جست‌وجوی استاد و پژوهشگر

Vineeth N Balasubramanianمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Computer Vision
• +۸ مورد دیگر

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

Andreas Bullingمشاهده پروفایل

استاد

• Human-Computer Interaction
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Andreas Bulling is a Professor at the Institute for Visualisation and Interactive Systems , University of Stuttgart, Germany. His research focuses on Human-Computer Interaction , Eye Tracking , and Computer Vision , with applications in Machine Learning , Virtual Reality , and Information Visualization . 2025 Publications: HOIGaze (Extended Reality), ChartQC (Data Visualization), HAIFAI (Human-AI Interaction), SummAct (Behavioral Summarization), Chartist (Chart Reading). 2024 Contributions: HumanEYEze (Multimodal AI), HOIMotion (3D Object Detection), MultiMediate'24 (Engagement Estimation), Unified Model of Saliency (Scanpath Prediction). His recent work explores gaze estimation , interactive behavior modeling , and privacy-preserving eye-tracking systems . Key subfields include Extended Reality , Neural Networks , and Behavioral Biometrics . While no explicit scientific awards are mentioned, his research has been widely cited (8,003 total citations) and downloaded (132,740 times). Andreas leads projects in Interactive Systems and collaborates with institutions such as Aalto University , KU Leuven , and National University of Singapore . His lab focuses on eye movement analysis , human motion forecasting , and task-driven input modeling .

• Speech Technology
• Computational Linguistics
• Human-Computer Interaction
• +۴ مورد دیگر

Jens Edlund is a Professor at KTH Royal Institute of Technology's Division of Speech, Music and Hearing. His research focuses on speech technology, dialogue systems, prosody, and evolutionary phonetics. He has contributed to foundational work on speech synthesis, conversational interaction, and multimodal corpora like the D64 corpus. Key projects include the MonAMI Reminder system and analysis of primate vocalizations to understand speech evolution. Edlund has collaborated extensively with global researchers, producing over 150 peer-reviewed works. His work integrates computational methods with linguistic and biological insights, emphasizing human-like dialogue systems and cross-species vocal analysis. Education: Ph.D. in Speech Technology (2011, KTH) Grants: Multiple EU and Swedish Research Council grants for speech technology and interdisciplinary studies Research labs include the KTH Speech, Music and Hearing Lab and collaborations with institutions like Max Planck Institute for Evolutionary Anthropology. Current work explores evolutionary origins of speech biomechanics and AI-driven speech synthesis evaluation.

Judy Hoffmanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Domain Adaptation
• +۷ مورد دیگر

Judy Hoffman is an Associate Professor in the College of Computing at Georgia Institute of Technology, with a joint appointment in the School of Interactive Computing and affiliation to the Machine Learning Center . She received tenure in April 2025 after joining Georgia Tech as an Assistant Professor. Her research focuses on enabling AI systems that are reliable, fair, and resource-efficient. PhD in Electrical Engineering and Computer Science (2016, UC Berkeley) Postdoctoral Fellowships at Stanford (2017) and UC Berkeley (2018) Former Research Scientist at Facebook AI Research Her work intersects computer vision and machine learning , with specialization in domain adaptation , adversarial robustness , and algorithmic fairness . She has published over 40 peer-reviewed articles, including the award-winning DeCAF (ICML 2024 Test of Time Award) and co-founded Women in Computer Vision (2015), which has sponsored ~40 women annually to premier conferences. ICML Test of Time Award (2024) NSF CAREER Award (2022) PAMI Distinguished Young Researcher (2023) Samsung AI Researcher of the Year (2021) Dr. Hoffman has served as Program Chair for CVPR 2023, Associate Editor for T-PAMI (2021-2023), and co-organizer of workshops at major AI conferences. She has delivered over 70 invited talks and contributes to open-source projects like cycada_release (567 stars) and lsda (47 stars).

Natalie H. Britoمشاهده پروفایل

دانشیار

• Developmental Psychology
• Neurocognitive Development
• Early Childhood Development
• +۳ مورد دیگر

Natalie H. Brito is an Associate Professor of Applied Psychology at New York University (NYU), affiliated with the Steinhardt School of Culture, Education, and Human Development. Her research focuses on how early social and cultural contexts shape neurocognitive development in infants and toddlers, particularly in areas of attention, memory, and socio-emotional skills. Prior to NYU, she completed a postdoctoral fellowship at Columbia University Medical Center and was a Robert Wood Johnson Health and Society Scholar. Dr. Brito’s work bridges developmental psychology, neuroscience, and public policy, emphasizing the need for equitable environments that support healthy child development. She has received prestigious awards such as the APS Rising Star Award and NIH grants, reflecting her impactful contributions to understanding developmental trajectories. Her research also extends to policy implications, such as the effects of paid maternal leave on infant brain function and the role of structural inequities in maternal mental health. Key themes include early life stress, gut microbiome influences, and the neurobiological underpinnings of cognitive development. Dr. Brito has published extensively in journals like Child Development , Developmental Cognitive Neuroscience , and JAMA Psychiatry , with a focus on innovative methodologies (e.g., the OWLET gaze-tracking tool). She teaches courses on developmental psychology and the principles of applied psychology, fostering interdisciplinary approaches to human development. Her scientific accolades include recognition from the International Society of Developmental Psychobiology and the American Psychological Association, underscoring her leadership in advancing developmental science and equity-focused research.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

