جست‌وجوی استاد و پژوهشگر

Daniel S. Hainمشاهده پروفایل

دانشیار

• Data Science
• Network Analysis
• Machine Learning
• +۷ مورد دیگر

Professor Daniel S. Hain is an Associate Professor at Aalborg University Business School, part of Aalborg University's Faculty of Social Sciences and Humanities. He is affiliated with the IKE Research Group (AI for the People) and the MASSHINE Cluster for Research on Innovation and Development. His research focuses on innovation dynamics, artificial intelligence, network analysis, and technology forecasting. Hain has been actively involved in strategic initiatives such as the Danish Industry Foundation's AI Denmark program and the Generative Ethnographic AI (GE-AI) seed project, demonstrating his commitment to advancing AI applications in societal contexts. His work bridges academic research with practical industry challenges, particularly in areas like patent analytics (e.g., Patentsberta model), renewable energy innovation, and global venture capital patterns in developing economies. Hain has received the OECD IPSDM “Big Data Analytics” Challenge award (2018) for collaborative research on inventive activity visualization. He contributes to policy discussions on AI governance, the privatization of research talent, and regional green technology development. Key projects include the Danish Research Unit for Industrial Dynamics (DRUID) since 1995, exploring innovation systems, and the EIS project on energy innovation competitiveness. Hain’s recent focus on generative AI ethics and SME predictive analytics reflects his dual expertise in technical methodologies and socio-economic impacts.

Christos Thrampoulidisمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Theory
• Mathematical Optimization
• +۴ مورد دیگر

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Vancouver. Previously, he held positions at the University of California, Santa Barbara (UCSB). His research focuses on machine learning theory, deep learning principles, mathematical optimization, and statistical signal processing. He leads the Machine Intelligence and Learning Design (MILD) group at UBC, emphasizing foundational studies of large language models, neural collapse, and high-dimensional data analysis. Education: PhD and MS in Electrical Engineering from the California Institute of Technology (Caltech), with a minor in Applied and Computational Mathematics. Undergraduate degree (Diploma) in Electrical and Computer Engineering from the University of Patras, Greece. Research interests include the implicit geometry of next-token prediction, optimization bias in neural networks, and robust generalization in overparameterized models. Key contributions include work on adversarial training, neural collapse phenomena, and safe reinforcement learning. His group actively explores theoretical frameworks for language models and develops methodologies to analyze their mechanics and limitations. Awards: NSERC Discovery Grant (2021). Teaching includes courses on optimization and signals & systems at UBC. He has advised numerous graduate students (PhD and MSc) and undergraduates, focusing on rigorous mathematical approaches to machine learning problems. Active collaborations span academia and industry, with publications in top venues such as NeurIPS, ICASSP, and AAAI. Current projects include understanding language model geometry, decentralized learning, and mitigating imbalances in training data. The group prioritizes equity and diversity, welcoming students with strong mathematical backgrounds and curiosity about foundational ML questions.

Gudrun Klinkerمشاهده پروفایل

استاد

• Industrial Augmented Reality
• Sensing
• Ubiquitous Tracking (Sensor Fusion)
• +۴ مورد دیگر

Overview Prof. Gudrun Klinker is a Professor at Technische Universität München (TUM), leading the Chair of Computer Science Applications in Medicine & Augmented Reality. She holds a PhD from Carnegie-Mellon University and has extensive experience in academia and industry, including roles at Digital Equipment Corporation and Fraunhofer Institute. Affiliations Professor (C3/W2) since 2000 Head of the FAR (Augmented Reality) research group Member of the CAMP (Computer Aided Medical Procedures) interdisciplinary team Research Dr. Klinker specializes in augmented reality (AR) with focuses on industrial applications, sensor fusion, 3D interaction, and healthcare integration. Her work bridges computer science with medical and industrial challenges, emphasizing user-centric design and real-world deployment. Key projects include: AR-based maintenance systems for complex machinery Healthcare applications like serious games for nutrition education VR/AR training environments for medical and aerospace domains Awards and Recognition Robert Sauer Prize (2010) for contributions to Bavarian science ISMAR Lasting Impact Award (2014) for influential AR research Teaching and Mentorship Prof. Klinker teaches courses on 3D user interfaces, AR fundamentals, and game design. She advises students in thesis projects, focusing on AR applications, HCI, and medical computing. Labs and Collaborations Her research is supported by partnerships with industry and institutions, leveraging TUM's interdisciplinary environment to advance AR/VR systems for real-world impact.

