Valeria Anna Sovrano is an Associate Professor at the Center for Mind/Brain Sciences (CIMeC) and the Department of Psychology and Cognitive Science at the University of Trento. Her research focuses on comparative cognition , brain asymmetries , and spatial-numerical skills in fish and reptiles , with a particular emphasis on visual perception of illusions and environmental geometry encoding . She leads the Lower Vertebrates Behavior and Neurobiology Unit at CIMeC and has supervised numerous doctoral projects. PhD in Experimental Psychology (University of Padua, 2004) Master degrees in Legal Psychology, Criminology, and Human Rights Recent work explores neurodevelopmental disorders in zebrafish models , inhibitory control in reptiles , and environmental pollutant impacts on cognition . Her studies on fish spatial navigation and numerical discrimination have been widely cited across cognitive science and developmental psychology. Notable awards include the AIP Section of Experimental Psychology award (2000) and the Gold Medal of the Italian President (2007) . She has taught courses like Biological bases of social behavior , Animal Cognition , and Psychobiology of stress , while coordinating STEM outreach programs and serving on equality & diversity committees.
Shweta Bhardwaj is a Researcher at Google Research (via Optimum InfoSys Ltd), working in the Mixed-Mode User Understanding team. Previously, she served as a Data Scientist at Flipkart (2019-2021) and held a research internship at NVIDIA Bangalore (2018). She holds an M.S. (by Research) from IIT Madras (2016-2019), advised by Dr. Mitesh M. Khapra, and a B.Tech. in Computer Science from Guru Nanak Dev University. Education: M.S. (by Research), IIT Madras, 2016-2019 B.Tech. in Computer Science, Guru Nanak Dev University Her research focuses on Computer Vision , Explainable AI , and Model Compression . She emphasizes ethical AI applications for societal challenges like criminal justice and healthcare. Recent work includes developing input-adaptive neural networks for fine-grained visual recognition and benchmarking AI systems for sustainability goals. Her publications span topics like video classification optimization, neural network plasticity, and interpretable subspaces in image representations. She collaborates on benchmarks like Colorbench (2025) and Editval (2023), advancing AI robustness and multimodal understanding. Advising & Grants: Advised by Dr. Gaurav Aggarwal (Google Research), Dr. Shourya Roy (Flipkart), and Dr. Mitesh M. Khapra (IIT Madras). No explicit grants mentioned in the text. Labs & Teams: Associated with RBC-DSAI Lab at IIT Madras (led by Dr. Balaraman Ravindran) and currently part of Google's Mixed-Mode User Understanding team.
Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.
Wei-Chiu Ma is an Assistant Professor of Computer Science at Cornell University, where he leads research at the intersection of 3D/4D computer vision and robotics. His work focuses on building AI systems that can understand, reconstruct, and re-simulate our dynamic world to enable more robust autonomous systems and advance entertainment applications. Prior to joining Cornell, Dr. Ma was a Young Investigator/Postdoc at AI2 / University of Washington. He received his Ph.D. from MIT, working with Antonio Torralba and Raquel Urtasun. Before his Ph.D., he was a Senior Research Scientist at Uber ATG R&D and Waabi working on self-driving vehicles, and completed his M.S. in Robotics at Carnegie Mellon University under the advisement of Kris M. Kitani. Dr. Ma's research interests span several interconnected areas in computer vision and robotics: 3D/4D Computer Vision: Focused on scene reconstruction, modeling, and understanding from visual inputs Neural Radiance Fields (NeRF): Developing techniques for novel view synthesis and scene representation Robotics and Simulation: Creating realistic sensor simulation for autonomous systems Digital Twins: Building interactive, game-engine compatible virtual environments Self-Driving Technology: Working on scene flow estimation, sensor simulation, and vehicle localization His recent publications demonstrate a strong focus on pushing the boundaries of 3D scene understanding, with particular emphasis on extreme-view geometry, neural rendering techniques, and creating realistic simulations for autonomous systems. His work often bridges theoretical computer vision with practical applications in robotics and autonomous vehicles. Dr. Ma has received several notable recognitions including being selected as a Cyber-Physical Systems (CPS) rising star and a Siebel Scholar. He has also earned the Best Application Award for his work on TDTOS: T-Shirt Design and Try On System. As an educator and mentor, Dr. Ma is actively involved in guiding the next generation of researchers. He hosts pro bono office hours for students from underrepresented groups and is committed to fostering diversity in the field. He is currently building his research group at Cornell and plans to hire 1-2 graduate students during the 2024-25 cycle. Dr. Ma has organized several influential workshops including the "Synthetic Data for Computer Vision" workshop at CVPR 2025, the "Agent in Interaction, from Humans to Robots" workshop at CVPR 2025, and the "3D Modeling, Reconstruction, and Generation in the Wild" workshop at ECCV 2024, demonstrating his leadership in the computer vision community.
