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Francesco De PaceView profile

Researcher

• Augmented Reality
• Virtual Reality
• Human-Robot Interaction
• +5 more

Francesco De Pace is a PostDoc Researcher at the Institute of Visual Computing and Human-Centered Technology within the Faculty of Informatics at Vienna University of Technology (TU Wien). His research focuses on the intersection of augmented reality, virtual reality, and human-robot interaction, with particular emphasis on developing innovative interfaces for industrial applications. Dr. De Pace's research spans several key areas in immersive technologies and human-computer interaction. His primary interests include Augmented Reality (AR) and Virtual Reality (VR) systems, Human-Robot Interaction (HRI), Brain-Computer Interfaces (BCIs), and advanced tracking and localization techniques. He has made significant contributions to the development of AR/VR interfaces for industrial robots, outdoor tracking with Real-Time Kinematic GPS, and SLAM (Simultaneous Localization and Mapping) systems. His work bridges theoretical research with practical industrial applications, focusing on creating more intuitive and effective human-machine interfaces. Dr. De Pace's recent publications demonstrate a clear trajectory toward enhancing human-robot collaboration through immersive technologies. His research shows increasing sophistication in integrating multiple modalities (visual, spatial, and neural) to create more natural interaction paradigms. A notable trend is the application of AR/VR to industrial settings, particularly for assembly tasks, path planning, and training scenarios. His systematic evaluation of RTK-GPS for wearable AR represents important foundational work for outdoor AR applications, addressing critical challenges in positional accuracy across different environmental conditions. While specific awards are not prominently documented in the available information, Dr. De Pace's research has been supported by significant funding mechanisms, including grants from the Austrian Research Promotion Agency (FFG) under Grant Agreement No FO999886342 KIRAS MRespond, indicating recognition of the importance and potential impact of his work. Dr. De Pace appears to be actively involved in multiple research projects, including PostDisaster and MRespond as indicated in his profile. His collaborative approach is evident through his extensive co-authorship with researchers across institutions. While specific student advising information isn't detailed in the available materials, his role as a PostDoc Researcher likely involves mentoring junior researchers and contributing to the academic development of students working on related projects. Dr. De Pace is associated with the Mixed Reality Lab at TU Wien, as indicated in the website navigation. His research appears to be conducted within collaborative frameworks that include both academic and industry partners. His work on the MRespond project suggests engagement with emergency response applications of mixed reality technologies, indicating interdisciplinary team involvement spanning computer science, engineering, and potentially public safety domains.

Nadja AlthausView profile

Researcher

• Object Categorization in Infants
• Language Acquisition
• Visual Processing
• +3 more

Nadja Althaus is a Postdoctoral Researcher at the University of Oxford's Department of Experimental Psychology, affiliated with the Oxford Babylab and the Oxford Centre for Developmental Science. Previously, she held postdoctoral positions at Oxford Brookes University Babylab and the Centre for Brain and Cognitive Development, Birkbeck College, London. She completed her PhD at Birkbeck College and holds an MA in Linguistics, Psychology, and Computer Science from the University of Tübingen, with a visiting period at the University of Toronto. Education includes a PhD from the Centre for Brain and Cognitive Development, Birkbeck College, University of London (2006–2010), and an MA in Linguistics, Psychology, and Computer Science from the University of Tübingen (2000–2006). She also spent time as a visiting student at the University of Toronto's Department of Computer Science (2003–2004). Her research interests focus on infant object categorization, exploring how language and perceptual cues influence cognitive development. She employs eye-tracking and computational modeling to study visual and cross-modal processing, language acquisition, and machine learning applications in cognitive science. Recent publications highlight her focus on multi-sensory integration in development, the application of saliency maps in visual cognition studies, and foundational work on connectionism in neuroscience. These contributions underscore her interdisciplinary approach, merging empirical research with theoretical modeling. Nadja has advised no listed students but has contributed to grants through her research roles. She is a key member of the Oxford Babylab and collaborates within the broader developmental science community. Her work is centered at the Oxford Babylab, where she investigates infant cognition through innovative methodologies.

