جست‌وجوی استاد و پژوهشگر

Wolfgang Stammerمشاهده پروفایل

پژوهشگر

• Explainable AI
• Neuro-Symbolic Architectures
• Concept Learning
• +۳ مورد دیگر

Wolfgang Stammer is a PostDoc researcher in the Machine Learning Group at TU Darmstadt's Computer Science Department. His work focuses on making AI models more interpretable and interactive, particularly in explainable AI (XAI), neuro-symbolic architectures, and systematic compositionality challenges in neural networks. He completed his Ph.D. in Machine Learning at TU Darmstadt (2019–2025), an M.Sc. in Computer Science at Goethe University Frankfurt (2016–2018), and a B.Sc. in Cognitive Science at the University of Osnabrück (2011–2015). Research Interests : Stammer's research bridges gaps between human understanding and AI capabilities. Key areas include: Explainable AI (XAI) and interactive machine learning (XIL) Neuro-symbolic integration for logical reasoning and visual concepts Mitigating shortcut learning and confounding factors in datasets Concept discovery and program synthesis for interpretable models Publications : His work spans foundational contributions to AI benchmarks (e.g., V-LoL, SLR-Bench) and frameworks (Neural Concept Binder, Revision Transformers). Recent studies highlight AI's limitations in systematic generalization and propose solutions for aligning reinforcement learning agents with human values. Grants & Labs : He contributes to the Machine Learning Lab at TU Darmstadt and co-organized workshops like the Interactive Machine Learning Workshop @ AAAI 2022. His research bridges theoretical advances with practical applications in healthcare and ethical AI systems.

Lee Davidsonمشاهده پروفایل

مدرس ارشد

• Museum Studies
• Visitor Studies
• Intercultural Museum Practice
• +۷ مورد دیگر

Dr Lee Davidson is a Senior Lecturer in the Museum & Heritage Studies programme at Victoria University of Wellington, New Zealand. She is actively engaged in interdisciplinary research and international academic collaboration, notably serving as a Visiting Professor at the Escuela Nacional de Conservación, Restauración y Museografía (ENCRyM) in Mexico for the past two years. Her research interests include visitor studies (theory and methods), intercultural museum practice, leisure and cultural tourism (history and contemporary practices). Her interdisciplinary work spans museum studies, anthropology, tourism, and heritage studies, with a strong focus on cross-cultural exchange, identity, and public engagement. The trends in her recent scholarly contributions reflect a deep engagement with intercultural heritage experiences, touring exhibitions, and the sociopolitical dimensions of museum practices. Her work explores how heritage is interpreted and experienced across borders, with particular attention to Indigenous representation, visitor meaning-making, and collaborative curation. Themes of cultural diplomacy, affect, and critical heritage theory recur throughout her publications and presentations. Senior Lecturer, Museum & Heritage Studies programme, Victoria University of Wellington Visiting Professor, International Course of Museology, ENCRyM, Mexico (2021–2023) Dr Davidson regularly presents at international conferences and delivers invited seminars, contributing to global academic discourse in museum and heritage studies. While specific grant and advising information is not available, her extensive participation in high-level academic forums suggests active research leadership and mentorship. She is involved in professional and academic networks centered on critical heritage studies, intercultural dialogue, and the role of museums in shaping historical consciousness and social change.

Aidan O'Sullivanمشاهده پروفایل

دانشیار

• Artificial intelligence
• Machine learning
• Reinforcement learning
• +۷ مورد دیگر

Aidan O'Sullivan is an Associate Professor in Energy and Artificial Intelligence at University College London's Bartlett School of Environment, Energy & Resources, where he leads the Energy Systems and Artificial Intelligence Lab and the department's Data Analytics research theme. He is also the founding course director of the Energy Systems and Data Analytics MSc, an innovative program combining energy systems and sustainability with data science and machine learning. Current Position: Associate Professor, UCL Energy Institute Education: PhD in Mathematics, Imperial College London Prior Experience: Postdoctoral Researcher at MIT Civil Engineering Department Affiliation: Turing Fellow at the Alan Turing Institute (2018-2020) O'Sullivan's research focuses on applying artificial intelligence to energy system problems, particularly in power systems and heavy industry. He is a pioneer in using data science and machine learning with novel energy sector data sources like renewable generation and smart meter data. His specific AI interests include reinforcement learning, ensemble based methods, latent variable models and deep learning, with additional interest in complex network theory for understanding system interdependencies. His publication history shows a clear evolution from transportation and aviation applications toward direct power system applications, with reinforcement learning emerging as a consistent methodology across his work. The research demonstrates strong interdisciplinary integration of AI techniques with energy system challenges, particularly in grid management, optimization, and emissions reduction. O'Sullivan has received recognition including a Turing Fellowship from the Alan Turing Institute in 2018, the UK's leading center for AI and data science research. Turing Fellowship, Alan Turing Institute (2018-2020) As an educator, O'Sullivan serves as Deputy Director and Founding Course Director of the MSc in Energy Systems and Data Analytics. He teaches BENV0094 Statistics for Energy Analytics, focusing on the mathematical foundations of machine learning methods and their application to energy system datasets. His teaching directly addresses the industry need for professionals with integrated expertise in energy systems and data science. O'Sullivan leads the Energy Systems and Artificial Intelligence Lab at UCL, which focuses on applying advanced AI techniques to solve complex energy system challenges, particularly in grid management, renewable integration, and energy efficiency. His work aligns with UN Sustainable Development Goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 13 (Climate Action), reflecting the applied nature of his research in addressing global energy challenges.