Dr. David Vinson is a Senior Lecturer in the Department of Experimental Psychology at University College London (UCL), where he conducts research at the intersection of language, cognition, and emotion. His academic profile demonstrates a strong commitment to understanding how humans process and represent meaning across various communication modalities. Dr. Vinson received his Doctor of Philosophy from University College London in 2009 and his Bachelor of Arts from Northwestern University in 1991. His educational background spans transatlantic institutions, providing him with a diverse academic foundation for his interdisciplinary research. His primary research focuses on language representations and processes, with particular emphasis on word, phrase and sentence meaning and its relation to other cognitive functions. A significant thread throughout his work examines the relationship between language and emotion, investigating how emotional content influences semantic processing. Dr. Vinson also explores how different communicative expressions integrate in multimodal contexts, studying both spoken language and sign systems. His methodological approach combines behavioral experimentation with selective neuroimaging techniques to provide comprehensive insights into language processing mechanisms. Dr. Vinson's publication record reveals consistent scholarly output across multiple high-impact journals in psychology, linguistics, and cognitive neuroscience. His recent work demonstrates growing interest in interdisciplinary semantic frameworks, with contributions to understanding how semantic representations differ for concrete versus abstract concepts, how iconicity functions in language, and how multimodal cues contribute to meaning construction. His research increasingly bridges theoretical linguistics with empirical cognitive science, reflecting contemporary trends in psycholinguistic research. As an educator, Dr. Vinson serves as module convenor for PSYC0004 Language and Cognition, an undergraduate second-year course at UCL. He actively contributes to the academic community as an ad hoc peer reviewer for numerous journals in psychology and neuroscience, a role he has maintained since 2002.
Aleksandar Bošković is a Senior Lecturer in Bosnian/Croatian/Serbian at Columbia University's Department of Slavic Languages and Literatures, where he also serves as Co-Director of the Institute of East Central Europe. His academic career spans both American and European institutions, bringing a transnational perspective to his work on Eastern European cultural production. Bošković received his Ph.D. in Slavic Studies from the University of Michigan (2013) and an M.A. in Comparative Literature and Literary Theory from the University of Belgrade (2007). Prior to joining Columbia, he was a research fellow at the Institute of Literature and Art in Belgrade (2003-2008), where he worked on Serbian literature of the 20th century. His academic journey reflects a deep engagement with both Western academic traditions and Eastern European intellectual contexts. Bošković's research focuses on experimental art from the former Yugoslavia, examining how creative practices across literature, film, visual arts, and radio express decolonial aesthetics and radical epistemologies. His work demonstrates how Yugoslav artists used techniques of refusal and negation to reframe questions of value and imagine new cultural narratives. He argues that radical Yugoslav art practices offer insights applicable to both post-Balkan and European contexts, emphasizing that culture is founded not on identity but on responsibility. His current project, "Nothing (:) Made in Yugoslavia," investigates how negation practices across different media in former Yugoslavia relate to the notion of artistic value, while he is also completing "Constructivist Cinépoetry Book," which explores cine-dispositive concepts in relation to early Soviet agit-poetry books. Bošković's scholarly output reveals a consistent engagement with avant-garde traditions, experimental media forms, and the intersection of political theory with artistic practice. His publications span multiple disciplines including literary studies, film theory, visual culture, and memory studies, with a particular focus on how artistic practices respond to political and historical circumstances in Eastern Europe, especially regarding negation, refusal, and the rethinking of cultural value systems. Collegium de Lyon Fellowship (2019-2020) Michael I. Sovern/Columbia Affiliated Fellowship at the American Academy in Rome (2023-24) NIAS Fellow (Semester 1, 2025-2026) Research support from the Harriman Institute Bošković has made substantial contributions to academic discourse through his numerous publications, including monographs, edited volumes, journal articles, and book chapters. His work bridges historical analysis with contemporary theoretical concerns, particularly regarding how artistic practices engage with questions of value, memory, and political imagination in post-socialist contexts. His research demonstrates how artistic practices can serve as powerful tools for reimagining cultural narratives beyond conventional identity frameworks.
