Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.
Kevin Y Chen is an MS Student in Health Policy at Stanford University and a Clinical Instructor at Stanford Children's Health. He holds concurrent roles as an Intermountain Fellow in Population Health, Delivery Science, and Primary Care. His academic background includes a pediatrics residency at the University of Utah School of Medicine. His research focuses on improving healthcare quality and process efficiency through high-value care initiatives. Key technical interests span artificial intelligence, robotics, and autonomous systems, with recent work addressing reinforcement learning, LiDAR data transfer for autonomous vehicles, and language-conditioned robot behavior. His articles reflect interdisciplinary contributions to robotics, computer vision, and embodied AI, with a focus on navigation, 3D reconstruction, and human-robot interaction. Current research integrates AI-driven solutions to healthcare challenges such as workflow optimization and medical education. No scientific awards have been explicitly mentioned in the provided text. His advising and grant activities remain unspecified, though his roles suggest involvement in clinical and educational projects.
Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.
Jeronimo Grandi is an Assistant Professor in the Department of Computer & Cyber Sciences at the School of Computer and Cyber Sciences, Georgia Southern University. His academic work spans augmented reality (AR), virtual reality (VR), and human-computer interaction, with a focus on collaborative technologies and cybersecurity in extended reality (XR). He holds a Ph.D. and M.S. in Computer Science from Federal University of Rio Grande do Sul (2018 and 2014), and a B.A. in Game and Interactive Media Design from University of Caxias do Sul (2011). His research emphasizes AR/VR interfaces for first responders, public safety applications, and continuous authentication systems. Notable achievements include the Best 3DUI Contest Award (2023), Unreal Faculty Fellowship (2023), and a Ph.D. distinction (Cum Laude) in 2018. He teaches courses like Human-Computer Interaction and Dissertation Research, and actively reviews for conferences like ACM ISMAR and IEEE VR. Key research directions include modular AR frameworks for emergency response, cross-reality collaboration tools for sustainability education, and visual search optimization in VR. His work bridges theoretical HCI principles with practical implementations in law enforcement, healthcare, and disaster management scenarios.
Dr. Abhijit Mahalanobis is an Associate Professor at the University of Central Florida (UCF), affiliated with the Center for Research in Computer Vision (CRCV). Previously, he served as a Senior Fellow at Lockheed Martin and held academic positions at the University of Arizona and the University of Maryland. He earned his B.S. from UC Santa Barbara (1984) and M.S./Ph.D. from Carnegie Mellon University (1985/1987). His research focuses on computational sensing, imaging systems, and automatic target recognition (ATR). Notable contributions include work on correlation filters, compressive sensing, and 3D imaging. He has published over 170 papers, holds four patents, and co-authored a book on pattern recognition. Dr. Mahalanobis has received prestigious awards such as the IEEE Fellow (2015), OSA Fellow (2004), and SPIE Fellow (1997). He has been honored with the Lockheed Martin NOVA Award (2005), Scientist of the Year (2006), and Innovator of the Year (1999). He has served on editorial boards for journals like Applied Optics and Pattern Recognition , and chairs for OSA and SPIE conferences. His research spans topics including infrared target detection, compressive sensing architectures, and deep learning for target recognition. The CRCV lab, under his leadership, develops advanced algorithms for defense and surveillance applications.
Ilaria Boscolo Galazzo is a Temporary Assistant Professor at the Department of Engineering for Innovation Medicine at the University of Verona. Her academic work focuses on the intersection of engineering, neuroscience, and medical applications, with particular expertise in neuroimaging techniques and computational analysis of biomedical data. Academic Sector: IBIO-01/A - Bioengineering Research Sectors: PE6_11 (Machine learning and signal processing), LS5_17 (Imaging in neuroscience), LS5_16 (Systems and computational neuroscience) Her research interests center on neuroimaging, including structural (diffusion MRI) and functional (EEG, fNIRS, functional MRI) imaging techniques, as well as perceptual analysis through cognitive science methods. She employs advanced computer vision and pattern recognition methods to design numerical biomarkers for characterizing healthy and pathological conditions. Her work aims to gain a holistic view of the human brain through integration of multi-modal, multi-scale probing and modeling approaches. Dr. Boscolo Galazzo is actively involved in several research groups including the Neuroimaging Group and Vision, Images, Patterns and Signals (VIPS) group, where activities focus on analysis, recognition, modeling and prediction of multivariate multidimensional signals using AI and machine learning techniques. Her research spans image processing, computer vision, pattern recognition, and biomedical data analysis for both basic and translational research. She has led multiple research projects including EDIPO (focused on neuroimaging genetics in translational research), investigations into brain connectivity underlying physiological and pathological patterns in action tremor, and mapping functional connectivity patterns in neurological diseases using advanced MRI techniques. As an educator, she serves on multiple teaching committees including the Computer Science Teaching Committee, Mathematics and Data Science Teaching Committee, and Information Engineering Teaching Committee. She also participates in the Collegio dei Docenti del Dottorato in Ingegneria dei Sistemi Intelligenti and the Consiglio di Dipartimento Ingegneria per la Medicina di Innovazione.
