جستجوی استاد راهنمای دانشگاهی

• Computer Vision
• Self-Supervised Learning
• Computational Photography
• +۷ مورد دیگر

Alexei A. Efros is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where he holds the Howard Friesen Professorship and is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. He previously served on the faculty at the Robotics Institute of Carnegie Mellon University (CMU) and completed a postdoctoral fellowship at the University of Oxford. His research spans data-driven computer vision, self-supervised learning, computational photography, and applications to computer graphics and robotics. His research interests include: Data-Driven Computer Vision Self-Supervised and Unsupervised Learning Generative Models and Image Synthesis Visual Representation Learning Applications in Robotics and Human-Computer Interaction Intersections with Human Vision and the Humanities The recent publications highlight a strong trend toward self-supervised learning, visual reasoning, and generative modeling, particularly diffusion models and 3D scene understanding. His work increasingly bridges computer vision with language, robotics, and cognitive science, emphasizing interpretability and real-world applicability. There is a clear focus on leveraging unlabeled data and developing methods for robust, generalizable AI systems. His scientific awards and recognitions include: Berkeley Fellowship Google Fellowship Soros Fellowship NSF Fellowship SIGGRAPH Outstanding Doctoral Dissertation Award Facebook Fellowship Adobe Fellowship CMU School of Computer Science Distinguished Dissertation Award ACM Doctoral Dissertation Honorable Mention Alexei Efros has advised numerous PhD students and postdocs, many of whom have gone on to faculty positions at top institutions including CMU, Stanford, MIT, Columbia, NYU, and Georgia Tech. His lab has received research funding from major tech companies and federal agencies, though specific grants are not detailed in the text. He teaches core computer vision and machine learning courses at both undergraduate and graduate levels at UC Berkeley. His research group is highly active, with ongoing projects in 3D perception, generative modeling, and vision-language systems. He leads a vibrant research lab at UC Berkeley, part of the BAIR consortium, collaborating with leading researchers such as Jitendra Malik, Trevor Darrell, Pieter Abbeel, and Angjoo Kanazawa. His lab fosters strong interdisciplinary connections with institutions worldwide, including Oxford, INRIA, and École Normale Supérieure.

• Computer Vision
• Machine Learning
• Data-Driven Learning
• +۵ مورد دیگر

Alexei A. Efros is the Howard Friesen Professor in the EECS Department at the University of California, Berkeley, and a core member of the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at Carnegie Mellon University’s Robotics Institute. His research focuses on data-driven computer vision, self-supervised learning, computational photography, and generative models. He has pioneered advancements in visual representation learning, including seminal work on neural radiance fields and generative adversarial networks. Education Background: Efros holds a PhD in Computer Science from MIT, though specific details of his academic journey are not explicitly provided in the text. His career includes postdoctoral research at the University of Oxford with Andrew Zisserman and collaborative work with Team WILLOW at INRIA Paris. Research Interests: Efros explores how vast uncurated visual data can be leveraged for understanding and synthesizing the visual world. Key areas include self-supervised learning, generative models, and applications in robotics and art. His lab has contributed influential techniques such as Style Transfer, GAN-based image synthesis, and neural scene representation learning. Recent work emphasizes real-time adaptation (Test-Time Training), 3D perception models, and ethical AI implications of generative systems. Publications: Over 150+ publications span topics like Generative Adversarial Networks (GANs), unsupervised learning, and visual-linguistic models. Notable works include Unpaired Image-to-Image Translation (CUT/GAU), Style Transfer , and Swapping Autoencoder . His research has significant industry impact, with techniques adopted in Adobe’s software and generative AI applications. Grants & Collaborations: Efros has secured major funding from NSF, DARPA, and industry partnerships (e.g., Adobe, NVIDIA). He co-leads projects on scalable vision models, ethical AI, and real-world perception systems. Current collaborations include work with MIT, NYU, and INRIA Paris. Labs & Teams: Leads the BAIR Vision Group at Berkeley, fostering interdisciplinary research between computer vision, graphics, and robotics. The group emphasizes Slow Science principles, prioritizing deep exploration over rapid publication.

