Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).
Rachel Rudinger is an Assistant Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Her research focuses on Natural Language Processing (NLP), Machine Learning, and AI ethics, particularly addressing sociocultural biases and fairness in large language models (LLMs). She holds a PhD from Johns Hopkins University (2019) and a B.S. from Yale University (2013). Rudinger's work explores equitable cultural alignment in AI systems, common ground misalignment in dialog systems, and the mutual influence of gender and occupation in LLMs. She received the NSF CAREER Award in 2024 for her project on robust, fair, and culturally aware commonsense reasoning. Her recent publications investigate empathy gaps in LLMs, synthetic data effectiveness in disaster response, and bias measurement techniques across domains. As an advisor, she guides seven PhD students including Christabel Acquaye and Haozhe An. Her research spans diverse topics from legal language analysis to maternal health question answering, reflecting her commitment to interdisciplinary AI ethics. She actively contributes to workshops on commonsense representation and serves as a reviewer for top conferences in NLP and AI.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University (CMU) in the School of Computer Science , with dual appointments in the Language Technologies Institute and Robotics Institute . His research bridges Natural Language Processing (NLP) with robotics, focusing on grounded and embodied language understanding. Assistant Professor, Language Technologies Institute, CMU (2021–Present) Courtesy Appointment, Robotics Institute, CMU Research Themes : Language as a social codification of embodied experience Interpretable multimodal model training Human-robot collaboration frameworks Embodied question-answering systems Selected Trends : His recent publications show increasing focus on cross-modal attention mechanisms (Vid2Robot), error detection in toolchains (Tools Fail), and theory-of-mind reasoning in language agents (SOTOPIA). Multimodal integration spans vision, audio, and robotic control contexts (ANAVI). Labs & Collaborations : Founder of CLAW Lab (Connecting Language to Action and the World) Collaborations with Microsoft Research, Meta Inc, and CMU's REAL (Robotics, Embodied AI, Learning) community
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Karsten Borgwardt is a Professor and Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry. He holds a PhD in Computer Science (2007) from LMU Munich and has held academic positions at ETH Zürich, Universität Tübingen, and the Max Planck Institutes in Tübingen. His research focuses on machine learning applications in biology and medicine, including biomarker discovery, personalized medicine, and systems biology. Education: PhD in Computer Science (2007), LMU Munich M.Sc. in Biology (2003), University of Oxford Diplom (M.Sc. equivalent) in Computer Science (2004), LMU Munich Research Interests: Development of machine learning algorithms for large biomedical datasets Pattern recognition in genomic and clinical data Applications in sepsis biomarkers, antimicrobial resistance prediction, and personalized medicine Grants & Projects: Scientific Coordinator of Marie Curie Networks (2013-2022) Swiss National Science Foundation Starting Grant (2014) Personalized Swiss Sepsis Study (CHF 5.3M, 2018) Awards: Krupp Award (2013), Golden Owl Teaching Award (2017), multiple 'Top 40 under 40' recognitions (2014-2016). Labs: Leads the Machine Learning and Systems Biology Department at MPI, collaborating with 22+ labs in sepsis research and international networks.
Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.
Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he co-directs the RAIVN lab and leads the computer vision team at the Allen Institute for AI (Ai2). His research intersects computer vision , natural language processing , robotics , and human-computer interaction . PhD in Computer Science from Stanford University (2021) Bachelor's and Master's degrees from Stanford and Cornell His work has received best paper , outstanding paper , and orals at top conferences like CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV. Media outlets including Science , Forbes , and PBS NOVA have covered his research. He has been supported by grants from Google , Apple , NFS , and others. Ranjay advises a diverse group of 15 PhD and postdoctoral researchers , including Jieyu Zhang, Benlin Liu, and Cheng-Yu Hsieh. His teams have developed benchmarks like MemoryBench and The Colosseum , and his PathFinder framework achieved 74% accuracy in skin melanoma diagnosis—surpassing human experts by 9%. Notable contributions include: Perception Tokens for visual reasoning in MLMs SAM2Act for robotic manipulation with memory Synthetic Visual Genome dataset with 5.6M relationships
Professor Liyue Shen is a faculty member in the Department of Biomedical Engineering within the College of Engineering at the University of Michigan. Her research program focuses on cutting-edge applications of artificial intelligence in biomedical imaging and healthcare, with particular expertise in diffusion models and inverse problem solving for medical image reconstruction. Dr. Shen's research interests span biomedical AI, medical image analysis, biomedical imaging, machine learning, computer vision, signal and image processing, AI for precision health, and bioinformatics. Her work bridges theoretical advances in AI with practical clinical applications, developing novel methods for medical image reconstruction, segmentation, and analysis that can improve diagnostic accuracy and treatment planning. Analysis of her recent publications reveals a strong focus on diffusion models for solving complex inverse problems in medical imaging, with particular emphasis on patch-based approaches, latent space disentanglement, and efficient sampling techniques. Her research group has made significant contributions to 3D CT reconstruction, chest X-ray analysis, holographic phase retrieval, and patient-specific imaging studies, demonstrating both theoretical innovation and practical clinical relevance. While specific scientific awards aren't mentioned in the available materials, her extensive publication record in top venues demonstrates significant scholarly impact in the field of biomedical AI. Her research program appears well-funded through grants supporting her work in medical imaging and AI development.
Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
Xiaoxiao Long is a Tenure-Track Associate Professor at the School of Intelligence Science and Technology, Nanjing University. He joined NJU as an associate professor in February 2024. Previously, he earned his Ph.D. from the University of Hong Kong (HKU) under the supervision of Prof. Wenping Wang (IEEE & ACM Fellow) and Prof. Taku Komura. His educational background includes: Ph.D. in Computer Science from University of Hong Kong Bachelor's degree in Control Science & Engineering from Zhejiang University Dr. Long's research focuses on computer graphics and 3D computer vision, with particular emphasis on 3D Vision, Physical AI, and World Models. His long-term goal is to develop General-Purpose AI with spatial capabilities. His work bridges theoretical understanding of 3D spaces with practical implementations of spatial AI systems, with applications spanning robotics, virtual reality, and augmented environments. He employs innovative neural network approaches and geometric constraints to advance 3D scene understanding and reconstruction. His publication record shows strong momentum with multiple papers accepted to top-tier conferences including CVPR (5 papers in 2025 alone), ICML, ICLR, ECCV, and TPAMI. His research demonstrates a clear progression from foundational geometric estimation techniques (ASN++) toward more comprehensive spatial AI systems. His scientific recognition includes: Excellent Young Scholars Fund (Overseas) from NSFC Dr. Long has successfully mentored numerous students who have published at major venues and gone on to pursue advanced degrees at prestigious institutions including USTC, Beihang University, HKU, UCAS, Virginia Tech, and HKUST. He is currently recruiting Ph.D. and master's students for Fall 2026, seeking candidates interested in pushing the boundaries of 3D computer vision and spatial AI. His laboratory focuses on developing advanced techniques for 3D scene understanding, neural rendering, and physical AI. Current projects span Gaussian-based representations, neural radiance fields, and geometric estimation, with applications in robotics, virtual environments, and spatial reasoning systems.
Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Shih-Fu Chang is the Dean of Columbia Engineering and holds the Morris A. and Alma Schapiro Professorship at Columbia University. His research focuses on computer vision, machine learning, and multimedia information retrieval. He is recognized as a foundational figure in the field of content-based visual search and has pioneered innovations in image/video search engines, crime prevention systems, and brain-machine interfaces. His leadership roles include Chair of Columbia's Electrical Engineering Department (2007-2010), Editor-in-Chief of the IEEE Signal Processing Magazine (2006-2008), and Senior Executive Vice Dean at Columbia Engineering, where he drives strategic planning and international collaboration. Dr. Chang has received prestigious awards including the ACM Multimedia Technical Achievement Award, IEEE Signal Processing Technical Achievement Award, and IEEE Kiyo Tomiyasu Award. He is a Fellow of AAAS, ACM, and IEEE, and an Academician of Academia Sinica. His recent work emphasizes multimodal reasoning, few-shot learning, and vision-language systems, with applications in healthcare diagnostics and multimedia benchmarking. His research spans cross-modal understanding, event extraction, and adaptive AI systems. Key contributions include systems like Ferret-v2 for multimodal grounding and RESIN for schema-guided event tracking. He has advised multiple startups and actively contributes to curriculum development in AI and engineering education.
Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.