Brian Schollمشاهده پروفایل

استاد

• Visual Cognition
• Object Persistence
• Event Perception
• +۴ مورد دیگر

Brian Scholl is a Professor of Psychology at Yale University, specializing in visual cognition, perception, and cognitive science. His research explores how visual perception interacts with higher-level cognition, including object persistence, event perception, causality, and intuitive physics. He leads the Yale Perception & Cognition Lab, focusing on dynamic events, attention, and computational models. He earned his Ph.D. from Rutgers University in 1999. His work spans experimental psychology, neuroscience, and computational modeling, with a focus on phenomena like inattentional blindness, scaffolded attention, and event segmentation. He has authored numerous papers and presented at international conferences, contributing to debates on perception vs. cognition interactions. Key research areas include the perception of dynamic event types, the role of intuitive physics in visual processing, and the influence of event boundaries on memory and decision-making. His lab employs computer-based experiments and collaborations with computational models to study perception across species and clinical populations.

Elizabeth A. Simpsonمشاهده پروفایل

دانشیار

• Developmental Psychology
• Autism Spectrum Disorders
• Social Cognition
• +۷ مورد دیگر

Elizabeth A. Simpson is an Associate Professor in the Department of Psychology at the University of Miami's College of Arts and Sciences. She serves as Associate Director of the Child Division and directs the Social Cognition Laboratory, focusing on infant social and cognitive development through interdisciplinary methodologies. Education: Ph.D. in Psychology (unspecified institution) Key Research Areas: Developmental Psychology, Autism, Social Cognition, Primate Studies, Visual Attention, Infant Behavior Her research investigates individual differences in infant visual attention, social motivation, and physiological markers like salivary oxytocin. Recent studies demonstrate: Stability of attentional patterns from newborn to 14 months Sex differences in early face detection Links between oxytocin levels and social affect in infants Neurophysiological markers in newborns later diagnosed with ASD Scientific awards include the NSF CAREER grant for neonatal imitation research. She contributes to open science initiatives through the ManyBabies consortium and develops remote eye-tracking methods for broader accessibility. Current lab work explores: Parent-infant affective interactions during still-face episodes Predictive power of early attentional biases Development of pathogen avoidance behaviors Cross-species comparisons of social cognition

• Human-Computer Interaction
• Virtual Reality
• Social Virtual Reality
• +۴ مورد دیگر

Giulio Jacucci is a Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Helsinki Information Technology Research Institute. He serves as a Supervisor in the Doctoral Programme in Computer Science, mentoring PhD candidates in human-computer interaction and related fields. His academic career spans over two decades with continuous research productivity from 2006 through 2025. Professor Jacucci's research interests center on Human-Computer Interaction , particularly focusing on Virtual Reality , Brain-Computer Interfaces , and Neuroadaptive Systems . His work explores how technology can adapt to users' cognitive and physiological states, with significant contributions to social virtual reality environments, embodied agents, and information retrieval systems. He investigates how virtual representations affect social behavior, time perception, and communication in digital spaces. His publication record shows a clear evolution from foundational work in information retrieval toward increasingly sophisticated neuroadaptive systems. Recent publications (2024-2025) demonstrate his leadership in examining social dynamics within virtual reality platforms, including votekicking mechanisms, mirror watching behaviors, and communication tools like mutes. His work bridges technical innovation with deep understanding of human social behavior in digital environments. Professor Jacucci currently leads multiple research projects funded by the Academy of Finland, including DataLit: Datalukutaito ja vastuullinen päätöksenteko (2023-2026) and MyModel: Selitettävyys suositusten hallinnassa (2023-2027). These projects focus on data literacy, responsible decision-making, and explainability in recommendation systems, reflecting his commitment to ethical and user-centered technology development. His research has received media attention, particularly around brain-computer interfaces and virtual reality applications, with coverage in outlets discussing how deep learning transforms mobile applications and sensor landscapes. He has presented his work at major conferences including CHI 2017 and continues to be an active contributor to the international research community.

• Computer Vision
• Pattern Recognition & Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.

Manuel Kaufmannمشاهده پروفایل

مدرس

• 3D Human Motion Capture
• Computer Vision
• Sensor Data Processing
• +۳ مورد دیگر

Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.