Daniele Leonardis serves as Associate Professor of Applied Mechanics at the Institute of Mechanical Intelligence (IIM) of the Sant'Anna School of Advanced Studies in Pisa since October 2025. His research centers on wearable haptic interfaces and hand exoskeletons for clinical neurorehabilitation, virtual reality, and teleoperation applications. His primary research domains include: Development of miniaturized actuators for high-fidelity haptic rendering in wearable devices Clinical neurorehabilitation using serious games for children with Cerebral Palsy Integration of tactile feedback in teleoperation systems for complex manipulation tasks Soft exoskeletal devices for movement assistance in spinal/neurological patients Industrial robotics for railway infrastructure inspection Leonardis leads significant research initiatives: Coordinator and scientific director of the completed TELOS project (2024) for VR-based cerebral palsy rehabilitation Scientific director for SSSA in the European SUN project on augmented reality and haptic feedback Director of the SmartNest third-party research project Collaborator in TATTO, LEARN, and AVATAR projects Supervisor for RFI's mobile railway inspection system design His recent publications (2024-2025) demonstrate concentrated advancements in teleoperation interfaces, soft exoskeleton validation, and novel actuation methods for tactile feedback, with strong clinical and industrial validation components. He actively disseminates research through editorial roles in leading robotics journals and public demonstrations at international conferences. As founding partner of Next-Generation-Robotics spin-off, Leonardis bridges academic research and commercial applications in railway inspection robotics, reflecting his commitment to translational impact.
Konstantinos A. Tsintotas is an Assistant Professor at the Department of Information and Electronic Engineering, International Hellenic University. His research focuses on artificial intelligence, robotics, computer vision, and their applications in smart cities, healthcare, and manufacturing. He is actively involved in advancing AI-driven systems for critical infrastructure management, robotic vision, and embedded device technologies. His work spans theoretical advancements and practical implementations, including projects like SLAM algorithms for autonomous navigation, deep learning models for medical diagnosis, and IoT-integrated smart supply chains. Tsintotas also explores ethical implications of AI in human action recognition and contributes to neuromorphic computing through spiking neural networks. Key technical contributions include ReJSHand (real-time hand pose estimation), fall detection systems for embedded devices, and visual place recognition frameworks. His interdisciplinary approach bridges computer science, electrical engineering, and biomedical applications, reflecting a strong commitment to innovation at the hardware-software interface. Notable trends in his publications emphasize AI ethics, multimodal perception for robotics, and low-power embedded solutions. Ongoing work includes advancing digital twin technologies for supply chains and refining bio-inspired neural architectures for robotics applications.
Alberto Greco, Ph.D., is a Research Fellow in Bioengineering at the Department of Information Engineering, Faculty of Engineering, University of Pisa, Italy. He obtained his Laurea (2010) and Ph.D. (2015) in Biomedical Engineering and Automatics/Bioengineering from the University of Pisa, with a Visiting Fellowship at the University of Essex (2014). His work integrates biomedical signal processing , machine learning , and physiological modeling with applications in wearable monitoring systems and eye-tracking . Education Biomedical Engineering, University of Pisa (2010) Ph.D. in Automatics, Robotics, and Bioengineering, University of Pisa (2015) Visiting Fellow, University of Essex, UK (2014) His research focuses on autonomic nervous system assessment , central nervous system modeling , and affective computing , particularly through projects like cvxEDA —a convex optimization framework for electrodermal activity processing. He contributes to European research projects and develops smart wearable platforms for physiological data collection. Key application areas include consciousness disorders , mood assessment , and human-robot interaction . His technical expertise spans Bayesian statistics , sparsity-based signal modeling , and multimodal physiological analysis . He is affiliated with the Neuro-Cardiovascular Intelligence Lab and contributes to the TOLIFE project, ComEDA stress assessment tool, and ThermICA thermal response analysis system. His work bridges bioengineering , robotics , and social-emotional interaction domains.
