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Travis MitchellView profile

Lecturer

• Computational fluid dynamics
• Lattice Boltzmann methods
• Multiphase fluid dynamics
• +3 more

Dr. Travis Mitchell is a Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland , and an affiliate of the Centre for Multiscale Energy Systems . He holds a PhD in Multiphase Computational Fluid Dynamics and dual degrees in Mechanical Engineering (BE Hons) and Mathematics (BSc). Education: PhD in Multiphase Computational Fluid Dynamics, The University of Queensland BE (Hons) in Mechanical Engineering, The University of Queensland BSc in Mathematics, The University of Queensland Research Interests focus on numerical modeling of multiphase fluid dynamics in porous media , with applications spanning CO2 electrolysis , hydrogen production via methane pyrolysis , biomedical fluid-structure interaction , and geomechanical fracture analysis . His methodological expertise includes Lattice Boltzmann techniques and high-performance computing . Recent Work Trends encompass multiphase transport in fractured media , gas diffusion electrode optimization , fiber-based air filter design , and thermocapillary flow modeling , reflecting his interdisciplinary impact in energy, health, and resource engineering. Scientific Recognition includes the ICMMES-CSRC Award for multiphase lattice Boltzmann research and an EAIT Citation for Excellence in Student Learning (2023) . Teaching Portfolio includes coordination of MECH2700: Computational Engineering and Data Analysis and lectures in MECH3780: Computational Mechanics and MECH6480: Computational Fluid Dynamics .

Nambi NallasamyView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Ophthalmology
• +5 more

Nambi Nallasamy is an Assistant Professor at the University of Michigan, holding joint appointments in the Departments of Ophthalmology and Visual Sciences and Computational Medicine and Bioinformatics. His research focuses on the intersection of machine learning and ophthalmology, particularly in improving cataract surgery outcomes and analyzing surgical quality. His work applies methodologies such as computer vision, machine learning, and natural language processing to healthcare research, with a strong emphasis on clinical informatics and computer vision challenges in ophthalmology. He is actively involved in developing AI tools for surgical training and assessment, including systems like CatSkill and CatStep. Recent publications highlight his contributions to predictive modeling for intraocular lens (IOL) refraction, automated surgical phase classification, and understanding social risk factors in microbial keratitis. His research also extends to corneal transplantation techniques and the impact of steroid use on corneal opacity in veterinary ophthalmology. Nallasamy is a board-certified ophthalmologist and fellowship-trained cornea specialist, with clinical expertise in corneal and ocular surface diseases, including corneal transplantation (DSEK, DMEK, DALK, PKP), cataract and refractive surgery, and limbal stem cell deficiency treatments. He collaborates with multidisciplinary teams and researchers such as Shahzad Mian, Maria Woodward, and Binh Duong Giap, advancing AI applications in ophthalmology and surgical outcomes analysis. His work bridges computational methods with clinical practice to enhance diagnostic and surgical precision.

Vladimir GolkovView profile

Researcher

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +6 more

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

• Machine Learning
• Computer Vision
• Data Compression
• +4 more

Donald Adjeroh is a Professor and Associate Chair of Computer Science at the Lane Department of Computer Science and Electrical Engineering, within the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. He also serves as the Program Director for Computer Science Graduate Studies and is the Project Lead PI for the WVAR-CRESH (West Virginia Arkansas Research Collaboration in Engineering, Science, and Health) initiative. Dr. Adjeroh received his Ph.D. from the Chinese University of Hong Kong in 1997. His academic journey has led him to become a prominent researcher in multiple interdisciplinary fields, with a focus on developing computational methods for biomedical applications. Dr. Adjeroh's research spans several interconnected domains: Machine Learning approaches for biomedical data analysis Computer Vision techniques for medical image processing Data Compression algorithms with applications in bioinformatics Search Algorithms & Data Structures for large-scale biological data Bioinformatics and Computational Biology methods for genomic analysis Digital Health applications for healthcare cost reduction and improved outcomes His publication record demonstrates consistent contributions across these fields, with recent work focusing on parameterized string algorithms, probabilistic suffix arrays for protein family prediction, and robust texture analysis techniques. His research shows a clear trajectory toward increasingly sophisticated applications of computational methods to biomedical problems. Dr. Adjeroh has received significant research funding from various sources including the National Science Foundation (NSF), Department of Energy (DOE), NASA, Department of Defense (DOD), Department of Homeland Security (DHS), and others. His current projects include "NSF NRT: Bridges in Digital Health" and "RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health," among others. He has mentored numerous students through research projects and serves as an advisor for graduate students interested in pursuing PhD research in areas related to search data structures, computational biology, bioinformatics, biometrics, machine learning, and digital health. Dr. Adjeroh is actively involved in organizing academic events, including the Summer School on AI & Smart Health, and serves as a key collaborator in multi-institutional research efforts focused on leveraging artificial intelligence to improve healthcare outcomes.

