Find Academic Supervisors

• Quantum Information Theory
• Quantum Many-Body Systems
• Mathematical Physics
• +2 more

Dr. Angela Capel Cuevas is an Assistant Professor at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics since April 2024. Previously, she held a Junior Professorship (W1) at Eberhard Karls Universität Tübingen's Mathematical Physics area (2021-2024), and was an MCQST Distinguished PostDoc at Technische Universität München (2020-2021). Her research sits at the intersection of Quantum Information Theory and Quantum Many-Body Systems , focusing on quantum dissipative evolutions and their mathematical characterization through quantum functional inequalities and entropic bounds . 2023-26: CRC TRR 352 grant on "Mathematics of Many-Body Quantum Systems" (7M€) 2024-27: QuantERA project "Towards a useful quantum advantage" (TouQan, 1.25M€) Her recent work demonstrates rapid thermalization in 1D quantum systems with logarithmic time scaling and establishes exponential mutual information decay for Gibbs states. She co-organized BIRS-IMAG workshops on quantum information theory and received the Jack Keil Wolf ISIT Student Paper Award (2023). Her PhD thesis (ICMAT/Universidad Autónoma de Madrid, 2019) introduced quasi-factorization techniques for relative entropy, leading to groundbreaking results in quantum functional inequalities. 2023: Simons Emmy Noether Fellowship at Perimeter Institute 2023: Forbes 30 Under 30 Spain 2022: Vicent Caselles RSME-FBBVA Award

• Medical Imaging
• Radiation Oncology
• Machine Learning
• +4 more

Jing Wang, Ph.D., is a Professor of Radiation Oncology at UT Southwestern Medical Center, affiliated with the Department of Radiation Oncology’s Division of Medical Physics and Engineering. His research bridges medical imaging, machine learning, and radiation therapy optimization. B.Sc. in Material Physics, University of Science and Technology of China M.A. and Ph.D. in Physics, Stony Brook University Postdoctoral training in Radiology (Stony Brook) and Radiation Physics (Stanford) Dr. Wang’s work focuses on enhancing medical imaging quality for quantitative applications in image-guided radiation therapy (IGRT) and adaptive radiation therapy (ART) . Key areas include CT/MRI/PET reconstruction , deep learning for tumor localization , and radiomics-based survival prediction . His AIRT Lab develops AI algorithms for treatment outcome modeling and real-time imaging. Recent publications highlight advancements in transformer networks for anatomy prediction, uncertainty-aware segmentation , and delta radiomics for surgical margin analysis. Journals span Medical Physics , Physics in Medicine and Biology , and International Journal of Radiation Oncology .

Steven LeeView profile

Professor

• Biophysical Chemistry
• Single-Molecule Fluorescence Spectroscopy
• Super-Resolution Imaging
• +5 more

Professor Steven Lee is a leading figure in biophysical chemistry at the University of Cambridge , where he leads the TheLeeLab in the Yusuf Hamied Department of Chemistry . His research focuses on developing advanced single-molecule fluorescence and multidimensional super-resolution imaging techniques to probe fundamental biological processes at unprecedented spatial precision. Developed novel super-resolution microscopy approaches for 2D/3D visualization of T-cell membrane proteins and histone assembly in fission yeast nuclei Pioneer of 15-20nm resolution imaging strategies through fluorophore kinetics and image reconstruction algorithms Recipient of the 2017 Marlow Prize in Physical Chemistry , Lee's lab produces cutting-edge tools with applications in immunology , neurodegeneration , and cellular biophysics . His team maintains active collaborations with Prof Klenerman (FRS MedSci) and Prof Moerner (Nobel Chemistry 2014). Research Highlights : Molecular origins of immunity through T-cell membrane protein interactions 3D histone dynamics during DNA replication/repair Amyloid aggregate quantification for neurodegenerative disease diagnosis Volumetric imaging innovations via vLUME virtual reality platform

Luca GuarneraView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Multimedia Forensics
• +6 more

