جست‌وجوی استاد و پژوهشگر

Su Ryon Shinمشاهده پروفایل

استادیار

• Bioengineering
• Tissue Engineering
• 3D Bioprinting
• +۷ مورد دیگر

Dr. Su Ryon Shin is an Assistant Professor in the Division of Engineering in Medicine at Harvard Medical School and Brigham and Women's Hospital (BWH) in Cambridge, MA. She leads an active research laboratory focused on bioengineering, tissue engineering, and regenerative medicine, with particular expertise in 3D bioprinting, biomaterials, and organ-on-a-chip technology. Her research interests span biohybrid robotics, decellularized extracellular matrix, stem cell-based tissue engineering, and volumetric muscle regeneration . Dr. Shin's work integrates advanced biomaterials with cellular systems to create innovative solutions for tissue regeneration and disease modeling. She has pioneered approaches using human stem cell-derived materials for volumetric tissue regeneration and developed biohybrid neuromuscular robots powered by living cardiac muscle cells. Her publication record demonstrates consistent productivity with over 180 publications, including numerous first/senior author papers in high-impact journals like Science Robotics, Advanced Materials, and Nature Reviews Bioengineering . Her work shows a clear progression from fundamental biomaterials development to increasingly complex tissue engineering applications and translational research. Dr. Shin has received significant recognition including being named a 2025 BWH Health & Technology Innovation Awardee , Highly Cited Researcher 2024 by Web of Science, and multiple Stepping Strong Innovator Awards (2015, 2018, 2020). Her research has been featured in Nature Reviews Bioengineering for breakthrough work on biohybrid robots. She actively mentors students and postdocs, with former lab members accepted to prestigious programs like MIT's PhD program in Chemical Engineering. Her collaborative approach is evident through numerous interdisciplinary projects with researchers across Harvard Medical School, BWH, and international institutions.

Gabriela V. Cohen Freueمشاهده پروفایل

دانشیار

• Statistical Methodology
• High-Dimensional Data Analysis
• Robust Statistics
• +۶ مورد دیگر

Gabriela V. Cohen Freue is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus, and holds a Canada Research Chair (CRC Tier 2). She leads an interdisciplinary research program focusing on developing robust statistical methodologies for analyzing high-dimensional data in genomics and proteomics, with applications in medical sciences. Her work addresses challenges such as outliers, collinearity, and measurement errors, with applications in biomarker discovery for diseases like multiple sclerosis, cardiovascular disorders, and asthma. Her academic journey includes collaborations across disciplines, including with the BC Cancer Agency, PROOF Centre of Excellence, and iCAPTURE. She has pioneered methods like the Penalized Elastic Net S-Estimator (PENSE) and contributed to proteomic data analysis tools such as the Protein Group Code Algorithm (PGCA). She also co-developed the MDQC quality control method for microarrays. Research interests include robust regression, biomarker development, and statistical methods for big data. Her team includes postdocs, PhD, and MSc students, with a focus on training in both statistical rigor and interdisciplinary collaboration. Notable grants include a CANSSI Collaborative Research Team Project (CRT) award for robust causal inference and prediction modeling. Teaching responsibilities span statistical consulting, high-dimensional biological data analysis, and generalized linear models. She emphasizes active learning and real-world problem-solving in her courses. Her lab’s work is supported by grants from the Data Science Institute (DSI) and collaborations with institutions like the PROOF Centre. Alumni of her group hold positions in academia (e.g., George Mason University) and industry (e.g., Merck, BC Cancer Research Centre).

Xin Zhouمشاهده پروفایل

پژوهشگر ارشد

• Computational Biology
• Health Informatics
• Cardiovascular Disease Modeling
• +۳ مورد دیگر

Dr. Xin Zhou is an Oxford-Bristol Myers Squibb Fellow at the Department of Computer Science, University of Oxford. Her research integrates computational modeling, clinical data, and experimental findings to investigate cardiac disease mechanisms and develop human-based simulations for drug evaluation. BSc and MSc in Life Sciences, Beijing Normal University DPhil in Computational Biology, University of Oxford Her work focuses on multi-scale cardiac modeling , particularly in ischemic heart disease and heart failure, exploring ionic currents, tissue conduction, and organ-level dynamics. She develops electromechanical simulations to study cardiac alternans and arrhythmic risks, translating these into clinical applications for patient stratification and pharmaceutical testing. Recent publications emphasize in silico clinical trials , sex-specific cardiometabolic analysis, and Purkinje network modeling. Collaborative efforts with clinicians and pharmaceutical partners highlight her translational approach to regulatory science. Model of the Year 2024, BioModels EPSRC Impact Acceleration Account Microsoft Research Project Award Recognition Award, University of Oxford She supervises PhD and MSc students in computational cardiology, while serving on the editorial board of Frontiers in Physiology . Her current projects involve digital twinning and predictive cardiac safety models to reduce animal testing reliance.

