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Doctoral Candidate Kevin Lin Receives International Recognition at ICMVA 2024 Conference
I am proud to announce that I received international recognition with the Best Presentation Award in the “Image Based Data Analysis and Application System” session for my paper “Diffusion and Multi-Domain Adaptation Methods for Eosinophil Segmentation” at the 7th International Conference on Machine Vision and Applications (ICMVA 2024) held in Singapore.
That makes two back-to-back international conference awards in two continents (Europe and Asia) in less than six months. We might have a brand new Ph.D. program here at UVA School of Data Science but our name is already out in the international community.
Competition was extremely fierce with my session consisting of six other Ph.D. students and researchers from the Philippines, Austria, Belgium, Italy, Japan, and India. Jet lag was a major factor and I presented the day after a two-layover-35-hour-long trip from Charlottesville to Singapore.
ICMVA 2024 consisted of many researchers focused on the engineering and mathematical aspect of image analysis with topics including holography, metrology, and interferometry. Much of the work reminded me heavily of my structural engineering-focused applied mathematics master's thesis under Richard Uber.
Even five years later, his instruction remained instrumental for my understanding of these highly technical presentations. In terms of deep learning, I noticed a near-generational divide with many senior researchers extremely skeptical of deep learning techniques while many young researchers eagerly applied deep learning to their approaches.
Presenting at two international conferences in two different continents and winning back to back international awards for UVA School of Data Science in less than six months feels amazing and would not be possible without the support of my advisor Don Brown and the GI Data Science Lab. It's been quite the journey since two years ago when I started this Ph.D. program with zero idea how to code in Python and no clue what Machine Learning could do. Thank you all for everything you do.
My paper will be published under the International Conference Proceedings by ACM (ISBN: 979-8-4007-1655-3), which will be archived in ACM Digital Library, indexed by Ei Compendex and Scopus. Major contributions include a comparison of state-of-the-art generative AI techniques in medical disease diagnosis and exploration into domain adaptation approaches in computer vision.
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