• Artificial Intelligence
• Big Data Analytics
• Internet of Things
• +10 more

Dr. Anuoluwapo Ajayi is an Associate Professor in the Department of Business and Management at the Faculty of Business and Law, University of the West of England. With over 17 years of experience in research, teaching, and administration from Obafemi Awolowo University, Nigeria, and visiting roles at the University of Lorraine, France; University of Eastern Finland; and University of Zululand, South Africa, his work bridges Artificial Intelligence , Big Data Analytics , and Construction Informatics to address complex challenges in Energy , Healthcare , and Education . Education: PhD in Artificial Intelligence and Construction Informatics Visiting Scholar, University of Lorraine, France Visiting Scholar, University of Eastern Finland Visiting Scholar, University of Zululand, South Africa Dr. Ajayi's research focuses on applying Machine Learning , IoT , and Optimization to domains like Construction , Energy Systems , and Health Informatics . His publications span topics including Concrete Quality Prediction , Cryptocurrency Market Analysis , and Smart Infrastructure , reflecting interdisciplinary expertise in Computer Science and Engineering . Notable scientific achievements include being a fellow of the Massachusetts Institute of Technology International Science and Technology Initiatives (MISTI). His 15 most recent publications (2020-2023) show a trajectory focused on Deep Learning , Renewable Energy , and Industry 4.0 in construction. Dr. Ajayi also contributes to emerging fields like Medical Imaging and Digital Agriculture . Collaborations: Massachusetts Institute of Technology (MISTI) University of Lorraine University of Eastern Finland University of Zululand

• Computer Vision
• Image Processing
• Machine Learning
• +5 more

Prof. Ing. Adam Herout, Ph.D., is a faculty member at the Faculty of Information Technology (FIT) of Brno University of Technology (BUT). He serves as Deputy Head of the Department of Computer Graphics and Multimedia (DCGM), focusing on computer vision, image processing, and 3D reconstruction. His research spans traffic surveillance, augmented reality, and real-time processing, with applications in medical imaging and autonomous systems. Academic Rank: Professor Department: Computer Graphics and Multimedia Email: herout@fit.vut.cz Herout's research interests lie in computer vision, machine learning, and their interdisciplinary applications. He has pioneered techniques for camera calibration using vehicle landmarks, density-based traffic analysis, and medical imaging solutions like cranial implant design. His work combines theoretical advancements with practical implementations in real-time systems. Key trends in his 15 most recent articles (2019–2023) include deep learning for traffic analysis, medical image segmentation, and optimization of computer vision algorithms. Sub-fields span from automated checkout systems to CNN-based AFM data correction, reflecting his interdisciplinary expertise. Scientific recognitions include the Commemorative Medal of FIT BUT (Bronze 2012, Silver 2019) and the Imagine Cup 2013. He leads the Computer Graphics Research Group at FIT and chairs disciplinary committees.

• Microbial Pathogenesis
• Lipidomics
• Inflammation
• +4 more

Alison J. Scott is an Assistant Professor at the University of Maryland, Baltimore School of Dentistry in the Department of Microbial Pathogenesis, with an adjunct appointment in the Department of Microbiology & Immunology. Her research focuses on host-directed therapies for infectious diseases through lipid response analysis during inflammation. Her educational background includes: Postdoctoral Fellowship (2015-2016) at the University of Maryland, Baltimore under Dr. David Goodlett PhD in Molecular Microbiology & Immunology (2015) from the University of Maryland, Baltimore under Dr. Robert Ernst BS in Analytical Biochemistry (2004) from Eastern Michigan University Dr. Scott's research centers on lipid-driven inflammatory mechanisms in infectious and sterile pathologies. Using cutting-edge mass spectrometry imaging , her lab maps lipidomic and metabolomic dynamics at the host-pathogen interface to identify therapeutic targets. Key emphases include: Lipid-mediated pathogenesis in TLR4-stealth infections (e.g., Francisella) Development of 3D imaging techniques for pulmonary infection models Application of lipid modulation in neurodegeneration linked to sterile inflammation Her 2016-2019 publications reveal a cohesive trajectory in mass spectrometry imaging innovation, specifically advancing simultaneous visualization of host-pathogen lipid interactions and structural lipid modifications affecting immune responses. This work establishes lipids as central mediators in infection pathogenesis and sterile inflammatory conditions. No scientific awards are highlighted in the provided documentation. While specific advisees and grant details aren't documented, her laboratory actively develops host-directed therapeutic strategies using bespoke lipids across pulmonary infection and neurodegeneration models, indicating ongoing research funding and mentorship activities. Her laboratory integrates lipidomic/metabolomic mapping with advanced imaging to decode disease mechanisms, currently operating across multiple experimental systems including Francisella infection models, sterile inflammation platforms, and neurodegeneration-linked immune response assays.

