Yan Wen is an experienced AI scientist with a rich background in computer vision, medical imaging, and data science. With a PhD in Computer Science, Yan has developed advanced algorithms for healthcare applications in lesion analysis, focusing on the challenges of small objects in images using AI. This research has been published in well-known journals/conferences such as ICCV, MIDL, IEEE Access, contributing to the field of computer vision and medical imaging significantly.Currently, as a Data Scientist at the University of Lincoln, Yan leads the development of a medical data platform powered by machine learning, funded by Innovate UK. This system stores diverse patient information facilitating comprehensive healthcare research. One of its innovative features is the kinematic analysis of motion videos from standard cameras, which replaces expensive and complex motion capture systems to evaluate patients' musculoskeletal conditions and track their recovery. Additionally, it also analyzes MR images and extracts biomarkers, providing a holistic evaluation of the patient's health. Actively used in clinical environments, this platform demonstrates real impact, enabling more precise and efficient patient care. Yan's responsibilities encompass system design, computer vision model development, and RESTful AI backend development. With outstanding experience in AI research and development, he uses Python and deep learning libraries like PyTorch, TensorFlow to bring cutting-edge AI solutions embedded in cloud computing platforms AWS, Google Cloud.During his PhD journey, Yan actively collaborated with several universities and institutions, including the University of York, University of Bedfordshire, Moorfield Eye Hospital, and the Institute of Cancer Research. These collaborations were funded by prominent organizations such as the British Council, ESPRC, and CRUK. These partnerships facilitated multidisciplinary research and development, enhancing the impact and reach of his work in various fields, including healthcare, robotics. Yan demonstrated good skills in presentation, communication, and collaboration. He effectively engaged with a wide range of stakeholders, including healthcare staff, patients, and international academics. His ability to convey complex technical concepts to non-professional stakeholders facilitated productive discussions and collaborations. By bridging the gap between technical and non-technical participants, Yan ensured that his research findings were accessible and actionable, driving real-world impact and innovation.
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Research TraineeUniversity Of YorkNewark-On-Trent, Gb -
Data ScientistUniversity Of Lincoln Apr 2023 - PresentLed the development of a medical data platform powered by machine learning called DataUnion. This system integrates diverse patient information, including radiology images, motion videos, and voice recordings, facilitating comprehensive machine learning research.Responsible for requirement analysis, system design, computer vision model development, and RESTful backend development. Collaborated closely with a front-end developer, engaging in in-depth discussions and co-designing solutions to… Show more Led the development of a medical data platform powered by machine learning called DataUnion. This system integrates diverse patient information, including radiology images, motion videos, and voice recordings, facilitating comprehensive machine learning research.Responsible for requirement analysis, system design, computer vision model development, and RESTful backend development. Collaborated closely with a front-end developer, engaging in in-depth discussions and co-designing solutions to ensure seamless integration and functionality. Worked in a real clinical environment, engaging with non-computer science professionals to gather feedback and refine system requirements, continuously improving the platform.Developed and deployed an innovative motion video analysis system (maimotion.com), which is currently available online and actively used in clinical settings, replacing expensive and complex motion capture systems for evaluating patients' musculoskeletal conditions and tracking their recovery. Show less -
Research TraineeUniversity Of York Jun 2022 - Apr 2023In the project focused on real-time underwater data quality analysis and control, I was responsible for evaluating underwater robots and related hardware available in the market. This involved conducting extensive research on the latest advancements in underwater technology and analysing various products to determine their suitability for meeting project requirements. To address the challenges of IoT deployment in real-world aquaculture environments, I proposed several effective solutions, such… Show more In the project focused on real-time underwater data quality analysis and control, I was responsible for evaluating underwater robots and related hardware available in the market. This involved conducting extensive research on the latest advancements in underwater technology and analysing various products to determine their suitability for meeting project requirements. To address the challenges of IoT deployment in real-world aquaculture environments, I proposed several effective solutions, such as integrating Real-Time Clocks (RTC) for Raspberry Pi and utilizing ESP32 microcontrollers (MCUs). Additionally, I applied the "Segment Anything Model" to generate Pseudo Masks from bounding boxes, thereby enhancing the dataset quality.I actively participated in university workshops, presenting our research to national and international academics, and served as a paper reviewer for ICRA 2023. These activities not only showcased our findings but also facilitated valuable feedback and collaboration opportunities. Show less
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Research AssistantUniversity Of Lincoln Jul 2019 - Aug 2020In collaboration with The Institute of Cancer Research and funded by Cancer Research UK, I worked as a research assistant on a project aimed at automating lung segmentation and registration in CT images for early risk prediction of lung cancer. As a one of the core contributors, I focused on analyzing and preprocessing raw image data, performing the registration of multiple CT images taken over different years, and employing deep learning methods to segment lung regions. My efforts ensured the… Show more In collaboration with The Institute of Cancer Research and funded by Cancer Research UK, I worked as a research assistant on a project aimed at automating lung segmentation and registration in CT images for early risk prediction of lung cancer. As a one of the core contributors, I focused on analyzing and preprocessing raw image data, performing the registration of multiple CT images taken over different years, and employing deep learning methods to segment lung regions. My efforts ensured the quality and consistency of the data, significantly enhancing the accuracy and efficiency of lung cancer risk prediction through automation. This research, in which I played a pivotal role, was published at MIDL 2024, underscoring its impact and contribution to the field. Show less
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Honorary Computer TechnicianMoorfields Eye Hospital Nhs Trust May 2018 - Aug 2018Bedford, England, United Kingdom -
Research TechnicianUniversity Of Lincoln Mar 2018 - Aug 2018One of the project I involved is the automation and quality assurance of ultrasound fetal measurements. The main objective is to develop and validate a deep learning-based algorithm that can automatically segment the fetal head and abdomen in ultrasound images. Further, I integrate this algorithm into an IoT board Jetson TX2. I evaluated the capabilities and limitations of the board and optimized the operating environment to achieve good accuracy.Additionally, I worked as a technician… Show more One of the project I involved is the automation and quality assurance of ultrasound fetal measurements. The main objective is to develop and validate a deep learning-based algorithm that can automatically segment the fetal head and abdomen in ultrasound images. Further, I integrate this algorithm into an IoT board Jetson TX2. I evaluated the capabilities and limitations of the board and optimized the operating environment to achieve good accuracy.Additionally, I worked as a technician funded by ESPRC (Engineering & Physical Sciences Research Council) in the implementation of a personalized health monitoring and self-management system at Bedford Moorfield Eyes Hospital and Cambridge Addenbrooke's Hospital. My role involved supporting the doctor in research project on-site, and later on, the collected health data was analyzed and summarized for further insights and decision-making. Show less
Yan Wen Education Details
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Awarded
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Transfer To Phd
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Upper Second Class
Frequently Asked Questions about Yan Wen
What company does Yan Wen work for?
Yan Wen works for University Of York
What is Yan Wen's role at the current company?
Yan Wen's current role is Research Trainee.
What schools did Yan Wen attend?
Yan Wen attended University Of Lincoln, University Of Lincoln, University Of Lincoln.
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