Miguel A. Carreira-Perpiñánمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Deep Learning
• +۷ مورد دیگر

Professor Miguel A. Carreira-Perpiñán is a faculty member in the Department of Computer Science & Engineering at the University of California, Merced's School of Engineering. His current research focuses on the intersection of optimization and machine learning, particularly algorithms for deep neural networks and nonlinear embeddings. He has advised multiple PhD students in areas spanning decision trees, clustering, and robotics applications. PhD in Computer Science (2001), University of Sheffield Licenciado en Informática (1995), Technical University of Madrid Research interests include: Machine learning algorithms and representations Optimization for deep learning and nested systems Dimensionality reduction and unsupervised learning Applications in computer vision, speech processing, and robotics His recent publications demonstrate trends in tree-based optimization (TAO algorithm), neural network compression techniques, and interpretable machine learning models. Papers from 2022-2015 highlight extensions of the method of auxiliary coordinates (MAC) to distributed systems, binary autoencoders, and nonlinear embeddings. Scientific awards and grants include: NSF Career Awards (2006-2011) Google Faculty Research Award (2013-2014) NSF Grant IIS #2007147 (2020-2023) for tree alternating optimization Notable Paper Award at AISTATS 2014 Current professional service includes area chair positions at NeurIPS 2025, ICML 2025, and AAAI 2025. He leads the Learning-Compression (LC) algorithm development for neural network optimization and collaborates with researchers at institutions including Meta AI, Google DeepMind, and the University of Iowa.

• Cardiac Imaging
• Magnetic Resonance Imaging
• Computed Tomography
• +۶ مورد دیگر

Ronald M Peshock is a Professor of Radiology and Internal Medicine at the University of Texas Southwestern Medical Center (UTSW), with a career spanning over four decades since completing medical school at UT Southwestern in 1976. His academic journey includes Internal Medicine training at Parkland Health & Hospital System (internship 1977, residency 1979) followed by a Cardiology fellowship (1982). Dr. Peshock's research focuses on cardiac imaging, particularly using MRI and CT technologies to assess cardiovascular risk and disease. His work heavily involves the Dallas Heart Study, examining left ventricular hypertrophy, atherosclerosis, and cardiovascular risk factors across diverse populations. In recent years, he has become a prominent researcher in artificial intelligence applications for radiology, with numerous publications on AI implementation in clinical workflows, diagnostic accuracy improvement, and integration of AI into radiology training programs. His publication record shows consistent productivity from 1979 through 2025, with 218 total publications according to available records. Recent work demonstrates particular expertise in cardiac CT, pulmonary embolism imaging, and developing quantitative imaging biomarkers for cardiovascular disease. His Scopus profile indicates an h-index of 67 with 14,914 citations, reflecting significant scholarly impact in his fields. Dr. Peshock maintains active clinical and research affiliations with Parkland Health & Hospital System and serves as a key researcher in the Dallas Heart Study. His work examining ethnic differences in cardiovascular disease, left ventricular hypertrophy patterns, and aortic stiffness has contributed significantly to cardiovascular imaging literature.

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Biomedical Engineering
• Voice Analysis
• Artificial Intelligence
• +۵ مورد دیگر

Daria Hemmerling , PhD, Eng., is a Lecturer at the Department of Metrology and Electronics under the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. Her research focuses on the intersection of biomedical engineering, voice analysis, and artificial intelligence, particularly for neurological and cardiovascular disease diagnostics. Research Interests : Applying mixed reality and AI for Parkinson’s disease assessment Voice/speech biomarkers for heart failure and neurodegenerative disorders Deep learning techniques in medical imaging (e.g., skull segmentation/reconstruction) Haptic feedback systems for multisensory interaction Simulation training in electrophysiology education Unsupervised learning and modality translation in biomedical signal processing Scientific Trends : Her recent work emphasizes multimodal diagnostic systems integrating voice analysis, VR/MR visualization, and deep learning. She explores explainable AI for medical classification tasks, data augmentation strategies, and innovative haptic/gamification interfaces.