Thanh Nho Doمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Surgical Robotics
• Soft Robotics
• +۵ مورد دیگر

Dr. Thanh Nho Do is a Scientia Senior Lecturer at the Graduate School of Biomedical Engineering (GSBmE), UNSW Sydney, and Director of the UNSW Medical Robotics Lab. He holds a PhD in Mechanical Engineering (Surgical Robotics) from Nanyang Technological University (NTU), Singapore, and a B.Eng. in Manufacturing Engineering from Ho Chi Minh City University of Technology, Vietnam. His research focuses on soft robotics, wearable technologies, and biomedical devices, including flexible surgical systems, soft actuators, and haptic interfaces. Education PhD in Mechanical Engineering (Surgical Robotics), NTU Singapore, 2015 B.Eng. in Manufacturing Engineering, Ho Chi Minh City University of Technology, Vietnam Research Interests Soft robotics for medical applications (e.g., NOTES systems, wearable haptics) Functional materials for biomedical devices Cardiovascular engineering and assistive devices Advanced control algorithms for medical robotics Key Contributions His work spans bioprinting, motor-free robotic systems, and soft wearable technologies. Recent studies include self-deploying cardiac compression devices and bioinspired artificial muscles. Awards 2025: CINSW Career Development Fellow 2024: NSW Young Tall Poppy Science Award 2023: Best Poster Awards at EMBC and ICRA Grants & Funding Includes NHMRC Ideas Grant (Lead CI), Cancer Institute NSW Fellowship, and UNSW Scientia Grant. Active projects address cardiovascular interventions and wearable robotics. Labs & Teams Leads the UNSW Medical Robotics Lab, collaborating on devices like soft robotic catheters and textile-driven exosuits.

Stephen A. Boydمشاهده پروفایل

استاد

• Environmental Chemistry
• Soil Microbiology
• Contaminant Remediation
• +۵ مورد دیگر

Stephen A. Boyd is a University Distinguished Professor in the Department of Plant, Soil and Microbial Sciences within Michigan State University's College of Agriculture and Natural Resources. His research spans environmental chemistry and microbiology with a focus on soil systems. His educational background includes a B.S. in Chemistry from Central Michigan University (1975), and M.S. and Ph.D. in Soil Chemistry from Purdue University (1978, 1980). Dr. Boyd's research investigates organic contaminant movement in soil, microbial/catalytic degradation mechanisms, and remediation technologies for contaminated soils/sediments. His work features innovative approaches including chemically modified clays for contaminant sorption and degradation, mechanistic studies of toxicant interactions with natural/modified clays, and development of in-situ soil modification technologies. He extensively examines biodegradation of xenobiotics (particularly PCB reductive dechlorination) and bioavailability of soil-bound contaminants to degrading bacteria. His 15 most recent publications reveal strong trends in clay-based contaminant immobilization, pharmaceutical/water pollutant interactions, dioxin chemistry, and nanomaterial applications for environmental remediation, with dominant fields being environmental chemistry, soil science, and contaminant toxicology. University Distinguished Professor (MSU, 2005) Jackson Award in Soil Science (SSSA, 2004) Highly Cited Researcher (Institute for Scientific Information, 2002) Distinguished Faculty Award (MSU, 2001) Soil Science Research Award (SSSA, 1999) Dr. Boyd has secured significant research funding including a recent $750K USDA grant (2022) for PFAS mitigation research. His laboratory focuses on clay chemistry applications for environmental remediation, with notable breakthroughs in soil cleansing technologies and biochar applications. Current work emphasizes advanced contaminant degradation pathways and practical field applications of his soil modification technologies.

Sudin Bhattacharyaمشاهده پروفایل

دانشیار

• Systems Toxicology
• Computational Biology
• Gene Expression Analysis
• +۳ مورد دیگر

Sudin Bhattacharya is an Associate Professor at the BioMolecular Science Gateway, Michigan State University, with affiliations in the Genetics & Genome Sciences Program and Cell & Molecular Biology Program. His research bridges computational biology and toxicology to understand complex biological systems. Email: sbhattac@msu.edu Research Interests Dr. Bhattacharya specializes in systems toxicology, focusing on computational modeling of gene regulatory networks, single-cell transcriptomics, and molecular dynamics in response to environmental toxicants. His work examines how chemical exposures disrupt cellular pathways and contribute to disease mechanisms. Article Trends His recent publications emphasize: Single-cell and single-nucleus RNA sequencing for toxicological profiling Computational models of circadian rhythms and intercellular communication Dose-dependent responses to environmental chemicals like TCDD and heavy metals Mechanistic studies of adipose tissue remodeling and hypertension Applications of machine learning in chemical risk assessment Integrative approaches to liver metabolism and disease modeling Scientific Contributions Dr. Bhattacharya has pioneered multiscale modeling of biological systems, particularly in hepatic and vascular contexts. His work on the aryl hydrocarbon receptor and PPARα signaling networks has advanced predictive toxicology frameworks.