Ren-Cang Li is a Professor of Mathematics at The University of Texas at Arlington, with affiliations including past roles as Chair Professor at Hong Kong Baptist University and positions at University of Kentucky and Oak Ridge National Laboratory. He earned his BS from Xiamen University (1985), MS from Chinese Academy of Sciences (1988), and PhD in Applied Mathematics from UC Berkeley (1995). His research focuses on numerical linear algebra, eigenvalue problems, high-performance computing, and machine learning, with impactful contributions like secular equation solvers in LAPACK and work on matrix equations. Awards include the NSF CAREER Award (1999) and multiple distinguished paper awards. Education: PhD in Applied Mathematics, UC Berkeley, 1995 MS in Computational Mathematics, Chinese Academy of Sciences, 1988 BS in Computational Mathematics, Xiamen University, 1985 Research interests span numerical algorithms, eigenvalue computations, optimization, and applications in machine learning. Notable contributions include methods for large-scale eigenvalue problems, matrix equations, and compressed sensing. His work on the secular equation solver is foundational in scientific computing libraries. Key Awards: Friedman Memorial Prize in Applied Mathematics (1996) NSF CAREER Award (1999) Three Distinguished Paper Awards from ICCM (2018-2020) INFORMS Data Mining Best General Paper (2019) Advising and Grants: Supervised numerous PhD students and led federally funded projects totaling over $5M, including work on probabilistic graph learning and nonlinear matrix equations. Active in editorial roles for journals like SIAM Journal on Matrix Analysis and Mathematical Communications .
Peter W. Kalivas is a Professor in the Department of Neuroscience at the Medical University of South Carolina (MUSC). His academic role focuses on understanding the neural mechanisms underlying addiction and reward processing, with particular emphasis on synaptic plasticity and neuropharmacology. He holds a PhD in Neuroscience and has been a leading figure in translational research bridging basic science and clinical applications in substance use disorders. His research interests span addiction neurobiology, neurophysiology, and the development of novel therapeutic targets for psychiatric and substance use disorders. Key areas include studying synaptic adaptations in the nucleus accumbens and ventral pallidum, the role of astrocytes and extracellular matrix in drug relapse, and the impact of stress and cannabinoids on reward circuits. Dr. Kalivas has published extensively on topics such as GABAergic/glutamatergic interactions, matrix metalloproteinases, and the efficacy of N-acetylcysteine (NAC) in treating addiction and PTSD. His work integrates animal models (e.g., rodents) with preclinical trials to explore mechanisms of vulnerability/resilience in opioid and heroin use disorders. Key affiliations: College of Medicine, Neuroscience Department at MUSC Research Themes: Synaptic plasticity, addiction neurobiology, drug relapse mechanisms Notable Projects: Multi-center studies on NAC in PTSD, genome-wide association studies in rats His contributions highlight the role of non-neuronal elements (astrocytes, extracellular matrices) in addiction, challenging traditional neuron-centric views. Collaborations span neuropharmacology, computational modeling (e.g., video denoising for microscopy), and translational medicine.
Scott Widmier is an Associate Professor in the Department of Marketing & Professional Sales at Kennesaw State University. He holds a PhD from Arizona State University and a BBA from Texas Christian University. His research focuses on Sales Strategy, International Marketing, and Direct Selling, with notable interests in institutional theory and global market selection. Education: PhD in Marketing, Arizona State University BBA, Texas Christian University MBA coursework completed at Arizona State University Research Interests: Marketing Strategy in Global Contexts Ethical and Sustainable Sales Practices Impact of Regulatory Environments on Sales Behavior Executive Education in Global Selling Skills Awards & Recognition: Nominated for NCSM Proceedings Editor KSU Foundation Distinguished Service Award Nominee Achievement Awards from NCSC His work combines academic rigor with practical insights, emphasizing institutional frameworks and factor endowment theory in market selection. Current research explores dark triad personality influences in hospitality and innovative survey methodologies for social sciences.
