Find Academic Supervisors

• Gender Schema Theory
• Political Advertising
• Cognitive Processes
• +4 more

Jacqueline Claire Hitchon is a Professor in the Charles H. Sandage Department of Advertising and the Institute of Communications Research at the College of Media, University of Illinois Urbana-Champaign, specializing in communication and media effects research. Her core expertise spans Gender Schema Theory , political advertising , cognitive processing in media consumption, television attention mechanisms , and cause-related marketing . She investigates how viewers process political advertisements with emphasis on gender schemas applied to female candidates, the impact of brand familiarity on advertising effectiveness, and audience responses to cause-related campaigns amid negative news contexts. Her publication trends (2002-2005) reveal consistent focus on cognitive and schematic processing across political communication, advertising, and television viewing. Key themes include gendered interpretation of political ads, strategic attention patterns during television consumption, and contextual effects of news framing on marketing responses, predominantly employing experimental methodologies to analyze viewer behavior and cognitive load.

• Knowledge Graphs
• Machine Learning
• Semantic Web Technologies
• +3 more

Bram Steenwinckel is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. He holds an FWO Junior Postdoctoral Fellowship and contributes to IMEC research projects. His work combines knowledge graphs with machine learning for explainable hybrid AI applications. FWO-funded projects on anomaly detection and knowledge graph embeddings Co-author on 15+ publications in semantic web, AI, and healthcare informatics Research focuses on integrating domain knowledge into machine learning pipelines Involvement in both academic research and applied engineering solutions His research explores knowledge graph creation for smart monitoring systems across healthcare and industrial domains. The INK methodology enables semantic rule mining while TALK provides context-aware activity recognition . Current work extends these approaches to zero-shot classification and automotive quality control . Scientific contributions include: Explainable knowledge graph embeddings Context-aware IoT data stream analysis Hybrid AI for ambulatory health monitoring Dynamic dashboarding architectures

Samira Si-Said CherfiView profile

Professor

• Data Quality
• Conceptual Modeling
• Ontology Engineering
• +5 more

Samira Si-Said Cherfi is a Professor at the Centre d'études et de recherche en informatique et communications (CEDRIC) within the Conservatoire National des Arts et Métiers (CNAM) in Paris. With a research career spanning over 25 years, she has established herself as a leading expert in data quality, conceptual modeling, and ontology engineering. Her work bridges theoretical computer science with practical applications in healthcare systems, business process management, and knowledge representation. Her research interests focus on data quality assessment , conceptual modeling methodologies , ontology engineering for complex systems , and cyber-physical security in healthcare infrastructures . She has pioneered approaches for evaluating RDF data completeness, developing quality metrics for conceptual schemas, and creating ontologies for healthcare security. Her work demonstrates how formal modeling techniques can solve real-world problems in information systems. Analysis of her recent publications reveals a strong trend toward cyber-physical security and healthcare information systems , where she applies semantic technologies to address cascading effects in critical infrastructures. Her work consistently connects theoretical foundations in conceptual modeling with practical applications in knowledge graphs and data integration. She has made significant contributions to understanding how OWL semantics can be effectively utilized in RDF-based knowledge graphs. As an active member of the academic community, she has served as guest editor for special journal issues and contributed to major international conferences including RCIS, CAiSE, and EDOC. Her leadership in the field is evident through her editorial roles and collaborative research projects. Professor Si-Said Cherfi leads research within the CEDRIC laboratory, specifically contributing to the 'Complex data, machine learning and representations' and 'Data mining and statistics' research teams. Her work often involves interdisciplinary collaboration with healthcare professionals, security experts, and industry partners to address complex challenges in information systems security and data quality.

