جست‌وجوی استاد و پژوهشگر

Uwe Gellertمشاهده پروفایل

استاد

• Social inequalities in mathematics education
• Mathematics and language
• Mathematics teacher education
• +۴ مورد دیگر

Uwe Gellert serves as University Professor at the Faculty of Education and Psychology, Free University of Berlin, leading the Mathematics Education and Society research group since 2010. His academic career spans prestigious institutions including Universität Hamburg and international guest professorships in Bolivia, Chile, and Frankfurt, establishing him as a leading sociological voice in mathematics education. His educational trajectory features a Physics studies period at INSA Lyon (1987, DAAD-funded), Mathematics and Physics First State Examination at TU Berlin (1985-1991), Ph.D. (1997), and Habilitation (2002), all from Freie Universität Berlin. This interdisciplinary foundation informs his critical approach to mathematics pedagogy. Gellert's research centers on seven interconnected domains: social inequalities in mathematics education; mathematics-language relationships; teacher education; sociology of mathematics; crosscultural studies; classroom interaction microanalysis; and affective dimensions of mathematical engagement. He applies Bernstein's sociological frameworks to dissect how power structures manifest in mathematical discourse, particularly examining how classroom interactions perpetuate or challenge social hierarchies through linguistic practices and assessment rituals. His recent publications reveal a pronounced critical-sociological orientation, analyzing how 'mathematics for all' initiatives intersect with neoliberal policies, how academic performance gets socially constructed through teacher-student interactions, and how digital technologies reshape mathematical epistemologies. Key thematic threads include the yellowing of critical pedagogies under institutional pressures, cross-national pathway disparities, and the linguistic construction of mathematical competence in multilingual contexts. Through extensive editorial work including major C.I.E.A.E.M. sourcebooks and encyclopedia contributions, Gellert has shaped international discourse on mathematics education's sociopolitical dimensions. His theoretical integrations bridge anthropology, sociology, and linguistics to develop nuanced frameworks for understanding mathematics classroom dynamics. As an educator, Gellert has pioneered approaches connecting teacher education theory with practical classroom application, emphasizing how professional development must address the structural constraints shaping teaching practices. His work consistently examines the tension between teacher agency and institutional determinants in educational reform implementation. The Mathematics Education and Society group maintains active Latin American collaborations, conducting micro-ethnographic studies that reveal how global educational policies manifest in local classroom interactions, particularly examining how socioeconomic status and linguistic diversity intersect with mathematical reasoning development.

Hillel Adesnikمشاهده پروفایل

دانشیار

• neural basis of perception
• neuroscience
• neurobiology
• +۶ مورد دیگر

Hillel Adesnik serves as Associate Professor in the Department of Molecular and Cell Biology at the University of California, Berkeley, with additional affiliation in Neuroscience. His research program centers on deciphering how cortical microcircuits transform sensory input into perceptions and behaviors, utilizing cutting-edge approaches in awake behaving mice to bridge cellular mechanisms with cognitive functions. Adesnik's research investigates the neural basis of perception through three integrated pillars: (1) dissecting horizontal and vertical connections in cortical layers for sensory feature extraction, (2) developing high-resolution optical tools like 3D-SHOT for single-neuron manipulation in intact brains, and (3) analyzing cross-cortical communication for percept synthesis. His lab combines two-photon imaging, optogenetics, electrophysiology, and computational modeling to study tactile processing in barrel cortex and visual perception, revealing how specific neuron types and synaptic mechanisms generate perceptual codes. Key discoveries include layer-specific inhibitory control, supra-linear feature summation, and gamma-band synchronization mechanisms. Analysis of Adesnik's publication record shows consistent focus on cortical microcircuit dynamics across sensory modalities, with increasing emphasis on tool development since 2017. His work demonstrates how precise neural manipulations can establish causal links between circuit activity and perception, particularly through innovations in holographic optogenetics. Recurring themes include the role of somatostatin interneurons in layer-specific processing, cross-laminar interactions in feature coding, and the development of quantitative frameworks for neural population decoding. Scientific recognition includes: Chan Zuckerberg Biohub Investigator (2022 cohort) Adesnik mentors a robust research team comprising postdoctoral fellows (Lamiae Abdeladim, Janine Beyer, Conor Dorian, Will Hendricks, Uday Jagadisan, Mora Ogando, Masato Sadahiro, Kevin Sit, Savitha Sridharan, Andrea Zazzi) and graduate students (Genesis Ferrer Imbert, Courtney Kim, Madi McCloud, Ravi Srinivasan). His lab operates through structured collaboration with engineering groups for optical tool development and maintains active partnerships for disease-model applications. Funding sources include the Chan Zuckerberg Biohub and NIH grants supporting neurotechnology innovation. The Adesnik Lab maintains three core research thrusts through an integrated experimental pipeline: in vivo circuit interrogation in behaving animals, in vitro synaptic analysis, and novel optical instrument development. Current work emphasizes translating high-resolution manipulation techniques to disease models including autism and epilepsy, while expanding into multi-area cortical dynamics during complex behavioral tasks.