Dr. Nigel Rogasch is an ARC Externally-Funded Research Fellow at the University of Adelaide's School of Biomedicine within the Faculty of Health and Medical Sciences. He is affiliated with the SAHMRI (South Australian Health and Medical Research Institute) and the Adelaide Health & Medical Sciences Building (AHMS). His primary research focuses on combining transcranial magnetic stimulation (TMS) with neuroimaging techniques (EEG, MRI) to investigate brain dynamics, plasticity, and their roles in healthy and disordered cognition. Research Interests: Understanding mechanisms of working memory and short-term memory Developing TMS-EEG methods to study cortical networks Exploring excitation/inhibition imbalances in schizophrenia and other mental illnesses Modeling how brain stimulation interacts with cortical circuits His work emphasizes translational applications of brain stimulation in clinical settings, such as treating aphasia and autism spectrum disorder. He actively supervises Honours and HDR students in cognitive neuroscience, neurophysiology, and engineering-related fields. Labs & Teams: Brain stimulation, imaging and cognition group at SAHMRI and AHMS.
Julia M. Stephen serves as an Adjunct Professor in the Department of Physics and Astronomy at the University of New Mexico and holds dual appointments as Director of the MEG/EEG Core and Professor of Translational Neuroscience at the Mind Research Network (MRN). Her primary research基地 focuses on advanced neuroimaging techniques to investigate brain development across the human lifespan, with particular emphasis on translational applications for neurological and psychiatric disorders. Dr. Stephen earned her PhD from the University of Minnesota, establishing the foundation for her specialized work in biophysics and neuroimaging. Her educational background directly supports her current methodological expertise in magnetoencephalography and multimodal brain imaging approaches. Her research program centers on developmental neuroscience, specifically examining typical and atypical brain maturation from infancy through aging. Key investigation areas include fetal alcohol spectrum disorders (FASD), autism spectrum disorders, schizophrenia, and Alzheimer's disease, with a focus on identifying neural biomarkers through MEG, EEG, and fMRI. She employs rigorous multimodal approaches to study sensory processing, cognitive development, and neural network dynamics, particularly investigating how prenatal exposures and aging processes alter brain function. Analysis of Dr. Stephen's publication record reveals consistent focus on developmental trajectories of brain function, with strong emphasis on FASD (7/15 articles), schizophrenia (4/15), and neuroimaging methodology development (3/15). Her work demonstrates increasing specialization in infant and child neurodevelopment since 2018, with recent publications highlighting MEG applications for early biomarker identification in prenatal exposure conditions and multisensory integration deficits. Dr. Stephen leads multiple NIH-funded research initiatives including the DevMind Study (tracking childhood brain development), investigations into prenatal alcohol exposure markers, and schizophrenia-related multisensory processing projects. Her laboratory infrastructure includes the MEG/EEG Core facility at MRN, which provides advanced neuroimaging capabilities for developmental and clinical neuroscience research. As a Principal Investigator, she directs interdisciplinary teams comprising neuroscientists, physicists, and clinicians to advance understanding of brain-behavior relationships across the lifespan.
Li Yi is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology, where they lead cutting-edge research at the intersection of computer vision, 3D graphics, and robotics. Their work focuses on advancing neural rendering, point cloud processing, and embodied AI with applications in human-object interaction and robotic manipulation. Research interests span Computer Vision , 3D Graphics , Robotics , Point Cloud Processing , Neural Rendering , and Human-Object Interaction . Recent work explores language-grounded spatial reasoning, dexterous manipulation, and 4D dynamic content generation, with publications appearing in top venues like CVPR, ICCV, and NeurIPS. Their research bridges theoretical advances with practical applications in embodied AI systems. The publication trends reveal a strong focus on neural rendering techniques (particularly NeRF variants), embodied AI for robotic manipulation , and multimodal understanding integrating vision, language, and action. Recent work increasingly incorporates large language models and focuses on generalizable approaches that transfer from simulation to real-world settings. As an advisor, Professor Li has mentored numerous students including Yunze Liu, Xueyi Liu, Zekun Qi, and Runpei Dong, who frequently appear as first authors on collaborative publications. Their research has been supported by significant grants enabling work on human-robot interaction, 3D scene understanding, and embodied AI systems. Professor Li leads a research group focused on developing comprehensive frameworks for spatial reasoning, object manipulation, and dynamic scene understanding. The team works on creating benchmarks like TACO for tool-action-object understanding and developing systems like MobileH2R for human-robot handover tasks. Current work emphasizes real-world applicability with a focus on generalizable solutions that work across diverse settings.
