Дејан Д. Ранчић је редовни професор на Електронском факултету у Нишу (Универзитет у Нишу), где је изабран 2015. године након дугогодишњег рада као доцент од 2005. године. Дипломирао, магистрирао и докторирао је на истом факултету у области Електротехника и рачунарство. Његово образовање укључује: Дипломирани инжењер електротехнике и рачунарства (1993) Магистар електротехнике и рачунарства (1997) Доктор електротехнике и рачунарства (2004) Ранчићев истраживачки фокус је на ГИС технологијама , 3D визуелизацији терена и обраги радарских сигнала . Кључни доприноси укључују развој алгоритама за мобилне ГИС апликације, паралелно рендеровање великих терена и радарску обраду података за сузбијање града. Његов рад комбинује теоријску рачунарску геометрију са практичним инжењерским решењима. Анализа његових радова показује еволуцију од радарских система (1997-1999) ка иновативним ГИС решењима (2000-2006), са посебним нагласком на визуелизацију терена и мобилне платформе. Најзначајнији радови објављени су у часописима као што су Computer and Geosciences и WSEAS Transactions on Computers . Тренутно учествује у 3 национална и 1 интернационални истраживачки пројекат, а као рецензент ради за WSEAS серију часописа. Иако детаљи о менторству нису наведени, његова публикацијска активност и пројекти указују на значајну улогу у развоју геопросторних технологија у Србији.
Peggy Chi is a Staff Research Scientist at Google DeepMind and a Visiting Associate Professor at National Taiwan University's Department of Computer Science and Information Engineering. She holds a Ph.D. in Computer Science from UC Berkeley and an M.S. from MIT Media Lab. Her research focuses on interactive systems, accessibility technologies, and AI-driven interfaces to enhance creativity and user experience in areas like video creation, non-visual access for visually impaired users, and cross-device interaction. Key research interests include Human-Computer Interaction (HCI), accessibility, and the application of AI in user-facing technologies. She has pioneered projects such as TacNote (tactile/audio note-taking for BVI users), Slide Gestalt (non-visual slide structure extraction), and Bespoke (LLM-based interface generation). Her work bridges theory and practice, with contributions to top venues like ACM CHI, UIST, and CVPR. Received a Best Paper Award at ACM CHI and a Google PhD Fellowship . Published over 20+ papers in HCI and AI, focusing on accessibility, video technology, and cross-device systems. Her research also extends to educational tools (e.g., MixT for mixed-media tutorials) and ubicomp systems for health and daily life, such as calorie-aware smart kitchens. Peggy collaborates with industry and academia to advance interactive technologies that empower users through innovation and inclusivity.
Dr. Craig Abbey is a Researcher at the University of California, Santa Barbara (UCSB), affiliated with the Vision and Image Understanding Lab. His work focuses on optimizing medical image analysis through mathematical modeling of human visual perception and engineering parameters like image processing algorithms. He held prior roles including Assistant Professor at UC Davis Biomedical Engineering and postdoctoral fellowships in Medical Physics. Abbey’s research integrates computational models with experimental psychophysics to improve medical imaging applications, particularly in breast cancer screening and CT/SPECT imaging. Educations: PhD in Applied Mathematics (University of Arizona, 1998) Research interests center on how visual tasks (e.g., lesion detection) are influenced by image noise textures, display engineering, and visual adaptation effects. He explores AI-driven tools for enhancing radiologist performance and developing anthropomorphic model observers that replicate human visual search strategies in 3D medical imaging. Recent work addresses sequential reading effects in mammography, non-Gaussian noise properties in CT, and deep learning-based denoising techniques for SPECT/CT. His studies highlight practical applications like optimizing screening protocols and improving diagnostic accuracy through adaptive image ordering. Labs/Teams: Vision and Image Understanding Lab at UCSB.
