Gurprit Singh is a Researcher at the Max Planck Institute for Informatics, Saarbrücken, Germany. His work focuses on advancing Monte Carlo integration techniques and their applications in generative AI, physically based rendering, and optimization. He has contributed to conferences such as SIGGRAPH, Eurographics (EG), and Pacific Graphics (PG), serving in roles like Technical Program Committee member and co-chair for Doctoral Consortium programs. Roles: Associate Senior Researcher, Conference Co-chair (EGSR 2021), and active in academic service. Research Interests: Monte Carlo methods, MCMC sampling, gradient-based optimization, and generative models. His research bridges rendering, optimization, and AI, with notable work on noise optimization in diffusion models and perceptual error minimization. He has received the Best Student Paper Award at ICPRAM 2025.
Dr. Wei Yan is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's Herbert Wertheim College of Engineering. With an extensive publication record spanning computer science education, augmented reality applications, and culturally responsive computing, Dr. Yan has established a significant research presence with numerous publications in top-tier conferences and journals from 2023-2025. Dr. Yan's research interests focus on culturally responsive computing education, particularly with Indigenous communities including the Navajo Nation. Their work bridges the gap between technical computing concepts and culturally relevant pedagogy, with a special emphasis on spatial reasoning and mathematics education through augmented reality technologies. The research program has produced innovative AR classroom applications that help students understand complex spatial transformations and matrix algebra through interactive 3D visualizations. Through collaborations with researchers like Ashish Amresh, Paige Prescott, Maya Israel, and Heather Burte, Dr. Yan has developed several educational technology interventions that address inclusion in computer science education. Their publications reveal a strong commitment to broadening participation in computing, particularly among underrepresented groups, with a focus on teacher professional development and curriculum design that respects cultural contexts. Dr. Yan's technical expertise spans both educational technology development and core computer science topics, as evidenced by publications ranging from spatial reasoning in AR classrooms to advanced topics in integer coding and neural image compression. This interdisciplinary approach allows for the development of sophisticated educational tools grounded in solid computer science principles. Current research directions include AI-enhanced educational applications, culturally responsive computing curriculum development, and the integration of conversational AI with augmented reality for improved learning experiences. The work has significant implications for how computing education can be made more accessible and relevant to diverse student populations.
Aanjaneya Mridul is an Associate Professor in the Department of Computer Science at Rutgers University, where he joined in 2017. He is also a member of the Computational Biomedicine Imaging and Modeling Center (CBIM). His research spans computer graphics, scientific computing, programming languages, and robotics, with a focus on developing numerical methods in computational physics that leverage modern computing power. Dr. Aanjaneya completed his doctoral studies in Computer Science from Stanford University in 2013 under Ronald Fedkiw, followed by postdoctoral studies at the University of Wisconsin-Madison with Eftychios Sifakis. He received his undergraduate degree in Computer Science and Engineering from the Indian Institute of Technology Kharagpur in 2008. His research interests include: Computer graphics and visual simulation Scientific computing and numerical methods Programming languages for computational physics Robotics and differentiable physics engines Computational physics for interdisciplinary applications Dr. Aanjaneya's recent work has focused on applying numerical methods to robotics for learning unknown physical parameters, and designing correctly-rounded implementations of elementary functions for new floating-point variants. His long-term goal is to enable next-generation algorithms that facilitate interdisciplinary collaboration with researchers in engineering and medicine. He has received several prestigious awards including the Ralph E. Powe Junior Faculty Enhancement Award (2019) and the NSF CAREER Award (2023). Ralph E. Powe Junior Faculty Enhancement Award (2019), sponsored by Oak Ridge Associated Universities (ORAU) NSF CAREER Award (2023) At Rutgers, Dr. Aanjaneya directs the Laboratory for Interactive Virtual Environments (LIVE), where he mentors PhD students and postdoctoral researchers. He has secured multiple NSF grants including the CAREER award, an NSF NRI grant with Professors Boularias and Yu, and grants with Professor Santosh Nagarakatte for projects on formally certified low-dimensional linear programs and correctly rounded math libraries. Outside of his academic work, Dr. Aanjaneya enjoys running, rock climbing, hiking, watching movies, and playing piano.
