جست‌وجوی استاد و پژوهشگر

Ismail Ben Ayedمشاهده پروفایل

دانشیار

• Computer vision
• Optimization
• Medical image analysis
• +۱ مورد دیگر

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

Shayan Aziznejadمشاهده پروفایل

پژوهشگر

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +۳ مورد دیگر

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

• Radiomics
• Medical Imaging Analysis
• Machine Learning in Healthcare
• +۷ مورد دیگر

Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.

Ehsan Sameiمشاهده پروفایل

استاد

• Medical Imaging Physics
• Radiology
• Computed Tomography (CT)
• +۵ مورد دیگر

Ehsan Samei is the Reed and Martha Rice Distinguished Professor of Radiology at Duke University. He holds concurrent professorships in Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering. His leadership roles include Chief Imaging Physicist at Duke University Health System, Director of the Carl E. Ravin Advanced Imaging Laboratories, and Director of the Center for Virtual Imaging Trials (CVIT). Education: University of Michigan (PhD, 1997; MEng, 1995) Key Appointments: Radiology (Clinical Science Departments), Biomedical Engineering (Pratt School of Engineering), Physics (Trinity College of Arts & Sciences) Dr. Samei's research bridges medical imaging physics with clinical applications. His work focuses on photon-counting CT technology, virtual imaging trials, and AI-driven harmonization of CT images. He develops computational models for organ dosimetry, disease quantification, and procedural optimization in radiology. Recent publications emphasize virtual imaging trials for evaluating CT technologies, radiation dose reduction strategies, and AI integration in medical imaging. His studies compare photon-counting CT with conventional systems for lung density, liver lesion detection, and cardiac imaging, while advancing radiomics and dose monitoring frameworks. Scientific Awards Fellow of AAPM (FAAPM) Fellow of SPIE (FSPIE) Fellow of AIMBE (FAIMBE) Fellow of IOMP (FIOMP) Fellow of ACR (FACR) President of AAPM (2023) President of SDAMPP (2010-2011) Dr. Samei has secured major grants from NIH, NCI, and industry partners like GE Healthcare and Siemens. He leads the Center for Virtual Imaging Trials and directs multiple residency training programs in medical physics. His laboratory develops simulation toolkits, 3D-printed phantoms, and dose analytics platforms.

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +۳ مورد دیگر

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

• Interactive Global Illumination
• Perceptual Graphics
• Non-physical Graphics
• +۱ مورد دیگر

Tobias Ritschel is a Professor of Computer Graphics at University College London . His research spans advanced rendering techniques, perceptual modeling, and data-driven graphics, with a focus on bridging physical accuracy and artistic flexibility in visual computing. Key research themes include: Interactive Global Illumination : Real-time simulation of complex lighting effects on GPUs Perceptual Graphics : Human vision-driven rendering and display optimization Non-physical Graphics : Beyond-photorealistic techniques for artistic expression Data-driven Graphics : Leveraging large datasets for novel rendering and modeling approaches His recent work emphasizes neural rendering , differentiable graphics , and X-ray tomography , with applications in 3D reconstruction , NeRF manipulation , and holographic imaging . Notable scientific achievements include the Eurographics Young Researcher Award 2014 and Eurographics Thesis Award 2011 . He has advised multiple PhD students including Philipp Henzler (EG PhD Award 2024) and Thomas Leimkühler (Otto Hahn Medal 2019), while actively contributing to conference leadership as co-chair for EGSR 2024 and Pacific Graphics 2024 . His team collaborates on X-ray reconstruction with Pablo Villanueva-Perez and works on 3D perception with Anthony Steed.

Leif Kobbeltمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +۳ مورد دیگر

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Simon Mochrieمشاهده پروفایل

استاد

• Biophysics
• Condensed Matter Physics
• Single Molecule Biophysics
• +۷ مورد دیگر

Simon Mochrie is a Professor of Physics and Applied Physics at Yale University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. His research focuses on experimental biophysics and condensed matter physics, with emphasis on chromatin dynamics, nuclear mechanics, and super-resolution microscopy. He holds a Ph.D. from MIT (1985) and has pioneered techniques such as optical tweezers and STED microscopy to study biological systems like the ubiquitin-proteasome system in yeast. Current projects include single-molecule measurements on nucleosomes and developing novel imaging methods like LIVE-PAINT for live-cell super-resolution imaging. Educations: Ph.D., Physics, MIT (1985) Research interests center on understanding how chromatin organization influences nuclear mechanics, with studies on heterochromatin condensation, cohesin-driven loop extrusion, and chromatin-envelope interactions. His lab develops advanced microscopy techniques to visualize protein dynamics and subnuclear structures in real time. Recent work explores diffusive states of membrane proteins and the role of phase separation in heterochromatin mechanics. His articles demonstrate a focus on interdisciplinary approaches, combining biophysical experimentation with computational modeling to elucidate fundamental mechanisms in cell biology and soft matter physics. Notable themes include the interplay between chromatin structure and nuclear stiffness, loop extrusion dynamics, and quantitative analysis of intrachromosomal contacts. Teaching contributions include developing introductory physics courses tailored for life sciences students, emphasizing applications in biology and medicine. He actively participates in STEM education initiatives, including collaborative research networks for graduate students in physical biology. The Mochrie Lab also emphasizes instrumentation innovation, such as building fast-scanning STED microscopes and reversible peptide-based imaging systems.

