جست‌وجوی استاد و پژوهشگر

Wenjia Baiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Medicine
• Medical Imaging
• Machine Learning
• +۵ مورد دیگر

Wenjia Bai is a Senior Lecturer in Artificial Intelligence in Medicine at Imperial College London, jointly appointed in the Department of Computing and the Department of Brain Sciences. She leads the Medical Vision group at the Data Science Institute and is a faculty member of the Biomedical Image Analysis (BioMedIA) Group. Prior to her current role, she held a Lectureship position from 2018 and became Senior Lecturer in 2022. Bai has a B.Eng and M.Eng in Automation from Tsinghua University (Distinction) and a D.Phil in Engineering Sciences from the University of Oxford, supervised by Prof. Sir Michael Brady. She previously collaborated with Prof. Daniel Rueckert before her lectureship. Education: B.Eng in Automation, Tsinghua University M.Eng in Automation, Tsinghua University D.Phil in Engineering Sciences, University of Oxford Research Interests: Bai's work focuses on advancing artificial intelligence and machine learning in medical imaging, particularly in cardiac and neurological applications. She develops algorithms for automated segmentation, motion analysis, and generative models to uncover disease mechanisms and improve clinical decision support. Her research also integrates genomics with imaging phenotypes to identify genetic determinants of cardiovascular and neurological disorders. She emphasizes open science, contributing to datasets like CMRxRecon2024 and deploying tools for large-scale studies such as the UK Biobank. Articles Trends: Her recent work highlights contributions to medical vision-language models, universal learning frameworks for cardiac reconstruction, and unsupervised clustering techniques for urban development analysis. She bridges AI with cardiology and neurology, addressing challenges in limited data scenarios and enhancing diagnostic accuracy through multimodal integration. Scientific Awards: Best Paper Award for Journal of Cardiovascular Magnetic Resonance (2018) Imperial College President’s Award for Outstanding Research Team (2019) Dudley Pennell Prize (2022) Advising & Grants: Bai advises PhD student Doga, whose work culminated in a successful viva. She leads grants as Principal Investigator for EPSRC, BHF New Horizon, and NIHR pilot projects. As a research stream leader in the SmartHeart programme (2016–2021) and co-investigator in CVD-Net (2024–2029), she drives cardiovascular research initiatives. Additionally, she serves on grant panels for Heart Research UK and the Multiple Sclerosis Society. Labs & Teams: She is affiliated with the Biomedical Image Analysis (BioMedIA) Group and leads the Medical Vision group within the Data Science Institute, fostering interdisciplinary collaboration in medical AI and imaging.

Peter O'Tooleمشاهده پروفایل

استاد

• Fluorescence Imaging
• Flow Cytometry
• Microscopy Technology Development
• +۳ مورد دیگر

Dr. Peter O'Toole is Head of Imaging and Cytometry and a Professor in the Department of Biology at the University of York. He leads the Imaging and Cytometry Laboratory, a top European facility equipped with state-of-the-art equipment from collaborators like Carl Zeiss and Beckman Coulter. His research focuses on microscopy technology development, with expertise in fluorescence imaging, flow cytometry, and super-resolution techniques. He holds grants from Wellcome Trust, BBSRC, and others, including a £954k Wellcome Trust grant for hyperspectral imaging technology. O'Toole is also Vice President of the Royal Microscopical Society and chairs major microscopy conferences. His educational background includes a PhD in Cell Biophysics from the University of Essex (1998) and a BSc from the same institution (1994). Research interests include developing imaging tools for biomedical applications, immunometabolism, and live-cell analysis. He collaborates internationally on projects like PHENOSPOT and the UK Coronavirus Immunology Consortium. His lab supports cutting-edge projects such as spatial omics and therapeutic vaccine development for leishmaniasis. O'Toole organizes microscopy courses and has pioneered facility meetings for the microscopy community. Grants total over £20M, with recent focuses on deep learning in imaging, immune system metabolism, and hyphae characterization. Key roles include Director of the Bioscience Technology Facility and membership in grant panels for BBSRC, MRC, and Wellcome Trust. His team includes Senior Technicians and PhD students like Laura Wiggins, advancing imaging technologies and collaborative research.

