Dr. Jan Dvorak is a Lecturer in French Linguistics at the Université Toulouse - Jean Jaurès , affiliated with the Cognition, Languages, Ergonomics (CLLE) laboratory. He teaches Introduction to French Syntax and Phonetics/Phonology to undergraduate students, while preparing candidates for the 18th-century French language test in modern literature certification. PhD in Linguistics (2021) from ENS de Lyon - Université de Lyon Supervised by Céline Guillot-Barbance and Olga Nádvorníková Research Focus His research operates within Löbner’s Concept Types and Determination Theory , investigating: Grammaticalization of Czech ten into definite articles Deictic semantics in spoken French demonstratives Contrastive referential strategies between Czech and French Pragmatic effects in emotional demonstrative uses Emergent grammatical structures in informal registers Publication Trends Recent work spans 2019–2024 with a focus on: Comparative analysis of Czech-French demonstrative systems Grammaticalization processes in Slavic and Romance languages Corpus-driven studies of spoken language Definiteness markers in superlative constructions Deixis and reference chain dynamics Scientific Recognition Recipient of Prix Gallica for best dissertation in language sciences (2022) Published in Discours , Travaux de Linguistique , Scolia , and conference proceedings Technical Expertise TXM textometric software proficiency Corpus annotation and analysis Functional linguistic theory application Cross-linguistic typology
Josette Rebeyrolle is a Lecturer in Language Sciences at the University of Toulouse - Jean Jaurès , affiliated with the Cognition, Languages, Language, Ergonomics (CLLE) research center. Her work spans discourse analysis, semantics, and corpus linguistics, focusing on referential continuity and discourse markers in educational contexts. Research Themes: Referential continuity ties, discourse markers, text world theory, corpus-based studies, and pragmatic markers in French. Collaborations: Frequent collaborator with Lydia-Mai Ho-Dac on annotation methodologies and student narrative analysis. Her recent publications explore the evolution of temporal expressions like "à un moment donné" and the pragmatic functions of discourse markers in both written and oral French. She has developed annotation tools like the RESOLCO and ANNODIS corpora for studying textual cohesion.
Brian Litt is a Professor of Neurology and Bioengineering focusing on computational neuroscience, clinical systems, and neurologic disorders. His work bridges neurology and bioengineering to develop advanced tools for epilepsy diagnosis and treatment. Research interests include: Computational modeling of seizure dynamics AI applications in neurophysiological data analysis Development of implantable neuromodulation devices Medical informatics for epilepsy phenotyping Recent publications emphasize AI-driven seizure outcome extraction, low-field MRI applications, and EEG-based network modeling. His studies often leverage generative models, medical records, and collaborative platforms like Pennsieve. Scientific contributions include: Framework for brain atlases Tools for seizure severity quantification Pipelines for intracranial electrode reconstruction Analysis of network controllability in epilepsy Litt’s work has significant implications for epilepsy monitoring, surgical planning, and personalized treatment strategies.
Mehmet Can Yavuz is an Assistant Professor at Işık University's Faculty of Engineering and Natural Sciences, Department of Computer Engineering. As Principal Investigator of the Multimedia Lab, he bridges machine learning with artistic expression through projects like Arky Multimedia, ConvergedMachine, and Duyukoru. His research spans biomedical imaging, human-computer interaction, and cross-modal analysis of multimedia storytelling. 2019-2023: PhD in Computer Science & Engineering, Sabancı University 2010-2016: MS in Physics, Boğaziçi University 2006-2010: BS in Physics, Işık University 2004-2010: BS in Electrical-Electronics Engineering, Işık University Current research explores: Advanced machine learning architectures (Variational Contrastive Learning, Cross-D Convolution) Biomedical imaging applications for disease detection Computational analysis of dramatic/literary works through graph theory and sentiment analysis AI-driven threat detection systems using sensor fusion Creative technology intersections in multimedia production His lab develops frameworks for: Noisy data processing in semi-supervised learning Cross-dimensional knowledge transfer Ensemble approaches in 2D/3D medical imaging Temporal-sentiment analysis of urban events Document embedding-based character analysis Projects include: ARKY MULTIMEDIA - Combining creative exploration with ML DUYUKORU - Machine learning-enhanced sensor threat detection CONVERGEDMACHINE - Multimodal ML research repository He oversees the Işık University Multimedia Lab , which integrates medical image computing with animation production, pushing boundaries in both scientific and artistic domains.