Baharan Mirzasoleimanمشاهده پروفایل

استادیار

• Machine Learning
• Data Efficiency
• Robustness
• +۴ مورد دیگر

Baharan Mirzasoleiman is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where she leads the BigML research group. Prior to joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. She received her Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. Her research focuses on addressing sustainability, reliability, and efficiency of machine learning, with particular emphasis on improving big data quality by developing theoretically rigorous methods to select the most beneficial data for efficient and robust learning. Her work spans several critical areas including data efficiency, robustness against label noise and data poisoning, and addressing spurious correlations in machine learning models. She has made significant contributions to understanding how neural networks exploit spurious features that correlate with certain categories during training but fail to generalize to minority groups. Professor Mirzasoleiman's research demonstrates how theoretically grounded approaches can lead to practical improvements in model robustness and efficiency across various applications including medical diagnosis and environmental sensing. Her work has resulted in the development of the SpuCo package, a Python library that provides modular implementations of state-of-the-art methods to address spurious correlations, along with controllable synthetic datasets like SpuCoMNIST and large-scale vision datasets like SpuCoAnimals. She has received numerous prestigious awards including the ETH medal for Outstanding Doctoral Thesis, being selected as a Rising Star in EECS by MIT, an NSF Career Award, a UCLA Hellman Fellows Award, and an Okawa Research Award. Her students have also received multiple fellowships and awards including Amazon Doctoral Student Fellowships and an OpenAI Superalignment Fast Grant. Professor Mirzasoleiman actively contributes to the academic community through invited talks at major conferences including ICML, ICLR, NeurIPS, and KDD, as well as co-organizing workshops on new frontiers in adversarial machine learning and sparsity in neural networks. She has developed educational resources including tutorials on Foundations of Data-efficient Learning presented at ICML 2024.

Zeynep Akataمشاهده پروفایل

استاد

• Multimodal Learning
• Explainable Artificial Intelligence
• Zero-Shot Learning
• +۲ مورد دیگر

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence . Education: PhD, University of Grenoble / INRIA Rhône-Alpes, 2014 MSc, RWTH Aachen University, 2010 BSc, Trakya University, Turkey, 2008 Research Interests: Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning . A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning . Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing. Publication Trends: Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision . Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems. Scientific Awards: Lise-Meitner Award for Excellent Women in Computer Science (2014) Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019) ERC Starting Grant, European Commission (2019) DAGM German Pattern Recognition Award (2021) ECVA Young Researcher Award (2022) Alfried Krupp Award (2023) Advising & Funding: Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.). Labs & Teams: She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.

Yanzhi Wangمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Neuromorphic Computing
• +۷ مورد دیگر

Yanzhi Wang is a Professor in the Department of Electrical and Computer Engineering at Northeastern University , affiliated with the Institute for Experiential AI and the Institute for the Wireless Internet of Things . He holds a PhD from the University of Southern California (2014). His research focuses on real-time AI systems, deep neural network compression, neuromorphic computing, and non-von Neumann architectures. Notable projects include NSF-funded initiatives on age-inclusive urban design, superconducting computing (DISCoVER), and edge device optimization (PatDNN). He has received prestigious awards such as the Army Research Office Young Investigator Award and the Constantinos Mavroidis Translational Research Award. His work emphasizes algorithm-hardware co-design for energy efficiency, with grants from NSF, ARO, and industry partners like Google. Recent research trends reflect his focus on accelerating vision transformers, diffusion models, and large language models for edge computing. He has pioneered methods like AutoViT and Fastcar, addressing latency and resource constraints in mobile platforms. Collaborations span academia and industry, driving innovations in superconducting circuits and neuromorphic systems.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +۷ مورد دیگر

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

• Inverse problems and reconstruction
• Neuroimaging
• Quantitative MRI
• +۱ مورد دیگر

