Find Academic Supervisors

• Artificial Intelligence
• Explainable AI
• Computational Argumentation
• +3 more

Antonio Rago is a Researcher in the Department of Computing at Imperial College London. He specializes in Explainable Artificial Intelligence (XAI) with a focus on computational argumentation frameworks and their integration with data-driven AI systems. His work bridges symbolic AI and machine learning to enhance transparency and societal benefit in AI applications. Education: PhD in Computing (2019) from Imperial College London, supervised by Prof. Francesca Toni and Dr. Marco Aurisicchio, with an MEng in Automotive Engineering from Loughborough University (2012). Research: Explores explainable AI through argumentation semantics, counterfactual reasoning, and hybrid symbolic-statistical methods. Application domains include e-learning, Formula One race strategy, healthcare, and mechanical engineering. Workshops: Organizer of international workshops like Arg&App 2025 and ArgXAI-25, and co-organizer of previous events at KR, ECAI, and COMMA conferences. Publications: Active in top AI venues (KR, IJCAI, AAAI, AAMAS) with over 25 publications since 2016, emphasizing argumentation-based explanations and robust AI systems. Industry Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014) and Project Manager at Green Lifting Ltd. (2014-2017).

• Machine Learning
• Information Fusion
• Data Clustering
• +3 more

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

• Geophysics
• Machine Learning
• Joint Inversion
• +7 more

Jiajia Sun is an Associate Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston. Her research focuses on advancing subsurface imaging, uncertainty quantification, and mineral exploration through interdisciplinary approaches combining geophysics, machine learning, and computer vision. Education : PhD in Geophysics (2015, Colorado School of Mines); BS in Geophysics (2008, China University of Geosciences, Wuhan). Research Interests : Jiajia specializes in deep learning for geophysical inversion, multi-physics data integration, and probabilistic geological modeling. Her work leverages computational resources like GPUs and clusters to solve inverse problems and tackle magnetic remanence challenges. Recent Publications : Her research includes applying Bayesian frameworks, deep generative models, and joint inversion algorithms to airborne geophysics for critical mineral mapping and hydrogen reservoir detection. She emphasizes open-source tools like SimPEG for reproducibility. Awards : J. Clarence Karcher Award (SEG) Advising & Collaborations : She mentors PhD students in geophysics and collaborates with institutions like Amazon’s Generative AI Innovation Center, Stanford University, and University College Dublin. Her team also tests drones and magnetometers at the UH Coastal Center.

Sriram SankararamanView profile

Professor

• Machine Learning
• Computational Genetics
• Genomics
• +7 more

Professor Sriram Sankararaman is a computational scientist with triple appointments at the University of California, Los Angeles in the Computer Science Department , Department of Human Genetics , and Department of Computational Medicine . His interdisciplinary research bridges computer science, statistics, and biomedicine. PhD in Computer Science from UC Berkeley Postdoctoral training at Harvard Medical School Research focuses on statistical and computational methods for understanding genomic datasets, including: Population genetic inference and archaic admixture analysis Genetic architecture of complex traits Machine learning for clinical data integration Privacy-preserving genomic analysis Recent publications address scalable algorithms for biobank-scale data, evolutionary insights from archaic DNA, and interpretable machine learning models. His lab develops tools like SCOPE and RHE-mc for population structure and variance components analysis. Scientific Honors NSF Career Award (2020) Microsoft Investigator Fellow (2019) Northrop Grumman Excellence in Teaching (2019) Okawa Foundation grant (2017) Alfred P. Sloan Fellow (2017) NIH Pathway to Independence Award (2014) Visvesvaraya Medal (2004) As director of the Machine Learning and Genomics Lab , he leads research on evolutionary genomics, medical genetics, and scalable algorithms for integrating multi-modal biomedical datasets.