Luca Guarnera is a Fixed-term Assistant Professor (RTDA) of Informatics at the Department of Mathematics and Computer Science, University of Catania. Born in Catania on October 26, 1992, he has been a research fellow in Computer Science at the University of Catania since January 1, 2022. His academic journey includes a PhD in Computer Science (XXXIII cycle, PON number E37H18000330006) from the University of Catania, with part of his research conducted at the University of Hertfordshire under Prof. Salvatore Livatino. PhD in Computer Science, University of Catania (2018-2021) Master's Degree in Computer Science (cum laude), University of Catania (2015-2017) Bachelor's Degree in Computer Science, University of Catania (2011-2015) Guarnera's research focuses on Computer Vision, Machine Learning, and Multimedia Forensics, with special emphasis on Deepfake Detection across images, video, audio, and multimodal data. His work explores intrinsic traces left during content creation processes, VR applications for forensic analysis, and deep learning approaches to forensic problems. He has contributed significantly to forensic firearms ballistics analysis through immersive VR observation and handwritten document analysis. His publication record shows a strong trajectory in deepfake detection technologies, evolving from early work on convolutional traces to current research distinguishing between GAN and Diffusion Model outputs. His research spans both theoretical foundations and practical applications in digital forensics, with increasing focus on multimodal approaches and real-world implementation challenges. Guest Editor for Special Issue 'Advancements in Deepfake Technology, Biometry System and Multimedia Forensic' in MDPI Journal of Imaging Guarnera actively participates in the academic community as a reviewer for international journals and has contributed to prestigious international events as a Technical Program Committee member. His research is supported through collaborations with industry partners like iCTLAB srl and academic institutions. He is involved in the HEALTHY-UNICT project studying dietary habits of college students through ecological momentary assessment approaches. As a member of IPLab (Image Processing Lab) since 2015, Guarnera has been involved in various research initiatives including participation in Mohamed Bin Zayed International Robotics Challenge (MBZIRC) competitions and international summer schools (ICVSS, MISS, S3P). His work bridges theoretical computer science with practical forensic applications, particularly in the rapidly evolving field of deepfake detection and multimedia authentication.

• Digital Humanities
• Visual Culture
• Computational History
• +5 more

Dr. Thomas Smits is a faculty member at the Faculty of Humanities of the University of Amsterdam , specializing in Digital Humanities , Visual Culture , and Computational History . His research bridges artificial intelligence with historical image analysis, focusing on media history, colonial visual representations, and algorithmic approaches to archival studies. Recent publications highlight his work on multimodal datasets, generative AI in protest memory reconstruction, and the ethical implications of machine learning for historical research. Smits employs computational methods like CLIP and computer vision to analyze large-scale visual collections, including magic lantern slides, historical advertisements, and news images. Key research areas: Digital Humanities, Visual Memory, Algorithmic Analysis, Colonial Media, Gender Studies, Historical Epistemology. Methodological focus: Stochastic modeling, multimodal machine learning, distant reading of visual datasets, and AI-driven archival enhancement.

• Medical ultrasound
• Image guided surgery
• Adaptive imaging
• +4 more

Gregg Trahey is the Robert Plonsey Distinguished Professor of Biomedical Engineering at Duke University, with additional appointments in Radiology. He leads pioneering research in medical ultrasound imaging and serves as a Bass Fellow, reflecting his significant contributions to both research and education. B.S. from University of Michigan, Ann Arbor (1975) M.S. from University of Michigan, Ann Arbor (1979) Ph.D. from Duke University (1985) Dr. Trahey's research focuses on medical ultrasound, image guided surgery, adaptive imaging, imaging of tissue's mechanical properties, and radiation force imaging . His laboratory develops and evaluates novel ultrasonic imaging methods with current projects involving high resolution imaging of the breast and mechanical characterization of both breast and cardiovascular systems. They conduct comprehensive testing through phantom models, animal trials, ex vivo experiments, and human clinical trials, with current clinical applications focusing on vascular plaque imaging and breast lesion characterization. Analysis of Dr. Trahey's recent publications (2022-2025) reveals a strong emphasis on spatial coherence techniques, adaptive ultrasound imaging systems, and quantitative tissue characterization. His work bridges engineering innovation with clinical applications, particularly in cardiac and breast imaging, with key themes including clutter reduction, real-time adaptive systems, and mechanical property assessment of tissues. Fellow, Institute of Electrical and Electronics Engineers (IEEE), 2022 MERIT Award, National Institutes of Health, 2009 Fellows, American Institute for Medical and Biological Engineering, 1999 Dr. Trahey has taught courses including MEDPHY 738: Radiology in Practice, ECE 392: Projects in Electrical and Computer Engineering, and BME 848L: Radiology in Practice. His research is supported by significant funding, particularly from the National Institutes of Health as evidenced by his prestigious MERIT Award, which provides extended grant support to researchers with exceptional performance. Dr. Trahey leads an active research laboratory that conducts comprehensive studies from phantom development through clinical trials. His team collaborates extensively with clinicians for translational research applications, particularly in cardiology and radiology. Current projects focus on high-resolution imaging techniques, mechanical tissue characterization, and development of novel ultrasound methods for improved diagnostic capabilities while maintaining patient safety.