Mark Burkhardt is a doctoral researcher and research associate at the Institute for System Dynamics within the Cluster of Excellence IntCDC at the University of Stuttgart. His work focuses on control systems and automation technologies for construction machinery, particularly tower cranes. Current affiliation: Institute for System Dynamics, University of Stuttgart Academic rank: Researcher Specializations: Crane control systems, cyber-physical construction platforms, automation of timber structures Research Interests: Burkhardt's research spans control systems engineering, automation technologies, and cyber-physical systems. His work addresses critical challenges in: Load sway damping for top-slewing cranes Path planning and absolute positioning of tower cranes Modeling and control of collaborative crane systems Development of novel grab systems for construction automation Georeferenced payload tracking Multi-agent crane coordination Teaching: He has taught courses on system dynamics, control theory, and programming since 2019, including: "Flache Systeme" (Flat Systems) "Systemdynamische Grundlagen der Regelungstechnik" (System Dynamics Fundamentals of Control Engineering) "Einführung in die Programmiersprache C" (Introduction to C Programming) "Praktikum Automatisierungstechnik/Systemdynamik" (Automation/System Dynamics Practical) Education: He holds an M.Sc. in Technical Cybernetics from the University of Stuttgart (2016-2019) and a B.Sc. in the same field (2013-2016).
Prof. Dr. Frank Haußer is a Professor at Beuth University of Applied Sciences Berlin in the Department II Mathematics - Physics - Chemistry. He serves as the academic advisor for the Applied Mathematics program and teaches courses including Numerical Mathematics, Mathematical Methods of Digital Image Processing, and Computational Engineering. His consultation hours are held during semesters and by appointment, with availability both in-person and online. Haußer's research focuses on: Numerical mathematics and scientific computing Mathematical modeling with applications in MATLAB/Octave Machine learning for medical imaging and insect monitoring Digital image processing techniques for biomedical applications Partial differential equations and computational engineering methods He has authored a textbook on mathematical modeling with MATLAB/Octave and leads interdisciplinary projects at the intersection of mathematics and technology. Analysis of his 15 most recent publications shows strong emphasis on: Medical imaging algorithms (especially retinal OCT analysis) Nanostructure dynamics and material science Computational physics and finite element methods Machine learning applications in biology and medicine Advanced mathematical modeling techniques His work consistently bridges theoretical mathematics with practical engineering applications. Haußer actively supervises student research, including: Machine learning for insect classification and localization Medical image processing algorithms Computational methods in engineering Finite element analysis applications Optimization and simulation techniques He has guided over 30 bachelor's and master's theses since 2009. His research projects include: KInsekt (2020-2023): AI-based insect monitoring funded by BMUV BeCRF (2015-2017): Medical image quality validation funded by BMWi QM ROCT (2013-2015): Automated OCT quality management funded by IFAF These interdisciplinary collaborations involve institutions across Germany.
Ravi Ramamoorthi serves as Professor in the Department of Computer Science and Engineering at the University of California, San Diego within the Jacobs School of Engineering, where he directs the UC San Diego Center for Visual Computing. His academic foundation includes a Ph.D. in Computer Science from the University of California, Berkeley, establishing his expertise in computational visual phenomena. Professor Ramamoorthi's research pioneers physically-based rendering and inverse vision problems , with seminal contributions to spherical harmonic lighting, frequency analysis of rendering, and neural radiance fields. His work bridges theoretical computer graphics with practical applications in virtual reality and computational photography, fundamentally advancing how light transport is modeled and reconstructed. His exceptional contributions have earned recognition through: ACM Fellow (2017) for transformative work in rendering and physics-based vision ACM Distinguished Member (2015) Eurographics Outstanding Technical Contribution Award (2015) SIGGRAPH Significant New Researcher Award (2005) He actively mentors graduate students while securing research funding from the National Science Foundation and technology partners, driving innovations in real-time rendering and appearance capture. His leadership extends to organizing major conferences and serving on editorial boards for top-tier graphics publications. The Center for Visual Computing under his direction integrates computer vision, graphics, and machine learning research, fostering collaborations across academia and industry to solve complex visual perception challenges.
Dr. Manolya Kavakli-Thorne serves as Associate Professor and Honorary Professor at Macquarie University's School of Computing, specializing in advanced human-computer interaction systems. Her research bridges virtual reality, augmented reality, and gaming technologies with practical applications across diverse domains including healthcare, film production, and transportation safety. Her primary research interests focus on: Virtual reality systems design and implementation Human-computer interaction paradigms Augmented reality applications in real-world contexts Computer game mechanics and serious gaming Gesture recognition systems for 3D modeling Generative adversarial networks for image synthesis Analysis of her recent publications reveals a strong trajectory toward practical implementations of immersive technologies. Her work demonstrates increasing focus on real-world applications of virtual production systems in filmmaking, gamification for behavioral change, and medical applications of VR/AR technologies. The interdisciplinary nature of her research connects computer science with psychology, engineering, and creative industries. Dr. Kavakli-Thorne has secured significant research funding through 15 projects including the Centre for Elite Performance Expertise and Training (CEPET), Virtual-Reality intervention platforms for Cerebral Palsy patients, and Mobile Augmented Reality Systems (MARS) Design. Her collaborative approach is evident in projects spanning lifeguard performance enhancement and virtual reality dome systems development. Her laboratory work centers around immersive simulation environments, virtual production studios, and mobile augmented reality systems. Current research directions include refining generative AI applications in medical imaging, developing more intuitive multi-modal interfaces for 3D modeling, and expanding the use of serious games for safety-critical training scenarios.