Scott L. Diamondمشاهده پروفایل

استاد

• Mechanobiology
• Thrombolysis
• Coagulation
• +۱۰ مورد دیگر

Scott L. Diamond is the Arthur E. Humphrey Professor of Chemical and Biomolecular Engineering and Bioengineering at the University of Pennsylvania's School of Engineering and Applied Sciences. He serves as Director of the Penn Center for Molecular Discovery, Director of the Penn Biotechnology Masters Program (one of the largest in the country with over 130 students), and Associate Director of the Institute for Medicine and Engineering (IME). His laboratory is located in the Roy and Diana Vagelos Laboratories at 3340 Smith Walk, 1020 Vagelos Research Laboratories, Philadelphia, PA. Diamond's research spans multiple interconnected fields in blood biology and biotechnology. His work focuses on mechanobiology, thrombolysis, coagulation, bioadhesion, gene therapy, drug/device development, proteomics, drug discovery, systems biology, and microfluidics. His laboratory has developed numerous specialized microfluidic devices for studying blood clotting under various flow conditions, including 8-channel devices for high-throughput clotting assays, side-view devices for clot structure analysis, stenosis devices for high shear clotting assays, and impingement-post devices for studying von Willebrand factor fibers. Diamond's research group has pioneered approaches to model and predict blood function using systems biology principles. His team has developed computational models that integrate reaction-transport phenomena with platelet signaling networks to predict thrombus formation under flow. These models have enabled the development of 'virtual blood' computer simulations that can predict the effectiveness of anticoagulation drugs for individual patients, contributing significantly to personalized medicine approaches in hemostasis and thrombosis. His extensive publication record demonstrates a consistent focus on understanding the fundamental mechanisms of blood clot formation and dissolution. Recent work has emphasized microfluidic approaches for point-of-care diagnostics, patient-specific modeling of platelet function, and the development of novel therapeutic strategies for thrombotic disorders. His research bridges engineering principles with clinical hematology to address significant challenges in cardiovascular medicine. NSF National Young Investigator Award NIH FIRST Award American Heart Association Established Investigator Award AIChE Allan P. Colburn Award George Heilmeier Excellence in Research Award Elected Fellow of the Biomedical Engineering Society (BMES) Diamond has secured significant research funding, including a $2.8 million NIH grant for 'Blood Systems Biology' and a $9.5 million NIH grant for the Penn Center for Molecular Discovery. His laboratory has developed numerous microfluidic devices for blood analysis and has collaborated extensively with clinicians and industry partners. Diamond has served on advisory committees for NSF, NIH, AHA, and NASA, and has consulted extensively for industry and government. With over 180 publications and patents, his work has significantly advanced the understanding of blood clotting mechanisms and the development of diagnostic and therapeutic approaches for thrombotic disorders.

• Computer Vision
• Machine Learning
• Biomedical Informatics
• +۴ مورد دیگر

Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.

Prashant Maliمشاهده پروفایل

استاد

• Genome Engineering
• RNA Editing
• Stem Cell Research
• +۴ مورد دیگر

Prashant Mali is a Professor in the Department of Bioengineering at the University of California, San Diego . His research bridges genome engineering, RNA biology, and biomedical applications, with a focus on CRISPR-Cas systems and ADAR-mediated RNA editing. Education : Ph.D. in Bioengineering Key Affiliations : UC San Diego, Altman Clinical and Translational Research Institute Dr. Mali's work centers on CRISPR-Cas9 technology , RNA editing , and human pluripotent stem cells . His lab develops tools for programmable gene regulation, synthetic lethal screens, and metabolic pathway analysis in disease contexts. Recent publications highlight innovations in circular RNA engineering , ADAR activity mapping , and metabolic reprogramming in cancer . His team employs multi-omics approaches and in vivo models to translate genome editing into clinical applications. Students and Collaborators Current Lab Members : Sami Nourreddine (Postdoc), Amir Dailamy (Graduate), Andrew Portell (Graduate), Michael Tong (Graduate) Alumni : Kyle Ford (PhD 2022), Nathan Palmer (PhD 2022), Udit Parekh (PhD 2021) Research Themes CRISPR Screens : Synthetic lethal interactions, oncogenic pathways, metabolic vulnerabilities RNA Editing : ADAR engineering, circular guide RNAs, clinical translation Tissue Engineering : Vascularized organoids, cardiac maturation, ex vivo models

Jean Provostمشاهده پروفایل

استاد

• Biomedical Engineering
• Biomedical Technology
• Digital Signal Processing
• +۵ مورد دیگر

