Color Vision Defects Professors and Supervisors

Francis OgokeView profile

Assistant Professor

Carnegie Mellon UniversityQatar

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

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David OwenView profile

Clinical Professor

Imperial College LondonUnited Kingdom

Dr. David Owen is a Clinical Professor in Molecular Pharmacology and Experimental Medicine at Imperial College London’s Department of Brain Sciences (Faculty of Medicine). He leads the Clinical Studies division at the NIHR Imperial Clinical Research Facility, which bridges laboratory discoveries to clinical treatments. His research focuses on the mitochondrial 18kDa Translocator Protein (TSPO), exploring its roles in neurodegenerative diseases (e.g., MS, Alzheimer’s) and pulmonary vascular disorders. He also contributes to medical education via Pharmacology teaching at the Imperial Medical School and directs the MRES in Clinical Research program. Key affiliations include the Centre for Advanced Therapeutics, Immuno-Pathology Network, and Section of Neurology. He chairs critical committees: North West London Integrated Formulary (2.6M patients) and Subregional IVIG stewardship across 6 NHS Trusts. His work integrates clinical practice (20% time in acute medicine) with translational research, emphasizing TSPO as a potential drug target and imaging biomarker. Research interests span neuroinflammation, imaging technologies (PET, MRI), and therapeutic development. Recent work includes WAND multi-modal brain datasets, MS pathology analyses, and TSPO’s role in microglial biology. He collaborates on open-source neuroimaging tools like the Welsh Advanced Neuroimaging Database.

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Ruplal ChoudharyView profile

Professor

Indiana UniversityUnited States

Ruplal Choudhary is a Professor of Food and Bioprocess Engineering in the Agricultural Systems and Education department at Southern Illinois University Carbondale's College of Agricultural Sciences. With over 20 years of academic and industrial experience in food processing equipment design and development, his career spans multiple institutions including the University of Manitoba, South Dakota State University, National Dairy Research Institute in India, and the Central Institute of Agricultural Engineering. Dr. Choudhary earned his Ph.D. in Biosystems Engineering from Oklahoma State University in 2004, an M.S. in Dairy and Food Engineering from IIT Kharagpur, India in 1994, and completed his undergraduate studies in Agricultural Engineering at J.N. Agricultural University, Jabalpur, India. His research focuses on improving food safety and quality through innovative nonthermal processing technologies. Dr. Choudhary specializes in using germicidal ultraviolet light, nonthermal plasma, power ultrasound, and nanotechnology for pathogen removal and food allergy mitigation. He also employs near infrared spectroscopy and color machine vision systems for rapid sensing of food safety, quality, and defects. His work spans across dairy products, soy, fruits, and vegetables, with particular emphasis on postharvest horticulture and agricultural systems. Analysis of Dr. Choudhary's recent publications reveals consistent focus on food safety technologies, particularly nonthermal processing methods and edible coatings for shelf-life extension. His research demonstrates strong integration of engineering principles with food science, with recurring themes in spectroscopic sensing, nanotechnology applications, and pathogen control across diverse food products including berries, dairy, and soybeans. Dr. Choudhary has received notable recognition including a Team Award from SIU's College of Agricultural Sciences. He serves as an Editorial Board Member for the Journal of Food Protection (IAFP) and as Associate Editor for the Processing Division of ASABE. As a dedicated educator and industry collaborator, Dr. Choudhary serves as Project Director for the USDA NIFA Food Safety Outreach Program, Lead Instructor for FSPCA Human Foods Preventive Control courses, Trainer for Produce Safety Alliance growers training, and Instructor for the FDA Better Process Control School. He is also recognized as an FDA Food Processing Authority in Southern Illinois. His laboratory work focuses on developing and testing food safety technologies, particularly nonthermal processing equipment and sensing systems for food quality assessment. His research team actively investigates edible coatings, antimicrobial treatments, and spectroscopic methods for food safety and quality monitoring.

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Jin Young LeeView profile

Assistant Professor

University of Hawaiʻi at MānoaUnited States

Jin Young Lee is an Assistant Professor at the School of Intelligent Mechatronics Engineering , Sejong University , specializing in image and video processing. He previously worked as a Staff Engineer at Samsung Electronics from 2008 to 2018. Education : Ph.D. (2018), M.S. (2008), B.S. (2006) from KAIST and Sungkyunkwan University Research Focus : Video coding standards, super-resolution, image denoising, and defect detection His research explores 3D video coding , multi-view compression , and deep learning for image restoration . Recent articles highlight applications in brain MR imaging and industrial quality control . Key article trends include lightweight neural networks (e.g., DCP-based U-Net), quaternion transformers for noise reduction, and distributed feature fusion in defect detection. Most works intersect computer vision , deep learning , and signal processing .

