About
Tianjie Chen
Personal statement will be added shortly! :-)
Personal statement will be added shortly! :-)
Passed the PhD qualifying exam on my birthday with exceptional feedback.
Joined the DREAM Research Center and started working on projects involving zero-knowledge proof systems and trusted execution environments.
Started my computer science PhD journey at NMSU and joined the PRISM research lab.
Obtained a Bachelor of Science in computer science from PSU.
Published my first first-author paper on increasing explainability of cancer prediction models.
Published my very first paper about analyzing dimensionality reduction techniques.
Started my journey in conducting research on AI/ML by developing an integrated solution for cancer diagnosis and prognosis.
Started my academic journey at PSU.
- Kim, J., Chen, T. and Kabir, M.F., 2025. Automated Image Segmentation Using Self-Iterative Training and Self-Supervised Learning with Uncertainty Scores. In Recent Advances in Deep Learning Applications (pp. 3-21). Chapman and Hall/CRC.
- Kim, J., Chen, T., Nguyen, H. and Kabir, M.F., 2024, December. YOLO-SCSA: enhanced YOLOv8 with spatially coordinated shuffling attention mechanisms for skin cancer detection. In 2024 International Conference on Machine Learning and Applications (ICMLA) (pp. 408-415). IEEE.
- Khatriya, N., Chen, T., Kabir, M.F. and Brearly, T., 2024, December. Identifying Probable Neurological Disorders with Explainable Machine Learning Techniques. In 2024 IEEE International Conference on Big Data (BigData) (pp. 5006-5013). IEEE.
- Chen, T. and Kabir, M.F., 2024. Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data. Plos one, 19(5), p.e0302947.
Kanjula, K.R., Datla, A.R., Chen, T. and Kabir, M.F., 2023, December. An edge internet of things framework for machine learning-based skin cancer detection models. In 2023 International Conference on Machine Learning and Applications (ICMLA) (pp. 2167-2173). IEEE.
- Kabir, M.F. Chen, T. and Ludwig, S.A., 2023. A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics, 3, p.100125.