Huamin Qu is a Chair Professor and founding Dean of the Academy of Interdisciplinary Studies at the Hong Kong University of Science and Technology (HKUST) . He leads the VisLab and coordinates the Human-Computer Interaction (HCI) group within the Department of Computer Science and Engineering. His academic journey began with a BS in Mathematics from Xi'an Jiaotong University , followed by an MS and PhD in Computer Science from Stony Brook University . Founding Head of HKUST's Division of Emerging Interdisciplinary Areas (EMIA) Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ) Director of IPO and senior administration team member As a pioneer in data visualization and human-computer interaction , his research bridges urban computing , explainable AI , social media analysis , and E-learning . Recent work focuses on human-AI teaming , multimodal communication , and augmented reality applications . His publications reveal a trajectory from foundational graph visualization and volume rendering to cutting-edge AI-integrated visual systems . Scientific awards include induction into the IEEE Visualization Academy , IEEE VGTC Technical Achievement Award , and multiple best paper/honorable mention awards at top conferences like IEEE VIS, ACM CHI, and IEEE VAST. His lab has graduated 48 PhDs and 21 MPhil students , with 21 PhDs now faculty members at institutions including UC Davis, University of Minnesota, and Zhejiang University. Technologies developed by his group have been adopted by Microsoft , IBM , and Google . His work on projects like Pulse of HKUST and ATMSeer has received global media coverage from MIT News , IEEE Spectrum , and NHK TV . He has served as Associate Editor of IEEE TVCG and held leadership roles in major conferences including IEEE VIS , PacificVis , and VINCI .
Dominik Banhold is a faculty member at the University of Würzburg , affiliated with the Faculty of Philosophy and the Institute for German Philology . He serves as part of the Chair of Didactics of German Language and Literature , focusing on German language education, linguistic standards, and educational media. His work spans textbook development, grammar instruction, and the historical analysis of language norms in educational contexts. Department: Institute for German Philology School: Faculty of Philosophy University: University of Würzburg Research interests include German didactics, linguistic standardization, educational media, historical linguistics, and pragmatic analysis. His work examines the evolution of language norms in school grammars, interactive grammar exercises, and the sociolinguistic aspects of communication genres like dating advertisements and partnership announcements. Recent publications focus on textbook development, grammatical variation, and educational methodologies. Notable works include German Competent 12/13 (2024, in preparation), Communicative English Grammar (2018), and studies on language codification in school grammars (2016). Contact: dominik.banhold@uni-wuerzburg.de
Nitin Gupta is an Associate Professor in the Department of Biological Sciences and Bioengineering at the Indian Institute of Technology Kanpur. His research focuses on understanding the fundamental mechanisms used by neural circuits for processing information, with initial emphasis on circuits in deeper layers of the olfactory system. PhD in Bioinformatics & Systems Biology, University of California San Diego (2009) B.Tech in Computer Science & Engineering, IIT Kanpur (2004) Dr. Gupta's research integrates experimental and computational approaches to study neural circuit function. He uses a variety of insects as model systems, including grasshoppers, flies and mosquitoes, and employs techniques such as in vivo electrophysiology, histology, behavioral observations, genetic manipulations, and computational modeling. His work bridges neuroscience, computational biology, and bioinformatics. His recent publications demonstrate a clear progression from more computational proteomics work during his PhD to increasingly neuroscience-focused research, particularly in olfactory processing. This reflects his transition from computational biology to systems neuroscience while maintaining strong computational approaches. NIH Fellows Award for Research Excellence (2013) Gordon Fellowship Medal and Graduate Engineering Leadership Award (2009) Diane Lin Prize (2009) Ratan Swarup Memorial Award (2004) National Talent Search Scholarship (1998) Dr. Gupta previously served as a Post-doctoral Fellow at the National Institutes of Health, USA (2010-2014) before joining IIT Kanpur as faculty. His interdisciplinary background combining computer science, bioinformatics, and neuroscience has positioned him to make significant contributions to understanding neural circuit mechanisms through both experimental and computational approaches. His laboratory at IIT Kanpur likely maintains insect model systems for neural circuit studies, with particular focus on olfactory processing.
Yongjun Zhang is an Assistant Professor at Stony Brook University jointly appointed in the Department of Sociology and the Institute for Advanced Computational Science. His research integrates computational methods with social science theory to analyze mobility patterns, segregation, and political polarization. Expertise in Big Data Analytics and Human Mobility Studies Recipient of OVPR Seed Grant and IACS Seed Grant for polarization and AI ethics research Developing large language/vision models for analyzing protest dynamics and environmental justice discourse Recent publications examine: Urban segregation through GPS and census data AI bias risks in social science research Political donations patterns among corporate elites Anti-AAPI hate speech monitoring systems He teaches Intro to Computational Social Science and Research Methods in Sociology , maintaining a lab that combines spatial analysis with social media data.