Mathieu Fontaineمشاهده پروفایل

دانشیار

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +۹ مورد دیگر

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.

• Multimodal Signal Processing
• Biomedical Signal Processing
• Image and Speech Processing
• +۳ مورد دیگر

Richard Dansereau is a Professor and Associate Dean (Graduate Studies) in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. His research focuses on signal processing, including biomedical applications, compressive sensing, medical imaging, and fractal complexity analysis. He has led the Signal Processing and Machine Learning Lab and advised numerous PhD students in areas like PET image reconstruction and drone detection through Riemannian geometry. His academic roles include serving as Clerk of Senate and Academic Editor for IET Signal Processing. Key research contributions span deep learning for compressive sensing (e.g., DEQ-based networks), cervical cell segmentation, and biomedical signal processing. Over 150 publications highlight his work on topics like PET-MRI fusion, audio-visual speech enhancement, and radar systems. Collaborations include projects on cardiac PET imaging and drone detection algorithms. Awards include the IEEE Senior Member designation. His teaching spans courses such as Digital Signal Processing, Wavelets, and Biomedical Systems across Carleton and the Georgia Institute of Technology.

Leslie Greengardمشاهده پروفایل

استاد

• Integral Equation Methods
• Electromagnetics
• Acoustics
• +۵ مورد دیگر

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

Julian Tachellaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Machine Learning
• Computational Imaging
• +۵ مورد دیگر

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

• Computer Vision
• Robotics
• 3D Reconstruction
• +۵ مورد دیگر

Thomas Läbe is a Researcher in the Department of Photogrammetry at the University of Bonn's Institute of Geodesy and Geoinformation. His roles include technical associate and software developer, with a focus on advancing robotics, computer vision, and agricultural automation. He holds a diploma in computer science (Dipl.-Inform. (FH)) from Cologne University of Applied Sciences (1994). His research spans image processing, bundle adjustment, visual odometry, and camera calibration, with notable contributions to SLAM (Simultaneous Localization and Mapping) for autonomous systems. Education: 1994: Dipl.-Inform. (FH) in Computer Science, Cologne University of Applied Sciences Research Interests: Focuses on integrating vision-based technologies into robotics for precision agriculture, 3D reconstruction, and robust localization in dynamic environments. His work addresses challenges in sensor calibration, panoptic segmentation, and semantic mapping for autonomous systems operating in agricultural and urban settings. Publications Trends: Recent work emphasizes agricultural robotics, including fruit completion, plant phenotyping, and robust localization under changing conditions. Key themes include LiDAR-based localization, semantic occupancy prediction, and end-to-end learning for 3D representations. Awards: 2001 Hansa Luftbild Price (Best PFG paper award, shared) Technical Contributions: Developed software frameworks for bundle adjustment, multi-camera systems, and obstacle detection. Collaborated on projects like the Phenoliner field phenotyping platform and UAV-based environmental monitoring systems. Labs/Teams: Active in the Photogrammetry professorship group, contributing to the PRBonn (Perception, Robotics & Bonn) robotics initiatives, with open-source tools like OverlapNet and PAg-NeRF for 3D perception.

• Multimodal signal processing
• Audio-video signal processing
• Biomedical signal processing
• +۱۲ مورد دیگر

Richard M. Dansereau is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. He currently serves as Associate Dean (Graduate Studies) and Clerk of Senate, reflecting his leadership roles within the university. His research interests include: Multimodal and audio-visual signal processing Biomedical and biometric signal processing Image and speech signal processing Compressive sensing and deep learning for reconstruction Fractal and multifractal complexity measures, including Rényi dimensions Applications in medical imaging, speech enhancement, and radar systems Recent publications highlight his lab's focus on advanced deep learning techniques for image reconstruction (e.g., deep equilibrium models for compressive sensing), medical image analysis (e.g., PET reconstruction and cervical cell segmentation), and Riemannian geometry in radar signal processing for drone detection. His work integrates theoretical signal processing with practical applications in healthcare and defense. Scientific awards associated with his research group include: Ontario Graduate Scholarship Alexander Graham Bell Canada Graduate Scholarship (CGS D) John Ruptash Memorial Fellowship NSERC Best Project Award 1st prize in poster competition at hSITE 2012 Finalist for World Congress Award at WSCTS’2006 Dansereau actively supervises graduate students, with a long list of Ph.D. and M.A.Sc. alumni who have worked on topics such as speech separation, ECG analysis, image registration, and radar signal processing. He collaborates with researchers at institutions like the University of Ottawa Heart Institute and Defence Research and Development Canada (DRDC). His lab, the Signal Processing and Machine Learning Lab, continues to publish in top journals and conferences, securing research opportunities for Canadian, American, and British citizens in speech intelligibility research.

René Vidalمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

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 .