I am a senior machine learning researcher and engineer, leading the research engineering team at NoriSpace in South Korea. I hold a PhD degree in computer science. My greatest interest for the moment is building multi-modal machine learning models to solve problems considered not possible for computers. With the latest advances of deep learning, notably, in language and vision, people have been building amazing tools and software that empower and help others. And I would very much like to be a part of this transformative technology.
Beside that, I like writing scripts to automate my workflow as well as playing guitar. I am concerned about the climate crisis, so I became vegan because it's the least I can do.
I am a team leader of the ML research and engineering team at NoriSpace. NoriSpace offers ML-powered solutions to automate business tasks. Business data allows access to massive and diverse data and involves many modes, such as texts, images, a lot of documents (images with texts), event logs, etc. It is a fantastic opportunity to study, learn, build, and deploy many types of models and to manage their cycles.
I did my PhD as a member of the advanced microscopy team in LOB. Our team dealt with large volume fluorescence microscopy images and develops non-linear microscopy. I researched and utilized self-supervised learning approach with deep neural networks to develop a generic filter for nuclei and cells, that covers various microscopy images. My advisors were Anatole Chessel and Emmanuel Beaurepaire.
I know it's a long and complex name ¯\_(ツ)_/¯. Likewise my course of study was a bit longer than usual and quite jumpy. To be precise, it took me 9 months in KHU and 20 months in École Polytechnique. I started off my Master's degree studying quantum engineering for future display, but came to know something called computer vision using machine learning and deep learning and it changed everything. I was soon absorbed by ML and DL entirely, and followed computer science and machine learning track in École Polytechnique.
I worked at a start-up called PandaScore as an intern in the Data Science team. Here I met real data scientists. Working in a start-up was absolutely a fascinating experience.
I came to France and first encountered machine learning and computer vision. Segmenting bio-medical fluorescence images was a cool thing. This opportunity was a turning point of my career towards computer science and machine learning.
I wanted to make a next generation display and decided to do Master's degree. In Quantum Information Display unit, I synthesized CVD (chemical vapor decomposition) graphene and put it on anodic aluminum oxide, which I grew in a custom built chemical chamber. The device showed some photo sensitivity.
I majored in what's called Information Display, an interdisciplinary engineering subject, covering electronics, optics, chemistry, and even a bit of quantum theory. You might be wondering what took me so long for Bachelor's degree. I did the military duty for 21 months. Yeah, it's still a thing in South Korea, if you didn't know...
bioimageloader is a python library to make it easy to load bioimage datasets for machine learning and deep learning. Bioimages come in numerous and inhomogeneous forms. bioimageloader attempts to wrap them in unified interfaces, so that you can easily concatenate, perform image augmentation, and batch-load them.
While supervised deep neural networks have become the dominant method for image analysis tasks in bioimages, truly versatile methods are not available yet because of the diversity of modalities and conditions and the cost of retraining. In practice, day-to-day biological image analysis still largely relies on ad hoc workflows often using classical linear filters. We propose NU-Net, a convolutional neural network filter selectively enhancing cells and nuclei, as a drop-in replacement of chains of classical linear filters in bioimage analysis pipelines. Using a style transfer architecture, a novel perceptual loss implicitly learns a soft separation of background and foreground. We used self-supervised training using 25 datasets covering diverse modalities of nuclear and cellular images. We show its ability to selectively improve contrast, remove background and enhance objects across a wide range of datasets and workflow while keeping image content. The pre-trained models are light and practical, and published as free and open-source software for the community. NU-Net is also available as a plugin for Napari.
I became a full-time Linux user when I got into serious programming. I found that the development experience on Linux was much painless than on Windows, and in fact it was much easier to use Linux than I had anticipated. Linux gives me much more control on my system and hardware as well. I like the Unix philosophy, and I love and support open source software. My favorite distro is Arch Linux. I use a tiling window manager (bspwm), neovim, lf, and others. Also, it happens that I enjoy writing small scripts to automate things and configuring software to my taste. This project basically contains all my configuration guides and files as well as scripts to make my current setup.
S. Lim, E. Beaurepaire, and A. Chessel, "NU-Net: a self-supervised smart filter for enhancing blobs in bioimages", BIC workshop, ICCV 2023. link (Also, check out my talk for the same work in I2K conference in 2022: link)
S. Lim, X. Zhang, E. Beaurepaire, and A. Chessel, “BioImageLoader: Easy Handling of Bioimage Datasets for Machine Learning,” arXiv.org, Mar. 02, 2023. link
[Dissertation] S. Lim, E. Beaurepaire, and A. Chessel, "Versatile machine learning for neurodevelopmental imaging", Institut polytechnique de Paris, École doctorale de l'Institut polytechnique de Paris, Oct. 2023. link