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Michelle A. Wood-TrageserView profile

Assistant Professor

University of PittsburghUnited States

Michelle A. Wood-Trageser, PhD is an Assistant Professor in the Division of Translational Pathology at the University of Pittsburgh School of Medicine. She serves as co-director for the CAP/CLIA certified Research Histology Services Laboratory, working alongside Dr. A. Jake Demetris to advance the field of transplant pathology through innovative histological techniques. Dr. Wood-Trageser earned her BS from Elizabethtown College in 2004 and her PhD from the University of Pittsburgh in 2009, establishing a strong foundation for her career in translational research. Her research program focuses on integrating traditional histopathological approaches with multiplex immunohistochemistry and digital image analysis to enhance understanding of disease processes within tissue. The laboratory's primary focus is on characterizing the immune microenvironment within biopsy tissues, with recent expansion into genomic capabilities for developing novel clinical tests that correlate geographical information from biopsies with molecular signatures. This integrative approach bridges the gap between classical pathology and modern molecular diagnostics. Dr. Wood-Trageser's publication record demonstrates consistent productivity across two major research domains: reproductive genetics (particularly ovarian biology and infertility) and transplant pathology. Her recent work (2020-2023) shows an increasing emphasis on digital pathology applications in transplantation, including immune synapse detection and T cell monitoring in allografts. She has developed expertise in combining genomic data with spatial tissue information to create more comprehensive diagnostic tools. Dorothy Lyet Research Endowment, Summer Research Stipend, Elizabethtown College (2002, 2003) Beta Beta Beta, Biological Honors Society, Elizabethtown College (2003-2004) Stephen L. Phillips Scientific Achievement Award, University of Pittsburgh, School of Medicine (2008) Gary Betty Scholar, University of Michigan (2009) Trainee Day Travel Award, Endocrine Society (2009) Outstanding Abstract Award, Endocrine Society (2011) Lalor Foundation Travel Fellow, Society for the Study of Reproduction (2014) Cornelia Post Channing New Investigator Award, Society for the Study of Reproduction (2014) Dr. Paul M. Rike Fellowship Award, Magee-Womens Research Institute (2014) As co-director of the Research Histology Services Laboratory, Dr. Wood-Trageser oversees a CAP/CLIA certified facility that serves as a hub for advanced histological techniques applied to both clinical and research specimens. Her mentorship approach emphasizes technical excellence in histopathology while encouraging innovation in digital and genomic pathology applications. Current research directions include developing methods to correlate spatial tissue architecture with molecular signatures, creating more precise diagnostic frameworks for transplant pathology and reproductive disorders.

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MUZAFFER ASLANView profile

Associate Professor

Bingol UniversityTurkey

Associate Professor Muzaffer Aslan is a faculty member at Bingöl University's Faculty of Engineering and Architecture, specializing in applied artificial intelligence research. His work bridges computer science, electrical engineering, and biomedical domains with practical implementations in industrial, medical, and energy systems. His academic journey includes a BSc in Electronic-Computer Education from Gazi University (1993), MSc from Fırat University (2004), and PhD in Electrical-Electronics Engineering from Fırat University (2016). This multidisciplinary foundation enables his cross-domain research approach. Professor Aslan's research centers on developing efficient deep learning solutions for real-world problems. His primary focus areas include medical imaging analysis (brain tumor and COVID-19 detection from X-rays), fall detection systems using depth sensors, emotion recognition from EEG signals, and appliance classification for smart grids. He innovates through hybrid architectures that combine CNNs with signal processing techniques like wavelet transforms and dispersion entropy, achieving high accuracy while maintaining computational efficiency. His publication record shows accelerating output since 2020, with 11 journal articles in 2021-2022 alone spanning medical diagnostics, agricultural technology, and industrial quality control. Recent work demonstrates increasing sophistication in model design, particularly in efficient architectures for resource-constrained environments as seen in his 2023 surface defect detection paper. As Principal Investigator for a TÜBİTAK 1002 project on appliance classification, he secures active research funding while mentoring graduate students. His supervision style emphasizes practical implementation, with students frequently co-authoring publications and contributing to textbook development. The collaborative nature of his work is evident in multi-institutional authorship patterns across his publications.

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Kristina SchädlerView profile

Professor

West Coast University of Applied SciencesGermany

Professor Kristina Schädler is a faculty member at the West Coast University of Applied Sciences (FH Westküste), where she serves as Professor of Data Processing within the School of Technology. She has been with the university since 2005 and also served as Dean of the Department of Technology. Her academic background includes a PhD in machine learning from TU Berlin, where she was awarded the Chorafas Research Prize for young scientists. West Coast University of Applied Sciences (since 2005) TU Berlin, Institute of Computer Science (1994-1999) Martin Luther University Halle/Wittenberg (1990-1994) Professor Schädler's research focuses on artificial intelligence and machine learning applications, particularly in image processing and data analysis. Her work spans multiple domains including industrial automation, agricultural technology, renewable energy, and animal husbandry. She has led numerous research projects that bridge academic theory with practical industrial applications, with particular emphasis on developing robust image processing systems that can be deployed in real-world settings. Her research portfolio demonstrates a consistent pattern of applying advanced machine learning techniques to solve practical problems across diverse industries. The ANIMET project, which developed facial recognition for horses, and the MaviSeg system for multichannel image segmentation represent her innovative approach to adapting computer vision technologies for specialized applications. Her work often involves close collaboration with industry partners to ensure practical relevance and implementation. Chorafas Research Prize for young scientists Innovationspreis at Equitana (2013) for the ANIMET project Professor Schädler has supervised numerous student theses that have resulted in practical applications across various domains. Her research group has secured funding from multiple sources including the European Commission, BMBF, and regional development agencies. She has established the CICAD project as a sustainable competence center for industrial image processing, which has trained multiple doctoral students through cooperative programs with the University of Lübeck. Her work demonstrates strong industry connections with companies like HIT Hinrichs Innovation + Technik, MBJ Solutions, and Fischer und Tausche Kondensatoren. Her research laboratory focuses on industrial image processing applications, with specialized equipment for 2D/3D imaging, spectral analysis, and machine learning implementation. The CICAD project established a dedicated competence center that continues to develop new applications of image processing technology across multiple industries.

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