• Biomedical Optics
• Optical Coherence Tomography (OCT)
• Quantitative Phase Microscopy
• +5 more

Adam Wax is a Professor of Biomedical Engineering and Professor of Physics at Duke University , where he leads cutting-edge research in optical spectroscopy for cancer detection , novel microscopy techniques , and low-coherence interferometry . As a member of both the Duke Cancer Institute Faculty Network and Duke Institute for Brain Sciences , he bridges engineering and biomedical applications. Education: B.S. from Rensselaer Polytechnic Institute (1993), M.A. (1996) and Ph.D. (1999) from Duke University Honors: Fellow, American Institute for Medical and Biological Engineering (2014); Fellow, SPIE (2010); Fellow, OSA (2010); NSF CAREER Award (2004) His research focuses on biomedical optics , particularly optical coherence tomography (OCT) and quantitative phase microscopy , with applications in early cancer detection , subcellular imaging , and clinical diagnostics . Recent work emphasizes low-cost, portable OCT systems for point-of-care applications and multimodal imaging combining spectroscopy with phase analysis. Scientific awards include: Fellow, American Institute for Medical and Biological Engineering (2014) Fellow, International Society for Optics and Photonics (2010) Fellow, Optical Society of America (2010) National Science Foundation CAREER Award (2004)

Jitendra MalikView profile

Professor

• Computer Vision
• Robotics
• Machine Learning
• +7 more

Jitendra Malik is the Arthur J. Chick Professor of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, with affiliations in Bioengineering, Cognitive Science, and Vision Science groups. He previously served as Chair of the Computer Science Division (2002-2004) and Department Chair of EECS (2004-2006 and 2016-2017). He also worked part-time as Research Director and Site Lead at Facebook AI Research (Meta Inc.) during 2018-2019. Education : B.Tech in Electrical Engineering (IIT Kanpur, 1980), PhD in Computer Science (Stanford, 1985) Current Research : Computer Vision, Robotics, Machine Learning, Computational Modeling of Human Vision, and Biological Image Analysis Making significant contributions to computer vision , robotics , and machine learning , Malik’s work includes foundational algorithms like anisotropic diffusion , normalized cuts , and R-CNN . His recent research focuses on embodied AI agents , dexterous manipulation , 3D scene reconstruction , and vision-language-action models . His publications have received 11 best paper awards , including test-of-time awards like the Longuet-Higgins Prize (3x) and Helmholtz Prize (3x). Malik has mentored over 80 PhD students and postdoctoral fellows , many of whom hold prominent positions at institutions like MIT, Caltech, Google, and Meta. His lab’s work spans computer vision , robot learning , and 3D modeling , with applications in autonomous systems, medical imaging, and computational biology. Scientific Awards Presidential Young Investigator Award (1989) Distinguished Alumnus Award, IIT Kanpur (2008) IEEE PAMI-TC Distinguished Researcher (2013) K.S. Fu Prize, IAPR (2014) ACM-AAAI Allen Newell Award (2016) IJCAI Award for Research Excellence (2018) IEEE Computer Society Pioneer Award (2019) Fellow, IEEE, ACM, AAAS Member, National Academy of Engineering and National Academy of Sciences As a leader in sensorimotor learning and humanoid robotics , Malik has pioneered approaches for vision-based quadcopter control , humanoid locomotion , and visuo-tactile perception , further advancing the field of artificial intelligence and computational vision .

