipient of DARPA, NSF, and Amazon funding Developed frameworks for vision-language co-segmentation and embodied navigation Keynote speaker at ACM PETRA 2022 on supervision signals in healthcare AI Awards & Recognitions: Best Vice Co-Chair at IEEE BigData 2023 Outstanding New Assistant Professor at Virginia Tech IBM Invention Plateau Award (2019)

Kerstin Preuschoffمشاهده پروفایل

دانشیار

• Neuroeconomics
• Neurofinance
• Decision Making Under Risk
• +۴ مورد دیگر

Kerstin Preuschoff is an Associate Professor at the University of Geneva affiliated with the Geneva Finance Research Institute . Her interdisciplinary research bridges Neuroeconomics and Neurofinance , focusing on the neural mechanisms of decision-making under risk, integrating neuroscience , financial economics , and psychology . Education : PhD in Computation and Neural Systems & Neuroeconomics, California Institute of Technology Previous Affiliations : Researcher and Lecturer at University of Zurich and EPF Lausanne Her research explores how the brain computes subjective value, processes surprise, and balances new vs. old information during learning. She employs fMRI , computational models , and behavioral experiments to investigate risk prediction errors and neural correlates of decision-making. Key trends in her publications include interdisciplinary work on reinforcement learning , cognitive flexibility , and neuroeconomic theory , with a focus on brain regions like the insula and temporo-parietal junction . She has contributed to executive courses on Psychology of Finance and Neurofinance , and her academic presentations span conferences like The Brain Conferences and Neurofinance Meetings .

• Artificial Intelligence
• Decision Support Systems
• Uncertain Data Analysis
• +۳ مورد دیگر

Zeineb El Khalfi is a Teacher-researcher at CESI Bordeaux Campus specializing in artificial intelligence and decision support systems, with research focused on mobility/smart transport and uncertain data analysis. Her academic credentials include: Doctorate from Paul Sabatier University of Toulouse and Higher Institute of Management of Tunis (2017) on "Lexicographic refinement in possibilistic sequential decision-making" Research Master's from École Polytechnique de Nantes (2014) in Data Mining and Knowledge Management Research Master's from Higher Institute of Management of Tunis (2014) in Computer Science and Knowledge Management Her research bridges theoretical AI frameworks with real-world applications, particularly in possibilistic reasoning for decision-making under uncertainty. Early work centered on lexicographic refinements in Markov decision processes and possibilistic decision trees, evolving toward smart mobility solutions where she applies spatio-temporal forecasting to micro-mobility redistribution challenges. This trajectory demonstrates a strategic shift from foundational AI theory to transportation-focused implementations while maintaining core expertise in uncertainty modeling. Analysis of her 2016-2023 publications reveals consistent contributions to possibilistic AI (6 conference papers, 3 journal articles), with recent work emphasizing practical transportation applications through micro-mobility systems and data-driven urban mobility solutions. No information is available regarding student supervision or research grants in the provided materials. She actively contributes to CESI's Engineering and Digital Tools Research Team, leveraging interdisciplinary collaboration to advance smart transportation technologies through computational methods.