Maria Brbic is an Assistant Professor of Computer Science at EPFL, previously a postdoctoral researcher at Stanford University under Jure Leskovec. Her research focuses on developing machine learning methods for biological and biomedical applications, particularly representation learning of high-dimensional datasets, open-world semi-supervised learning, and single-cell genomics. Her work includes the STELLAR method for spatial cell type discovery (Nature Methods 2022), the ORCA framework for open-world learning (ICLR 2022), and contributions to the Fly Cell Atlas (Science 2022). She is involved in the Chan Zuckerberg Biohub and Neuro-omics projects. She received the University of Zagreb's best thesis award, was recognized as a MIT Rising Star in EECS, and won the Basel Computational Biology Conference best poster award. Her research bridges computer science with cutting-edge biomedical discovery.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Doudou Zhou is an Assistant Professor of Statistics & Data Science at the National University of Singapore. Previously, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2024), and earned a Ph.D. in Statistics from UC Davis (2022), advised by Prof. Hao Chen. He holds dual B.S. in Statistics and B.E. in Computer Science from USTC (2019). His research focuses on developing statistical methods and machine learning techniques for electronic health records (EHR) data, including federated learning, reinforcement learning, graph neural networks, and high-dimensional statistics. Key interests include representation learning for multi-institutional data harmonization and precision medicine applications. Notable awards include the 2023 Harvard Data Science Initiative Postdoctoral Fellowship and 2022 ICSA Student Poster Award. He teaches Applied Natural Language Processing (ST5230) and actively contributes to journal reviewing (e.g., JASA, Biostatistics). His work spans algorithm development in federated reinforcement learning and transfer learning for heterogeneous domains. He leads a research group exploring topics such as knowledge graph integration, multimodal EHR analysis, and fair federated learning systems. His code contributions include implementations for federated offline RL and change-point detection methods.
ZHENG Baihua serves as Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS), concurrently holding leadership roles as Associate Dean for SCIS Post-Graduate Research Programmes and Director of the Master of Science in Computing programme. Currently on leave but maintaining full-time faculty status, his academic career spans over two decades with foundational training from Hong Kong University of Science and Technology. Professor Zheng's research program integrates artificial intelligence, data science, and urban computing to solve critical challenges in mobility and sustainability. His expertise centers on trajectory data management, social network analysis, and spatio-temporal modeling, with significant contributions to trajectory compression algorithms, influence minimization in social networks, and real-time traffic prediction systems. His work bridges theoretical database innovations with practical applications in smart city infrastructure and public health interventions. Analysis of recent publications (2024-2025) reveals a dominant focus on physics-informed trajectory processing, GPU-accelerated indexing for high-dimensional data, and transformer-based models for urban mobility prediction. Key trends include the fusion of graph neural networks with spatio-temporal dynamics, novel approaches to contact tracing through timeline graphs, and differentiable search techniques for structured data discovery. These works consistently target real-world deployment in transportation systems and epidemic control. No scientific awards are documented in available institutional records. Information regarding student supervision, research grants, laboratory facilities, or collaborative teams remains unspecified in current public profiles.
Enrique Dunn is an Associate Professor in the Department of Computer Science at Stevens Institute of Technology. He holds an academic position within the Charles V. Schaefer, Jr. School of Engineering and Science. His research focuses on 3D Computer Vision, emphasizing geometric and semantic relationships in imaged environments. Dunn earned a B.S. in Computer Engineering from the Autonomous University of Baja California (1999), an M.S. in Computer Science from the Ensenada Center for Scientific Research (2001), and a Ph.D. in Electronics and Telecommunications (2006). He held postdoctoral roles at UNC Chapel Hill (2008–2012) before joining Stevens in 2016. His research interests include 3D reconstruction, visual odometry, and large-scale visual analytics. Dunn has authored over 40 papers in top conferences/journals such as CVPR and ICCV. He serves as an Associate Editor for Elsevier's Image and Vision Computing journal and has held roles in program committees for ECCV and 3DV. Key contributions include VOLDOR-SLAM (2021), NeuroCS (2023), and methods leveraging dense optical flow residuals for visual odometry. His team comprises Ph.D. students Juan Carlos Dibene and Siyuan Cao, with alumni Zhixiang Min (Apple) and Xiangyu Xu (InnoPeak). Dunn's work addresses challenges in crowd-sourced imagery and dynamic scene modeling.
Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.
Giorgos Bouritsas is a machine learning scientist and postdoctoral fellow at the Archimedes AI unit / Athena Research Center, affiliated with the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens. He also serves as an adjunct lecturer at NCSR Demokritos. His research focuses on Geometric and Graph Deep Learning, with contributions spanning theoretical foundations of graph/geometric neural networks and generative modeling of graph/geometric data. His educational background includes a PhD in computer science from Imperial College London (2023), supervised by Prof. Michael Bronstein and Prof. Stefanos Zafeiriou, and an MEng in electrical and computer engineering from the National Technical University of Athens (2017). Dr. Bouritsas' research interests encompass Geometric Deep Learning, Graph Neural Networks, Weight Space Learning, Self-Supervised Learning, and Machine Learning applications in biology and chemistry. His work intersects with areas such as computer vision, network science, and physics, with publications in leading conferences (NeurIPS, CVPR, ICCV, ECCV) and journals (TPAMI). His recent publications demonstrate trends in geometric deep learning, graph neural networks, and applications in bioinformatics and computer vision. His research on Scale Equivariant Graph Metanetworks was accepted for an oral presentation at NeurIPS 2024, highlighting his contributions to advancing the theoretical foundations of graph neural networks. Outstanding reviewer award, NeurIPS '21 Outstanding reviewer award, NeurIPS '23 Outstanding reviewer award, ICML '22 Outstanding reviewer award, ICML '24 Dr. Bouritsas regularly engages in educational activities, teaching postgraduate courses such as Deep Learning at the MSc in AI program at NCSR Demokritos. He also provides academic service as a reviewer for major machine learning conferences and journals. His current work at Archimedes AI centers on weight space learning, with applications in automating machine learning processes and predicting ML model behavior.
Dr. Xin Xia is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing machine learning techniques, particularly in the domain of recommender systems, with an emphasis on on-device deployment, graph-based learning, and self-supervised methods. He has contributed to optimizing recommendation systems for edge computing environments and improving their efficiency in resource-constrained settings. Key research areas include: On-device recommendation systems and edge AI Graph neural networks for recommendation Self-supervised learning and contrastive techniques Efficient model optimization and knowledge distillation His publications analyze trends such as transitioning cloud-based systems to on-device applications, reducing communication overhead in distributed models, and simplifying graph contrastive learning frameworks. These contributions highlight his expertise in bridging theoretical advancements with practical, deployable solutions. Dr. Xia’s work has been presented at top-tier conferences such as SIGIR, CIKM, and AAAI, reflecting the academic rigor and industry relevance of his research.
Professor Aline Villavicencio is a Chair in Natural Language Processing at the University of Sheffield's School of Computer Science. She holds a PhD and MPhil from the University of Cambridge and an MSc from the Federal University of Rio Grande do Sul (Brazil). Her research focuses on lexical semantics, multilinguality, and cognitively motivated NLP, with applications including Multiword Expression (MWE) treatment and text simplification for English and Portuguese. She has held a CNPq Research Fellowship (2007–2017) and currently leads projects like Modeling Idiomaticity in Human and Artificial Language Processing (EPSRC grant). Her roles include PC co-chair for CoNLL-2019, Area Chair for ACL-2019 and NAACL-2018, and co-chair of PROPOR 2018. She advises WiNLP, contributes to TACL, JNLE, and other journals, and collaborates with the Neurocomputational and Language Processing Lab in Brazil. Notable work includes corpus development (e.g., BRWAC for Brazilian Portuguese), idiomaticity detection, and cross-lingual transfer learning. Recent grants include £446k from EPSRC and £370k from Horizon Europe (Atrium). Key Projects: Modeling Idiomaticity, Cross-lingual Adaptation, Speech Segmentation Grants: EPSRC (£446k), Horizon Europe (£370k), Royal Society (£74k) Publications: Over 100 papers in ACL, EMNLP, and journals like Natural Language Engineering . Her lab work involves developing NLP tools for under-resourced languages and exploring neural models' limitations in idiomatic understanding. She co-edited books on cognitive aspects of language acquisition and MWE processing, and her teaching includes advanced NLP modules.