Simon WattView profile

Senior Lecturer

• Sensorimotor Control
• Multisensory Integration
• 3D Vision
• +3 more

Simon Watt is a Senior Lecturer in the Department of Psychology at Bangor University's School of Psychology and Sport Science. His research focuses on sensorimotor control, multisensory integration, and 3D vision, with applications to prosthetic limb design, virtual reality systems, and surgical robotics. PhD in Psychology from University of Surrey Postdoctoral research at UC Berkeley Joined Bangor University faculty in 2004 Director of the Cognitive Neuroscience Institute His work explores how humans control hands and tools in 3D space, examining whether tools become incorporated into body schema and how sensorimotor mechanisms adapt to different tool geometries. Key research areas include: Tool use and body schema modification Visual-tactile integration Reaching and grasping mechanics Stereoscopic display optimization Recent studies investigate the functional requirements for tool control resembling body parts, revealing both successful integration and surprising failures depending on tool properties. This aligns with his broader approach in 'Pasteur's Quadrant,' combining fundamental science with practical applications. Affiliation: Bangor University, School of Psychology and Sport Science, Department of Psychology

Recep Firat CekinelView profile

Researcher

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +7 more

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

Niranjan BalasubramanianView profile

Assistant Professor

• Natural Language Processing
• Information Retrieval
• Question Answering
• +3 more

Niranjan Balasubramanian is an Assistant Professor in the Department of Computer Science at Stony Brook University with additional affiliations in the Department of Biomedical Informatics and the Center of Excellence in Wireless & Information Technology (CEWIT). His research focuses on Natural Language Processing and Information Retrieval systems that extract, understand, and reason over textual information. Education: PhD, University of Massachusetts Amherst (Center for Intelligent Information Retrieval) MS, Computer Science, University at Buffalo (2003) His research spans question answering for elementary education, event schema generation from news, machine learning for information retrieval, energy-efficient mobile search, and automatic Wikipedia content generation. Recent work explores causal reasoning in event extraction, multimodal claim verification, authorship fairness, and secure coding with large language models. Analysis of his 15 most recent publications (2024-2025) reveals intensive focus on advancing NLP through causal/temporal reasoning, multimodal verification, and LLM optimization. Key trends include psychological modeling of human language, energy-efficient architectures, and addressing misattribution in authorship analysis. Scientific Awards: No awards mentioned in provided text Advising and grants information was not provided in the source materials. His postdoctoral background at the University of Washington's Turing Center and industry experience at Syracuse University's Center for Natural Language Processing inform his applied research approach. Labs and Teams: Affiliated with Stony Brook's Center of Excellence in Wireless & Information Technology (CEWIT), contributing to interdisciplinary AI initiatives while maintaining primary focus in the Computer Science department.

H. Andrew SchwartzView profile

Assistant Professor

• Large and scalable language analyses
• Psychological and health discovery
• Computational social science
• +6 more

H. Andrew Schwartz is an Assistant Professor at Stony Brook University's Department of Computer Science. His research bridges computational social science, natural language processing, and psychological discovery, focusing on social media language analysis for health and demographic insights. Education : PhD in Computer Science (2011) from the University of Central Florida Past Affiliations : Postdoctoral Research Fellow and Visiting Assistant Professor at University of Pennsylvania's Computer & Information Science department Current Role : Lead Research Scientist for the World Well-Being Project, a multidisciplinary collaboration between computer scientists and psychologists Dr. Schwartz's research utilizes machine learning to analyze large-scale social media data for mental and physical health prediction, automatic lexicon refinement, and measuring human temporal orientation. His work has been recognized by Almetric's top 25 most discussed research papers in 2013. Recent publications examine psychological state detection through NLP, including PTSD analysis, mental health screening algorithms, and LLM adaptation for personality modeling. He develops methodologies like adaptive question-asking systems and self-supervised learning frameworks for behavioral analysis. Key scientific contributions include: Developing the WWBP-SQT-lite system for identifying mental health change moments Creating AlBA (Adaptive Language-Based Assessments) for mental health evaluation Advancing temporal analysis of social media language for health informatics

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Benjamin DelawareView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Program Verification
• +5 more

Benjamin Delaware is an Assistant Professor in the Department of Computer Science at Purdue University since 2016. He earned his Ph.D. in Computer Science from the University of Texas at Austin in 2013 under William Cook and Don Batory, following an M.Sc. in Computer Science from Washington University in St. Louis (2007) and a dual B.S. in Computer Science and B.A. in Russian from Truman State University (2005). Research focuses on program synthesis , formal verification , and relational program properties . Key contributions include KestRel (relational verification), PALM (LLM-assisted proof automation), and Clotho (distributed system testing). His publications span top venues like OOPSLA, PLDI, and POPL, with recent work (2025) on coverage-type-guided synthesis and LLM-integrated proof repair . He has received multiple scientific awards , including a SIGPLAN Distinguished Paper Award (2023) and CRII Award from NSF (2018). Ben advises a research group producing graduates such as Qianchuan Ye (now at SUNY Buffalo) and Kia Rahmani (UT Austin postdoc). He teaches graduate courses on program reasoning (CS560) and programming language design (CS456/CS565), with earlier teaching experience at UT Austin. Active in academic service , he served as Program Committee Co-Chair for RocqPL 2026 and CoqPL 2025 , and as Diversity, Equity, and Inclusion Chair at POPL 2023. His grants include NSF funding for input generator verification (2023-2026) and Cisco research on privacy-preserving computation (2022-2023).