• Machine Perception
• Computer Vision
• Machine Learning
• +4 more

Dr. Vasileios Balntas is a leading researcher in machine perception, currently serving as a Research Science Manager at Reality Labs Research. He holds an Honorary Research Associate position at the Department of Electrical and Electronic Engineering , Imperial College London, and previously led research at Scape Technologies and conducted post-doctoral work at the University of Oxford. His research spans computer vision , machine learning , and artificial intelligence , focusing on camera pose estimation 3D scene reconstruction image retrieval local feature descriptors egocentric vision systems privacy-preserving AI The 15 most recent publications highlight his work in neural rendering, adversarial learning, and structured language models for visual perception. Key trends include egocentric multi-modal AI 3D localization descriptor security autoregressive scene modeling infilling techniques second-order loss functions At Imperial College London, he contributes to the Faculty of Engineering 's research in electrical engineering and collaborates across Reality Labs Research and Scape Technologies on cutting-edge machine perception projects.

René VidalView profile

Professor

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

• Biostatistics
• Neuroimaging Analysis
• Causal Inference
• +3 more

Brian Caffo is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, with an active research profile evidenced by 2024-2025 publications and GitHub contributions. He is a key contributor to Neuroconductor (an R platform for medical imaging analysis) and the Acute to Chronic Pain Signatures (A2CPS) project, while maintaining significant educational impact through Coursera data science courses and open-source textbooks. His research spans biostatistics, neuroimaging analysis, and causal inference methodology, with emphasis on functional MRI data, multi-omics integration, and machine learning applications in public health. Current work focuses on advanced neural architectures for brain imaging, causal mediation in autism research, and biomarker discovery for chronic pain and neurodegenerative diseases. Analysis of his 15 most recent publications reveals dominant themes in transformer-based neuroimaging analysis (28%), causal inference applications (27%), and multi-omics/pain signature research (20%). His work consistently bridges statistical methodology with clinical applications in HIV prevention, autism spectrum disorders, and long COVID prediction through large collaborative projects like the National COVID Cohort Collaborative. Dr. Caffo leads the JHU Data Science Lab and contributes to multiple interdisciplinary initiatives including shell MEA development for neural organoids and DREAM-02 HIV microbicide trials. His GitHub profile shows active maintenance of educational repositories like LittleInferenceBook and regmodsbook, supporting widespread adoption of data science methods in biomedical research.

Maria BrbicView profile

Assistant Professor

• Machine Learning
• Computational Biology
• Biomedical Informatics
• +3 more

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.

Yanxiang DengView profile

Assistant Professor

• Spatial Omics
• Single-Cell Analysis
• Microfluidics
• +3 more

Yanxiang Deng is an Assistant Professor in the Department of Pathology and Laboratory Medicine at the Perelman School of Medicine, University of Pennsylvania. His research pioneers spatial omics technologies to decode tissue architecture in development and disease, with seminal contributions including spatial-CUT&Tag and spatial-ATAC-seq for epigenetic mapping. His educational background includes a PhD from Rensselaer Polytechnic Institute (2018) followed by postdoctoral training at Yale University (2018-2022). Key appointments span Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Deng's lab focuses on developing microfluidic platforms for spatial multi-omics, enabling pixel-level profiling of histone modifications, chromatin accessibility, and proteome-transcriptome interactions. His work bridges engineering and biomedicine to address cancer mechanisms and neurodegenerative disorders, with technologies allowing unprecedented resolution of cell-type-specific epigenetic landscapes in intact tissues. Analysis of his 15 most recent publications (2023-2025) reveals accelerating innovation in multimodal spatial mapping, particularly FFPE tissue compatibility, DNA methylation-transcriptome co-profiling, and neuroscience applications. His methods increasingly integrate chromatin features with proteomic data, expanding from foundational 2022 Science and Nature papers to clinical translation in depression and cancer. Major recognitions include: Blavatnik Awards for Young Scientists, Regional Laureate in Life Sciences (2023) Founders Award of Excellence, Rensselaer Polytechnic Institute (2015) National Scholarship (2008) He actively mentors 8 trainees including 6 graduate students and 2 postdocs, with research supported by NIH grants and institutional funding. His lab's deterministic barcoding approach (DBiT-seq), highlighted as Nature Methods' "Method of the Year," underpins multiple high-impact collaborations in immunology and neuroscience. The Deng Lab operates from Stellar Chance Laboratories, employing interdisciplinary teams to develop next-generation tools for spatial multi-omics. Current projects include Spatial-DMT for DNA methylation mapping and spatial-Mux-seq for quadruple-modality profiling, leveraging microfluidics expertise to unlock archival tissue repositories for disease research.