Julien FageotView profile

Researcher

• Sparsity
• Random Processes
• Approximation Theory
• +7 more

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

Ali MadooeiView profile

Teaching Professor

• Computer Science Education
• Software Engineering
• Instructional Design
• +4 more

Ali Madooei is a Teaching Professor in the Department of Computer Science at Johns Hopkins University, affiliated with the Whiting School of Engineering. He specializes in computer science education, software engineering, instructional design, and learning technology, with a focus on hands-on project-based learning. Department: Computer Science School: Whiting School of Engineering Academic Rank: Teaching Professor Email: madooei@jhu.edu His research interests revolve around enhancing software development education through project-based learning environments. He established the Coding, Learning, and Application-Building Lab (COLLAB) , which provides students with unstructured, self-directed software project experiences. He also leads the AI-Assisted Instruction Lab , exploring how artificial intelligence can improve educational technology. Madooei's work emphasizes modern technology stacks, employability, and flexible remote collaboration, with projects often aligning with industry standards. Additionally, he has contributed to biomedical imaging through prior research in color modeling and hyperspectral analysis for skin lesion diagnostics. Ali Madooei has received the Whiting School of Engineering Excellence in Teaching Award (2024) , highlighting his commitment to pedagogical innovation in computer science education. As an educator, Madooei promotes independent learning, mentorship, and leadership development in his labs. His labs support a dozen students each term with academic credit and practical experience, offering paid, volunteer, and self-directed project opportunities. Projects are structured around sprint cycles and remote collaboration, with students encouraged to propose ideas or join existing initiatives. He has previously taught at the University of British Columbia, Simon Fraser University, and Kwantlen Polytechnic University, bringing diverse institutional experience to his current role.

• Microrobotics
• Magnetic Control Systems
• Targeted Drug Delivery
• +3 more

Dongfang Li is a Research Fellow in the Department of Neurosurgery at Yale University's School of Medicine, specializing in magnetically-driven microrobotics for neurosurgical applications and brain cancer therapy. His research focuses on: Microrobot design and magnetic actuation Image-guided navigation in dynamic biological environments Swarm control for targeted drug delivery Degradable materials for precision cancer treatment Magnetic hyperthermia-chemotherapy integration Analysis of his 15 most recent publications (2019-2025) reveals a cohesive trajectory advancing microrobot technology for brain interventions. Key innovations include OCT-guided in vivo navigation, torque-driven microswimmers, vessel-targeting nanoclovers, and rotating-field control systems. His work bridges robotics, nanotechnology, and neurosurgery to enable non-invasive, cell-level therapeutic precision with significant implications for glioblastoma treatment.

• Medical Image Processing
• Multiple Sclerosis Research
• MRI Analysis
• +2 more

Daniel García Lorenzo is a Research Fellow at the McConnell Brain Imaging Centre (BIC) within the Montreal Neurological Institute, McGill University, working under Dr. D. Louis Collins in the Image Processing Lab. His research develops automatic MRI-based pipelines for Multiple Sclerosis (MS) analysis with focus on atrophy quantification. His academic background includes: Ph.D. in robust MS lesion segmentation, Université de Rennes I (INRIA, Rennes), 2010 M.Sc. in image processing & AI, Telecom-Bretagne (Brest, France) M.Eng. in telecommunications, Universidad de Valladolid (Spain) His research centers on computational neuroimaging for MS, developing segmentation algorithms that integrate expectation maximization, mean shift, and graph cuts to handle multi-sequence MRI data. He investigates workflow dependencies in lesion segmentation and longitudinal atrophy analysis to predict disease progression. Publications from 2008-2010 reveal consistent focus on MS neuroimaging, with methodological innovations in adaptive fusion, workflow optimization, and bias correction. His work bridges computer vision (Markov models, pixon representation) and clinical neurology, extending to Parkinson's disease through SPECT/PET analysis of deep brain stimulation. Daniel contributes to the Image Processing Lab's open-source ecosystem, with segmentation algorithms available for online use through the BIC platform.