Daan Christiaens is a tenure track lecturer at KU Leuven's Faculty of Medicine and Faculty of Engineering Sciences. He is affiliated with the Department of Electrical Engineering (ESAT) and Department of Imaging & Pathology, serving as a member of the Medical Imaging Division and the KU Leuven Brain Institute (LBI). His academic responsibilities include membership in the Faculty Councils of Engineering Sciences and Medicine. His research focuses on: Inverse problems in medical imaging reconstruction Neuroimaging techniques for brain analysis Advanced quantitative MRI methodologies Diffusion-weighted imaging for microstructural assessment Dr. Christiaens' recent publications (2023-2025) demonstrate a consistent focus on diffusion MRI innovations, including novel reconstruction algorithms, neonatal brain development mapping, and clinical applications for neurodegenerative disorders. Key technical themes include motion correction, multi-shell modeling, and AI-enhanced image processing, while clinical applications span Alzheimer's disease, cerebral palsy, and autism research. He leads significant research projects including: MRI reconstruction with dynamic field monitoring (2024-2028) Compressed sensing for microstructure imaging (2022-2026) Neonatal diffusion MRI network connectivity analysis (2024-2028) As a core developer of the MRtrix3 software framework for medical image processing, he contributes to essential tools in neuroimaging research.

• Computer Vision
• Machine Learning
• Computational Photography
• +۵ مورد دیگر

Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.

Merry Maniمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Signal Processing
• Image Reconstruction
• +۴ مورد دیگر

Dr. Merry Mani is an Associate Professor in Radiology and Imaging Sciences and Biomedical Engineering, specializing in biomedical imaging and signal processing. Her work focuses on advancing MRI-based imaging technologies to study neurological disorders such as Alzheimer's, Autism, and Epilepsy. She holds a Ph.D. in Electrical and Computer Engineering from the University of Rochester (2014) and completed a postdoctoral fellowship at the University of Iowa School of Medicine (2018). Her research combines biophysical modeling with machine learning to explore brain microstructures. Key achievements include the NNARSAD Young Investigator Grant and NIH-funded projects like 'Fast Multi-dimensional Diffusion MRI with Sparse Sampling'. Her lab develops cutting-edge reconstruction methods like qModeL and MUSSELS, prioritizing high spatio-temporal resolution imaging. Major contributions span diffusion MRI acquisition, model-based deep learning, and clinical applications in neurodegenerative diseases. Notable grants include NIH R01EB031169 for Alzheimer’s neurodegeneration studies and projects on rTMS for depression. Her work bridges imaging innovation with clinical impact, aiming to improve diagnosis and treatment through advanced imaging biomarkers.

Changyou Chenمشاهده پروفایل

دانشیار

• Multi-Modal Learning
• Vision and Language
• Foundation Models
• +۷ مورد دیگر

Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.

• MRI
• Diffusion MRI
• Perfusion MRI
• +۶ مورد دیگر

Professor Fernando Calamante is a Professor of Biomedical Engineering at The University of Sydney and Director of Sydney Imaging Core Research Facility. He leads the National Imaging Facility node and focuses on advanced MRI methodologies, particularly Diffusion and Perfusion MRI, to study brain connectivity and neurological disorders. His work includes developing the MRtrix software, widely used in diffusion MRI analysis. He holds extensive funding (~$50M) and has been recognized with awards like ISMRM Fellowship and NHMRC grants. His research spans super-resolution imaging, brain connectomics, and clinical applications in stroke and tumors. Education: BSc (Physics, Argentina), PhD (Magnetic Resonance Imaging, University College London). Career highlights include leadership roles at The Florey Institute and ISMRM presidency (2021-2022). Research interests include: Novel MRI methods for brain connectivity and super-resolution imaging Applications of Diffusion and Perfusion MRI in neurology Integration of structural and functional connectomics Key achievements: Over 200 publications, software innovations, and leadership in global MRI societies.