Claudia Schonمشاهده پروفایل

پژوهشگر

• Automated Reasoning
• Artificial Intelligence
• Knowledge Representation
• +۳ مورد دیگر

Claudia Schon is a Researcher at the Institute of Computer Science, Department 4, University of Koblenz-Landau. Her work focuses on automated reasoning, cognitive systems, and knowledge representation. She has contributed to projects like the Corg Project, exploring cognitive reasoning mechanisms and integrating commonsense knowledge into automated theorem provers. Her research bridges logic-based AI with human reasoning models, addressing challenges in uncertainty management, associative reasoning, and deontic logic. Key areas of research include commonsense reasoning, deductive systems, and the application of large language models to knowledge selection. She has published extensively on topics such as negation in cognitive reasoning, concept contraction in description logics, and intentional forgetting in AI systems. Her work often emphasizes interdisciplinary approaches, combining formal logic with cognitive science and philosophy. Her publications from 2019 to 2024 reflect a steady focus on advancing automated reasoning methodologies, integrating human-like cognitive processes into AI systems. Recent work explores the next generation of deduction systems and the use of word embeddings for axiom selection. She has also contributed to workshops on linguistic and cognitive approaches to dialog agents, highlighting her interest in human-AI collaboration. Despite no listed scientific awards, her contributions are recognized through active publication in top-tier conferences and journals. She is affiliated with the E-KRHyper project, developing reasoning tools for description logics. Her advising roles and grants are not explicitly documented in the provided text.

• Riemannian Geometry
• Symplectic Geometry
• Geometric Group Theory
• +۳ مورد دیگر

Mikhael Gromov is the Jay Gould Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a D.Sc. and Ph.D. from the University of Leningrad (now Saint Petersburg State University), specializing in Mathematics. University: New York University School: Courant Institute of Mathematical Sciences Department: Mathematics His research interests span Riemannian Geometry, Symplectic Geometry, Geometric Group Theory, and interdisciplinary studies on learning processes. Recent work includes applying geometric principles to biology and probability theory. Recent publications explore isoperimetric inequalities, entropy in geometric structures, and topological probability frameworks. He has also contributed to understanding protein conformation spaces and mathematical linguistics. Education: D.Sc. Mathematics, University of Leningrad, 1973 Ph.D. Mathematics, University of Leningrad, 1969 Gromov teaches advanced courses on topology, probability, and geometry, including a 2022-23 course on 'Probability and Topology' and 2024 lectures on geometric theorems. His work often bridges pure mathematics with applications in biology and computer science.

Richard Zowallaمشاهده پروفایل

پژوهشگر

• Health Informatics
• Web Data Mining
• Text Mining
• +۴ مورد دیگر

Dr. sc. hum. Richard Zowalla is a researcher at the Faculty of Computer Science, Heilbronn University, specializing in health informatics and software engineering. He works at the Interdisciplinary Center for Machine Learning (ZML) and focuses on health web analysis, text mining, and open source software. Education: Doctorate in human sciences (Dr. sc. hum.), dissertation on German health web data analysis (2022) Research interests include health information systems, focused web crawling, text mining, and software quality management in agile environments. His work bridges healthcare and computer science through data-driven analysis of online medical resources. Publication trends show expertise in health web readability, multilingual health data comparison, and AI-driven service innovation. His articles emphasize practical applications of machine learning and data visualization in healthcare contexts. Teaching: Software Lab (2016-2025), Advanced Programming Techniques (2020-2024), Database Internship (2014-2025), Distributed Systems (2016-2017) Labs & Teams: Active member of the Interdisciplinary Center for Machine Learning (ZML) at Heilbronn University, contributing to collaborative research in health data analysis.

Saana Svärdمشاهده پروفایل

استاد

• Assyriology
• Ancient Near Eastern Studies
• Gender Studies
• +۵ مورد دیگر

Saana Svärd is a Professor at the University of Helsinki in the Faculty of Arts, Department of Languages. She specializes in ancient Near Eastern studies with a focus on gender analysis, digital humanities, and emotional history in Mesopotamian texts. Academic Affiliation: University of Helsinki (Faculty of Arts, Department of Languages) Research Themes: Neo-Assyrian Empire, gender studies, digital approaches to cuneiform texts Her recent research explores emotional semantics through computational methods and bodily mapping in ancient texts. She directs the Centre of Excellence in Ancient Near Eastern Empires (2018-2025) and leads the 'Embodied Emotions' project (2022-2026). Notable awards include the 2021 Helsinki Science Award and 2023 honorary mention for co-editing Muinaisen Lähi-idän imperiumit . Key projects include: Digital Humanities approaches to Akkadian texts Quantitative analysis of Neo-Assyrian religious affiliation Gender roles in ancient Mesopotamian societies