Robert Azencottمشاهده پروفایل

استاد

• Genomics
• Proteomics
• Medical Imaging
• +۹ مورد دیگر

Robert Azencott is a Professor of Mathematics at the University of Houston and holds the title of Emeritus Professor at École Normale Supérieure in France. He specializes in interdisciplinary research at the intersection of mathematics, biosciences, and imaging. His work spans stochastic processes, data mining, and medical imaging applications such as 3D-echocardiography analysis and deformable shape matching. Azencott has contributed to probabilistic methods for bacterial genetic evolution models and financial market microstructure analysis. His research also addresses texture classification, kernel-based learning, and stochastic differential equations (SDEs) in finance and biology. Research Interests Genomics & Proteomics : Sparse modeling of gene interactions, microRNA impact on cancer survival, proteomic mass spectra analysis. Bacterial Evolution : Stochastic models for genetic evolution with periodic selection and large deviations theory. Medical Imaging : 3D-echocardiography strain analysis, deformable shape dynamics via diffeomorphic matching, and ROI reconstruction in X-ray tomography. Probability & Statistics : SDE parameter estimation, kernel-based clustering, and applications to algorithmic trading and option pricing. Labs & Collaborations Azencott collaborates on projects such as digital stains for live-cell microscopy and intra-cardiac echography analysis to assess myocardium deformations. His work intersects with teams in cardiology, computational biology, and financial engineering.

Thierry Epicierمشاهده پروفایل

پژوهشگر

• Environmental Electron Microscopy
• Nanoparticles
• Heterogeneous Catalysis
• +۴ مورد دیگر

Thierry Epicier is an Emeritus Researcher at the Department of Materials Science and Engineering, Claude Bernard Lyon 1 University, affiliated with the CNRS unit IRCELYON (UMR 5256). His research focuses on Environmental Transmission Electron Microscopy (ETEM) to study nanocatalysts under operando conditions, including gas and temperature environments. He leads the MEME group (Methodologies in Environmental Microscopy) and coordinates major projects like the ANR 'WATEM' (2020-2023), analyzing water interactions in aerosols and catalytic systems. Dr. Epicier holds a State Doctorate in Materials Science from INSA Lyon and has extensive experience in electron microscopy techniques, including development of software tools like Diffraction-Workshop and contributions to deep learning approaches for nanoparticle tracking. His academic roles include organizing international microscopy congresses (e.g., EMC2016) and teaching advanced microscopy courses at institutions like Texas A&M University. Research interests span heterogeneous catalysis, nanoparticle dynamics, and environmental applications of ETEM. Key achievements include pioneering studies on Pd-Au core-shell catalysts, in situ observation of catalytic deactivation mechanisms, and the use of neural networks for automated nanoparticle analysis. He has held leadership positions in CNRS federations (e.g., METSA) and served on ANR evaluation committees. Current work explores cloud formation mechanisms via water condensation studies in ETEM, bridging nanoscale materials science with atmospheric chemistry. Teaching responsibilities include advanced microscopy training programs and M2 courses on materials characterization. His lab facilities include the IRCATECH characterization platform with operando capabilities. Despite his emeritus status, Dr. Epicier remains active in cutting-edge projects, emphasizing real-time material behavior under functional conditions.