Professor Robert J. Kittel is a leading neuroscientist at the University of Leipzig, holding the Chair of Animal and Behavioral Physiology within the Institute of Biology, Faculty of Life Sciences. His research program investigates molecular mechanisms of neuronal communication with a particular focus on the presynaptic active zone. He leads an active research group employing neuro- and optogenetic approaches, electrophysiology, behavioral analyses, and high-resolution light microscopy in Drosophila melanogaster models. Dr. Kittel's research interests center on synaptic plasticity, sensory physiology, and neuronal circuits in the context of adaptive behavior. His work spans three main areas: Plasticity (how active zone physiology is modified by activity-induced plasticity in behaving animals), Pathology (investigating nociception and therapeutic potential of receptors like Latrophilin/CIRL), and Evolution (exploring active zone properties across species to identify conserved features versus specializations). His team has made significant contributions to understanding molecular dynamics at single synapse resolution and neurotransmission in intact organisms. Analysis of his recent publications reveals a strong focus on connectomics, synaptic plasticity mechanisms, and molecular neuroscience. His work frequently appears in high-impact journals including Nature , with notable contributions to the fruit fly connectome project. The research demonstrates consistent integration of advanced imaging techniques with behavioral analysis to understand neural circuit function. Emmy Noether Fellowship Principal Investigator for DFG-funded Research Training Group NeuroTune Member of Clinical Research Unit KFO 5001 Co-leader of second funding phase for orthodontic research project 'ResolvePain' Professor Kittel actively mentors PhD and Master's students, with numerous alumni including Daniel Bidell, Kim Borstel, and Sven Dannhäuser. His research is supported by multiple DFG grants, including the NeuroTune Graduate School and the Priority Program SPP 2205 'Evolutionary Optimization of Neural Processing'. His laboratory collaborates extensively with clinical colleagues like Heike Rittner and Tobias Langenhan, bridging basic neuroscience with translational applications. The Kittel Lab maintains strong connections with the Pauls Lab and Selcho Lab, forming an integrated neuroscience research community at the University of Leipzig. The team has recently contributed to the publication of the fruit fly connectome in Nature and continues to investigate molecular mechanisms of active zone plasticity in the context of neuronal information coding and memory formation.
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.
Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.
Estefanía Talavera Martínez is an Assistant Professor specializing in Datamanagement & Biometrics , with research spanning artificial intelligence, computer vision, and health informatics. Her work addresses surveillance, emotion recognition, and egocentric data analysis.
Liane G. Benning is a distinguished researcher specializing in biogeochemical processes on the Greenland Ice Sheet. Her work focuses on the interplay between microbial communities, mineral dust, and climate change in driving ice albedo reduction and subsequent melt dynamics. She has co-authored numerous high-impact studies on cryospheric systems using field observations, remote sensing, and genomic analyses. Key Research Areas: Biological and abiotic controls on ice albedo Mechanisms of nutrient cycling in extreme environments Microbial community composition and biosynthetic potential Remote sensing validation for cryospheric monitoring Article Trends: Her recent publications analyze the formation of dark ice zones, orbital drift impacts on satellite data, and the role of organic ligands in mineral transformation. These studies often integrate multi-disciplinary approaches, including metagenomics, hyperspectral imaging, and climate modeling. Notable Collaborations: Shunan Feng Joseph Mitchell Cook Alexandre Magno Anesio Martyn Tranter
Dr. James Atlas is a Senior Lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury's Faculty of Engineering, where he has been employed since July 2018. His primary affiliations include full-time academic responsibilities at this institution. His research spans: Core CS : Artificial intelligence, machine learning, constraint optimization Systems : Distributed and high-performance computing Applied domains : Medical data analysis (CT reconstruction, health prediction), earth/space exploration (flood mapping, environmental monitoring), and multi-agent systems Education : Curriculum development, threshold concept gamification Recent publication analysis (2019-2025) reveals dominant themes in deep learning applications for medical imaging (spectral CT reconstruction, bioacoustic analysis) and environmental sensing (3D tree modeling, vineyard monitoring, flood prediction), with consistent interdisciplinary collaboration. He actively supervises 10 graduate students researching: Machine learning for global flood exposure mapping CT reconstruction algorithms and artifact correction EMG-EEG hybrid prosthetics Hierarchical reinforcement learning Automated 3D digital tree modeling Bioacoustic monitoring systems
Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.