Mathieu FontaineView profile

Associate Professor

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +9 more

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.

Jürgen HesserView profile

Professor

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +7 more

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Xavier DécoretView profile

Researcher

• Computer Graphics
• Real-time Rendering
• Visibility
• +3 more

Xavier Décoret is a researcher at INRIA since October 2003, specializing in computer graphics with core expertise in real-time rendering, visibility algorithms, and level-of-detail techniques. He teaches courses at Grenoble (Master IVR program) and École Polytechnique, including Java programming, virtual image creation, and advanced image synthesis. His educational background includes: PhD in Computer Graphics under François Sillion Post-doctoral position at MIT under Frédo Durand Décoret's research spans non-photorealistic rendering, shadow computation, and GPU-accelerated algorithms. He develops practical graphics tools including XdkWRL (VRML parsing), Argstream (command-line arguments), and XdkBibTeX (BibTeX handling), emphasizing real-world implementation for interactive systems. His 2008 publications reveal trends toward efficient interactive rendering techniques, with contributions in dynamic stylization, label placement, soft shadows, and GPU voxelization—highlighting innovations in plausible visual effects for real-time applications. He is affiliated with the Artis research team at INRIA, focusing on virtual reality and computer graphics advancements.

Ana LucicView profile

Assistant Professor

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

Jared TannerView profile

Professor

• Compressed Sensing
• Matrix Completion
• Signal and Image Processing
• +7 more

Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.

Deborah J. RubensView profile

Professor

• Medical Imaging
• Ultrasound
• Computed Tomography
• +7 more

Deborah J. Rubens is a Professor (Part-Time) in the Department of Imaging Sciences at the University of Rochester Medical Center, with a secondary appointment in Biomedical Engineering. She serves as Vice Chair for Faculty and Professional Development and Medical Director of the Rochester Center for Biomedical Ultrasound. Education : Biology (BS, Stanford University), Medicine (MD, University of Rochester) Board Certifications : Diagnostic Radiology Leadership : Former Director of Body MRI, Head of Ultrasound Division Her research focuses on ultrasound innovations including: Prostate cancer detection via sonoelasticity 3D ultrasound and contrast agent development US/MR/CT fusion imaging Shear wave dispersion for liver disease Telemedicine ultrasound applications in Peru She has led NIH-funded research on 3D ultrasound elastography and co-investigated studies on venous thrombosis prophylaxis. Scientific accolades include Fellowships in the American College of Radiology and American Institute of Ultrasound in Medicine, plus the Armed Forces Institute of Pathology Distinguished Scientist award. Her clinical expertise spans ultrasound, CT, and MRI diagnostics for genitourinary and hepatobiliary conditions.

Rafael SebastianView profile

Professor

• Cardiac Computational Modeling
• Artificial Intelligence in Medicine
• Medical Imaging
• +4 more

Rafael Sebastian is a Full Professor at Universitat de Valencia and General Director for Science and Research of the Generalitat Valenciana. He leads the Computational Multiscale Simulation Lab (CoMMLab) and collaborates with institutions like Oxford University and Yale University. Department of Computer Science, Universitat de Valencia CoMMLab Founder Spanish Network of Excellence in Cardiac Modeling His research focuses on multi-scale computational models and artificial intelligence for patient-specific cardiac simulations , aiming to improve arrhythmia risk stratification and therapy planning . Key topics include cardiac conduction system modeling , scar-related ventricular tachycardia , and machine learning pipelines for clinical applications. Recent publications emphasize automata-based simulations for atrial arrhythmias, machine learning in arrhythmia localization, and 3D geometric characterization of aortic diseases. Trends show integration of computational modeling with clinical data and medical imaging . Scientific Awards: Best Poster Award, Functional Imaging and Modeling of the Heart (2021) Cum Laude Award, SPIE Medical Imaging (2009) Student Presentation Award (2011) He has supervised 7 PhD/Master students and led grants exceeding €1 million, including projects like iSARC-GENETICS and iCardioTwins , focusing on digital twin technology and cardiac disease stratification .