• Molecular Genetics
• Bioinformatics
• Computational Biology
• +۴ مورد دیگر

Li You serves as a Researcher in the Department of Molecular Genetics at Erasmus Medical Center, Erasmus University Rotterdam. Their work integrates computational methodologies with biomedical research to develop AI-driven solutions for healthcare challenges, particularly in genomic analysis and medical imaging. Research focuses on Molecular Genetics and Bioinformatics, with specialized expertise in RNA sequencing, transcriptomics, and single-cell analysis. Key investigations include tumor cell dynamics, expression-level modeling, and transfer learning applications. The interdisciplinary approach bridges computer science and molecular biology to address complex biological questions through emerging technologies like deep learning and neural networks. Recent publications (2023-2025) reveal a consistent trajectory in applying artificial intelligence to medical diagnostics and biological imaging. Core themes include arrhythmia classification, cardiac segmentation, 3D reconstruction for metaverse applications, and real-time cell analysis. This work demonstrates strong cross-disciplinary integration of computer vision, biomedical engineering, and molecular genetics to advance precision medicine tools.

Viji Draviam Sastryمشاهده پروفایل

استاد

• Cell Division
• Cytoskeleton Dynamics
• Genomic Instability
• +۳ مورد دیگر

Professor Viji Draviam Sastry is a Professor of Quantitative Cell and Molecular Biology and Director of Industrial Innovation at Queen Mary University of London. She leads the Draviam Lab, focusing on cell division mechanisms, cytoskeletal dynamics, and genomic stability. Her work integrates high-resolution microscopy, molecular biology, and computational tools to study chromosome segregation errors and their implications in cancer. Draviam holds a PhD from Trinity College, Cambridge, and completed postdoctoral research at MIT and Harvard Medical School. Her research interests include mitotic spindle dynamics, kinetochore-microtubule interactions, and the development of automated imaging tools like SpinX. She collaborates with industry partners such as Zeiss and Image Solutions to advance drug discovery and precision medicine. Key achievements include identifying novel chromosomal instability aiding variants (CIVa) and elucidating mechanisms regulating chromosome attachment stability. Draviam supervises multiple PhD students and has contributed to over 60 publications, including work on Astrin-PP1 pathways and MARK2 kinase roles in spindle positioning. She chairs the BBSRC LIDo-DTP program and co-leads AI-driven drug discovery initiatives. Her lab’s interdisciplinary approach bridges cell biology, bioinformatics, and engineering to address fundamental and translational questions in cell division.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

Daniel Angمشاهده پروفایل

پژوهشگر

• Quantum Sensing
• Precision Measurement
• Quantum Diamond Microscopy
• +۴ مورد دیگر

Daniel Ang is a Researcher and staff scientist at the Quantum Technology Center (QTC) of the University of Maryland, working under Prof. Ron Walsworth. He leads efforts in quantum sensor development for directional detection of dark matter and neutrinos using nitrogen-vacancy (NV) centers in diamond. Previously, he completed a PhD in atomic physics at Harvard University, contributing to the ACME EDM experiment to measure the electron's electric dipole moment. Education: PhD in Atomic Physics, Harvard University (2023) Bachelor's in Physics, Amherst College (2015) Research Focus: Quantum sensing with NV centers for dark matter/neutrino detection Development of quantum materials (hexagonal boron nitride, silicon carbide) Super-resolution microscopy and strain sensing techniques Machine learning-driven molecular dynamics simulations ACME Experiment Contributions: Optimized ThO molecule lifetime measurements Improved photodetector and data acquisition systems Enhanced magnetic field control for EDM sensitivity Current Projects: 3-stage dark matter detector design using diamond segments 3D quantum diamond microscopy for nanoscale track imaging Collaboration with Sandia National Lab on ion-implanted damage tracks Labs/Teams: Quantum Technology Center (QTC) at University of Maryland, Walsworth Group.

Jean-Christophe Olivo-Marinمشاهده پروفایل

استاد

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +۱۳ مورد دیگر

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.

