جست‌وجوی استاد و پژوهشگر

Hongyang Gaoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Hongyang Gao is an Assistant Professor at Iowa State University, focusing on AI, Machine Learning, and Data Science. His work bridges theoretical foundations and practical applications in neural networks, graph representation learning, and cybersecurity. Education: PhD in Computer Science, Texas A&M University MS in Computer Science, Tsinghua University B.S. in Biomedical English, Peking University Research Interests: Explores neural ODEs, graph neural networks (GNNs), and their applications in molecular modeling, vulnerability detection, and trustworthy AI systems. His recent work emphasizes model interpretability (e.g., MotifExplainer), optimization theory for implicit networks, and graph-based explainability techniques. Publications Trends: Recent papers (2022-2025) highlight advancements in graph learning frameworks, such as G2T-LLM for molecule generation and MAGE for GNN explainability. His work on Neural ODEs explores activation function impacts on convergence, while cybersecurity papers apply dataflow analysis for vulnerability detection. Advising: Collaborates with students on projects involving motif-based methods (e.g., Motifpiece), meta-learning (Meta-AdaM), and trustworthy AI (data preconditions research). No grants explicitly listed in provided texts.

Francisco Martínez Álvarezمشاهده پروفایل

استاد

• Time Series Analysis
• Big Data Analytics
• Deep Learning
• +۳ مورد دیگر

Francisco Martínez Álvarez is a Full Professor in the Computer Science Division at Universidad Pablo de Olavide's School of Engineering. He received his PhD in Computer Science in 2010, honored with the extraordinary doctoral prize. He has completed research stays at Université de Lyon, New York University, and Universidad de Chile. His research focuses on: Big data time series analysis Deep learning for forecasting Seismicity pattern recognition Explainable AI systems Recent publications cover applications in energy forecasting, environmental monitoring, and quantum machine learning. He has supervised 10 doctoral theses and co-founded the Data Science & Big Data Lab. Recognized among the world's top 2% scientists by Stanford University, he has led multiple national and European projects on big data streaming and sustainable AI.

Yuan Zhangمشاهده پروفایل

دانشیار

• Statistical Network Analysis
• Nonparametric Inference
• U-statistics
• +۴ مورد دیگر

Yuan Zhang is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics and Translational Data Analytics (TDA) initiative. He holds a BSc in Statistics from the University of Science and Technology of China (2011) and a PhD in Statistics from the University of Michigan (2016). His research focuses on statistical network analysis, nonparametric inference, U-statistics, and conformal prediction. He advises several PhD students, including Meijia Shao (recipient of the 2023–2024 Whitney Research Award), Torey Hilbert, Kiljae Lee, Fangyi Wang, and Qian Zhou. Key research contributions include advancements in network-based statistical methods, robust distribution-free testing, and conformal prediction frameworks. His work bridges theoretical statistics with practical applications in neuroscience, genomics, and high-dimensional data analysis. Notable publications include work on U-statistic reduction (Journal of the American Statistical Association, 2025), stable conformal prediction (ICLR 2025), and neuroimaging connectivity analysis (Biometrics, 2025). Zhang collaborates extensively with researchers in biostatistics and computational science, contributing to projects in voxel-wise genome-wide association studies and brain connectivity analysis. His research is supported by affiliations with TDA and interdisciplinary collaborations at Ohio State.

Sirisha Rambhatlaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Representation Learning
• +۴ مورد دیگر

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

Alper Kürşat Uysalمشاهده پروفایل

دانشیار

• Text Classification
• Feature Selection
• Pattern Recognition
• +۲ مورد دیگر

Alper Kürşat Uysal is an Associate Professor of Computer Engineering at Alanya Alaaddin Keykubat University. His research develops advanced text classification algorithms and feature selection methods, with applications ranging from spam detection to educational analytics. Research foci: Novel feature selection metrics for text data Imbalanced text classification techniques Short text analysis methodologies Multilingual spam detection systems Affective computing in educational contexts Uysal's publications introduce innovative approaches to feature selection, including the Extensive Feature Selector method and specialized metrics for short text datasets. His work addresses practical challenges in Turkish-language social media content moderation and develops automatic classification systems for educational applications. Recent research examines how feature selection impacts classifier performance on imbalanced text corpora.