Bertrand Schneiderمشاهده پروفایل

دانشیار

• Assessment
• Cognitive Development
• Evidence-Based Intervention
• +۱۰ مورد دیگر

Bertrand Schneider is an Associate Professor of Education at Harvard University's Graduate School of Education (GSE). He holds a Ph.D. in Learning Sciences and Technology Design from Stanford University (2015) and previously served as a postdoctoral researcher at Stanford's Transformative Learning Technologies Laboratory. His research focuses on educational interfaces (e.g., augmented reality, tangible tools) and multimodal learning analytics, particularly in collaborative and informal learning environments. He investigates how technologies like gaze-tracking and motion sensors can assess student learning trajectories and improve instructional strategies in makerspaces and remote teaching contexts. Education : Ph.D., Learning Sciences and Technology Design, Stanford University (2015) Postdoctoral Researcher, Transformative Learning Technologies Laboratory, Stanford University Research Interests : Educational technologies (AR, tangible interfaces, multitouch) Multimodal data analysis (gaze, body movement, speech) Collaborative learning in formal/informal settings Remote teaching strategies using shared gaze visualizations AI applications in personalized feedback and misinformation discernment Key Contributions : Developed methodologies for capturing joint visual attention in collaborative learning Advocated for ethical implementation of multimodal learning analytics Explored AR's dual-edged impact on student engagement and collaboration Labs/Teams : Current research aligns with GSE's focus on innovative learning technologies Prior affiliation with Stanford's Transformative Learning Technologies Lab

Hyowon Gweonمشاهده پروفایل

دانشیار

• Computational Approaches
• Developmental Approaches
• Education
• +۶ مورد دیگر

Hyowon Gweon is an Associate Professor in the Department of Psychology at Stanford University. As the leader of the Social Learning Lab, her research focuses on how humans learn from others and help others learn, employing interdisciplinary methods including developmental, computational, and neuroimaging approaches. She holds a PhD in Cognitive Science from MIT (2012) and joined Stanford in 2014 after a postdoc at MIT. Her research interests span computational approaches to social learning, developmental psychology, neuroimaging, and education. She has received notable awards such as the APS Janet Spence Award (2020), James S. McDonnell Scholar Award (2018), and Marr Prize (2010). Her work explores topics like counterfactual reasoning, social cognition in infants, and embodied AI benchmarks. Labs/Teams: Social Learning Lab Key Themes: Prosocial behavior, theory of mind, cognitive development, and human-AI interaction. Her recent articles investigate infant gaze behavior, temporal reasoning in children, and strategic communication in preschoolers. Grants and advising details are not explicitly listed in the provided text.

Scott T. Actonمشاهده پروفایل

استاد

• Signal and Image Processing
• Machine Learning
• Bioimage Analysis
• +۳ مورد دیگر

Scott T. Acton is the Lawrence R. Quarles Professor and Chair of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in Biomedical Engineering. He leads the VIVA lab, specializing in biological image analysis, machine learning, and AI for education. His research spans medical imaging, signal processing, and computer vision. Professor Acton holds a B.S. (Virginia Tech, 1988), M.S. (UT Austin, 1990), and Ph.D. (UT Austin, 1993) in Electrical Engineering. He has authored over 325 publications and served as Editor-in-Chief of IEEE Transactions on Image Processing and General Co-Chair of the IEEE International Symposium on Biomedical Imaging. His research interests include bioimage analysis, machine learning applications, and medical imaging technologies. The VIVA lab focuses on problems like cell tracking in bacterial biofilms, gait recognition using LiDAR, and AI-driven classroom activity analysis. Awards: IEEE Fellow (2013), All-University Teaching Award (2009), Outstanding Young Electrical Engineer (1996). Courses Taught: How the iPhone Works, Digital Image Processing, Signals and Systems. Labs/Teams: VIVA - Virginia Image and Video Analysis lab. Recent work emphasizes AI for education (e.g., automated classroom activity classification) and medical imaging advancements like 3D biofilm segmentation and LiDAR-based human identification. His contributions bridge engineering and healthcare, with applications in neuroscience and clinical decision support.

Niels Henzeمشاهده پروفایل

استاد

• Human-Computer Interaction
• Predictive Models in Interactive Systems
• Mobile Human-Computer Interaction
• +۵ مورد دیگر

Niels Henze is a Professor at the Chair of Media Informatics within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg, where he has been serving since May 2018. His research centers on human-computer interaction, with a strong focus on predictive models in interactive systems, mobile interaction, augmented and virtual reality, and attention-aware computing. His research interests include: Human-Computer Interaction (HCI) Predictive modeling for runtime adaptation Mobile and wearable interaction Augmented and Virtual Reality (AR/VR) Attention-aware and context-sensitive systems Sociocognitive aspects of interactive technologies The analysis of his recent publications reveals a consistent focus on leveraging user behavior and contextual cues to build intelligent, adaptive interfaces. His work integrates machine learning with interaction design, emphasizing real-time model updates, implicit feedback, and cognitive load awareness to improve usability and user experience across mobile and immersive platforms. Scientific awards: No awards mentioned in the provided text. Niels Henze leads research in adaptive interactive systems and advises students in the field of media informatics. He has previously held a junior professorship at the University of Stuttgart and completed his doctorate at the University of Oldenburg under Susanne Boll. He is actively involved in advancing the theoretical and practical foundations of predictive and attention-aware computing. Grants and funding sources are not specified in the text. He is associated with the Institute for Information and Media, Language and Culture (I:IMSK) at the University of Regensburg, contributing to a multidisciplinary environment that bridges informatics with cultural and linguistic studies.