Ignacio IZEDDIN is an Associate Professor ( Maître de conférences ) at the Institut Langevin, ESPCI Paris, which is part of CNRS and Université PSL. His research sits at the critical intersection of advanced optical imaging, biophysics, and molecular cell biology, with a particular focus on pushing the boundaries of what can be visualized and understood about molecular distribution and cellular dynamics. His primary research interests include Single-Molecule Localization Microscopy (SMLM), super-resolution imaging techniques, single particle tracking (SPT), biophotonics, diffusion processes in cell biology, molecular cell biology, transcription regulation, DNA repair mechanisms, and light-matter interactions at the nanoscale. Dr. IZEDDIN's work aims to develop innovative microscopy tools that overcome current limitations in spatial and temporal resolution, enabling the capture of rapid, dynamic cellular processes with unprecedented clarity. The trends in his recent publications reveal a strong focus on developing event-based sensor technology for high-speed single-molecule localization, creating novel 3D microstructured substrates for cellular imaging and calibration, studying light-matter interactions at the nanoscale through fluorescence lifetime imaging, and applying these advanced techniques to understand fundamental biological processes like DNA repair, chromatin dynamics, and cellular differentiation. His work consistently bridges physics, engineering, and biology to solve complex imaging challenges. Dr. IZEDDIN is actively involved in research funding and recruitment, with current projects including a European LIGHTinParis COFUND PhD position analyzing alpha-synuclein diffusion in neurons using super-resolution microscopy based on event sensors, and hiring for a software engineer position to develop data processing tools for event-based SMLM technology. His laboratory employs a highly interdisciplinary approach, combining physics, biology, and engineering expertise to tackle challenging problems in cellular imaging. Current projects involve collaborations with neuroscientists, physicists, and engineers to study everything from molecular diffusion in neurons to macrophage differentiation on 3D topographical substrates.
John F Hughes is a Professor of Computer Science at Brown University's School of Engineering. His work bridges computer graphics and mathematics, with a focus on intuitive interfaces for 3D modeling and visualization. He has made significant contributions to sketch-based interfaces, art-based graphics, and shape modeling. Education: PhD in Mathematics, University of California, Berkeley (1982) MA in Mathematics, University of California, Berkeley (1982) BA in Mathematics, Princeton University (1977) Professor Hughes's research centers on computer graphics with strong mathematical foundations. He specializes in the modeling of shape and form at multiple scales, human-computer interaction, and art-based graphics. His work explores how artists' techniques can be utilized to enhance human-computer communication about shape. He has recently expressed interest in machine learning applications to graphics problems. His approach emphasizes informal modes of input and output, particularly sketching as a means to describe shape and expressive renderings for information communication. His publication record shows a consistent focus on sketch-based interfaces for 3D modeling, art-based rendering techniques, and mathematical approaches to graphics problems. Over time, his work has evolved from foundational mathematical approaches to more applied interactive systems, while maintaining a strong connection to mathematical principles. Recent publications indicate expanding interests into machine learning applications for graphics and computational approaches to sparse data. Scientific Awards: User Interface Software and Technology (UIST) Best Paper Award Professor Hughes has received substantial research funding from major technology companies and government agencies. His funded research includes a gift from Pixar supporting graduate fellowships in computer graphics (since 2000), research grants from Microsoft, NSF, and collaborations with IBM and Sun Microsystems. His work has been instrumental in advancing sketch-based interfaces and art-based rendering techniques, with applications ranging from character animation to document navigation interfaces. He maintains active collaborations with researchers across the computer graphics community, particularly in the areas of sketch-based interfaces and modeling. His work with Takeo Igarashi, Tomer Moscovich, and other collaborators has been highly influential in the computer graphics community, shaping how we interact with 3D content through intuitive sketching interfaces.