Heng-Ming TaiView profile

Professor

• Signal Processing
• Image Processing
• Power System Reliability
• +1 more

Heng-Ming Tai is a Professor of Electrical and Computer Engineering at The University of Tulsa, where he serves as the graduate advisor in the ECE department. His academic journey began with a B.S. in Electrical Engineering from National Tsing-Hua University in 1980, followed by an M.S. in 1983 and a Ph.D. in 1987, both from Texas Tech University. Dr. Tai's research spans multiple critical areas in electrical engineering, with primary focus on signal/image processing , power system reliability , and power electronics . His work bridges theoretical foundations with practical applications, particularly in thermal management of IGBT module, monitoring technique for power converter, image encryption, biomedical image segmentation using neural network technique, and load control using age-of-information scheme. His research addresses contemporary challenges in power systems, including the integration of renewable energy sources and the development of more reliable and efficient power electronics systems. Analysis of Dr. Tai's recent publications reveals a strong trend toward interdisciplinary research that combines power systems engineering with advanced computational techniques. His work increasingly focuses on the intersection of power electronics, control systems, and data science, particularly in areas like age-of-information based control systems, reliability evaluation of composite power systems with renewable integration, and advanced thermal management strategies for power semiconductors. His research demonstrates a consistent evolution from fundamental power electronics toward more complex system-level challenges in modern power grids. As the graduate advisor for the Electrical and Computer Engineering department, Dr. Tai plays a crucial role in mentoring the next generation of engineers. While specific details about his grant history are not provided in the available information, his extensive publication record spanning multiple decades suggests a sustained research program with likely significant external funding support. Dr. Tai's work is centered in the Electrical and Computer Engineering department at The University of Tulsa, where he contributes to both the academic and research missions of the college. His research on power electronics and thermal management likely involves laboratory facilities for power converter testing and thermal analysis, though specific lab names are not mentioned in the available information.

• Osteoarthritis
• Magnetic Resonance Imaging
• Knee Osteoarthritis
• +3 more

Felix Eckstein is a Professor at the Institute of Anatomy and Cell Biology, Paracelsus Medical University in Salzburg, Austria, leading the Research Program for Musculoskeletal Imaging. With over 770 publications spanning 30+ years, his work focuses on quantitative MRI techniques for osteoarthritis assessment, particularly knee cartilage morphometry and biomarker development for clinical trials. His research centers on osteoarthritis pathophysiology, magnetic resonance imaging innovation, cartilage/bone quantification, and sexual dimorphism in joint anatomy. Key interests include transverse relaxation time (T2) mapping, automated segmentation methods, biomechanical factors in disease progression, and validation of imaging endpoints for drug trials. His work bridges engineering precision with clinical rheumatology to establish imaging standards for osteoarthritis therapeutics. Recent publications (2024-2025) emphasize advanced MRI methodologies including fully automated cartilage analysis across vendors, laminar T2 mapping for early disease detection, and multi-component endpoints for virtual knee replacement. Critical themes involve sensitivity to longitudinal change, diabetes-comorbidity interactions, alignment effects on joint space width, and validation of biomarkers against clinical outcomes in cohorts like OAI and IMI-APPROACH. Scientific recognition includes: Forschungsgruppenpreis der Deutschen Akademie für osteologische und rheumatologische Wissenschaften für Leistungen in den Jahren 2012-2014 (2015) As Principal Investigator, he secured major grants including 'Childhood determinants of adulthood knee cartilage thickness' (2016-2017) and 'NanoDiaRA' for nanotechnology diagnostics in arthritis (2010-2014). His work drives international consortia standards through collaborations with Osteoarthritis Initiative and IMI-APPROACH. He directs the Musculoskeletal Imaging Program developing FDA/EMA-acceptable imaging protocols for osteoarthritis trials, and has organized pivotal conferences including the 109th Anatomical Society Meeting (2014), establishing frameworks for quantitative imaging in regulatory science.

• Bayesian Statistics
• Statistical Modeling
• Data Science
• +4 more

Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics

• Biomedical Image Analysis
• Artificial Intelligence in Medicine
• Cardiac Imaging
• +3 more

Iulia Popescu is a Postdoctoral Research Fellow at the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (Oxford BRC) and the Radcliffe Department of Medicine (RDMOxford), University of Oxford. Her work focuses on biomedical image analysis, particularly cardiac T1 mapping standardization across global multi-centre studies. She specializes in deep learning , AI , data visualization , and automated quality control in medical imaging. She earned her DPhil in digital health at the University of Oxford, developing personalized models for cardiac wall motion abnormality and scar quantification using multimodal imaging. Her research spans cardiac magnetic resonance (CMR) , quantitative tissue characterization , and clinical translation of imaging protocols. She has contributed to tools like the Total Mapping Toolbox (TOMATO) and patented methods for medical image enhancement and quality validation . She collaborates with clinical and industrial partners, working on multi-centre clinical trials and executive-level multidisciplinary teams . Her recent publications address T1-mapping standardization , automated segmentation reliability , and multi-vendor imaging consistency , with applications in hypertrophic cardiomyopathy and scar quantification . She is funded by the NIHR Oxford BRC and associated with the Piechnik Group and Ferreira Group at Oxford.