Shizhe Chenمشاهده پروفایل

پژوهشگر

• Vision-and-Language
• Embodied AI
• 3D Computer Vision
• +۲ مورد دیگر

Shizhe Chen is a researcher at the WILLOW team in INRIA Paris , focusing on vision-and-language and embodied AI. Previously, he completed postdoctoral research at WILLOW with Ivan Laptev and Cordelia Schmid. PhD in Computer Science from Renmin University of China (2020) BSc in Computer Science from Renmin University of China (2015) His research bridges vision-and-language with embodied AI , emphasizing 3D scene understanding , robotic manipulation , and vision-language navigation . Recent projects include the GRAIL workshop (CVPR'25) on generalizable robotic manipulation and NextBestPath (ICLR'25) for efficient mapping. Key publication trends show expertise in 3D reconstruction (HORT, Online 3D Scene Reconstruction), robotic navigation (ViViDex, Object Goal Navigation), and vision-language systems (DUET, Think Global Act Local). Collaborations span INRIA, Microsoft Research Asia, and Carnegie Mellon University. 2022 REVERIE Challenge (2nd place) 2021 REVERIE & SOON Challenges (winner) 2019-2024 multiple TRECVID, CVPR, and ACM Multimedia awards 2020 Jingdong Scholarship & Beijing Outstanding Graduate Award 2016-2017 National Scholarships As co-supervisor, he guides PhD students in robotics and computer vision projects at INRIA Paris, collaborating with teams at Microsoft, Carnegie Mellon, and Louis Vuitton. His DUAL-SCALE GRAPH TRANSFORMER (CVPR'22 Oral) framework significantly advanced vision-language navigation benchmarks.

Eric Fosler-Lussierمشاهده پروفایل

استاد

• Speech Processing
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Eric Fosler-Lussier is the John I. Makhoul Professor and Acting Department Chair of Computer Science and Engineering at The Ohio State University. He holds courtesy appointments in Linguistics and Biomedical Informatics, and serves as Assistant Dean for Faculty Lifecycle in the College of Engineering. His research focuses on speech processing, natural language understanding, and clinical NLP applications. PhD in Computer Science, University of California, Berkeley (1999) B.A.S. in Computer and Cognitive Science, University of Pennsylvania (1993) B.A. in Linguistics, University of Pennsylvania (1993) His research spans core technologies for speech recognition and NLP, with applications in medical training systems and clinical record analysis. Recent work includes neural diarization techniques, conversational AI for healthcare, and cross-domain text-to-SQL frameworks. Recent publications highlight innovations in end-to-end speech systems, knowledge transfer between textual and speech models, and robust error prediction frameworks. His lab has produced 15+ publications since 2022, with a focus on educational and medical applications. NSF CAREER award recipient IEEE Signal Processing Best Paper Award IMIA Yearbook Best Paper (twice) IBM Faculty Award IEEE and ISCA Fellow He advises numerous PhD students and has mentored graduates now working at Amazon, Google, IBM, and universities worldwide. His Speech and Language Technologies Laboratory develops systems for speech enhancement, virtual patients, and knowledge transfer.