Mehwish Nasim is a Lecturer in Computer Science at The University of Western Australia (UWA) and holds adjunct positions at the University of Adelaide and Flinders University. She is an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers and a member of the Equity and Diversity Committee. With 17+ years in technology, she has served as a Research Scientist at the University of Konstanz and a lecturer at the National University of Sciences and Technology (Pakistan). Her PhD from the University of Konstanz focused on inferring social relations in partially observable networks. Research Interests Her work spans social network analysis, machine learning, medical image processing, and health analytics. Current projects include predicting population-level events in Australia, detecting misinformation, modeling social media polarization, and improving decision-making via complex systems approaches. Grants & Funding Key grants include a Defence AI Research Network grant (~$100K), a Defence Innovation Partnership grant ($150K), and Flinders Impact Seed Funding ($10K) targeting vaccine misinformation in migrant communities. She also secured ACEMS grants for social media polarization studies and led proposals in wargame modeling and transnational research. Teaching She teaches courses such as Algorithms, Agents & AI (UWA), Data Engineering (Flinders), and Database Modelling. Past roles include lecturing at NUST and tutoring network dynamics at Konstanz. Media & Outreach Featured in 'Women in STEM' series (2022), Flinders News (2022), and CSIRO's Data61 spotlight (2021). She has been interviewed on disinformation campaigns and podcast discussions on 'fake vs fact' (ACEMS, 2020).
Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.
Professor Yi-Bing Lin is a distinguished faculty member in the Department of Computer Science at National Yang Ming Chiao Tung University, Taiwan, where he leads pioneering research in Internet of Things (IoT) systems and applications. His work primarily focuses on developing the IoTtalk platform and its numerous derivatives across various domains including smart agriculture, smart homes, environmental monitoring, and creative applications. His research interests span Internet of Things, Edge Computing, Smart Agriculture, Sensor Networks, AI Integration, Wireless Networking, and Smart Home Systems. Professor Lin has developed the IoTtalk framework that enables rapid development of IoT applications with numerous specialized implementations including VoiceTalk, SensorTalk, AgriTalk, and many others that address specific domain challenges. His work emphasizes practical implementations with real-world impact, particularly in precision agriculture where his team has developed systems for orchid disease detection, rice blast monitoring, turmeric farming, and watermelon ripeness prediction. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating AI with IoT systems, particularly for agricultural applications and smart environments. His work increasingly incorporates advanced techniques like continuous wavelet transform, deep learning, and computer vision to solve practical problems in precision farming and environmental monitoring. The publications also show growing interest in creative applications of IoT technology for performing arts, interactive experiences, and educational contexts. Professor Lin has received recognition through consistent high-volume publication output in top-tier venues including IEEE Internet of Things Journal, IEEE Access, and Sensors. His collaborative network is extensive, with frequent co-authorship with researchers like Yun-Wei Lin, Wen-Liang Chen, and Min-Zheng Shieh. His advising has produced numerous researchers who continue to work in IoT and related fields, with many former students maintaining collaborative relationships. Professor Lin's research has been supported by multiple grants enabling the development of practical IoT systems with real-world implementations. His lab has developed numerous specialized IoT applications through the IoTtalk framework, creating a cohesive research ecosystem. Current work shows expansion into new application domains including interactive miniature worlds, simultaneous performance across locations using IoT-based motion capture, and IoT-based musical instruments like piano playing robots and violin robots, demonstrating the versatility of his research approach.
Riccardo Spezialetti is a researcher at the University of Bologna's Department of Computer Science and Engineering, specializing in advanced 3D vision and deep learning. He earned his PhD in 2020 with a thesis titled 'Learning to understand the world in 3D,' focusing on geometric deep learning and 3D object representation. His work bridges computer vision, machine learning, and neural representations, with notable contributions to unsupervised domain adaptation, LiDAR processing, and neural field-based 3D reconstruction. Key research interests include equivariant descriptors, implicit neural representations, and self-supervised learning for 3D data. He collaborates frequently with Samuele Salti and Luigi Di Stefano, co-authoring over 25 publications in top venues like CVPR, ICCV, and IEEE PAMI. His research has practical applications in autonomous systems, robotics, and photorealistic 3D reconstruction.
Lazaros T. Tsochatzidis is a researcher with significant contributions to computer vision , machine learning , and their applications in medical imaging and autonomous driving . He has published extensively in top-tier venues like Pattern Recognition , IEEE Access , and Robotics and Autonomous Systems , often collaborating with researchers such as Ioannis Pratikakis and Konstantinos Zagoris . Research Interests Specializes in LiDAR-based 3D object detection for autonomous vehicles Developed domain adaptive object detection techniques using mean teacher models Innovated part-aware graph fusion for skeleton-based action recognition Expert in medical image analysis for breast cancer diagnosis Contributed to historical document transcription and sclera segmentation research Scientific Awards No explicit awards mentioned in available data Notable Collaborations Long-term collaboration with Konstantinos Zagoris (6 co-authored papers) Extensive work with Ioannis Pratikakis (11 co-authored publications) Multidisciplinary collaborations with experts in medical imaging , autonomous systems , and biometric security
Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.