Nicolas Boutryمشاهده پروفایل

استادیار

• Discrete Topology
• Mathematical Morphology
• Spectral Graph Theory
• +۲ مورد دیگر

Nicolas Boutry is Assistant Professor at EPITA, where he leads the Data Science and Artificial Intelligence specialization. His research spans discrete topology, mathematical morphology on shape trees, spectral graph analysis for cybersecurity, and explainable AI. Current projects investigate connections between topological data analysis and mathematical morphology, AI-based radiotherapy prediction, and lightweight computer vision transformers. Applications include biomedical imaging and cybersecurity threat detection. Publications reflect innovations in interpretable machine learning, brain tumor segmentation, and graph processing frameworks. Research consistently bridges theoretical mathematics with practical implementations in GPU-accelerated environments.

• Graph Data Management
• RDF and Semantic Web
• Database Theory
• +۳ مورد دیگر

Maxime Jakubowski is a PostDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Databases and Artificial Intelligence research group. Based in Room HA0320 at Favoritenstrasse 9, he can be contacted at maxime.jakubowski@tuwien.ac.at and maintains an ORCID profile (0000-0002-7420-1337). His research centers on graph data management within the Semantic Web ecosystem, specializing in RDF validation through shape constraint languages like SHACL and ShEx. He investigates formal foundations, expressiveness boundaries, and practical implementations including SQL compilation and neighborhood-based graph description. Current projects include FRESH (2021–2026), KtoAPP (2018–2025), and TARGET (2024–2028), focusing on graph data theory and implementation. Recent publications (2021–2025) demonstrate consistent contributions to RDF validation standards, with 14 articles addressing shape language formalization, compilation techniques, and provenance tracking. His work bridges theoretical database concepts with industrial applications in knowledge graph validation. Dr. Jakubowski supervises bachelor theses and teaches courses including Management of Graph Data (192.161) and Project in Computer Science. His research collaborations span international projects and the Dagstuhl Seminar 24102 on Shapes in Graph Data. He is an active member of the Databases and Artificial Intelligence research group, contributing to TU Wien's leadership in graph data management and Semantic Web technologies through both theoretical research and practical tool development.

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +۷ مورد دیگر

Yiwen Zhang is a researcher affiliated with Anhui University in Hefei, China, with a focus on Recommender Systems , Machine Learning , and Graph Neural Networks . Their work spans Data Mining , Imbalanced Data challenges, and Big Data optimization. Recent publications highlight advancements in robust recommendation via heterogeneous graphs, causal feature selection , and anomaly detection in time series. Key research areas: Recommender Systems, Machine Learning, Graph Neural Networks, Data Mining, Edge Computing. Notable applications: Imbalanced Data Classification, Social Recommendation, Edge Server Placement, Causal Discovery. Their 2025 and 2024 contributions include 15+ papers on topics like parameter-free sampling , graph attention networks , and causal inference . These works emphasize robustness , uniformity , and low-cost optimization in complex scenarios. Collaborations: Lei Sang, Dengcheng Yan, Qiang He, Yun Yang, Zhaolong Ling. Published in journals: Applied Intelligence , IEEE Transactions on Knowledge and Data Engineering , Expert Systems with Applications .

Paul Krabbeمشاهده پروفایل

پژوهشگر

• Health Outcome Measurement
• Patient-Reported Outcome Measures (ePROMs)
• Health Status Instruments
• +۳ مورد دیگر