Ananya Choudhuryمشاهده پروفایل

استاد

• Radiation Therapy
• Prostate Cancer
• Bladder Cancer
• +۹ مورد دیگر

Professor Ananya Choudhury serves as Chair and Honorary Consultant in Clinical Oncology at the University of Manchester, where she is also Co-Group Leader of the Translational Radiobiology Group within the Division of Cancer Sciences. She joined The Christie NHS Foundation Trust in 2008, specializing in urology and sarcoma, and has since focused on radiotherapy-related research in prostate and bladder cancers. Professor Choudhury is clinical lead for advanced radiotherapy, including the groundbreaking MRLinac project, and plays a key role in national radiotherapy research initiatives. Professor Choudhury earned her BA (Hons) in 1993, MB. BChir (Cantab) in 1995, and MA (Cantab) in 1997 from Trinity College, Cambridge. She completed her Clinical Oncology training at the Yorkshire Deanery from 2000-2008, during which she earned her MRCP in 2000 and F.R.C.R in 2004. She completed her PhD in 2008 through the University of Leeds and Princess Margaret Hospital in Toronto, Canada, where she studied the molecular epidemiology of DNA double strand break repair in bladder cancer. Professor Choudhury's research program focuses on optimizing and personalizing radiotherapy using advanced imaging technology to deliver high doses while minimizing side effects. Her work centers on prostate and bladder cancers, with particular interest in predictive biomarkers, hypoxia, and the integration of magnetic resonance imaging to improve treatment precision. She has pioneered research in radiotherapy dose optimization, biomarker development, and the identification of patients who would benefit most from different treatment approaches. Her extensive publication record demonstrates a strong focus on radiation therapy, particularly in genitourinary cancers. Recent work explores MRI-guided radiotherapy, hypoxia biomarkers, and personalized treatment approaches across multiple cancer types. She has made significant contributions to understanding how imaging technology can improve radiotherapy precision and effectiveness while reducing side effects, with several publications appearing in top journals through 2025. Professor Choudhury has received multiple prestigious awards recognizing her contributions to the field: Cancer Research-UK/Royal College of Radiologists Clinical Training Fellowship (2005) Fellowship for the 10th ECCO-AACR-ASCO Workshop on Methods in Clinical Cancer Research (2007) Outstanding Contribution, Greater Manchester Clinical Research Awards (2017) RCR Research Fellowship (2005) Research Fellowship, Princess Margaret Hospital, Toronto (2004) Professor Choudhury has supervised numerous doctoral and master's students across multiple cancer types, with current students expected to complete through 2024. She is Principal Investigator on multiple research grants, including 'Measuring tumour radioresistance to improve radiotherapy outcomes' and the 'MAESTRO Programme' as part of CRUK RadNet. Her research program is supported by significant funding from NIHR Manchester Biomedical Research Centre and other major funding bodies. As Co-Group Leader of the Translational Radiobiology Group, Professor Choudhury collaborates extensively with leading researchers including Peter Hoskin, Catharine West, Corinne Faivre-Finn, and Marcel van Herk. Her team is at the forefront of integrating advanced imaging with radiotherapy to improve cancer treatment outcomes, with active projects spanning from basic radiobiology to clinical implementation of novel radiotherapy techniques.

Kep Kee Lohمشاهده پروفایل

مربی ارشد

• Comparative primate neuroanatomy
• Sulcal anatomy
• Frontal cortex function
• +۴ مورد دیگر