Tommi Jauhiainenمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Language Technology
• Egyptology
• +۳ مورد دیگر

Tommi Jauhiainen is a Researcher at the University of Helsinki , affiliated with the Department of Digital Humanities and the Centre of Excellence in Ancient Near Eastern Empires (ANEE) . He specializes in Language Technology and Digital Humanities , focusing on computational methods for analyzing historical texts. His research interests include Egyptology , Automatic Text Dating , Cuneiform Script , and Computational Linguistics . He has contributed to projects like Automatic Classification and Analysis of Texts from Egyptian Antiquity (2024–2029) and FIN-CLARIAH (2024–2025). Recent articles highlight his work on machine-readable hieroglyphic texts , automated chronotopical analysis , and language resource development for underrepresented languages. He actively participates in peer review and conferences like the International Congress of Egyptologists .

Piotr Gmytrasiewiczمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Multiagent Systems
• Decision Theory
• +۷ مورد دیگر

Piotr Gmytrasiewicz is an Associate Professor at the Department of Computer Science, University of Illinois at Chicago (UIC). He leads the Multiagent Systems Group within the Artificial Intelligence Laboratory at UIC. His research focuses on rationality in artificial agents , particularly in environments with multiple interacting agents. Key areas include Interactive Decision-Making Bayesian Modeling for Agent Communication Recursive Belief Frameworks Time Pressure and Computational Trade-offs Evolution of Agent Communication Languages Dynamic Resource Allocation His recent work examines the rationality of insincere communication and methods to discount potentially insincere information. Past projects include modeling emotions in agent design and developing decision-theoretic approaches to game theory. He has secured funding from prestigious institutions such as the National Science Foundation (NSF) , Office of Naval Research (ONR) , and DARPA . Current and past projects emphasize Automated Linguistic Competence Evolution Emergent Communication Protocols Scalable Multiagent Learning Strategic Coordination under Uncertainty He earned his Ph.D. from the University of Michigan, Ann Arbor (1992) and has previously collaborated with the Department of Computer Science and Engineering (CSE) at the University of Texas at Arlington on DARPA-funded research.

Haim Dubossarskyمشاهده پروفایل

مدرس

• Natural Language Processing
• Artificial Intelligence
• Historical Linguistics
• +۳ مورد دیگر

Haim Dubossarsky is a Lecturer in Computer Science at the School of Electronic Engineering and Computer Science , Queen Mary University of London , and an Affiliated Lecturer at the Language Technology Lab , University of Cambridge. His work bridges Natural Language Processing (NLP) , Artificial Intelligence (AI) , and interdisciplinary studies in linguistics , cognitive neuroscience , and mathematical modeling . Dr. Dubossarsky's research explores semantic change and language variation through computational methods, focusing on: Detecting historical and contemporary linguistic shifts Integrating neurocognitive insights into NLP systems Developing interpretable AI models for language tasks Recent publications highlight his contributions to multilingual NLP , word-in-context (WiC) tasks , and robust semantic analysis . His work includes collaborations on hate speech detection , topological representations , and temporal modeling in language evolution. Dr. Dubossarsky leads projects funded by: UKRI AI Hub (£1,026,205, 2024–2029) RJ Riksbankens Jubileumsfond (£381,511, 2022–2027) Innovate UK (£320,887, 2024–2025) He supervises PhD students at Queen Mary's Centre for Human-Centred Computing , including Alaa Bazaid , Roksana Goworek , and Dina Pisarevskaya , working on topics like multilingual polysemy disambiguation and language variation analysis .

Nick Droutsasمشاهده پروفایل

مدرس

• Game Design
• Web Accessibility
• Human-Computer Interaction
• +۳ مورد دیگر

Nick Droutsas is a PhD Student and Lecturer in the Department of Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences. He teaches Interactive Learning Skills and Communication (NV0600/NV1600) and ICT Skills (NG0601) at Brunel University London Pathway College while contributing to undergraduate/postgraduate Computer Science modules including Human Computer Interaction and Digital Design Methodologies. His educational background includes: MA in Game Design (120 credits) from Uppsala University with Master’s Thesis: Gamers with the Purpose of Language Resource Acquisition: Personas and Scenarios for the players of Language Resourcing Games-With-A-Purpose (Pass with distinction) BSc (Hons) in Informatics and Computer Engineering (240 credits) from University of West Attica with Bachelor’s Dissertation: Alternative Approaches and Tools in Automatic Morphological Processing for Morphologically Rich Languages (10.00/10.00) His research centers on Purposeful Game Solutions for constructing linguistic resources to enhance web accessibility, spanning Game Design, HCI, NLP, and Digital Media Accessibility. Current projects include Simul8 (accessibility simulation tool), Healthy Ageing Practices Serious Games, and Physiological Evaluation in Virtual Environments/360 Video. As a Research Assistant, he contributes to interdisciplinary projects bridging game design, accessibility engineering, and physiological response analysis in immersive media applications.