Paul Krabbe is a Researcher affiliated with the Faculty of Medical Sciences at the University of Groningen, specifically within the Value, Affordability and Sustainability (VALUE) department. His work contributes to the UN Sustainable Development Goals related to health and well-being. Dr. Krabbe specializes in developing electronic patient-reported outcome measures (ePROMs) and improving health outcome measurement methodologies, including instruments like the CS-Base generic health status tool and the Infant Health-Related Quality of Life instrument (collaborating with Nestlé). He has pioneered methods such as the HealthFan, HealthSnApp, and the MAPR model, advancing digital health assessment tools and platforms like the Château Sante cloud platform. His research interests span health economics, pediatric health outcomes, and instrument design. Recent work includes multinational studies validating health-state valuation models (e.g., EQ-5D-5L) and patient-centered approaches to instrument development. Krabbe has delivered keynotes and academic presentations on topics like value-based healthcare and modern HRQoL measurement trends. He has been recognized with awards such as the 2017 SHARE PhD Top Publication Award and the 2012 Kwaliteitsprijs Medisch Specialisten. His contributions include over 199 research outputs, 6 datasets (e.g., on prostheses usage features and EQ-5D health values), and supervision of 7 academic works. He collaborates internationally and engages in policy-related research, as seen in his work referenced in policy sources.

• Biomedical Engineering
• Paediatrics and Reproductive Medicine
• Computation Theory and Mathematics
• +۳ مورد دیگر

Priya Rani is a Lecturer in the School of Engineering at RMIT University. Her research focuses on interdisciplinary areas including Biomedical Engineering, Artificial Intelligence, and Neurosciences. She actively supervises research projects such as developing peripheral electronics for neural recording systems and applying machine learning for clinical delirium diagnosis. Dr. Rani’s work bridges computational methods with healthcare applications, emphasizing robust machine learning models and medical imaging analysis. She is affiliated with RMIT’s City Campus and contributes to advancing technologies for sustainable and intelligent systems. Research Interests: Her primary areas include computational theory, biomedical engineering, and AI-driven solutions for healthcare challenges. Notable projects involve semantic segmentation using attention-augmented networks and damage monitoring in advanced materials. Dr. Rani’s research trends highlight machine learning robustness, medical image analysis, and smart home technologies for aging populations. Supervision Focus: Neural recording systems, clinical AI applications Key Projects: Cortical neural interfaces, delirium prediction algorithms Technical Expertise: Machine learning, image processing, medical diagnostics Collaboration and Impact: Her work addresses real-world challenges through interdisciplinary approaches, with publications spanning medical IoT, nanosensors, and composite materials analysis. Dr. Rani’s contributions emphasize practical applications of AI and computational methods in healthcare and engineering domains.

Maryclare Griffinمشاهده پروفایل

استادیار

• Statistical Inference
• Time Series Analysis
• Variable Importance
• +۳ مورد دیگر

Maryclare Griffin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research develops statistical methods for complex data analysis, with emphasis on time series modeling, variable importance measures, Bayesian computation, and interpretable machine learning. Current methodological work includes adaptive overdifferencing for non-stationary processes, mixture representations for Bayesian simulation, and structured shrinkage priors for high-dimensional inference. Applications span clinical genomics, environmental monitoring, and biophysical trend analysis. Recent publications demonstrate innovations in time series model identification, optimization algorithms for regularized regression, and variable importance frameworks for nonlinear models. Collaborative work extends to metastasis prediction in melanoma and lightning strike pattern modeling. Holds a PhD in Statistics from the University of Washington and completed postdoctoral training at Cornell's Center for Applied Mathematics. Maintains active research in computational statistics and model interpretability.

Sara Beeryمشاهده پروفایل

استادیار

• Computer Vision
• Artificial Intelligence
• Environmental Science
• +۳ مورد دیگر

Sara Beery is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on leveraging computer vision for environmental sustainability and conservation challenges. Beery's work spans computational vision, self-supervised learning, and multimodal AI systems. She has contributed to advancements in neural latent dynamics, face recognition bias analysis, and spatio-temporal dataset creation for autonomy. Her recent publications emphasize computer vision applications in conservation, medical imaging, and robotics. Notable topics include synthetic image generation, denoising low-SNR video, and cell segmentation using foundational AI models. Scientific Awards Resnick Graduate Scholar