Klaus Mueller is a Professor in the Department of Computer Science at Stony Brook University, where he also serves as Interim Chair of the Department of Technology and Society. He holds adjunct faculty positions in the Biomedical Engineering Department and Radiology Department, and is a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. His research spans visualization, visual analytics, explainable AI, computational fairness, and medical imaging, with significant contributions to volume rendering, GPU computing, and virtual reality. Dr. Mueller received his educational credentials from prestigious institutions: PhD in Computer and Information Science, The Ohio State University, 1998 MS in Computer and Information Science, The Ohio State University, 1996 MS in Biomedical Engineering, The Ohio State University, 1990 BS in Electrical Engineering, Polytechnic University of Ulm, Germany, 1987 Professor Mueller's research focuses on making complex data accessible and understandable through innovative visualization techniques. His work in visual analytics empowers users to explore high-dimensional data spaces, while his contributions to explainable AI help bridge the gap between complex machine learning models and human understanding. In medical imaging, he has pioneered GPU-accelerated reconstruction techniques that significantly improve CT imaging while reducing radiation exposure. His recent work explores the intersection of large language models with visualization, creating tools that enhance data understanding through natural language interaction. His extensive publication record shows a consistent focus on visualization techniques, with recent work increasingly incorporating AI and machine learning components. The trend shows a progression from foundational visualization techniques to more complex applications involving explainable AI, fairness in algorithms, and medical imaging applications. His work often bridges theoretical advances with practical implementations, particularly through GPU acceleration. Dr. Mueller's scientific achievements have been recognized with numerous prestigious awards: US National Science Foundation CAREER award (2001) SUNY Chancellor Award for Excellence in Scholarship and Creative Activity (2011) Inducted into the National Academy of Inventors (2018) Golden Core Award, IEEE Computer Society (2016, 2022) Meritorious Service Certificate, IEEE Computer Society (2016) IEEE Fellow (2024) Best Paper Award, IEEE Visual Data Science Symposium (2019) His research has been generously supported by major funding agencies including the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and Department of Homeland Security (DHS), as well as private industry partners. As Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022), he has shaped the direction of visualization research globally. He has advised numerous PhD students whose work has advanced the field of visual analytics and medical imaging. Dr. Mueller directs the Visual Analytics and Imaging (VAI) Lab at Stony Brook, which focuses on developing innovative visualization techniques for complex data analysis. The lab has been instrumental in creating tools for medical imaging, security applications, and data science. His team has developed frameworks for smoke and fire simulation, visual analytics for healthcare, and GPU-accelerated medical imaging algorithms. The lab fosters interdisciplinary collaboration between computer scientists, medical researchers, and domain experts to solve real-world problems through visualization.
Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.
Oh-Young Song is a Professor in the Department of Software at Sejong University's Ocean Humanities College, where he has been a faculty member since 2006. He also serves as a Researcher at the eXtended Reality Research Center and directs the Sejong University Graphics Lab, which focuses on natural phenomena simulation and related applications in special effects and animation. Sejong University, Department of Software (2006-present) Adobe Systems, Visiting Professor (2013-2014) Seoul National University, Automation and Systems Research Institute (2004-2006) Song earned his educational credentials from Seoul National University: B.S. (1998), M.S. (2000), and Ph.D. (2004), all in Electrical Engineering and Computer Science. His research spans computer graphics, VR/AR/MR, physics-based animation, fluid simulation, and deep learning . His work bridges theoretical computer graphics with practical applications in healthcare, IoT, and special effects. Song has developed physics-based animation techniques used in movies and games, with particular expertise in fluid simulation where he holds a US patent for simulating detailed fluid movements using derivative particles. His recent research increasingly integrates AI and deep learning approaches with traditional computer graphics techniques. Analysis of his 15 most recent publications (2021-2024) reveals a strategic expansion of his research into healthcare applications, with significant work in medical image analysis, gastrointestinal disease detection, and secure patient data transmission. His research shows strong interdisciplinary collaboration, particularly between computer graphics and medical informatics, with consistent publication in high-impact journals across computer science and healthcare domains. Song's scholarly achievements include 49 research outputs with 1922 citations and an h-index of 20. His most notable scientific achievement is the US-patented technique for simulating detailed fluid movements using derivative particles, with related papers published in ACM Transactions on Graphics. US Patent for Method of Simulating detailed movements of fluids using derivative particles 49 research publications across computer graphics, VR/AR, and medical AI domains 1922 total citations with h-index of 20 As an educator, Song teaches courses including Advanced Computer Graphics, Algorithms and Practice, and Physics Coding for General Public. He advises graduate students through Master's thesis research and Graduation Research and Career courses. His lab, the Sejong University Graphics Lab, actively collaborates with industry on physics-based animation techniques for special effects and animation in movies and games. Current research directions indicate growing emphasis on integrating extended reality technologies with healthcare applications, particularly in medical simulation and diagnostic support systems.