• Building Information Modeling
• Digital Twinning
• Information Management
• +5 more

Prof. Dr.-Ing. Sebastian Esser serves as Group Lead for Information Management at the Chair of Computing in Civil and Building Engineering at Technical University of Munich. His research focuses on advancing Building Information Modeling (BIM) methodologies, particularly in infrastructure and railway applications. He contributes significantly to international standardization efforts including IFC-Road and IFC-Rail projects, and leads research initiatives such as RIMcomb and BauPuls360. Dr. Esser's research spans several critical areas in digital construction: Graph-based version control systems for BIM collaboration Digital twin development for infrastructure management Semantic modeling of built environments BIM-based regulation checking for railway infrastructure Interdisciplinary model coordination techniques Knowledge representation in civil engineering His work bridges theoretical computer science with practical civil engineering applications, focusing on improving data interoperability and workflow efficiency in construction projects. Analysis of his recent publications reveals a strong emphasis on graph-based approaches to BIM challenges. His research has evolved from foundational work on BIM programming interfaces to sophisticated implementations involving knowledge graphs, semantic reasoning, and digital twin architectures. Key trends include increasing integration of semantic web technologies with BIM standards, development of specialized query interfaces like GraphQL for construction data, and application of formal methods to infrastructure modeling problems. Dr. Esser actively supervises numerous bachelor's and master's theses annually, with recent topics covering graph-based entity alignment, BIM-GIS integration for flood assessment, incremental model updates, and digital twin implementations. His teaching portfolio includes courses such as Bau- und Umweltinformatik, BIM.fundamentals, BIM.infra, and Semantic Modeling of the Built World, demonstrating his commitment to educating the next generation of digital construction professionals. He is involved in multiple research initiatives including DFG FOR 5672 (The information backbone of robotized construction), SPP 2187 (Adaptive modularized constructions), and AM2PM (Additive to Predictive Manufacturing). His laboratory work spans the BIM-Lab and related computational infrastructure supporting his research in digital construction technologies.

Adrien DoerigView profile

Visiting Professor

• Computational Neuroscience
• Cognitive Neuroscience
• Machine Learning
• +5 more

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

• Cognitive Science
• Brain-Computer Interfaces
• Robotics and Human-Robot Interaction
• +5 more

Dr. Artur Gunia is a researcher at the Department of Cognitive Science within the Faculty of Philosophy at Jagiellonian University in Kraków, Poland. His work spans the interdisciplinary field of cognitive science with particular focus on human-technology interaction, neurotechnology, and philosophical aspects of cognition. His research interests encompass brain-computer interfaces, cognitive enhancement technologies, virtual and augmented reality systems, robotics, and the ethical implications of emerging technologies. Gunia's work demonstrates a strong interdisciplinary approach, bridging cognitive science, computer science, philosophy, and psychology to explore how technologies can augment human cognitive capabilities while addressing associated ethical concerns. Analysis of his recent publications reveals a consistent focus on the intersection of cognitive science and technology, particularly examining how emerging technologies like brain-computer interfaces, virtual reality systems, and social robots affect human cognition, perception, and behavior. His work spans both theoretical investigations of cognitive frameworks and practical applications of cognitive enhancement technologies across various domains including education, safety systems, and assistive technologies. Gunia has made significant contributions to understanding the phenomenological aspects of body schema in virtual environments, trust dynamics in human-machine interaction, and ethical considerations in robotics. His publications demonstrate a consistent trajectory of research exploring how humans adapt to and integrate with technological systems, with particular attention to cognitive, affective, and ethical dimensions.

