جست‌وجوی استاد و پژوهشگر

• Developmental Psychology
• Cognitive Neuroscience
• Memory Processes
• +۳ مورد دیگر

Myriam C. Sander is a faculty member at the International Max Planck Research School on the Life Course (LIFE) and the Berlin School of Mind and Brain, affiliated with the Max Planck Institute for Human Development. Her research focuses on lifespan changes in memory processes, neural oscillations, and cognitive aging. PhD in Natural Sciences (2011), Humboldt University Berlin MSc in Psychology (2007), Humboldt University Berlin Her research interests include: Development of perception and memory across the lifespan Working and episodic memory mechanisms Neuronal correlates of aging (oscillations and activity patterns) Multivariate MRI/EEG methods Her recent publications analyze age-related differences in: Theta-gamma coupling precision Neural pattern similarity Alpha/beta desynchronization Memory consolidation during sleep Cortical and hippocampal structural integrity She advises doctoral students including Charlotte Pauley and Anna E. Karlsson.

Henrik Høeg Müllerمشاهده پروفایل

دانشیار

• Linguistics
• Typological Linguistics
• Syntax
• +۱۱ مورد دیگر

Henrik Høeg Müller is an Associate Professor and Head of Department in Spanish Business Communication at the School of Communication and Culture, Aarhus University, Denmark. He is actively engaged in linguistic research focusing on typological differences between Danish and Spanish, particularly in syntax, morphology, and lexical-schematic distinctions. Institution: Aarhus University School: School of Communication and Culture Department: Spanish Business Communication Position: Associate Professor, Head of Department Email: henhm@cc.au.dk His research centers on the structural and typological contrasts between Danish and Spanish, especially in nominal expressions, predicate formation, and lexical vs. syntactic encoding. Key interests include bare nouns, count/mass distinctions, telicity, motion verbs, and schematicity. His work often employs comparative and cognitive linguistic frameworks. The most recent publications reveal a strong trend in analyzing how lexical and syntactic strategies diverge across languages, especially in expressing similarity, kind, and event structure. Topics such as the Danish 'slags', complex predicates, and ambiguous nominal structures highlight his focus on fine-grained syntactic and semantic analysis. These works contribute to broader discussions in typology and cognitive linguistics. Henrik Høeg Müller has served as an editor for Ny Forskning i Grammatik and frequently presents at academic events, including workshops and conferences. He has no listed scientific awards in the provided text. Editorial Role: Editor, Ny Forskning i Grammatik Presentations: Regular lecturer at seminars and conferences on Danish and Spanish linguistics He is involved in academic networks related to Scandinavian and Romance linguistics, contributing to collaborative research and scholarly discourse. No labs or formal research teams are explicitly mentioned.

Claudia Wegenerمشاهده پروفایل

پژوهشگر

• Language Documentation
• Speech-Gesture Interaction
• Morphosyntax
• +۶ مورد دیگر

Dr. Claudia Wegener is a Researcher at the Department of Linguistics, University of Cologne, specializing in General Linguistics. Her work focuses on language documentation, speech-gesture interaction, and Papuan/Austronesian languages like Savosavo and Gela. She holds a PhD from Radboud University Nijmegen (2008) and has conducted extensive fieldwork in the Solomon Islands and Malaysia. Previous Positions: Research Assistant, University of Cologne (2017–present) Postdoctoral Researcher at MPI for Psycholinguistics, Radboud University (2002–2011) DoBeS Project Leader for Savosavo documentation Research Interests Her research spans: Socio-linguistic aspects of gesture systems Morphosyntactic typology of Papuan languages Lexical semantics and kinship terminology Documentation methodologies for endangered languages Publications Her work includes a landmark grammar of Savosavo (2012), co-edited volumes on language documentation (2011), and influential articles on gesture studies (2022) and motion event encoding (2022). Projects Leading the Savosavo-Gela Documentation Project (DoBeS-funded) Contributions to MPI's Endangered Languages Documentation Archive Collaborations with Australian National University and University of Manchester Her research bridges linguistic theory with applied documentation, emphasizing community-driven preservation strategies.