Margie Olbinadoمشاهده پروفایل

پژوهشگر

• X-ray Imaging
• Phase-Contrast Imaging
• Tomography
• +۵ مورد دیگر

Dr. Margie Olbinado is an Industrial Liaison Scientist at the Paul Scherrer Institute (PSI) in Switzerland, affiliated with the PSI Center for Photon Science and its Laboratory for Macromolecules and Bioimaging . She specializes in advanced X-ray imaging techniques at the TOMCAT beamline. Education: B.S. and M.S. in Physics, University of the Philippines Ph.D. in Physics, University of Tokyo (2010-2013) Research Interests: Dr. Olbinado is a leading expert in X-ray phase-contrast imaging , tomography , and time-resolved X-ray techniques . Her work focuses on industrial applications, including material characterization, failure analysis, and virtual histology, with recent innovations in X-ray ghost imaging. Publications: Her research spans ultra-fast X-ray microscopy, grating interferometry, and novel imaging modalities like Ghost Tomography. Key collaborations include institutions such as ESRF, Tohoku University, and ANAXAM - Analytics for Advanced Manufacturing. Collaborations: She works closely with PSI Technology Transfer, SLS TT AG, and international research teams to bridge academic advancements with industrial needs.

Lucie Brocمشاهده پروفایل

مدرس

• Developmental Psychology
• Oral Language Development Disorder
• Educational Psychology
• +۳ مورد دیگر

Lucie Broc is a Lecturer in the Department of Psychology at the University of Poitiers, affiliated with CeRCA (Center for Research on Cognition and Learning) and MSHS (Maison des Sciences de l'Homme et de la Société). Her research focuses on the typical and atypical development of oral and written language in children and adolescents, particularly those with Developmental Language Disorder (DLD) and High Functioning Autism (HFA). University of Poitiers CeRCA/MSHS Research Center Her work investigates the impact of transcription modes (typing vs. handwriting) on spelling performance, orthographic error patterns in neurodevelopmental disorders, and the role of narrative writing in mobilizing syntactic knowledge. She collaborates with Prof. Julie Dockrell (UCL), Prof. Marie Anne Schelstraete (UCLouvain), and CNRS Research Director Thierry Olive. Recent publications analyze keyboard typing interventions, working memory assessments, and cross-linguistic spelling differences. Her research employs both experimental studies and meta-analyses across European languages, with a focus on special education applications. Contact: lucie.broc@univ-poitiers.fr

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Vivek Gupta is an incoming Assistant Professor at the School of Computing and Augmented Intelligence , Arizona State University (ASU), starting Fall 2024. Previously, he served as a Postdoctoral Researcher at the University of Pennsylvania's Cognitive Computation Group , and as a Research Fellow at Microsoft Research India . His academic journey includes a Ph.D. in Computer Science from the Kahlert School of Computing, University of Utah (advisor: Prof. Vivek Srikumar), supported by the Bloomberg Data Science Fellowship, and an MTech/BTech Dual Degree from IIT Kanpur . Dr. Gupta's research focuses on Natural Language Processing for semi-structured data , particularly tables, flowcharts, and maps. His work addresses trustworthy AI systems , multilingual tabular inference , and knowledge integration in low-resource environments. Current projects explore temporal reasoning , multimodal table analysis , and neurosymbolic agents for complex data. Recent publications examine adaptive prompting for temporal table reasoning , flowchart attribution , and multimodal benchmarking . His MMTBENCH and TabXEval frameworks evaluate robustness and quality standards in table processing. Recruitments highlight his CORAL Lab seeking candidates for Trustworthy AI projects. Bloomberg Data Science Fellowship (2021-2023) Ericsson Innovation Award (2016) Best Paper, DeeLIO-2022 Outstanding Paper, NLP4ConvAI-2022 As a mentor, Dr. Gupta has guided 30+ students across institutions including ASU, University of Utah, IIT Kanpur, and UPenn. His professional service includes organizing NAACL Student Research Workshops , serving as iKDD Student Ninja , and coordinating the University of Utah Data Science Club . Contact: vgupt140@asu.edu , keviv9@gmail.com