Boyu Kuangمشاهده پروفایل

پژوهشگر ارشد

• Semantic Segmentation
• Object Detection
• Weakly Supervised Learning
• +۹ مورد دیگر

Dr. Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. His work bridges academic research with industrial applications in aviation, robotics, and energy systems. Current role: Research Fellow in Computer Vision and AI Key affiliations: Centre for Computational Engineering Sciences Research interests include semantic segmentation , object detection , weakly and self-supervised learning , multi-modal perception , and vision-language foundation models . His methodology emphasizes robust AI systems for low-resource industrial environments , with applications in aviation maintenance , robotics , and energy infrastructure monitoring . He leads the Artificial Intelligence and Machine Learning module at Cranfield. Key activities include the UKRI, ATI, and Airbus-funded ONEHeart project on autonomous systems and intelligent inspection. Collaborations span Stanford University , King’s College London , Civil Aviation University of China , and industry partners like Airbus and Leidos . Publications focus on vision-language models , multi-modal perception , and industrial AI . As an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), he contributes to academic governance. Peer review experience includes 60+ journal manuscripts for venues like IEEE Transactions on Image Processing and Elsevier Neural Networks . Facilities utilized include DARTeC and AIRC .

Erik Meijeringمشاهده پروفایل

استاد

• Biomedical Imaging
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Erik Meijering is a Professor of Biomedical Image Computing at the University of New South Wales (UNSW), School of Computer Science and Engineering. His research focuses on developing deep learning and machine learning methods for biomedical imaging analysis, including cell tracking, neuron reconstruction, and image segmentation. He holds an IEEE Fellowship and has served in key roles such as Chair of the IEEE Signal Processing Society's Bio Imaging and Signal Processing Technical Committee. Educations: MSc in Electrical Engineering (Delft University of Technology, 1996), PhD in Medical Image Analysis (Utrecht University, 2000). He previously worked at Erasmus University Medical Center (2002–2019) and Swiss Federal Institute of Technology (2000–2002). Research interests include biomedical image computing, computer vision, and the application of AI in microscopy and clinical imaging. Notable projects include the Cell Tracking Challenge and BigNeuron initiative, which benchmark computational methods in bioimage analysis. His work spans over 300 publications, including high-impact journals like Nature Methods and IEEE Transactions on Medical Imaging. He has supervised numerous PhD, Master's, and Bachelor's students, contributing to advancements in medical image analysis and AI-driven diagnostics.

• Process Control
• Real-Time Optimization
• Chemical Engineering
• +۳ مورد دیگر

Dominique Bonvin is a Professor at the School of Engineering (STI) of École Polytechnique Fédérale de Lausanne (EPFL), holding a position within the Automatic Control Laboratory (LA). His research focuses on advanced process control, real-time optimization, and model-based systems engineering. Key topics include modifier adaptation for uncertain systems, dynamic optimization of chemical processes, and control strategies for reaction systems and energy systems. He has published extensively in top-tier journals such as Computers & Chemical Engineering and Journal of Process Control , with over 450 scholarly works to date. His work addresses challenges in industrial processes like wastewater treatment plants, gas compressors, and solid-oxide fuel cells, emphasizing robustness against plant-model mismatches. Collaborations span global institutions, with notable contributions to bioprocess modeling and control of energy-harvesting systems such as kite power. Research Themes: Real-Time Optimization (RTO), Model Predictive Control (MPC), Process Automation, and Systems Engineering Key Technologies: Modifier Adaptation, Dynamic Optimization, Port-Hamiltonian Systems, and Extent-Based Reaction Modeling His recent efforts include accelerating optimization algorithms (e.g., gradient-directed modifier adaptation) and integrating transient measurements for improved plant modeling. He has also pioneered the use of vessel extents to simplify reaction system analysis, enabling decoupled state estimation without full kinetic knowledge.