Xiaoyi Jiang is a Professor at the Institute of Computer Science at the University of Münster, leading the Jiang Lab focused on Pattern Recognition and Image Analysis. He holds roles in the CiM-IMPRS Graduate Programme Management Board and participates in projects like the Multiscale Imaging Centre. His research emphasizes biomedical image analysis, machine learning, and medical applications such as tumor segmentation, vessel network extraction, and automated tracking systems for small organisms. Key contributions include FIMTrack software for locomotion analysis, the Voreen visualization framework, and complex-valued neural network architectures. He has authored over 200 papers and holds patents in imaging and segmentation technologies. His work bridges computer science with medical and biological applications, aiming to solve challenges in automated analysis and interpretation of biomedical data.
Enrique S. Quintana-Ortí is a Professor at the Technical University of Valencia and Jaume I University , Spain, specializing in Computer Science of Systems and Computers . His work bridges High-Performance Computing (HPC) , Parallel Computing , and Deep Learning , with a focus on optimizing Matrix Algorithms for modern architectures. Key research areas: Quantized Inference , GEMM-Based Convolutions , GPU Acceleration , and Performance Portability across ARM, RISC-V, and NVIDIA processors. Recent projects include RED-SEA (European interconnect solutions), GreenLightningAI (decoupled AI systems), and Ginkgo (GPU-based linear algebra frameworks). His publications (2023–2025) emphasize edge computing , mixed-precision techniques , and energy-efficient AI . Collaborative efforts span institutions like Xilinx , Fujitsu , and co-authors such as Adrián Castelló , Héctor Martínez , and Francisco D. Igual .
Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.
Hélder Filipe Oliveira is a Senior Researcher at INESC TEC and an Invited Assistant Professor at the University of Porto's Computer Science Department. He holds a Ph.D. in Electrical and Computer Engineering from FEUP. His roles include leading the Visual Computing and Machine Intelligence Area at INESC TEC and coordinating the Data Science Hub. He has extensive experience in research projects, including leading the LuCaS and MICOS initiatives, and has supervised 6 current PhD students, 1 concluded PhD, and 56 MSc students. Education: B.Sc. (2004), M.Sc. (2008), and Ph.D. (2013) in Electrical and Computer Engineering from FEUP. Research focuses on medical image analysis, bio-image processing, computer vision, and machine learning applications in healthcare. His work includes developing AI models for TBI patient prediction and lung cancer diagnosis, with notable contributions to interpretable deep learning systems. Publications span over 80+ peer-reviewed works, 1 patent, and datasets. He actively participates in academic events, organizing the VISUM summer school and serving as a keynote speaker. Labs/Groups: Visual Computing and Machine Intelligence Area, Breast Research Group, and Centre for Telecommunications and Multimedia.
Michael Adjeisah is a Research Fellow at Bournemouth University's CfACTs Research Centre, focused on interdisciplinary research in machine learning and artificial intelligence. His work spans computer vision, natural language processing, health informatics, and data science, with applications in cultural heritage technology, low-resource language processing, and medical systems. Notable research areas include Adinkra symbol recognition using deep learning, graph neural networks for classification tasks, and sentiment analysis models leveraging attention mechanisms. He has contributed to advancements in neural machine translation for low-resource languages and blockchain-enabled privacy solutions for electronic health records. Adjeisah's publications reflect a strong emphasis on practical applications of AI, with recent work addressing challenges in spoken digit recognition for Amharic and respiration-based biometric systems. His research often integrates multi-sensor fusion and data augmentation techniques to enhance model performance in real-world scenarios.
Dr. John Chiverton is a Senior Lecturer at the University of Portsmouth within the Faculty of Technology, School of Electrical and Mechanical Engineering and affiliated with the Institute of Life Sciences and Healthcare and Portsmouth AI and Data Science Centre . He specializes in signal and image processing with applications across biomedical engineering, computer vision, and assistive technologies. Research Interests : Automated 3D Imaging Analysis Physiological Signal Processing (Blood Pressure Forecasting) Computer Vision for Human Activity Recognition Machine Learning in Medical and Industrial Applications Multi-modal Sensor Data Integration Scientific Contributions include 49 research outputs (17 articles, 28 conference contributions) and 15 projects funded by entities like Google and Mitsui Sumitomo Insurance Welfare Foundation . His work spans biomedical imaging, robotics, and smart environment systems. Awards : IEEE Award IPEM Award Collaborations include institutions like University of Surrey, University of Bristol, and cross-disciplinary teams in medical imaging and smart material analysis . He supervises PhD students in areas like 3D Imaging and Physiological Signal Analysis .