Eneko AgirreView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Language Technology
• +7 more

Eneko Agirre is a Full Professor at the Faculty of Computer Science of the University of the Basque Country UPV/EHU, where he serves as the director of the HiTZ Centre on Language Technology. He is an active member of the Ixa Research Group and has established himself as a leading figure in Natural Language Processing, particularly in multilingual and low-resource language settings. His work bridges theoretical advances with practical applications for language technology. Agirre received his PhD from the University of the Basque Country in 1999 with a thesis on conceptual relationships and ontologies, supervised by Dr. Kepa Sarasola Gabiola and Dr. Arantza Díaz de Ilarraza Sánchez. His academic journey has been marked by significant contributions to computational linguistics and language technology. His research primarily focuses on Natural Language Processing challenges, with special emphasis on Word Sense Disambiguation, cross-lingual transfer learning, dialogue systems, and Large Language Models for low-resource languages. He has pioneered work on Basque language technology and has consistently addressed the challenges of multilingual AI systems, particularly examining how language models perform across different linguistic contexts and cultural settings. Analysis of his recent publications reveals a strong trajectory toward advancing Large Language Models for low-resource languages, with particular attention to Basque. His work spans vision-language models, information extraction techniques, and rigorous evaluation methodologies for NLP systems. A recurring theme is the exploration of how language models handle low-resource languages compared to high-resource ones, with groundbreaking findings about cultural knowledge transfer between languages. Fellow of the ACL (2021), one of only 74 research leaders worldwide National Research Prize on Informatics (2021) Best resource paper award at ACL 2024 Honourable mention paper award (top 1%) at EMNLP (2020) Outstanding Paper award (top 2%) at COLING (2020) Recipient of three Google Faculty Research Awards (2017, 2018, 2019) Agirre has supervised over 25 PhD students, many of whom have received prestigious awards including the EurAI Artificial Intelligence PhD Dissertation Award. His research has been supported by numerous European projects including LIHLITH (2018-2020) on lifelong learning for dialogue systems, and he has served as principal investigator for multiple CHIST-ERA and FP7 projects. His work with Google includes collaborative projects on entity dictionaries and conversational question answering systems. As director of the HiTZ Centre on Language Technology and member of the Ixa Research Group, Agirre leads a vibrant team focused on advancing language technology for Basque and other under-resourced languages. The center has developed significant resources including Latxa, an open language model for Basque, and has established itself as a hub for multilingual NLP research. His group actively collaborates with international institutions including Stanford, NYU, and various European universities, fostering a global network for language technology research.

• Autonomous Driving
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems at Esslingen University of Applied Sciences within the Department of Computer Science and Engineering. He concurrently holds the leadership position of Director at the Institute for Intelligent Systems, where he oversees research initiatives focused on intelligent systems development for real-world autonomous applications. His research program centers on computer vision for autonomous systems , with specialized expertise in visual-inertial SLAM, collective perception, and neural rendering techniques. Key investigation areas include environmental robustness across agricultural and urban settings, real-time processing constraints for embedded systems, sensor fusion methodologies (particularly camera-radar integration), and the application of generative models for perception enhancement. His work consistently addresses practical implementation challenges such as computational efficiency and sensor calibration in unstructured environments. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Advancement of lightweight perception systems through stixel-based representations and neural rendering; (2) Development of infrastructure-supported collective perception frameworks with datasets like CoopScenes and OPNV; and (3) Rigorous benchmarking of SLAM components in domain-specific contexts including agricultural robotics and multi-season navigation. His recent systematic review on LLM-based vulnerability detection also demonstrates expanding interest in software security for autonomous systems. As Director of the Institute for Intelligent Systems, Prof. Enzweiler leads a research ecosystem focused on translating theoretical advances into practical autonomous vehicle technologies. His team develops specialized datasets (Rover, OPNV) and software stacks for smart city environments, emphasizing the integration of novel perception approaches with vehicle dynamics modeling and real-time operational constraints.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +7 more