Seba Contrerasمشاهده پروفایل

پژوهشگر

• Complex Systems
• Dynamical Systems
• Infectious Diseases
• +۱ مورد دیگر

Seba Contreras is a Postdoctoral Researcher in the Physics of Disease Spread at the Max Planck Institute for Dynamics and Self-Organization . His work bridges mathematical modelling , dynamical systems , and infectious disease epidemiology to uncover principles in outbreak controllability and disease dynamics. Dr. rer. nat. (Physics of Biological and Complex Systems, 2023) - Georg-August-Universität Göttingen MSc (Extractive Metallurgy, 2019) - Universidad de Chile BEng & Dipl.-Eng. (Civil Engineering, 2017) - Universidad de Chile His research integrates complex systems and infectious diseases with a focus on: Non-pharmaceutical interventions during pandemics Competition and co-infection dynamics between diseases Information-disease feedback loops Statistical methods for epidemiological data correction Parameter inference from limited medical datasets Machine learning applications in protein engineering Recent publications demonstrate expertise in machine learning for biological datasets, epidemic models with heterogeneous populations, and data-driven public health policy . Collaborations span human genetics , theoretical ecology , protein engineering , and social sciences across institutions in Chile and Germany.

Signy Sheldonمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Behavioural Neuroscience
• Episodic Memory
• +۵ مورد دیگر

Signy Sheldon is an Associate Professor in the Department of Psychology at McGill University, specializing in cognitive and behavioral neuroscience. Her research focuses on understanding the neural mechanisms of episodic memory, including how the hippocampus and related structures support memory retrieval, imagination, and problem-solving. She also explores how aging and individual differences influence memory processes. Sheldon uses behavioral experiments, neuroimaging (fMRI), and patient studies to investigate memory's role in non-mnemonic functions like creativity and decision-making. Her work highlights the interplay between memory and broader cognitive abilities, such as schema formation and context processing. She leads the Sheldon Memory Lab , which employs multidisciplinary approaches to study memory's neural underpinnings. Though no specific awards are listed, her extensive publications reflect her contributions to memory science. No advising or grant details are provided in the text. Research interests span episodic memory, cognitive aging, neuroimaging, and the application of NLP to memory narratives. Her lab's work often addresses translational questions, such as using memory reconstruction to address clinical issues like social anxiety.

Justus-Jonas Erkerمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Representation Learning
• Dialogue Systems
• +۳ مورد دیگر

Justus-Jonas Erker is a PhD Candidate in Natural Language Processing at the UKP Lab, Technische Universität Darmstadt , supervised by Dr. Nils Reimers and Prof. Dr. Iryna Gurevych. His research focuses on distributed representation learning , particularly in dialogue systems and retrieval-augmented generation. Key areas include sentence-transformers, emergent properties via cosine similarity, and efficient NLP techniques. Research Interests: Representation Learning Dialogue Modeling Retrieval Augmentation Efficient NLP Algorithms Recent Work: His 2024 ACL paper introduced Triple-Encoders for efficient dialogue representation, while his 2023 work on Curved Contrastive Learning demonstrated novel approaches to dialogue planning. In 2022, he contributed a foundational cancel culture corpus for sociolinguistic NLP analysis. Affiliations: Active contributor to open-source projects (GitHub: 15 repos), including libraries for sentence-transformers and dialogue modeling. Engaged in academic outreach via Twitter/X and LinkedIn.

Sebastian Michelmannمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Memory Processes
• Neurophysiology
• +۷ مورد دیگر

Sebastian Michelmann is an Assistant Professor of Psychology at New York University's College of Arts and Science. His research focuses on understanding how temporal dynamic content is neurally represented and processed in episodic memory, with particular emphasis on fast-timescale memory retrieval mechanisms and their interaction with event knowledge. He employs electrophysiological methods (EEG/MEG), computational modeling, and behavioral experiments to study memory formation under naturalistic conditions. Education: PhD in Psychology from the University of Birmingham (2018), thesis awarded the Glushko Dissertation Prize (2020) Master's (Diploma) in Psychology from Julius-Maximilians-Universitaet Wuerzburg (2014) Postdoctoral research at Princeton University (2018–2023) under Professors Ken Norman and Uri Hasson Research interests include neural oscillations, event segmentation, and the role of generalized event knowledge in memory retrieval. His work bridges cognitive neuroscience with computational approaches, exploring how the brain encodes, stores, and retrieves temporally precise information. Key research directions involve studying how memory processes are embedded in continuous experience and developing tools to analyze neural data (e.g., intracranial EEG re-referencing). Recent studies explore event boundary detection using large language models and investigate the neural correlates of natural language comprehension through ECoG datasets. Scientific Awards: Glushko Dissertation Prize (2020) Laboratory activities focus on future projects combining electrophysiology with computational modeling to understand interactive dynamics of episodic memory. Collaborative efforts include developing semi-automatic alignment tools and cloud-based neuroimaging frameworks.