Torsten Zeschمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Educational Technology
• +۶ مورد دیگر

Prof. Dr.-Ing. Torsten Zesch serves as Deputy Scientific Director, Member of the Executive Board, and Head of the Research Professorship for Computational Linguistics at FernUniversität in Hagen since March 2022. He previously held W2 and W1 Professorships for Sprachtechnologie (Language Technology) at Universität Duisburg-Essen from 2014-2022. Zesch is also Spokesperson of the Advisory Board of the German Society for Computational Linguistics and Language Technology (GSCL) since 2024, having previously served as GSCL President from 2018-2024. Dr. Zesch completed his dissertation (Dr.-Ing.) in Computer Science at Technische Universität Darmstadt in 2009. His research spans robust language processing systems, analysis of non-standard language structures, and educational applications of language technology, with particular focus on automatic content scoring and spelling error correction for learner language. Zesch's recent publications reveal a strong focus on integrating language technology with educational applications. His work demonstrates expertise in developing practical NLP solutions for educational assessment, including transformer-based spelling error feedback systems, hierarchical automatic scoring methods, and leveraging LLMs for educational applications despite cold-start problems. He also maintains significant research in hate speech detection, particularly in multimodal contexts and visio-linguistic models. As leader of the Computational Linguistics research professorship within the CATALPA research center, Zesch directs a team investigating how language technology can support educational processes. His projects DAKODA and KISS-Pro focus on language acquisition and educational applications. He has contributed significantly to the field through numerous publications in top NLP and educational technology venues, with over 100 publications spanning from 2006 to present.

Rada Mihalceaمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Computational Linguistics
• +۵ مورد دیگر

Rada Mihalcea is a Professor at the University of Michigan in the Computer Science and Engineering department. She actively promotes Natural Language Processing (NLP) applications for social good and founded the ACL Year Round Mentorship program. University: University of Michigan School: College of Engineering Department: Computer Science and Engineering Academic Rank: Professor Research Interests include: Natural Language Processing for social good applications Text Mining and Sentiment Analysis Deception Detection and Multimodal Analysis Cross-Cultural Computational Linguistics Educational Technology and Gender Studies Recent research trends focus on: Fake news detection using multimodal features Lexicon-based cross-cultural value analysis Thermal imaging for affective computing Deception detection through linguistic and physiological cues Applications in healthcare and counseling analysis Scientific Awards : Sarah Goddard Power Award (2019) Best Paper Award at CICLING (2019) Best Paper Award at SocInfo (2018) Additional Contributions : Co-authored textbooks on Text Mining and Graph-based NLP Developed FunPython tutorial for children Collaborative work with the School of Music on the Ada Lovelace Opera Active in diversity initiatives in computing

Robert Klemmensenمشاهده پروفایل

استاد

• Political Behavior
• Political Psychology
• Behavioral Genetics
• +۴ مورد دیگر

Robert Klemmensen is Professor and Assistant Head of Department at Lund University's Department of Political Science, where he coordinates research activities. He is a member of the LU Profile Area: Natural and Artificial Cognition, an interdisciplinary initiative exploring cognition from biological and computational perspectives. His work examines how genetic and psychological factors shape political behavior using advanced quantitative methods. Klemmensen's research focuses on: Voter opinion formation and behavioral alignment with stated preferences Genetic influences on political attitudes through twin studies Quantitative text analysis of political rhetoric Machine learning applications in political ideology detection Normative implications of empirical findings in democratic systems His publications demonstrate consistent focus on genetic-behavioral interactions in politics, with recent work incorporating computational methods like deep learning for facial analysis and natural language processing. Article clusters examine: (1) heritability of political traits, (2) methodological innovations in text analysis, and (3) cross-disciplinary connections between political science and genetics. He currently co-edits Political Psychology and Research and Politics , and serves on the Independent Research Fund Denmark. Klemmensen leads the research project 'Divided Parliaments? Polarization, Moralization, and the Risk of Gridlock' (2024-2027), funded by the Bank of Sweden Tercentenary Foundation, examining governance challenges in polarized environments. Within Lund University's Natural and Artificial Cognition profile area, he collaborates with computer scientists and cognitive researchers to develop interdisciplinary approaches to political behavior analysis.

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.