Fatih Gedikliمشاهده پروفایل

استاد

• Artificial Intelligence
• Big Data Analytics
• Software Engineering
• +۵ مورد دیگر

Prof. Dr. Fatih Gedikli is a full-time Professor of Artificial Intelligence and Big Data at the Institute of Computer Science, Ruhr West University of Applied Sciences . His academic work spans software engineering, web engineering, and applied artificial intelligence, with a focus on recommendation systems . Key research areas: Recommender Systems , Big Data Analytics , Natural Language Processing , Deep Learning Contribution: Development of AI-based data pipelines for unstructured data analysis from news, social media, and scientific publications Entrepreneurial Activities : Co-founder and Co-CEO of graphworks.ai , a German AI startup offering student internships. Regular speaker at workshops and keynotes, including events on entrepreneurship and sustainable supply chains.

• Modal Logic
• Temporal Logic
• Automated Reasoning
• +۵ مورد دیگر

Cláudia Nalon is a full professor at the Department of Computer Science at the University of Brasília, where she has established herself as a leading researcher in modal logic and automated reasoning. Her academic career spans over two decades with continuous contributions to theoretical foundations and practical implementations of reasoning systems. Research Profile Professor Nalon specializes in proof methods for combined modal logics, particularly those allowing interaction between different logical components. Her PhD work focused on resolution-based methods for interactions between linear-time temporal logic and multi-modal S5. She has developed tableau-based methods for temporal logics of knowledge with both no learning and perfect recall scenarios. Her expertise extends to the theoretical foundations and implementation of reasoning tools for normal modal logics, including systems with symmetry, reflexivity, seriality, transitivity, and Euclideaness. More recently, she has pioneered resolution methods for Coalition Logics, a non-normal modal logic for reasoning about cooperative agency. Publication Trends Analysis of her 15 most recent publications (2020-2024) reveals consistent focus on modal logic reasoning with particular emphasis on resolution methods, reduction techniques, and model construction. Her work demonstrates a strong connection between theoretical foundations and practical implementations, with numerous papers presenting evaluated theorem-proving techniques and their applications. The publications span major venues in automated reasoning including IJCAR, TABLEAUX, and CADE. Professional Service Editorial board of The IfCoLog Journal of Logics and their Applications (since 2015) Editorial board of Annals of Mathematics and Artificial Intelligence (2025-2027) Steering committee for LSFA (2018-2021), TABLEAUX (2016-2023), CADE (2021-2024) Executive committee of the Brazilian Logic Society (2017-2021) Program committees for over 30 international conferences from 2010 to 2025 Mentorship and Software Development Professor Nalon has supervised multiple student projects resulting in significant software implementations for modal logic reasoning. She has personally developed several theorem provers including ltl2snf (for temporal logic translation), KSP (for multimodal K), and CLProver (for Coalition Logic). Her mentorship has produced numerous student-led implementations that extend these systems, demonstrating her commitment to training the next generation of researchers in formal methods. Community Building Professor Nalon has been instrumental in establishing research communities focused on logic and automated reasoning in Brazil. She served as co-chair for TABLEAUX 2017 in Brasília and has organized multiple editions of the Brazilian Logic Workshop (WBL). Her work with the LSFA workshop series has fostered a strong international community of researchers in logical and semantic frameworks.

