Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA
Murilo da Silva Baptista is a Reader at the Institute for Complex Systems and Mathematical Biology , within the School of Natural and Computing Sciences at the University of Aberdeen . He has been with the university since 2009, initially as a Senior Lecturer and promoted to Reader in 2014. He is actively accepting PhD students in Physics, Mathematics, and Engineering, and his research is internationally recognized in the fields of complex systems and chaos theory. His research focuses on understanding the relationship between function—such as information processing, collective behavior, and synchronization—and structure in large networked complex systems. He applies analytical methods, data science, nonlinear time series analysis, and machine learning to model systems in neuroscience, smart engineering, and Earth sustainability. He is a leading scientist in chaos-based communication, demonstrating how chaotic signals can enable smart and secure wireless and underwater communication systems. His work includes theoretical developments in phase definition in chaotic oscillators, chaos-based cryptography using Poincaré return times, and the discovery of phenomena like Collective Almost Synchronization, which enhances machine learning for EEG signal prediction. His recent publications (2023–2025) span a wide range of applications, including chaotic image and 3D model encryption, UAV surveillance using chaotic paths, causal feature selection in health systems, modeling neurological disorders, and socio-environmental analysis in Brazil. These works reflect a strong trend toward applying nonlinear dynamics and network science to real-world engineering, biomedical, and societal challenges. Scientific Contributions and Recognition: Proved a conjecture on the analytical calculation of Poincaré first return times using unstable periodic orbits. Contributed foundational work to chaos-based cryptography. Proposed a formula linking mutual information to Lyapunov exponents, supporting the Infomax theory of brain evolution. Discovered the phenomenon of Collective Almost Synchronization in complex networks. Demonstrated that causality is a space-time phenomenon, not purely temporal. Advising and Research Support: He is currently supervising PhD students in Physics, Maths, and Engineering, indicating active mentorship. His research is supported by analytical developments and data-driven modeling. He leads work on optimal wireless chaos communication, synapse modeling, brain network changes post-surgery, and socio-economic causality in Brazil. His collaborations span institutions in the USA, Brazil, Germany, and Portugal. Labs and Research Groups: He is affiliated with the Institute for Complex Systems and Mathematical Biology at Aberdeen, a hub for interdisciplinary research in nonlinear dynamics, network theory, and their applications across physical, biological, and social systems.
Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.
Lopez Martinez, Joan Antoni is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT) and the Department of Graphic and Design Engineering. He is a key member of the CATMech research group (Centre Avançat de Tecnologies Mecàniques), contributing to advanced mechanical technologies with a focus on microsystems and industrial applications. His research interests span Lab-on-a-Chip systems , microfluidics , microfabrication , medical devices , microplastics , product design , and sensors . His work integrates engineering principles with applications in health, environment, and education. He employs tools such as Ansys and Catia for simulation and design. The recent publications (2021–2025) reflect a strong trend in microfluidic diagnostics , gas and ionic sensors , environmental monitoring (especially microplastics), and educational innovation . These studies combine experimental design, numerical modeling, and practical implementation, often using 3D printing and advanced materials. His scientific awards include multiple recognitions for teaching excellence, such as the Reconeixement als mèrits docents d’especial qualitat and the Premi Societat Catalana de Tecnologia for student projects. These highlight his commitment to pedagogical innovation. Lopez Martinez has led and participated in numerous research and educational projects, including competitive R&D+i initiatives like SOCIAL BLOOMING HUB and EmotionalSigns , and educational innovations such as LAB-VIDEO UPC for remote engineering labs. His collaborations extend across departments and research groups at UPC, particularly with experts in mechanical, biomedical, and environmental engineering. He is actively involved in research labs and teams, primarily within the CATMech center and its Microtech division, where he contributes to industry-oriented microtechnology development. His work bridges fundamental research with practical applications in health, sustainability, and industrial innovation.
Gabriel Kerekes serves as a Postdoctoral Researcher and Group Leader of 3D Data Acquisition and Monitoring at the Institute of Engineering Geodesy Stuttgart (IIGS), part of the Faculty of Aerospace Engineering and Geodesy at the University of Stuttgart. He is actively affiliated with the Cluster of Excellence IntCDC (Integrative Computational Design and Construction for Architecture), contributing to cutting-edge research in computational construction methodologies. Dr. Kerekes specializes in terrestrial laser scanning (TLS), engineering surveying, and 3D data acquisition systems. His research addresses critical challenges in point cloud processing, stochastic modeling of geodetic measurements, and robotic total station networks for real-time construction monitoring. He develops innovative solutions for deformation analysis, geometric quality control of bio-based building elements, and integration of multi-sensor systems in architectural applications, with significant contributions to biomimetic shell construction and infrastructure monitoring projects. No scientific awards or fellowships were documented in the provided materials. Dr. Kerekes has supervised ten Master's and Bachelor's students since 2018, with research spanning TLS intensity analysis, atmospheric error modeling, and robotic positioning systems. His grant-funded projects include RP 16-2 (Spider Crane Robotic Platform) and AP 7 (Integrated Space-Time Point Cloud Modeling) under the IntCDC cluster. As leader of the 3D Data Acquisition and Monitoring team, he drives advancements in geodetic measurement technologies for next-generation construction processes.
Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.
Gianmarco Cherchi is a Tenure-Track Assistant Professor and Computer Science Researcher in the Department of Mathematics and Computer Science at the University of Cagliari, Italy, where he also completed his PhD. He teaches courses in Data Visualization and Web Programming at the undergraduate level. His research lies at the intersection of Computer Graphics and Geometry Processing, with a strong focus on surface and volumetric mesh generation, optimization, digital fabrication, and polycube-based modeling. His work combines algorithmic innovation with practical applications in fabrication, visualization, and interactive systems. The recent publications highlight a consistent trend in advanced hexahedral meshing techniques (e.g., HexBox, VOLMAP), robust geometric computation (e.g., mesh booleans), and interactive tools (e.g., ProtoSketchAR, Py3DViewer). His research spans theoretical algorithm development, benchmark creation, and applied systems for VR/AR and simulation. His scientific accolades include the Young Investigator Award 2024 from the Shape Modeling International Organization, and prior Best Thesis Awards from the Eurographics Italy Association for both his M.Sc. and Ph.D. work. Cherchi actively collaborates with researchers such as Marco Livesu, Riccardo Scateni, and others, contributing to major surveys and state-of-the-art methods in hexahedral meshing. His work is supported by publications in top venues like ACM Transactions on Graphics (SIGGRAPH), Computer Graphics Forum (Eurographics), and IEEE VR. He has also developed practical software tools like Py3DViewer for geometry processing prototyping. He leads research in digital fabrication pipelines, as evidenced by publications on polycube decomposition for manufacturing and automated flat pattern generation. His lab work involves developing interactive and robust systems for 3D modeling and analysis.
Dr. Antoni Jaume-i-Capó is a Full Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB) , affiliated with the Computer Graphics and Vision and Artificial Intelligence Group (UGiVIA) and serving as Director of the Artificial Intelligence Applications Laboratory (LAIA@UIB) . His work bridges Artificial Intelligence , Computer Vision , and Medical Imaging with applications in Motor Rehabilitation and Explainable AI (XAI) . Research Interests : Artificial Intelligence, Explainable AI, Computer Vision, Human-based Computation, Intelligent Systems for Motor Rehabilitation, Medical Imaging Processing. Teaching : Courses include Algorithmics and Data Structures , Final Degree Projects , and Practical Placements for Informatics Engineering degrees across multiple campuses. Scientific Contributions span 15 recent publications focusing on XAI fidelity metrics , medical image analysis , and crowdsourced diagnostics . These works integrate machine learning for Sickle Cell Disease classification and kinect-based rehabilitation systems . Scientific Awards : Premio al mejor trabajo JENUI 2010 Students : Supervised over 11 PhD/Master’s students, including Gabriel Moyà-Alcover and Pedro Marrero-Fernández. Projects : Leads EU-funded initiatives like EXPLAINME and EUGAIN , alongside regional efforts such as People4Sicklemia .
Linus Franke is a postdoctoral researcher at Inria Sophia Antipolis in France, affiliated with the GraphDeco research group under George Drettakis. He previously completed his PhD at the Chair of Visual Computing , Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Germany under Marc Stamminger. His research focuses on Computer graphics Perceptual rendering Computer vision Machine learning Neural rendering Novel view synthesis Key trends in his recent publications include neural rendering , 3D Gaussian splatting , radiance fields , and real-time rendering with applications in virtual reality and light field displays. His work often integrates point clouds , differentiable rendering , and GPU optimization techniques. His personal website at lfranke.github.io highlights projects like VET (Visual Error Tomography) and TRIPS (Trilinear Point Splatting for Real-Time Rendering).