Renate Delucchi Danhierمشاهده پروفایل

پژوهشگر

• Psycholinguistics
• Comparative Linguistics
• Multilingualism
• +۱۲ مورد دیگر

Renate Delucchi Danhier is a researcher at the Faculty of Cultural Studies, Technical University of Dortmund, where she has held positions as Temporary Academic Councilor (2018–2026) and Research Assistant (2015–2018). She holds a Ph.D. in Comparative Linguistics from Heidelberg University and an M.A. in German as a Foreign Language, Classical Archaeology, and Egyptology. Doctorate: Comparative Linguistics, Heidelberg University (2015) Master of Arts: German as a Foreign Language Philology, Classical Archaeology, Egyptology, Heidelberg University (2008) Her research focuses on psycholinguistics, multilingualism, bilingual education, language and cognition, and the role of language in inclusion. She investigates how linguistic structures affect perception, particularly in spatial and educational contexts. Her work often integrates eye-tracking methodologies and interdisciplinary collaboration, especially with Barbara Mertins. The 15 most recent publications reveal a strong trend in psycholinguistics, with emphasis on comparative analysis of German and Spanish, syntactic features in educational texts, and multilingual cognition. Her recent work extends into data visualization and diversity in cognition, showing a growing interest in humanistic data representation. She has contributed to significant interdisciplinary projects such as 'Psycholinguistic Foundations of Inclusion' and co-edited special issues on diversity in cognition. Renate Delucchi Danhier has not received any explicitly mentioned scientific awards. She has not listed any advisees or students. There is no information about labs or research teams she leads, but her collaborative publications suggest active participation in research groups focused on language, cognition, and education.

Goran Glavašمشاهده پروفایل

استاد

• Computational Semantics
• Multilingual Representation Learning
• Low-Resource NLP
• +۲ مورد دیگر

Goran Glavaš is a Professor at the University of Würzburg's Faculty of Mathematics & Computer Science, holding the Chair for Natural Language Processing (Computer Science XII) and affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and democratizing language technologies through fairness and sustainability. Former Assistant Professor at University of Mannheim (2017-2021) Interim Associate Professor at LMU Munich (2021-2022) Doctorate in 2014 at University of Zagreb under Jan Šnajder Recent research trends emphasize cross-lingual learning, multilingual knowledge integration, and ethical AI frameworks. His group contributes to robust multilingual models, vision-language systems, and sustainable NLP applications in social sciences. Outstanding Paper Award at ACL 2024 (IRCoder) Outstanding Paper Award at EACL 2024 (Kardeş-NLU) Extensive publications in EMNLP, ACL, NAACL, EACL, and TACL Advises a team of researchers at the University of Würzburg's NLP Chair, including Benedikt Ebing, Gregor Geigle, and Fabian David Schmidt. Leads the WüNLP group within CAIDAS, focusing on democratizing language technologies.

• Language production
• Message formulation
• Sentence formulation
• +۱۲ مورد دیگر

Dr. Agnieszka Konopka is a Lecturer in the School of Psychology at the University of Aberdeen, where she conducts research and teaches while actively accepting PhD students for supervision. Her office is located in Room G33 of the William Guild Building on the University of Aberdeen campus. Her academic appointments include: Lecturer, School of Psychology, University of Aberdeen SONA and research participation coordinator Associate Editor, Journal of Experimental Psychology: General Editorial Board Member, Journal of Experimental Psychology: Learning, Memory, and Cognition Editorial Board Member, Applied Psycholinguistics Dr. Konopka's research focuses on the cognitive mechanisms underlying language production and memory for language. She investigates how speakers plan and formulate messages and sentences across different languages, examining incrementality and flexibility in message and sentence formulation. Her work also bridges bilingual sentence processing with reconstructive memory research to understand how language learning affects memory for linguistic information. Her research interests include: Language production and message formulation Sentence formulation and incrementality Cross-linguistic differences in language processing Linguistic diversity and bilingualism Syntax and memory for language Source memory and metacognition Her publication record shows a consistent trajectory of high-impact research in psycholinguistics and cognitive psychology, with recent work expanding into cross-cultural aspects of emotion recognition and social category influences on cognitive processes. She frequently employs eye-tracking and other experimental methodologies to examine the precise time-course of linguistic processing. Her teaching responsibilities include: Memory & Language Methodology B MRes programme instruction Dr. Konopka maintains an active presence in the academic community through her editorial roles and continues to advance research at the intersection of language, cognition, and social processes.