Chloe Harrisonمشاهده پروفایل

مدرس ارشد

• Cognitive Stylistics
• Cognitive Linguistics
• Literary Analysis
• +۶ مورد دیگر

Chloe Harrison is a Senior Lecturer in English Language and Literature at Aston University's School of Social Sciences and Humanities (SSH). She specializes in cognitive stylistics and applied linguistics, focusing on cognitive grammar for literary analysis, reader response, and contemporary fiction. She currently directs MA English programs and co-leads the Aston Stylistics Research Group. Her research bridges theoretical linguistics and practical pedagogy, with notable contributions to forensic stylistics and cognitive poetics. Education: PhD in Cognitive Stylistics, University of Nottingham (2013) MA in Literary Linguistics, University of Nottingham (2010) BA in English Studies, University of Nottingham (2009) PG Cert in Academic Practice in Higher Education, Coventry University (2015) Research Interests: Application of cognitive grammar to literary analysis Reader response and re-reading phenomena Contemporary fiction and narrative structures Forensic stylistics and authorship analysis Educational dissemination of cognitive stylistics Awards & Grants: £7.3k 2024 grant for sleep narrative research £4k 2021 seed funding for forensic stylistics Multiple teaching and curriculum development grants COST-funded international training (2016) Academic Service: External Examiner at Anglia Ruskin University (2023-2027) Treasurer of the International Association of Literary Semantics Member of key professional associations including IASL, Poetics and Linguistics Association Teaching: Leads postgraduate modules in cognitive poetics, stylistics, and contemporary literature. Previously taught discourse analysis, creative writing, and qualitative research methods.

• Machine Learning
• Optimization Techniques
• Healthcare Operations Management
• +۵ مورد دیگر

Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics at the Leavey School of Business, Santa Clara University, and Program Director for the Master of Science in Information Systems (MSIS). He holds a PhD in Operations and Information Management from the University of Colorado Boulder and degrees in Computer Science from the University of Bologna. His research focuses on combining machine learning and optimization to improve business processes and healthcare systems. Key areas include reducing racial disparities in medical appointment scheduling, relational data mining, and metaheuristic algorithms. Collaborations include healthcare organizations dedicated to Black women, mental health nonprofits, and pharmaceutical companies. His work has been featured in MIS Quarterly , Manufacturing & Service Operations Management , and INFORMS Journal on Computing . Award highlights include the 2021 INFORMS Minority Issues Forum Paper Competition, AACSB's 2021 Innovations That Inspire, and inclusion in a United Nations report. Media features include Forbes and WIRED . He teaches courses such as Data Science with Python, Natural Language Processing, and Business Intelligence. His research emphasizes practical impact, addressing social equity through algorithmic fairness and operational efficiency in healthcare and retail. Ongoing projects explore ethical AI applications and decision support systems for vulnerable populations.

Sonja Genslerمشاهده پروفایل

دانشیار

• Digital Marketing
• Consumer Behavior
• Multichannel Management
• +۳ مورد دیگر

apl. Prof. Dr. Sonja Gensler is an Associate Professor at the Institute for Value-Based Marketing, affiliated with FB4. She specializes in bridging research and teaching to foster student development, emphasizing practical insights for their professional journeys. Her work focuses on digital marketing innovation, consumer behavior analysis, and multichannel strategies. She explores regulatory impacts on the sharing economy and employs advanced empirical methods like conjoint analysis and regression modeling. Key themes in her research include market trends, customer engagement, and the integration of technology in marketing practices. Her research interests span digital marketing strategies, consumer decision-making processes, and the application of statistical techniques to analyze complex datasets. Prof. Gensler’s contributions include studies on sharing economy governance, multichannel management, and the effects of social media on brand relationships. She has published extensively on methodologies such as finite mixture models, contingency analysis, and discriminant analysis, highlighting their practical relevance in marketing and economics. Prof. Gensler’s recent work reflects a trend toward addressing contemporary challenges like AI’s role in customer relationship management and the evolving dynamics of online-offline consumer interactions. Her empirical focus ensures her research remains grounded in real-world applications, benefiting both academic and industry stakeholders.