• Biomedical Image Processing
• Medical Image Analysis
• Bioimaging Sciences
• +4 more

James Duncan is the Ebenezer K. Hunt Professor of Radiology and Biomedical Engineering, and Professor of Biomedical Engineering at Yale University's School of Medicine. He leads the Department of Biomedical Engineering as Department Chair and holds joint appointments in Diagnostic Radiology and Electrical Engineering. His research focuses on biomedical image processing, model-based image analysis, and medical imaging technologies. Notably, he pioneered techniques for anatomical object segmentation using Bayesian strategies and deformable motion tracking in cardiac imaging. Education: PhD from University of Southern California (1982), MS from UCLA (1975), BSEE from Lafayette College (1973). Research interests include medical image segmentation, motion tracking, image-guided surgery, and AI-driven medical imaging solutions. His team developed foundational methods for MRI/SPECT analysis and cardiac biomechanics modeling. Over 176 publications demonstrate his expertise in machine learning applications for healthcare, with recent work in domain adaptation, diffusion models, and low-dose imaging techniques. Awards include IEEE Fellow (2000), NIH BRP grants, and multiple R01 grants. His lab collaborates widely, advancing translational imaging technologies and interdisciplinary engineering-medicine initiatives. Major grants include sustained NIH funding since 1993. He mentors interdisciplinary teams and chairs the Yale Biomedical Imaging Institute. Current projects address AI in diagnostic imaging, cancer immunotherapy monitoring, and personalized cardiac modeling.

Paul L BendichView profile

Adjunct Professor

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +4 more

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

• Machine Learning
• Neural Networks
• Quantum Computing
• +3 more

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

• Neurosciences
• Mental Health
• Neurobiology
• +6 more

Dr. Natalie Matosin is a Research Fellow and Director of the MINDS Lab (Mental Illness, Neurobiology, and Disorders of Stress) at the University of Sydney's Charles Perkins Centre. She holds prestigious fellowships, including the Sydney Horizon Fellowship and the Al & Val Rosenstrauss Rebecca L Cooper Medical Research Foundation Fellowship. Her research focuses on single-cell resolution analysis of brain cells to develop personalized therapies for brain illnesses such as mental disorders, dementias, and traumatic brain injury. She has established an independent laboratory at the University of Wollongong in 2018 before joining Sydney. Education: Dr. Matosin earned her PhD in neuroscience from the University of Wollongong in 2015. Postdoctoral training at UNSW Sydney and the Max Planck Institute of Psychiatry in Munich honed her expertise in next-generation sequencing and advanced histology techniques for analyzing postmortem human brain tissue. Research Interests: Her work integrates molecular biology, bioinformatics, and translational research to study stress physiology, synaptic pathology, and epigenetic mechanisms (e.g., DNA methylation of FKBP5). Key themes include the pathogenesis of psychiatric disorders such as schizophrenia and depression, as well as the impact of stress on brain structure and function. She emphasizes interdisciplinary approaches, leveraging spatial omics and multi-omic data to map disease mechanisms. Scientific Awards: Forbes 30 Under 30 in Europe (2020), TEDx speaker, NHMRC Fellow, International Brain Research Organisation Fellow, Humboldt Foundation Fellow. Advising & Grants: Matosin leads international collaborations and has secured grants including the Australian Research Council Discovery Projects (DP24), Sydney Horizon Fellowship (2023), and Faculty of Medicine and Health startup funding (2024). Her grants investigate adult neurogenesis, cortical aging effects, and stress-induced molecular changes in psychiatric disorders. While no specific advisees/students are listed, her lab (MINDS) actively engages in collaborative research across multiple countries. Labs & Teams: The MINDS Lab is central to her work, focusing on neurobiology of stress and mental health. Her team spans institutions in Australia, USA, Canada, Germany, Croatia, Iceland, and Sweden, emphasizing global partnership in translational neuroscience projects.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +3 more

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Chen LIView profile

Associate Professor

• Human Robot Interaction
• Industrial Robotics
• AI for Manufacturing
• +3 more

Chen LI is an Associate Professor in the Department of Materials and Production at Aalborg University, Denmark. His work focuses on advancing smart manufacturing through AI-driven robotics and 5G-enabled production systems. He leads research projects involving historical figure emulation in robotics, human-robot interaction, and AI at the edge for industry 5.0. His interdisciplinary approach bridges software architecture, industrial automation, and cognitive modeling. Key projects: Time-Traveling Conversations (Villum Foundation), Human Robot Interaction dialog systems, and AI REDGIO 5.0 for SME manufacturing Collaborations span institutions like Seoul National University and industry partners Research interests include: AI ethics in dialogue systems Edge computing for real-time manufacturing Virtual assistant architectures Robot speaking style imitation Over 70 publications (2009-2025) span topics like anomaly detection frameworks, defect prediction models, and hybrid power system analysis. Active in conferences such as HRI2021 and workshops on AI in performing arts.