Md Al Masum Bhuiyanمشاهده پروفایل

استادیار

• Applied Statistics
• High Frequency Data Analysis
• Machine Learning
• +۶ مورد دیگر

Dr. Md Al Masum Bhuiyan is an Assistant Professor in the Department of Mathematics and Statistics at Austin Peay State University, part of the College of STEM. He holds a Ph.D. and two Master's degrees in Applied Mathematics from The University of Texas at El Paso (UTEP) and a Bachelor's in Mathematics from the University of Dhaka. Dr. Bhuiyan specializes in Applied Statistics, high-frequency data analysis, machine learning, and stochastic modeling, with expertise in analyzing diverse datasets including financial markets, geophysics, meteorology, and public health. His educational background includes certifications in Big Data Analytics and SAS Base Programming. His research focuses on applying advanced statistical and computational methods to complex systems, such as financial volatility estimation, earthquake-seismic data analysis, and environmental monitoring. He teaches courses like Stochastic Processes, Probability, and Elements of Statistics at both undergraduate and graduate levels. Dr. Bhuiyan's work integrates interdisciplinary approaches, combining mathematical rigor with real-world applications. His publications span high-impact journals like Physica A and AIMS Environmental Science , addressing topics from ozone concentration prediction to volcanic activity modeling. His research trends emphasize predictive analytics, volatility modeling, and algorithmic solutions for geophysical and financial systems. While no formal awards are listed, his extensive publication record reflects significant contributions to computational science and applied mathematics. His teaching and research roles highlight a commitment to advancing data-driven methodologies across multiple disciplines.

Göran Kauermannمشاهده پروفایل

استاد

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +۲ مورد دیگر

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Sergio López-Sancioمشاهده پروفایل

پژوهشگر

• Psycholinguistics
• Natural Language Processing (NLP)
• Data Science
• +۳ مورد دیگر

Sergio López-Sancio is a psycholinguist specializing in human language processing and Natural Language Processing (NLP). He holds a PhD from the University of the Basque Country (UPV/EHU), an MA in Linguistics from the same institution, and a BA in English Philology from the University of Oviedo, where he earned the National Award for Excellence in Academic Performance and an Outstanding Degree Award for his honors thesis on the subjunctive mood in English and Spanish. His research focuses on real-time dependency processing, combining EEG and behavioral methods to study syntactic structures across languages. He has presented at prestigious conferences such as the Linguistic Society of America and AMLaP, addressing topics like dependency locality, island effects, and cross-linguistic variation in Basque, Spanish, and Italian. López-Sancio has contributed to research projects including OVO: Originating Variation from Order (MINECO-funded), The Bilingual Mind (Basque Government), and AThEME (EU-funded), totaling over €5M in funding. His current role at Amazon involves enhancing Alexa's Spanish language understanding through NLP tools. Key Awards: National Award for Excellence in Academic Performance, Outstanding Degree Award, Outstanding Cum Laude Dissertation Research Themes: Dependency Parsing, Locality Effects, Cognitive Modeling, Cross-Linguistic Syntax

Tuomas Virtanenمشاهده پروفایل

استاد

• Audio signal processing
• Machine listening
• Sound source separation
• +۵ مورد دیگر

Tuomas Virtanen is a Professor at Tampere University's Signal Processing Research Centre within the Faculty of Information Technology and Communication Sciences. His primary affiliation is the Computing Sciences department, where he leads the Audio Research Group . His work focuses on computational analysis of audio signals, machine listening, and acoustic scene understanding. Research Interests: Audio signal processing, content analysis of audio, sound source separation, acoustic scene classification, speech processing (including noise-robust ASR and speaker recognition), and machine learning techniques such as deep neural networks and statistical modeling. His group develops methods for sound event detection, localization, and tracking in complex acoustic environments. Key Contributions: Leading the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges, developing open-source datasets like TUT Acoustic Scenes and STARSS, and advancing techniques in audio captioning, privacy-preserving audio processing, and multimodal learning. Notable Projects: Creation of the Audio Research Group , development of the Clotho audio captioning dataset, and pioneering work in zero-shot audio classification using semantic embeddings. His research bridges signal processing fundamentals with modern machine learning paradigms.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۷ مورد دیگر

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.