• Wireless Communications
• Precision Agriculture
• Infrastructure Sensing
• +۳ مورد دیگر

James Krogmeier is an Associate Dean for Facilities and Planning and Professor of Electrical and Computer Engineering at Purdue University. He holds affiliations across multiple departments including Agricultural & Biological Engineering, Civil and Construction Engineering, and the School of Electrical and Computer Engineering. His administrative roles include membership in the Engineering Leadership Team, Engineering Cabinet, Space Planning and Allocation Committee, CoE Safety Committee, and Facilities Committee. His research focuses on interdisciplinary engineering challenges at the intersection of wireless communications, precision agriculture, and infrastructure monitoring. Key areas include IoT-enabled agricultural systems, pavement integrity assessment using radar data, and waveform design for advanced communication systems. He has contributed to projects like Meta Ag (automated agricultural metadata collection) and OATSMobile (underground sensor data hubs). His work integrates field data from telematics, GPS, and sensor networks to improve decision-making in agriculture and transportation. Recent trends in his publications show strong emphasis on practical applications of machine learning in infrastructure monitoring, rural IoT connectivity, and autonomous systems in farming. While no formal awards are listed, his extensive list of publications reflects sustained impact in his fields. His advising record shows no listed students, though collaborations with industry partners (e.g., on snowplow tracking systems) suggest applied research focus. He leads development of tools like the Pavement Patching Management software and ISOBlue HD agricultural data platforms. Labs/teams: Active involvement in Purdue's agricultural IoT initiatives, transportation infrastructure research groups, and wireless communication laboratories. His work bridges academia and practical applications through partnerships with departments across multiple engineering disciplines.

Pietro Peronaمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Signal Processing
• +۵ مورد دیگر

Pietro Perona is the Allen E. Puckett Professor of Electrical Engineering at the California Institute of Technology (Caltech). He holds a D.Eng. from the University of Padua (1985) and a Ph.D. from UC Berkeley (1990). His academic roles include serving as Director of the Center for Neuromorphic Systems Engineering (1999–2004) and Executive Officer for Electrical Engineering (2006–2010). His research focuses on computational vision, visual recognition, and machine learning. Key projects include the Visipedia initiative (smart apps for species identification like iNaturalist and Merlin Bird ID) and collaborations on neuroethology with Professors Anderson and Dickinson, studying fruit fly and mouse behavior. He also explores human visual perception and crowd-sourced learning. Perona has been recognized as an IEEE Fellow and leads the Caltech Vision Lab, which develops machine vision systems for applications in conservation, robotics, and neuroscience. His lab’s work spans foundational research to real-world tools, including datasets like the Caltech Fish Counting Dataset and MABe22 benchmarks. He teaches courses on computational vision and has advised numerous projects in AI and robotics. Current collaborations include partnerships with Amazon Web Services (AWS) and Disney on AI-driven solutions.

• Cyberinfrastructure
• Data Science
• Software Engineering
• +۳ مورد دیگر

Kenton Guadron McHenry serves as Associate Director for Software at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign, with an additional affiliate appointment at the Center for Digital Agriculture. McHenry's work bridges computational infrastructure development with domain-specific scientific research across multiple disciplines. McHenry's research interests focus on cyberinfrastructure, data science, and software engineering, with specific expertise in preprocessing techniques, automated search systems, and keyword spotting. The research fingerprint analysis shows strong concentrations in cyberinfrastructure (100%), preprocessing steps (93%), data types (88%), and keyword spotting (77%). This expertise enables advanced computational approaches for scientific discovery. Analysis of McHenry's publication record reveals an interdisciplinary research trajectory spanning computer science applications in environmental monitoring, public health, medical research, and workforce development. Recent work demonstrates particular strength in applying AI and data-intensive methods to real-world challenges, from permafrost mapping to pandemic response. This cross-domain approach reflects the increasingly collaborative nature of modern computational research. As Associate Director for Software at NCSA, McHenry leads a team responsible for developing and maintaining the software infrastructure that supports computational research across numerous scientific domains. This role involves strategic planning for software development initiatives that enable scientific discovery and innovation at the university and beyond.