Alejandro Luis CallaraView profile

Researcher

• Biomedical Signal Processing
• Neuroimaging
• Brain Connectivity
• +6 more

Alejandro Luis Callara serves as a postdoctoral researcher at the University of Pisa's Research Center "E. Piaggio," specializing in biomedical signal and image processing methodologies for neuroscience applications. His work bridges engineering and cognitive science through advanced computational approaches to physiological data analysis. His academic foundation includes: Bachelor’s Degree in Biomedical Engineering (2012), University of Pisa Master’s Degree in Biomedical Engineering (2015), University of Pisa PhD in Information Engineering (2019), University of Pisa Callara's research centers on developing analytical pipelines for EEG and fMRI data to map brain connectivity networks, with particular expertise in autonomic nervous system interactions during emotional processing. He actively contributes to the Brain matters team's development of neuronal segmentation tools for confocal microscopy data, extending his methodological focus to multi-scale tissue imaging. His technical proficiency spans directed coherence analysis, dynamic causal modeling, and thermal response quantification. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) autonomic physiology investigations examining parasympathetic-sympathetic coupling during emotional tasks using EDA and HRV metrics; (2) innovative neuroimaging techniques for brainstem fMRI analysis and sparse connectivity mapping; and (3) multimodal integration studies exploring olfactory-visual-auditory interactions in affective disorders through VR/AR paradigms. Methodologically, his work consistently employs advanced signal processing frameworks like PCA-based partial correlation and ThermICA for multivariate analysis. Within the Research Center "E. Piaggio," Callara collaborates across bioengineering and robotics initiatives, particularly through the Brain matters team where he applies computational neuroscience to neuronal morphology analysis. His current projects focus on refining contactless stress classification systems using thermal imaging and developing immersive VR scenarios for anxiety disorder research, demonstrating strong translational potential for clinical applications.

Michael MahoneyView profile

Professor

• Machine Learning
• Big Data
• Statistical Analysis
• +5 more

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

Shen Heng TaoView profile

Academic

• Artificial Intelligence
• Multimedia Computing
• Computer Vision
• +3 more

Shen Heng Tao is a Distinguished Professor and Dean of the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). He is also the Executive Dean of the AI Research Institute and Chief Scientist of Vision Intelligence at the Peng Cheng Laboratory. His academic journey includes a BSc (First Class Honours) and PhD from the National University of Singapore (2000 and 2004), followed by roles at the University of Queensland, where he became a Professor in 2011. His research focuses on artificial intelligence, multimedia computing, and computer vision, with notable contributions to cross-media intelligent analysis and real-time video retrieval systems. He has published over 400 peer-reviewed papers, including 160+ IEEE/ACM Transactions, and holds an H-index of 78. His work has been recognized with awards such as the IEEE Fellow, ACM Fellow, OSA Fellow, and Clarivate Highly Cited Researcher. He has led 15 major research grants, including projects from the Ministry of Science and Technology of China and National Natural Science Foundation. His service includes roles as Associate Editor for journals like ACM Transactions of Data Science and IEEE Transactions on Multimedia, and conference chairs like ACM Multimedia 2021. His lab, the Center for Future Media, explores advanced topics like multimodal learning, trustworthy AI, and human-robot interaction. Current research emphasizes cross-modal reasoning and real-world applications in metaverse, autonomous systems, and smart cities.

Luo JieboView profile

Professor

• Computer Vision
• Natural Language Processing
• Machine Learning
• +4 more

Jiebo Luo is a Professor of Computer Science at the University of Rochester, where he has held this position since 2014. He earned his BS and MS in Electrical Engineering from the University of Science and Technology of China (1989 and 1992) and a PhD in Electrical Engineering from the University of Rochester (1995). Prior to academia, he spent 15+ years at Kodak Research Laboratories as a Senior Principal Scientist. His research focuses on computational social science, natural language processing, digital health, computer vision, data mining, and multimedia. Dr. Luo’s work has been recognized through numerous awards, including the ACM SIGMM Technical Achievement Award (2021), Fellowships from ACM, AAAI, IEEE, IAPR, and SPIE. He has authored over 500 peer-reviewed papers, holds 90+ patents, and serves as Editor-in-Chief of the IEEE Transactions on Multimedia. He actively contributes to conference organization (e.g., ACM Multimedia, CVPR) and editorial roles for top journals. Key contributions include pioneering work in social media analytics, sentiment analysis, and digital health, as well as foundational research in multi-label classification and action recognition datasets like UCF 101. His labs and collaborations span the Goergen Institute for Data Science and the Greater Rochester Data Science Industry Consortium.