Fangjinhua Wang is a Researcher affiliated with the Department of Computer Science at ETH Zurich, working within the Professorship for Computer Science. Their role involves contributing to cutting-edge research in fields such as 3D reconstruction, computer vision, and neural networks. The research focuses on advancing methodologies like scene graph manipulation, multi-view stereo techniques, and holistic human-scene reconstruction. Collaborations and projects emphasize practical applications in robotics, computer graphics, and AI-driven systems. While no explicit education details are provided, the research trajectory reflects a deep engagement with computational methods for 3D modeling and vision-based systems. The work bridges theoretical advancements with real-world applications, addressing challenges in scene understanding, object interaction, and human-robot collaboration. Research interests are centered on interdisciplinary topics including neural representation learning, robust visual localization, and the integration of geometric priors for high-fidelity reconstructions. The output demonstrates a commitment to both foundational research and applied solutions in computer science and robotics.
Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.
Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)
Dr. Kevin Lu is a Reader at Brunel Business School, Brunel University London, specializing in digital transformation and business analytics. His research focuses on leveraging Generative Artificial Intelligence (GAI) technology for digital business strategy development. He has published over 30 research articles in leading journals, 3 book chapters, and more than 30 conference papers. His work has been supported by organizations such as SAMS, GCRF, Innovate UK, EPSRC, and the EU. Dr. Lu holds a BSc (Hons) in Mathematics, a PhD in Management, and a Postgraduate Certificate in Teaching and Learning in Higher Education. Before joining academia, he worked in industry as a computer software engineer, bringing practical experience to his research and teaching. His research interests span digital marketing and marketing analytics, understanding business development through data analysis, and Generative AI applications in marketing. Dr. Lu adopts a computer science perspective in his work, encompassing topics such as social networks, trust, web science, artificial intelligence, and fair and accountable interactions. His studies utilize methods including mathematical modeling, optimization, and econometrics. His recent publications demonstrate a strong focus on the intersection of AI and consumer behavior, particularly in e-commerce contexts, with increasing emphasis on Generative AI applications. Dr. Lu has successfully supervised ten PhD students to completion and currently supervises research on developing business strategies for digital products using Generative AI techniques. His research has attracted significant funding from prestigious organizations including SAMS, GCRF, Innovate UK, EPSRC, and the EU. Fellow of the Higher Education Academy (FHEA) Member of the SAS Academy Programme Chair for Business Analytics track at IEEE Conference on Business Informatics He teaches courses in Business Intelligence, Marketing Analytics, and Business Statistics, maintaining regular office hours and dedicated time for dissertation supervision. His work provides valuable insights for businesses seeking to leverage digital technologies for competitive advantage, particularly in understanding how AI applications can transform customer interactions and business processes.
Shih-Yang Su is a recent PhD graduate in Computer Science from the University of British Columbia, specializing in Human Motion Learning, 3D Vision, and Character Animation. His research bridges computer vision and graphics, with significant contributions to neural rendering and articulated human modeling. His primary research interests include: Human Motion Learning and Character Animation using neural representations Neural Radiance Fields (NeRF) for articulated objects and human bodies 3D Vision techniques for novel view synthesis and depth inpainting Reinforcement Learning applications in embodied environments His publication record shows a clear progression from reinforcement learning (2017-2018) toward neural rendering and human modeling (2020-2024), with increasing focus on articulated neural representations. Key publications include work on Neural Point Characters (ICCV 2023) and DANBO (ECCV 2022), which address fundamental challenges in representing articulated human bodies. Collaborations span multiple institutions including Meta Reality Labs (with Dr. Michael Zollhöfer and Dr. Timur Bagautdinov), University of Maryland (with Prof. Jia-Bin Huang), Borealis AI (with Dr. Hossein Hajimirsadeghi), and Academia Sinica (with Dr. Yi-Hsuang Yang and Dr. Li Su).
Senjian An is a Senior Lecturer in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University, Faculty of Science and Engineering. His research focuses on machine learning, computer vision, and structural health monitoring, with applications in 3D displacement measurement and anomaly detection. Key interests include physics-guided neural networks, unsupervised learning for time-series data, and vision-based structural analysis. His recent publications emphasize robustness in damage detection, synthetic data generation, and transformer-based models for industrial monitoring. An's work integrates deep learning with engineering challenges, advancing non-contact measurement techniques for civil infrastructure. No students or awards are noted. Collaborative projects involve vibration analysis and maritime surveillance, reflecting a strong applied focus in AI and computer vision.