• Multimodal Literacy
• Science Education
• Multilingual Education
• +۳ مورد دیگر

Kristina Danielsson is a Researcher at Linnaeus University , affiliated with the Faculty of Arts and Humanities and the Department of Swedish Language . Her work focuses on multimodal literacy , science education , and multilingual learning practices , particularly in early childhood and primary education contexts. She leads interdisciplinary projects exploring how digital tools , physical objects , and multisensory representations facilitate meaning-making in linguistically diverse classrooms. Her research spans collaborations with the Linnaeus University Centre for Intermedial and Multimodal Studies (IMS) and the Digi Group , examining topics like: Transformation of representations in science pedagogy Metaphor use in disciplinary learning Development of digital animations for conceptual understanding Recent publications analyze: 2025: Hybrid learning activities for six-year-olds 2024: Disciplinary text structures in science 2023: Multimodal fairy tales and ecology education

• Environmental Art
• Landscape Studies
• Interdisciplinary Art Practice
• +۳ مورد دیگر

Amanda Thomson is a Lecturer in Fine Art-Painting & Printmaking at The Glasgow School of Art, specializing in interdisciplinary approaches to landscape, place, and environmental studies. Her practice integrates art-making, nature writing, and archival intervention, with a focus on multi-modal methodologies. Education: PhD in Art Practice-Based Research from University of Aberdeen Key Themes: Landscapes as living texts, human-environment interactions, decolonizing natural collections Collaborations: Regularly works with Elizabeth Reeder on hybrid art-writing projects Thomson's recent artistic output includes The Royal Botanic Garden Edinburgh's silent archive project and the 2024 Boundary Layers installation. She explores landscape through Antlers of Water (Canongate, 2020) and Pedagogy of Place (Intellect, 2023), which examine environmental identity and cross-disciplinary learning. Her creative practice spans: Printmaking and drawing Experimental nature writing Fieldwork-based performance Botanical documentation Sound and video installation Archival recontextualization Thomson has participated in numerous environmental art dialogues including: 2023 Edinburgh International Book Festival's Future of Nature Writing 2022 Scottish Government Bute House literary reception 2021 BBC Radio 4 Art and Ideas broadcast

Yves Boubenecمشاهده پروفایل

دانشیار

• Auditory Cognition
• Neural Basis of Auditory Processing
• Attention Mechanisms
• +۵ مورد دیگر

Yves Boubenec is an Associate Professor (maître de conférences) in neuroscience at the École Normale Supérieure (ENS) in Paris, where he is affiliated with the Department of Cognitive Studies and the Perceptual Systems Laboratory. His research focuses on the neural basis of auditory cognition, particularly how attention, learning, and memory interact with auditory processing at the cortical level. He teaches neurophysiology of auditory perception at ENS through the IMaLiS and Cogmaster programs. His research spans three main conceptual angles: Attention , examining how top-down attention modulates auditory processing; Learning , studying how exposure to natural sounds shapes auditory perception across multiple time scales; and Memory , investigating how exposure to sounds leaves long-term traces in auditory cortex. He employs mesoscopic-scale neural recording techniques including electrophysiological recordings of neuronal populations and large-scale functional UltraSound (fUS) neuroimaging. Boubenec's recent publications (2023-2024) reveal a strong focus on speech processing, task engagement effects on auditory cortex, evidence integration across sensory modalities, and the neural mechanisms underlying categorical perception. His work frequently involves cross-species comparisons (particularly using ferrets) and combines experimental approaches with theoretical modeling. His laboratory offers multiple internship opportunities for students at various levels (L3, M1, M2, DENS) focusing on hemogenetic imaging, cross-species comparison of rhythm perception, and learning dynamics in biological and artificial networks. Boubenec collaborates extensively with researchers including Shihab Shamma, Srdjan Ostojic, and various interdisciplinary teams across neuroscience, acoustics, and computational modeling fields. His work bridges fundamental neuroscience with applications in speech processing and artificial intelligence.