Feng LiuView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +11 more

Feng Liu is an Assistant Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He previously served as a Postdoctoral Researcher at Michigan State University's Department of Computer Science and Engineering. His research spans computer vision, machine learning, and biometric recognition, with a focus on 3D scene understanding, generative AI, and human-AI interaction. Research Interests: 3D Computer Vision: 3D object/scene understanding, 3D generation, VR/AR, 3D vision+language understanding 3D Human Digitization: Modeling, reconstruction, rendering, biomechanics Generative AI: Explainability, generalization, controllability in generative models, DeepFake detection Biometric Recognition: Face and gait recognition, person re-identification AI + X: Applications in Education and Healthcare Publication Trends: Dr. Liu's recent publications demonstrate a strong focus on advancing biometric recognition systems, particularly in open-set scenarios and video-based person re-identification. His work integrates 3D vision, diffusion models, and large-scale benchmarking, with increasing emphasis on real-world challenges such as aerial-ground integration, long-range recognition, and controllable synthetic data generation for training robust models. Scientific Recognition: Area Chair for BMVC 2025, ACM MM 2025, FG 2025, IJCNN 2025 Area Chair for FG 2024 Advising and Engagement: Dr. Liu is actively recruiting PhD students and interns, indicating an expanding research group. He regularly presents at major conferences including CVPR, NeurIPS, and WACV, and participates in workshops such as ELFA, demonstrating active engagement with the research community. His collaborations span multiple institutions, including Michigan State University, Queensland University of Technology, and others. Labs and Teams: While specific lab names are not mentioned in the provided texts, Dr. Liu leads a research group focused on computer vision and AI, with projects involving 3D reconstruction, biometrics, and generative models. His work on AG-VPReID and HAMoBE suggests leadership in developing large-scale benchmarks and hierarchical recognition systems.

Jie CaoView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +4 more

Dr. Jie Cao is an Assistant Professor at the School of Computer Science, University of Oklahoma, affiliated with the Gallogly College of Engineering and the Data Science and Analytics Institute. He holds a PhD from the University of Utah, and MS/BS degrees from Huazhong University of Science and Technology. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), dialogue systems, structured prediction, and trustworthy AI applications in education and healthcare. Prior to OU, he was a postdoctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, and worked as a research scientist at WeChat AI, Amazon, and in software engineering at Alibaba and Baidu. Education: PhD in Computer Science, University of Utah MS in Computer Science, Huazhong University of Science and Technology BS in Information Security, Huazhong University of Science and Technology Research Interests: Dr. Cao’s work spans multi-party/multimodal dialogue analysis, LLM alignment, efficient structured prediction, and robust AI deployment. Notable projects include AI partners for collaborative classrooms, discourse analysis in psychotherapy, and database workload characterization. His research bridges foundational NLP/ML advancements with interdisciplinary applications in education, healthcare, and material science. Key Achievements: Recipient of the iSAT Trainee Grant (2023) and Alternative Textbook Grant (2025) Lead developer of MatterChat, a multi-modal LLM for material science Published over 20 peer-reviewed papers in top venues like ACL, COLING, and IEEE TVCG Co-instructor for NLP courses integrating LLM and agentic AI materials Labs & Teams: Leads the OUNLP lab at OU, focusing on advancing dialogue systems and trustworthy AI solutions.

Gerard Renardel de LavaletteView profile

Professor

• Computer Science
• Mathematical Logic
• Discrete Mathematics
• +4 more

Prof. Gerard Renardel de Lavalette is a Professor in the Department of Fundamental Computing Science at the University of Groningen’s Faculty of Science and Engineering. His research focuses on mathematical logic, formal methods, and theoretical computer science, with notable expertise in interpolation theory, modal logics, and belief revision systems. Academic Appointments: Deputy Programme Director for Computing Science. Research Interests: Intersection of discrete mathematics, intuitionistic logic, and computational logic applied to software verification and social simulation models. His work integrates foundational logic with practical applications in agent-based systems and GDPR-compliant data anonymization. Recent contributions include dialogical models for opinion dynamics and studies on pseudonymisation techniques. He has authored over 50 peer-reviewed publications across journals and conferences. Key research themes include formal semantics of modularization, Lindelöf properties in infinitary logics, and the design of knowledge-based programming frameworks.