Pedro Felzenszwalb is a Professor of Engineering and Computer Science at Brown University , with a research focus spanning computer vision, artificial intelligence, machine learning, and algorithms. Born in Rio de Janeiro, Brazil, he earned his BS in Computer Science from Cornell University (1999) and MS/PhD in EECS from MIT (2001/2003). He previously held a faculty position at the University of Chicago (2004-2011) before joining Brown in 2011. Education : PhD in EECS, MIT (2003) MS in EECS, MIT (2001) BS in Computer Science, Cornell University (1999) His research integrates computer vision and AI, emphasizing scalable algorithms for object recognition, image segmentation, and probabilistic modeling. Key methodologies include deformable part models, belief propagation, and dynamic programming. His work has significant applications in early vision tasks, scene understanding, and geometric constraints in 3D object recognition. Pedro’s publications demonstrate a trajectory from foundational graph/image algorithms (2004-2006) to advanced machine learning approaches (2010-2023), with recurring themes in optimization, clustering, and multiscale modeling. Notable journals include Journal of the ACM , IEEE Transactions , and Communications of the ACM . Scientific Awards : ACM Grace Murray Hopper Award IEEE Technical Achievement Award PASCAL Visual Object Challenge Lifetime Achievement Prize Longuet-Higgins Prize NSF CAREER Award He has received NSF funding for projects including Graph Cut Algorithms (2012-2015) and Object Recognition with Hierarchical Models (2008-2013). At Brown, he teaches graduate courses in machine learning, linear systems, and pattern recognition.
Nikola Savanovic serves as an Assistant Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic work centers on cybersecurity, machine learning, and IoT systems within the Department of Informatics and Computing. Research Focus: His primary fields include cybersecurity (particularly intrusion detection in healthcare IoT systems), metaheuristic optimization of machine learning models, web technologies, and healthcare informatics. His recent work demonstrates strong interdisciplinary connections between computer science, medical technology, and financial systems. Publication Trends: Analysis of his 15 most recent articles reveals a dominant focus on cybersecurity applications (40%), machine learning optimization (30%), and healthcare technology (20%), with growing emphasis on metaheuristic algorithms for solving complex security and diagnostic problems. His work frequently appears in high-impact journals like Mathematics and Sustainability . Academic Background: PhD in Advanced Protection Systems (2016-2024), University of Singidunum Master's in Contemporary Information Technologies (2013-2016), Singidunum University Bachelor's in Informatics and Computing (2007-2013), Singidunum University Mechanical Technician for Computer Engineering (2003-2007), Polytechnic High School - School for New Technologies Authorship: He has co-authored 5 academic books including Web Design and Multimedia Systems (2023) and Knowledge Discovery in the Cyberspace (2017), demonstrating expertise in both theoretical and applied computing fields. His collaborative research spans multiple international institutions with consistent publication output since 2013. Technical Leadership: His conference presentations reveal active involvement in Serbia's academic technology community through events like IcETRAN and SINTEZA conferences, focusing on practical implementations of cybersecurity frameworks and educational technology solutions.
Aleš Jaklič is an Assistant Professor affiliated with the Computer Vision Laboratory . His work spans computer vision, 3D reconstruction, and educational technologies, with a focus on practical applications in archaeology, meteorology, and STEM-C education. Research interests include: 3D modeling from point clouds Superquadric parameter prediction from depth images IoT-based educational tools for computer science Historical artifact digitization Image processing algorithms His publications since 2000 demonstrate expertise in geometric modeling, computer vision, and educational technology. Highlights include work on superquadric recovery (2000, 2003), archaeological modeling (2015), and IoT education frameworks (2020). Recent research (2021) explores neural network approaches to depth image analysis. Active in research programs funded by the Slovenian Research Agency (ARRS) since 2009, including the ongoing P2-0214 - Computer Vision program (2019-2024). He has also contributed to the ŠIPK 5 project on IoT education (2020).
Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.
Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.
Ganggang Xu is an Associate Professor (with tenure) in the Department of Management Science at the Miami Herbert Business School , University of Miami . He specializes in advanced statistical methodologies, particularly in nonparametric and semiparametric modeling, spatial statistics, and point process theory. Education: Ph.D. in Statistics, Texas A&M University (2011) B.S. in Statistics, Zhejiang University (2006) Research Interests: His research spans several key areas in modern statistics and data science. He has made significant contributions to nonparametric and semiparametric regression , particularly in the context of functional data analysis and spatial-temporal modeling . His work on point processes includes marked, multivariate, and clustered point processes, with applications ranging from neuroscience to social media behavior. He also explores Bayesian hierarchical models and model selection techniques, often integrating computational efficiency with theoretical rigor. Publications Overview: His recent publications (2023–2025) reflect a strong focus on machine learning-enhanced statistical modeling , including tree-based estimation of intensity functions, network autoregressive models, and quantized inference. He has also contributed to applied domains such as medical imaging and inventory control , demonstrating the broad applicability of his methodological work. Grants & Collaborations: While specific grants are not listed in the provided text, his extensive publication record with multiple co-authors across institutions suggests active collaboration and possible funding from NSF or NIH-equivalent bodies in statistics and data science. Labs & Teams: Though no specific lab is mentioned, his affiliations and co-authorships imply involvement in interdisciplinary research teams at the University of Miami, especially within the business analytics and statistical modeling domains.