Dr Will Bailey is an Industry Collaboration Fellow at the University of Salford's School of Science, Engineering & Environment. He holds a PhD in Acoustics and Audio Engineering (2019) and completed postdoctoral research at the Universities of Cambridge and Sheffield, focusing on machine learning for assistive hearing devices. His current work at Salford Acoustic Labs involves commissioned projects across hearing science, sound design, and acoustics innovation. Research interests include applied machine learning (TinyML), human auditory perception, spatial audio technologies, and forensic analysis of animal cruelty cases. He has contributed to international challenges such as the Clarity Prediction Challenge and ICASSP SP Clarity Challenge, advancing speech enhancement and intelligibility prediction for hearing aids. Education: PhD in Acoustics and Audio Engineering (University of Salford, 2019) Postdoctoral Research: University of Cambridge (Machine Learning for Hearing Devices) Postdoctoral Research: University of Sheffield (Assistive Technology) Awarded the Noise Abatement Society John Connell Award (2024) and Best Business Collaboration (Rising Star) at the University of Salford (2024). His work bridges academic research with industry applications, emphasizing collaborative innovation. Associated with the Acoustics Innovation Institute and Salford Acoustic Labs, focusing on applied acoustic solutions for healthcare, urban environments, and immersive technologies.
Overview Gualtiero Volpe is a Professor affiliated with the Department of Informatics at the University of Genoa, Italy. His research focuses on Human-Computer Interaction (HCI), Affective Computing, and Multimodal Interaction, with particular emphasis on analyzing social interactions, embodied cognition, and expressive movement qualities in contexts such as music performance and education. He has collaborated extensively with researchers like Antonio Camurri, contributing to interdisciplinary projects that bridge computer science, cognitive science, and the arts. Research Interests Analysis of human movement and affect through computational methods Design of multimodal interactive systems for education and healthcare Investigation of laughter and social dynamics in human interactions Development of technologies for music performance and collaborative creativity Key Contributions Volpe's work spans over 140 publications in top-tier venues such as IEEE Transactions on Affective Computing, ACM Conference on Human Factors in Computing Systems (CHI), and International Conference on Multimodal Interaction (ICMI). His research has led to innovations in: Automated analysis of movement qualities using machine learning Sonification platforms for exergames and rehabilitation Embodied interaction frameworks for children's education Computational models of group cohesion and synchronization Recent Trends in Publications Recent studies emphasize the integration of cognitive science principles into movement analysis and the application of multimodal systems to address challenges in post-pandemic social interaction (e.g., digital commensality). His work on 'Embodied Multisensory Training' and 'Hybrid Co-Working in Industry 5.0' highlights a growing focus on real-world applications of HCI research. Awards & Grants No specific awards or grants are explicitly mentioned in the provided data, though his prolific publication record and leadership in collaborative projects suggest sustained academic recognition and funding support. Labs & Teams Volpe is part of the EyesWeb XMI research group at the University of Genoa, developing open-source platforms for real-time multimodal interaction analysis. He collaborates with institutions like the University of Trento and Goldsmiths, University of London on projects related to music technology and social robotics.
Andrea Cavallaro is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), serving as Director of the Idiap Research Institute. His roles span academia and leadership, including directing the Centre for Intelligent Sensing and holding editorial positions in top journals. He specializes in machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro leads projects like CORSMAL (multimodal object recognition) and GraphNEx (explainable AI). Education: PhD in Electrical Engineering from EPFL (2002). Notable awards include the Royal Academy of Engineering Teaching Prize (2007) and IEEE AVSS Best Paper Award (2009). He is a Fellow of the Higher Education Academy, International Association for Pattern Recognition, and ELLIS. Research focuses on aligning AI with societal values, particularly privacy and trustworthiness. Recent work explores adversarial attacks, privacy personas, and robotic manipulation. His 2025 publications address 3D reconstruction, privacy-aware models, and human-robot interaction. PhD students: 11 advisees in deep learning and robotics. Grants/Projects: AlignAI (trustworthy LLMs), GraphNEx (GNNs for XAI), CORSMAL (multimodal sensing). Labs/Teams: Idiap Lab (EPFL), leading CORSMAL consortium, and Turing Institute collaboration.
