Research
I'm a researcher and engineer working at the intersection of computer vision, deep learning, and applied machine learning. My work focuses on building AI systems for image understanding. I develops models that can learn robust visual representations, generalize across datasets and conditions, and support real-world decision-making whether the goal is detecting subtle patterns in clinical scans or extracting actionable insights.
Beyond core model development, I interested in the full lifecycle of ML: evaluation, interpretability, and visualization to better understand model behavior, diagnose failure modes, and communicate results clearly. I also contributes to system development, translating research ideas into reliable pipelines and tools that are reproducible, scalable, and easier for others to use. In addition, I has experience applying machine learning to financial data, bringing a cross-domain perspective to modeling, validation, and deployment.
Develop computer vision models that turn satellite and aerial imagery into meaningful, actionable information.
Build deep learning methods to analyze medical images and support accurate, trustworthy clinical insights.
Create evaluation and visualization tools to understand model behavior, improve reliability, and explain results clearly.
Design end-to-end ML systems and pipelines that are reproducible, scalable, and ready for real-world use.
model architecture and pipeline of my works.
Publications
Selected first, full list available. Add paper/code links as you fill in.
- Extraction and Conversion of Carboxymethyl Cellulose from Okara Soybean Residue via Soda AQ Pulping: Integration of Predictive Models and Process ControlPreeyanuch Srichola, Titinunt Kitrungrotsakul, Kuntawit Witthayolankowit, Chaiyaporn Sampoompuang, Keowpetch Lobyaem, Prapakorn Khamphakun, Rawiwan TumthongPolymers 17(6): 777 : 2025
- A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy ImageTitinunt Kitrungrotsakul, Yutaro Iwamoto, Sari Ipponjima, Satoko Takemoto, Hideo Yokota, Tomomi Nemoto, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Jingsong Li, Yen-Wei ChenIEEE/ACM Transactions on Computational Biology and Bioinformatics 18(5) : 2021
- Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19Titinunt Kitrungrotsakul, Yutaro Iwamoto, Hikari Jinbo, Sari Ipponjima, Tomomi Nemoto, Satoko Takemoto, Hideo Yokota, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei ChenIEEE Journal of Biomedical and Health Informatics 25(7) : 2021
- VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentationTitinunt Kitrungrotsakul, Xian-Hua Han, Amir Hossein Foruzan, Yutaro Iwamoto, Hideo Yokota, Tomomi Nemoto, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei ChenComputerized Medical Imaging and Graphics 75 : 2019
- A Cascade of CNN and LSTM Network with 3D Anchors for Mitotic Cell Detection in 4D Microscopic ImageTitinunt Kitrungrotsakul, Xian-Hua Han, Yutaro Iwamoto, Satoshi Tanaka, Hideo Yokota, Tomomi Nemoto, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei ChenIEEE ICASSP 2019: 958-962 : 2019
- Liver segmentation using superpixel-based graph cuts and restricted regions of shape constrainsTitinunt Kitrungrotsakul, Xian-Hua Han, Yen-Wei ChenIEEE International Conference on Image Processing (ICIP) : 2015