Nikola Savanović is a researcher and academic at Singidunum University, specializing in cybersecurity, machine learning, and web technologies. He holds a PhD in Advanced Security Systems (2024) from Singidunum University, following a Master's in Contemporary Information Technologies (2016) and a Bachelor's in Informatics and Computing (2013), both from the same institution. He also completed high school in Computer-Aided Design at Politehnika School for New Technologies (2003-2007). His research focuses on intrusion detection systems, machine learning optimization for cybersecurity applications, and adaptive web technologies. He has contributed to educational technology through virtual learning environments and game-based learning. His mathematical work includes fixed point theory in b-metric spaces. He has authored/co-authored multiple books on web design, multimedia systems, and digital marketing. Publications highlight interdisciplinary work: hybrid CNN-XGBoost models for medical diagnostics, metaheuristic algorithms for software defect detection, and cybersecurity innovations in healthcare IoT systems. His articles span computer science, mathematics, and data science, emphasizing practical applications in education and healthcare. Despite no listed awards, his extensive publication record and teaching roles reflect significant academic contributions. He actively collaborates on university projects and advises students in informatics and computing disciplines, though no formal student list is provided.
Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.
George Labahn is Professor and Director of the Symbolic Computation Group at the University of Waterloo's David R. Cheriton School of Computer Science. His research spans computer algebra, computational finance, and pen-based mathematical interfaces. Key contributions include fundamental algorithms for matrix normal forms (Hermite/Smith forms), symbolic-numeric polynomial computation, and development of the MathBrush system for handwritten mathematics recognition. Research focuses on efficient algorithms for polynomial matrix arithmetic, rational approximation, and solving differential equations with elliptic coefficients. Computational finance work develops numerical methods for option pricing under jump diffusion models. Current projects include fast Hermite form computation and rank-sensitive matrix algorithms. Serves as associate editor for Journal of Symbolic Computation (JSC) and previously for ACM Transactions on Mathematical Software (TOMS). Authored core Maple programming guides and implemented key packages for symbolic integration, differential equations, and graphics in Maple.
Markus Schütz is a Researcher at the Department of Computer Graphics, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. Dr.techn. and BSc. His work focuses on real-time rendering of massive point clouds, GPU acceleration, and interactive visualization. Key projects include 'Bringing Point Clouds to WebGPU' and 'Instant Visualization and Interaction for Large Point Clouds'. He has developed the Potree library for web-based point cloud visualization. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) Doctor of Technical Sciences (Dr.techn.) from TU Wien Research Interests: His research emphasizes real-time rendering techniques for large-scale point clouds, GPU optimization, and efficient data processing. He explores areas such as level-of-detail generation, compute shader utilization, and web-based visualization tools like Potree. Recent work includes software rasterization of 2 billion points and simultaneous LOD generation for point clouds. Awards: Best Paper Award at EGPGV2024 High-Performance Graphics 2022 Best Paper Award Second Place in SIGGRAPH Poster Student Research Competition (2018) AGEO AWARD 2017 Projects & Grants: Bringing Point Clouds to WebGPU (2024–2025, netidee Foundation) Instant Visualization and Interaction for Large Point Clouds (2023–2026, WWTF) IVILPC (Interactive Visualization of Large Point Clouds) project Labs & Teams: Active in TU Wien's Computer Graphics Group, collaborating on GPU-accelerated rendering and real-time visualization systems.
Katharina Krösl is a Lecturer at Vienna University of Technology, affiliated with the Institute of Computer Graphics and Algorithms within the Rendering and Modeling workgroup. Her research bridges computer graphics and assistive technologies through immersive virtual reality (VR) and augmented reality (AR) applications. PhD in Computer Science (2016-2020), supervised by Michael Wimmer Master's thesis on interactive photon tracing for lighting design Her work focuses on simulating vision impairments (e.g., cataracts), developing real-time rendering techniques, and creating VR-based training systems for disaster management and attention disorders. Publications span path tracing optimization, XR accessibility, and perceptual modeling. Scientific recognitions include the Young Experts Award 2021 and IEEE VR 2020 Best Research Demo Award. She contributes to IEEE and ACM publications while integrating luminaire design with VR/AR workflows.
Irene Ballester Campos is a Researcher at TU Wien's Faculty of Informatics, affiliated with the Department of Computer Graphics and Algorithms. Her work focuses on applying computer vision and AI to develop assistive technologies for healthcare contexts, particularly in dementia care and clinical applications. She is involved in the visuAAL project (2020–2026) and previously contributed to the DIANA project (2020–2023). Her research emphasizes privacy-sensitive systems, 3D sensor technologies, and human-centered design for assistive applications. Key areas include dementia behavior monitoring through depth sensors, action recognition in privacy-sensitive scenarios, and ethical implications of assistive systems. She also explores clinical applications such as Parkinson's disease severity estimation through motion analysis. Publications highlight contributions to 3D human pose estimation, dynamic object tracking in robotics, and vision-based solutions for toileting assistance. Her work bridges computer science with healthcare, emphasizing practical deployment in real-life clinical settings.