Jean Provost is a Full Professor in the Department of Engineering Physics at Polytechnique Montréal , with affiliations to the Montreal Heart Institute , IVADO , and the Institute of Biomedical Engineering . His research focuses on ultrasound imaging , cardiac and cerebral vascular imaging , and superresolution image reconstruction using machine learning and optimization . Based on 96 publications, his work emphasizes ultrasound localization microscopy , neural network applications , and microvascular hemodynamics . Education : Ph.D. (Columbia University), MPhil (Columbia University), M.Sc.A. (École Polytechnique Montréal), Engineering Degree (École Centrale Paris), License (Université Paris XI), B.Eng. (École Polytechnique Montréal) Research trends from 15 recent articles include: 3D and dynamic ultrasound localization microscopy for microvascular mapping Deep learning for image reconstruction and neural network pruning Machine learning-driven aberration correction and superresolution imaging Acoustoelectric and cavitation-based imaging techniques Applications in cardiac diagnostics and dementia detection Supervision includes 2 Ph.D. and 8 Master's theses completed at Polytechnique Montréal (2023), covering topics like optical ultrasound detection , microbubble modulation , and spatiotemporal sampling .

Ningyuan Caoمشاهده پروفایل

استادیار

• Machine Learning Acceleration
• Real-Time Systems
• Low-Power Circuits
• +۷ مورد دیگر

Ningyuan Cao is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame, College of Engineering. He leads the Circuit and System Intelligence Research Lab , focusing on the intersection of advanced hardware design and real-time/low-power machine learning applications. Education : Ph.D., Electrical and Electronics Engineering, Georgia Institute of Technology (2020) M.S., Electrical Engineering, Columbia University (2015) B.S., Electrical and Electronics Engineering, Shanghai Jiao Tong University (2013) His research investigates custom analog/mixed-signal circuits , digital architecture , and micro-system design for machine learning acceleration, distributed intelligence, and data-driven IC design automation. Key application domains include Internet-of-Everything, tactile internet, and mixed reality systems. Recent publications highlight work on Bayesian neural networks , privacy-preserving bio-signal encoders , transformer-based surrogate models , and compute-in-memory architectures . Technical themes span neuromorphic computing, uncertainty quantification, and hardware security.

Michael Harrisonمشاهده پروفایل

استادیار

• Vascular Biology
• Regenerative Medicine
• Developmental Biology
• +۱۱ مورد دیگر

Michael Harrison is an Assistant Professor in the Department of Cell and Developmental Biology at Weill Cornell Medicine, where he leads the Regeneration and Development Lab within the Graduate School of Medical Sciences. His research focuses on vascular development and regeneration using zebrafish as a model organism, with emphasis on coronary and cerebral vasculature. Education: B.Sc. in Genetics, University of Edinburgh (2005) Ph.D. in Developmental Genetics, University of Sheffield (mentor: Vincent Cunliffe) Postdoctoral Fellowship, Saban Research Institute, Children’s Hospital Los Angeles (CIRM Fellow) Harrison's research centers on understanding how blood and lymphatic vessels form and regenerate, particularly in the heart and brain. His lab investigates coronary vessel development, the role of lymphatic systems in inflammation and regeneration, and revascularization after injury. By leveraging zebrafish genetics and advanced imaging, his work aims to uncover pathways that could be harnessed for regenerative therapies in humans. His recent publications reveal key signaling mechanisms such as Cxcr4-Cxcl12 in coronary development and the two-step formation of cardiac lymphatics. The 15 most recent articles demonstrate a strong, consistent research trajectory in vascular biology and regeneration, with increasing use of advanced techniques like single-nuclei multiomics, fluidic imaging devices, and CRISPR-based genome editing. His work spans developmental mechanisms, functional imaging, and translational applications in cardiac repair. Scientific Awards: No awards explicitly mentioned in the provided text. Harrison actively mentors a team of postdoctoral fellows, research assistants, and students, several of whom have progressed to medical school or research careers. His lab collaborates extensively, particularly with Ching-Ling Lien's group. He has secured research space and funding to support ongoing projects in cardiac and cerebral vasculature. The lab is actively recruiting rotation students from BCMB, PBSB, IMP, and Tri-Institutional programs, as well as postdoctoral researchers and research assistants, indicating an expanding research team and active grant support. Labs and Teams: Regeneration and Development Lab, Weill Cornell Medicine Collaborations with Ching-Ling Lien Lab Member of Tri-Institutional PhD Programs Active participation in BCMB, PBSB, and IMP training programs

Christian Waldمشاهده پروفایل

پژوهشگر ارشد

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +۳ مورد دیگر

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

James S. Duncanمشاهده پروفایل

استاد

• Biomedical Image Processing
• Quantitative Image Analysis
• Cardiac Function Analysis
• +۴ مورد دیگر

James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.

• 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.