Lisa M. DinellaView profile

Professor

• Gender Development
• Child Psychology
• Social Psychology
• +5 more

Lisa M. Dinella is Professor of Psychology at Monmouth University, serving as Principal Investigator of the Gender Development Laboratory and Director of the Program in Gender and Intersectionality Studies (PGIS). Her work bridges academic research with real-world impact through White House engagements and corporate consulting on gender development. Her educational background includes: Ph.D. from Arizona State University M.S. from Arizona State University B.A. from The College of New Jersey Dr. Dinella's research centers on how toys, media, and play experiences shape gender identity from early childhood through adulthood. She investigates the cognitive mechanisms behind gender-typed toy preferences, the impact of color-coding (particularly pink) on play choices, and how childhood experiences influence career trajectories. Her work reveals how seemingly innocuous elements like toy propulsion mechanics or costume choices serve as critical vectors for gender socialization. Analysis of her 2014-2018 publications shows consistent focus on gender development through play, with emerging emphasis on career pathways and family models. Key methodological approaches include experimental toy preference studies, longitudinal career interest tracking, and interdisciplinary analysis of media influences. Scientific Awards: No formal awards listed in source materials As laboratory director, she leads research on gender development through play experiences while consulting for corporations on research design and analytics. Her work has generated significant media engagement including BBC World Service features and White House invitations, though specific grant details remain unspecified. She actively collaborates with educators to implement science-based practices in schools. The Gender Development Laboratory serves as her primary research hub, examining how early play experiences impact lifelong development through empirical studies of toy preferences, media exposure, and gender identity formation. Her team's findings directly inform corporate toy design and educational policy discussions.

Jennifer KarnoppView profile

Assistant Professor

• Educational Leadership
• School Improvement
• Educational Equity
• +4 more

Dr. Jennifer Karnopp is an Assistant Professor in the Department of Educational Leadership at San Diego State University. Her research focuses on transforming educational systems to better engage marginalized learners through examining the interplay between organizational structures and social relationships. She investigates leadership practices that foster equity and inclusion, reform implementation dynamics, and contextual influences on school improvement. Dr. Karnopp's primary research interests include: Educational leadership for equity and inclusion Organizational change and school improvement Social network analysis in educational contexts Rural education and knowledge mobilization Systemic reform implementation Teacher belonging and support systems Her methodological approach combines social network analysis with qualitative and mixed-methods designs. Her publication record demonstrates consistent focus on educational change mechanisms. Recent works (2022-2025) cluster around three themes: teacher belonging/support, equity-focused historical/systemic analysis, and networked improvement communities. Earlier research established foundations in organizational learning, rural agency, and leadership development. Dr. Karnopp has co-authored two books on systemic educational transformation and maintains research collaborations with scholars across multiple institutions. While no awards are mentioned in the source material, her publication in top-tier journals demonstrates scholarly recognition. No specific information about student advising, research grants, or laboratory affiliations is present in the source materials.

Alexander BucheltView profile

Researcher

• Artificial Intelligence
• Data Science
• Natural Language Processing
• +3 more

Alexander Buchelt serves as a Researcher in the Department of Computer Science and Security at University of Applied Sciences St. Pölten, contributing to the Data Science and Artificial Intelligence (BA) program. His industry background includes data analytics roles at Hutchison Drei Austria (2014-2018) and MindTake Research GmbH (2019-2020), followed by academic experience as a Student Assistant at FH St. Pölten (2020-2022). His research spans artificial intelligence with emphasis on natural language processing for German content moderation, fractal-based neural network architectures, and AI policy frameworks. Current work demonstrates strong focus on real-world implementation including drone applications in forest ecology, multilingual social media analysis, and regulatory compliance for emerging AI systems. Technical approaches integrate machine learning with domain-specific challenges across environmental science, media analysis, and telecommunications. Publication trends since 2022 reveal increasing emphasis on ethical AI deployment and practical system robustness, with 2024-2025 works addressing content moderation safeguards, neural network innovation, and environmental monitoring solutions. His research consistently bridges theoretical AI development with tangible societal applications, particularly in European regulatory contexts and German-language environments. As part of the Department of Computer Science and Security, Buchelt contributes to Austria's applied research ecosystem focused on practical technology solutions. His work aligns with the university's mission in developing industry-relevant AI applications while addressing emerging challenges in security, ethics, and cross-domain implementation.