• Computer Science
• Artificial Intelligence
• Cybernetics
• +۳ مورد دیگر

Dilip Sarkar serves as an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, University of Miami. His academic profile demonstrates a strong research focus across multiple domains of computer science with particular emphasis on theoretical foundations and practical applications. Dr. Sarkar's research interests include: Artificial Intelligence and Cybernetics Parallel Algorithms and Programming Combinatorics Image Processing and Compression Internet of Things Security Machine Learning Applications in Healthcare Quantum Computing Approaches His publication record from 2018-2024 reveals a researcher engaged with cutting-edge computational challenges. Recent work spans quantum tensor networks for time series analysis, security frameworks for single sign-on systems, efficient computation in belief theoretic models, and machine learning applications for traumatic brain injury recovery prediction. His research demonstrates consistent integration of theoretical computer science with practical problem-solving across diverse application domains. As a member of CCS (Center for Computational Science) at the University of Miami, Dr. Sarkar participates in interdisciplinary research initiatives that bridge computational methods with scientific discovery across multiple fields. His technical expertise spans both theoretical frameworks and practical implementations, with notable contributions to image compression techniques, particularly for medical applications, and security protocols for modern web and IoT environments.

Francesca Ievaمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Statistical Learning
• +۵ مورد دیگر

Francesca Ieva serves as Associate Head of the Health Data Science Centre at Human Technopole and Associate Professor of Statistics at the Politecnico di Milano, where she leads the MOX - Modeling and Scientific Computing laboratory within the Department of Mathematics. She also co-leads the Di Angelantonio & Ieva Group, a collaborative research team focused on integrating molecular data with clinical information to advance precision medicine. Dr. Ieva received her PhD in Mathematical Models and Methods for Engineering in 2012. Her academic journey has positioned her at the forefront of health data science, bridging statistical methodology with biomedical applications. Her research focuses on statistical learning in biomedical sciences, with particular emphasis on developing advanced models for integrating complex clinical data to inform predictions in clinical decision-making. Francesca's work spans multiple domains including: Development of novel survival analysis techniques for healthcare applications Integration of DNA methylation data for cardiovascular risk prediction Application of machine learning to clinical pathway analysis in mental health Innovative approaches to polygenic risk scoring incorporating SNP interactions Development of radiomics models for cancer prognosis and treatment response Analysis of Dr. Ieva's recent publications reveals a strong trend toward federated learning approaches in healthcare data analysis, reflecting growing concerns about data privacy while maintaining analytical power. Her work increasingly integrates multiple data types (genomic, epigenetic, imaging, and clinical records) to create more comprehensive patient profiles for precision medicine applications. A notable pattern is her focus on translating complex statistical models into clinically actionable tools that can directly inform patient care and healthcare policy decisions. As Associate Head of the Health Data Science Centre at Human Technopole, Dr. Ieva oversees a research group comprising epidemiologists, statisticians, and data scientists working collaboratively to bridge the gap between genotype and phenotype. The Di Angelantonio & Ieva Group develops innovative studies that integrate biomolecular data with medical records, imaging, and portable medical device data. Her team utilizes both existing healthcare data and newly generated population-based studies, applying novel analytical methods that integrate clinical epidemiology with health research to improve data interpretation. Dr. Ieva actively mentors several PhD students including Andrea Lampis, Katherine Marie Logan, Alessia Mapelli, Michela Carlotta Massi, and Andrea Mario Vergani. Her research has been supported by collaborations with major healthcare institutions and likely receives funding from research councils and health technology initiatives, though specific grant details are not provided in the available information.

Dokyun Leeمشاهده پروفایل

دانشیار

• Generative AI
• Economics of Unstructured Data
• Human-AI Collaboration
• +۵ مورد دیگر

Dokyun Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Computing & Data Science at Boston University , where he founded the Business Insights through Text (BIT) Lab and co-leads the BU Digital Business Institute Generative AI Lab . His research focuses on the responsible application, development, and impact of AI in business, particularly analyzing unstructured data (e.g., text, images) across digital consumer management, platform design, and human-AI collaboration. Education : PhD in Operations, Information & Decisions (Wharton School), MS in Statistics (Yale University), BA in Computer Science (Columbia University) His work addresses Generative AI (unintended consequences, human-integration frictions), Economics of Unstructured Data (content extraction, marketing), and AI Regulation in contexts like advertising, market competition, and creativity. Recent articles explore LLMs in corporate risk nowcasting, generative AI's effects on online communities, and interpretable deep learning for churn management. His research trends span NLP, computer vision, and causal inference, with applications in digital marketing, e-commerce, and innovation analytics. Scientific Awards : INFORMS ISS Gordon B David Young Scholar, INFORMS ISS Sandy Slaughter Early Career, CDO Magazine Leading Academic Data Leader, AMA Don Lehmann Award 2024, MSBA Teaching Award He has secured grants from Adobe, Google, Microsoft, MassMutual, and Prudential Foundation. His labs (BIT Lab, Generative AI Lab) mentor students and collaborate on AI-driven solutions for business challenges.