Kai Hanمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Kai Han is an Assistant Professor at The University of Hong Kong's School of Computing and Data Science, where he directs the Visual AI Lab. His research focuses on computer vision, machine learning, and artificial intelligence with specific interests in open-world learning, 3D vision, generative AI, and foundation models. He aims to achieve principled visual understanding and build reliable AI systems that close the intelligence gap between machines and humans. Dr. Han's research interests span multiple areas in visual AI, with particular emphasis on developing methods for open-world visual understanding. His work addresses fundamental challenges in category discovery, visual correspondence, 3D reconstruction, and generative modeling. He has made significant contributions to novel category discovery, open-set recognition, and visual correspondence problems, with his AutoNovel framework being particularly influential in the field. His current research explores the intersection of generative models and visual understanding, particularly focusing on how foundation models can be leveraged for comprehensive visual analysis. His publication record demonstrates a clear evolution from traditional computer vision problems toward more challenging open-world scenarios and generative approaches. Early work focused on 3D reconstruction of transparent and mirror surfaces, while more recent publications explore category discovery, visual correspondence, and generative AI. The trend shows increasing focus on foundation models, large language model integration with vision systems, and creating more robust visual understanding systems that can handle real-world open-set scenarios. Best Paper Runner-Up Award at CVPR Workshop on Continual Learning in Computer Vision, 2022 Outstanding Reviewer for ICCV 2021 (top 5%) Outstanding Reviewer for CVPR 2021 Outstanding Reviewer for CVPR 2020 Travel Award, ICLR 2020 Doctoral Consortium Travel Grant, ICCV 2017 Dr. Han actively mentors PhD students and postdocs, with numerous students appearing as first authors on his publications. His lab has secured multiple funding opportunities including HKU-PS, HKPFS, PGS, HKU-BICI, and HKU-ASTRI scholarships. He serves as Area Chair for major conferences including CVPR 2026, ICLR 2026, and AAAI 2026, demonstrating his standing in the research community. His lab, the Visual AI Lab, focuses on creating robust visual understanding systems that can handle real-world scenarios beyond closed-set recognition.

Albert Salahمشاهده پروفایل

استاد

• Human Behavior Analysis
• Affective Computing
• Computer Vision
• +۴ مورد دیگر

Prof. dr. Albert Salah (Utrecht University) serves as Professor of Social and Affective Computing in the Department of Information and Computing Sciences. His research spans computer vision, machine learning, and computational social science with applications in behavioral analysis, mental health, migration studies, and animal welfare. He leads the AI and Animal Welfare Lab and contributes to the Utrecht Platform for Applied Data Science. Chair: Social and Affective Computing Editorial Roles: Associate Editor for Pattern Recognition and International Journal on Human-Computer Studies Leadership: IEEE FG Steering Committee, ACM ICMI Steering Committee Research focuses on: Human behavior analysis at individual, group, and societal scales Multimodal interaction modeling Mobile phone data applications for migration and mobility Computer vision for animal pain detection Ethical AI in asylum procedures Key scientific activities include: Co-organizing 11th International Symposium on Brain and Cognitive Science (2025) Workshops at ACM ICMI 2025 and IEEE FG conferences Founding Dutch chapter of IEEE Biometrics Council His work appears in journals like: Science Advances IEEE Transactions on Affective Computing EPJ Data Science Image and Vision Computing

Brian McFeeمشاهده پروفایل

استادیار

• Machine learning
• Music information retrieval
• Recommender systems
• +۱ مورد دیگر

Brian McFee is an Assistant Professor of Music Technology and Data Science at New York University's Music and Performing Arts Professions school. He specializes in machine learning applications for music and multimedia data, with particular focus on structure analysis and audio processing. Director of Graduate Studies, MS Researcher at Center for Data Science Research interests: McFee pioneers methods in music information retrieval, recommender systems, and multimedia signal processing. His recent work explores event-based metrics for music structure, hierarchical embedding learning, and sound event detection technologies. Key awards: ISMIR Best oral presentation and best poster presentation awards (2014) Academic contributions: McFee has advised multiple students including Qingyang (Tom) Xi (Music Technology) and Elena Georgieva (Data Science). His research spans both theoretical developments and practical software implementations like resampy and librosa libraries.