Kep Kee Loh is a Senior Tutor in the Department of Psychology at the National University of Singapore (NUS). Currently, he also holds an NUS Overseas Postdoctoral Fellowship position at both the Montreal Neurological Institute (McGill University) and the University of Oxford. His research focuses on comparative primate neuroanatomy, examining what makes the human brain special compared to other primates through multimodal MRI techniques. Ph.D. in Neuroscience from Université Claude Bernard Lyon I (2014-2018) M.Sc. in Cognitive Neuroscience from University College London (2011-2012) B.Soc.Sci. (Hons.) in Psychology from National University of Singapore (2007-2011) Dr. Loh's research primarily investigates the anatomical organization of brains across humans and various primate species including chimpanzees, baboons, and macaques. He employs different magnetic resonance imaging techniques (anatomical, resting-state, diffusion-weighted MRI) to compare brain organization across species, with particular focus on the medial frontal cortex, sulcal anatomy, and the evolution of speech and language in the human brain. His work adopts a multimodal approach to provide an integrative view of what sets human brains apart from other primates. His recent publications demonstrate a strong focus on comparative neuroanatomy across species, with particular emphasis on primate brain evolution, frontal cortex organization, and language-related neural pathways. The research spans multiple disciplines including neuroscience, cognitive science, and evolutionary biology, with increasing attention to methodological advancements in neuroimaging techniques for cross-species comparisons. NUS Overseas Postdoctoral Fellowship (2021) Institute of Language, Communications and the Brain (ILCB) Postdoctoral Fellowship (2019) Fondation Recherche Médicale (FRM) Fin de Thèse (PhD funding) (2017) BRAIN Student Travel Award, 6th Motivation and Cognitive Control Symposium (2016) Dr. Loh has been involved in numerous collaborative research projects across international institutions, including the French Institute of Health and Medical Research (Stem Cell and Brain Research Institute), Aix-Marseille Université, and currently McGill University and the University of Oxford. His work has received significant recognition with over 1,000 citations for his 33 publications. While specific grant information isn't detailed in the provided text, his postdoctoral fellowships indicate successful competitive funding. Dr. Loh collaborates with several research groups including the Montreal Neurological Institute at McGill University and research teams at the University of Oxford. His work connects with broader initiatives like the collaborative resource platform for non-human primate neuroimaging, indicating participation in larger research networks focused on advancing primate neuroscience through shared resources and methodologies.

Hee Kwon Songمشاهده پروفایل

دانشیار

• MRI Physics
• Biomedical Engineering
• Medical Imaging
• +۲ مورد دیگر

Hee Kwon Song, Ph.D. is an Associate Professor of Radiology at the University of Pennsylvania , affiliated with the Institute for Translational Medicine and Therapeutics and the Penn Center for Musculoskeletal Disorders . His expertise lies in MRI physics and MRI engineering , focusing on novel dynamic imaging strategies. Education: BS in Electrical Engineering, Northwestern University (1991) MS in Bioengineering, University of Pennsylvania (1994) PhD in Bioengineering, University of Pennsylvania (1999) Master of Law, University of Pennsylvania Carey Law School (2021) Dr. Song’s research specializes in dynamic contrast-enhanced MRI (DCE-MRI) using radial data acquisition and KWIC reconstruction for ultra-fast imaging. His work enables retrospective respiratory motion compensation and flexible resolution adjustments in tumor treatment monitoring ( lung, liver, breast, kidney, ovarian ). Applications extend to non-contrast MRA and tissue T1 mapping . Selected Publications demonstrate his focus on accelerated MRI , bone-selective imaging , and deep learning reconstruction , with collaborations spanning Medicine, Obstetrics and Gynecology, Neurology , and Radiation Oncology . Contact: heekwon.song@pennmedicine.upenn.edu

• Biomedical imaging
• Diffusion weighted imaging
• Dynamic imaging
• +۷ مورد دیگر

Brad Sutton is a Professor of Bioengineering at the University of Illinois Urbana-Champaign and Technical Director of the Biomedical Imaging Center at Beckman Institute. He holds affiliate roles in the Neuroscience Program, Department of Electrical and Computer Engineering, and is a Health Innovation Professor at the Carle Illinois College of Medicine. His roles also include fellowship positions with the National Center for Supercomputing Applications and the CZ Biohub Chicago. Education: Ph.D. in Biomedical Engineering from the University of Michigan (2003). Research Interests: Focus on advanced MRI techniques for structural and functional brain imaging, including diffusion-weighted imaging, dynamic imaging, and neuromuscular coupling studies. His work emphasizes multi-scale bioimaging to understand brain function across interventions, aging, and disease. Publications: Over 180 peer-reviewed articles in 2025-2024 highlight innovations in MRI technology and applications in neuroscience, including breakthroughs in laminar fMRI specificity, myelin development modeling, and Alzheimer’s biomarker studies. Recent work extends to clinical applications like aortic imaging automation and mixed reality training tools. Awards: AIMBE and ISMRM Fellowships (2017/2024), Abel Bliss Scholar (2014-), and over 9 patents in imaging techniques. Labs & Teams: Leads the Magnetic Resonance Functional Imaging Lab. Collaborates with interdisciplinary teams across engineering, medicine, and computational science to advance imaging technologies and their clinical translation.