Gonzalo Besuievsky is Associate Professor at the Department of Computer Science and Applied Mathematics of the University of Girona , Spain. He leads research on physically-based rendering and global illumination, with particular emphasis on Monte-Carlo methods and dynamic radiosity environments. Education: PhD in Computer Science, Universitat Politècnica de Catalunya , 2001 — Dissertation: "A Monte Carlo Approach for Animated Radiosity Environments" Research Interests: His work spans several inter-related domains: Global Illumination & Radiosity: developing algorithms for realistic light transport in synthetic scenes. Monte Carlo Techniques: adaptive and hierarchical sampling to accelerate rendering. Dynamic Environments: efficient update schemes for animated lighting and moving light sources. Daylighting Simulation: integrating sunlight models into architectural 3-D workflows. Motion Blur & Temporal Coherence: novel methods to render motion-blurred radiosity images. Publication Trends: Across his 1993–2006 publications, a clear evolution is visible from foundational stochastic ray-tracing work toward sophisticated Monte-Carlo radiosity frameworks that handle dynamic lighting, daylighting, and frame-to-frame coherence for animations. Labs & Groups: He is affiliated with the Girona Graphics Group , a research team devoted to advanced graphics and visualization technologies.
Sheila Sutjipto is a Postdoctoral Research Fellow at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). Her research focuses on robotics, virtual reality, and human-robot interaction, with applications in mining safety, assistive technology, and industrial automation. She has published extensively in top robotics conferences and journals, demonstrating expertise in teleoperation systems, digital twin technology, and sensor fusion for robotic applications. Dr. Sutjipto's research spans multiple domains of robotics and human-computer interaction. Her work particularly emphasizes virtual reality interfaces for robot teleoperation , digital twin technology for industrial applications , and multi-modal perception systems . She has made significant contributions to mining safety through rock scaling robotics, assistive technology for visually impaired individuals, and haptic feedback systems for industrial robots. Her interdisciplinary approach integrates mechanical engineering, computer vision, and human factors to develop intuitive and safe robotic systems. Dr. Sutjipto's recent publications demonstrate a strong trajectory toward practical applications of robotics in challenging environments. Her work on digital twin-based teleoperated rock scaling robots addresses critical safety issues in mining operations. She has also pioneered multi-modal perception systems combining RGB, event cameras, and LiDAR for assistive robotics. A consistent theme across her publications is enhancing human-robot interaction through intuitive interfaces, whether via virtual reality, haptic feedback, or annotation-assisted control systems. Dr. Sutjipto has been involved in several funded research projects, including the HALO rock scaling robot project (stages 2 and 3), IntelliForce Tooling for hydraulic safety, and integration of live surround video for VR digital twins. She has collaborated with industry partners like Ausdrill and BTP Parts Pty Ltd. Her grant history shows a strong industry-academia collaboration focused on practical robotics applications. Dr. Sutjipto appears to be part of the robotics research group at UTS's School of Mechanical and Mechatronic Engineering. Her collaborations suggest involvement with teams focused on mining robotics, assistive technology, and industrial automation. She frequently collaborates with researchers like G. Paul, D.T. Le, and K. Nguyen, demonstrating strong interdisciplinary teamwork across engineering disciplines.
Pete Willemsen is a Professor and Department Head in the Department of Computer Science at the University of Minnesota Duluth, Swenson College of Science and Engineering. His research focuses on urban microclimate simulation, virtual reality education, and human-computer interaction. Current research includes One Night in Tarrytown (interdisciplinary VR learning environments) Development of Quick Environmental Simulation (QES) for urban wind and pollution modeling Investigation of radiance field content generation for VR applications Prior projects have explored: Locomotion mechanics in VR training systems GPU-accelerated environmental simulations Human spatial perception in virtual environments Low-cost Kinect-based tracking systems Collaborations span multiple disciplines including Communications, Digital Arts, and Mechanical Engineering. Current students include Asif Sijan (VR content generation), Christianah Adigun (spatial cognition), and Noah Miller (VR controller ergonomics).