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

Aysim TokerView profile

Researcher

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Aysim Toker is a Ph.D. candidate at the Chair for Computer Vision and Artificial Intelligence , affiliated with the Technical University of Munich . She works under the supervision of Prof. Dr. Laura Leal-Taixe and Prof. Dr. Xiaoxiang Zhu on interdisciplinary projects. Research Focus: Deep learning, sequence analysis, and remote sensing. Key Contributions: Advancing video object segmentation, Earth observation models, and anonymization techniques. Collaborations: Partnered with international researchers across multiple institutions. Publications span top venues like eLife , ICCV , NeurIPS , and CVPR , with a focus on: Deep learning architectures for multi-modal tasks Image and video analysis in remote sensing and anonymization Tracking and segmentation algorithms Her work bridges foundational computer vision research with applications in Earth science and privacy-preserving technologies.

Jeffery D. WeirView profile

Adjunct Professor

• Operations Research
• Decision Analysis
• Risk Analysis
• +6 more

Dr. Jeffery D. Weir is an Adjunct Professor in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). A former U.S. Air Force officer, he specializes in decision analysis, risk analysis, and multi-objective optimization, with applications spanning military logistics, energy systems, and transportation modeling. His research addresses complex operational challenges such as aircraft scheduling, RNA foldability prediction, and cost analysis for military installations. Doctor of Philosophy (Ph.D.), Industrial Engineering & Operations Research, Georgia Institute of Technology, 2002 Master of Science (M.S.), Operations Research, Air Force Institute of Technology, 1995 Bachelor of Science in Electrical Engineering (B.S.E.E.), Georgia Institute of Technology, 1988 Dr. Weir's research bridges theoretical and applied domains, focusing on metaheuristics, data envelopment analysis, and robust optimization. His recent work includes RNA structure prediction frameworks, multi-modal distribution networks, and energy model recommendation systems. His publications span journals like BMC Bioinformatics , European Journal of Operations Research , and IEEE Transactions on Evolutionary Computation , reflecting interdisciplinary expertise in computational biology, military logistics, and building energy systems. Scientific awards include the Toulmin Medal for best article in 2004. He has received grants from the Defense Intelligence Agency, United States Transportation Command, Air Force Materiel Command, and other military organizations. Dr. Weir is a member of the Institute for Operations Research and the Management Sciences (INFORMS), Military Operations Research Society, and Institute of Industrial Engineers. His work emphasizes practical decision support tools for military personnel and resource management.

Alexander T. AdamsView profile

Assistant Professor

• Ubiquitous and Wearable Sensing Systems
• Equity-Driven Design in Healthcare
• Signal Processing
• +10 more

Dr. Alexander T. Adams is an Assistant Professor at the School of Interactive Computing, College of Computing, Georgia Institute of Technology. His research focuses on designing and fabricating novel ubiquitous and wearable sensing systems through equity-driven design principles to advance healthcare technology. Educational Background: BS in Computer Science (2014, University of North Carolina at Charlotte), PhD in Information Science (2021, Cornell University). Professional Journey: Former Research Scientist at Cornell Tech's Precision Behavioral Health Initiative (2021-2022); current faculty member at Georgia Tech since Fall 2022. Research Themes: Multi-modal sensing systems, mental and physical health assessment, mobile health technologies, and intersections of fabrication, signal processing, and public health. Affiliations: Core Faculty in IRI Robotics, Affiliated Faculty in Bioengineering and Bioscience People and Technology at Georgia Tech.