Hannah Spitzerمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Spatial Omics
• +۴ مورد دیگر

Hannah Spitzer is a Research Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig Maximilian University of Munich and an associated Research Group Leader at Helmholtz Munich's Computational Health Center. She leads the Spitzer Lab, focusing on computational analysis of multimodal brain datasets to advance understanding of neurovascular and neurodegenerative diseases. Her educational background includes: PhD in Computer Science from Heinrich-Heine University Düsseldorf and Research Center Jülich (2015-2020) Master's in Computer Science from RWTH Aachen (2013-2015) Bachelor's in Computer Science from RWTH Aachen (2009-2013) Dr. Spitzer's research integrates computational biology and machine learning to decode brain complexity, with emphasis on spatial omics analysis , interpretable image representation learning , and cross-modal data integration . Her group develops tools like squidpy and campa for spatial omics while applying graph neural networks to epilepsy lesion detection through the international MELD project, prioritizing biological interpretability in AI models. Recent publications reveal strong trends in leveraging graph neural networks for subtle brain lesion detection and creating computational frameworks for spatial omics integration. Her work consistently bridges advanced machine learning with clinical neuroscience to uncover disease mechanisms in neurodegeneration and vascular disorders. Dr. Spitzer actively mentors students including current PhD candidate Beatrice Guastella and alumni Deniz Fettahoglu (MSc) and Katia Berr (PhD). Her lab operates through major collaborations including the MELD epilepsy consortium and Helmholtz Imaging Project, with funding supporting computational pipeline development for small-vessel disease prediction and multimodal brain atlasing. The Spitzer Lab comprises postdoc Wasim Aftab and PhD student Beatrice Guastella, working on computational pipelines that integrate histology, spatial omics, and neuroimaging data to decode brain disease mechanisms through interpretable AI approaches.

Aleksandra Sarcevicمشاهده پروفایل

استاد

• Computer Science
• Human-Computer Interaction
• Healthcare Informatics
• +۷ مورد دیگر

Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.

Amber Dumfordمشاهده پروفایل

استاد

• Student Engagement
• Gender Issues in Higher Education
• Arts Education
• +۶ مورد دیگر

Dr. Amber Dumford is a Professor at the University of South Florida in the College of Education , specializing in Higher Education and College Student Affairs . She holds a Ph.D. in Higher Education (with a cognate in Sociology) from Pennsylvania State University , a M.P.A. from Indiana University Bloomington , and a B.S. in Mathematics from the same institution. Teaches courses in Student Affairs Assessment, College Teaching, and Globalization in Higher Education Currently serves on doctoral dissertation committees Active in research on student engagement, gender issues, and data quality in higher education Reviews for 20+ peer-reviewed journals and serves on editorial boards of top journals Consultant for National Center for Educational Statistics and institutions in US/Canada Research Interests include: Factors influencing student engagement and learning Gender disparities in higher education Arts and engineering education outcomes Creativity in academic settings Quantitative reasoning and assessment Data quality in educational research Food insecurity among diverse college students Publications reflect trends in student engagement , career readiness , data-driven educational practices , and equity in higher education . Her recent works focus on socioecological factors affecting food pantry usage and validity of survey instruments in measuring student behaviors. Service Contributions : Active member of university committees and councils Board member of Research in Higher Education and Journal of College Student Development Statistical modeling consultant for National Center for Educational Statistics Teaching Experience spans traditional mathematics instruction (2002-2014) to higher education seminars and globalization studies at USF. She plans to develop data analytics and institutional research courses.

Andrés Bruhnمشاهده پروفایل

استاد

• Computer Vision
• Optical Flow
• Scene Flow
• +۵ مورد دیگر

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

• Reinforcement Learning
• Neural Network
• Deep Reinforcement Learning
• +۵ مورد دیگر

Abhinav Verma is an Assistant Professor in Computer Science and Engineering, focusing on reinforcement learning, neural networks, and formal verification of control systems. His research bridges machine learning, program synthesis, and theoretical computer science to ensure interpretable and verifiable AI. Research Interests : Reinforcement learning, neurosymbolic AI, policy synthesis, formal guarantees in control systems. Key Contributions : Developed frameworks combining neural networks with formal methods for verifiable sequential decision-making, contributed to explainable AI through programmatic policy learning. Article Trends : Recent work emphasizes temporal logic constraints, counterfactual experience replay, and reduced-variance reinforcement learning. His publications highlight compositional approaches, differentiable programs, and neurosymbolic integration. Collaborations : Collaborates with institutions and researchers in computer science, with a focus on stochastic systems and interpretable machine learning.