Narrative CV

Tung-Han Hsieh is a Research Specialist specializing in computational physics, HPC system development, and IoT maintenance. His research interests focus on biomedical image processing and machine learning, and dedicated to advancing knowledge and innovation in this area. He obtained his Ph.D. from Department of Physics, National Taiwan University and have contributed to multiple international research projects.

He developed a HPC system for RCAS, now applied in the first principle calculations and machine learning, impacting the researches of RCAS.

He has collaborated with Dr. Fu-Liang Yang on Deduction Learning model, leading to non-invasive blood glucose and cuffless blood pressure estimations. As a mentor, he has guided 1 graduate student, fostering new talent in machine learning. He also served as a reviewer for NSTC projects, Diagnostics, Photonics, and Sensors (MDPI) journals, and contribute to the organization of OPTIC2024 international conference.

His future research aims to deepen biomedical image processing with machine learning and enhance interdisciplinary collaboration. He strives to translate academic discoveries into real-world applications, ensuring a lasting impact on both academia and society.

Education

• Ph.D. Physics Department, National Taiwan University, 2002
• B.S. Physics Department, National Taiwan University, 1996

Positions and Career

• Research Specialist, Research Center for Applied Sciences, Academia Sinica (2020 – present)
• Associate Research Specialist, Research Center for Applied Sciences, Academia Sinica (2016 – 2020)
• Assistant Research Specialist, Research Center for Applied Sciences, Academia Sinica (2006 – 2015)
• Assistant Professor, Physics Division, Commission of General Education, National United University (2005 – 2006)
• Postdoctoral fellow, Physics Department, National Taiwan University (2002 – 2005)

Honors and Awards

• 2023 19th National Innovation Award in the Academic Research Category
• 2005 The Excellent Ph. D. Thesis Award of the Physical Society of the Republic of China

Selected Publications

• Dao-Ming Chang, Heng-Hua Hsu, Ping-Liang Ko, Wei-Jen Chang, Tung-Han Hsieh, Hsiao-Mei Wu, and Yi-Chung Tung, “Rapid time-lapse 3D oxygen tension measurements within hydrogels using widefield frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) and image segmentation”, Analyst 149, 1727 (2024).
• Wei-Ru Lu, Wen-Tse Yang, Justin Chu, Tung-Han Hsieh, and Fu-Liang Yang, “Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training”, Sci. Rep. 12, 6506 (2022).
• Wei-Che Huang, Chin-Dian Wei, Shimshon Belkin, Tung-Han Hsieh, and Ji-Yen Cheng, “Machine-learning assisted antibiotic detection and categorization using a bacterial sensor array”, Sensors and Actuators B: Chemical, 355 (2022) 131257.
• Justin Chu, Wen-Tse Yang, Wei-Ru Lu, Yao-Ting Chang, Tung-Han Hsieh, and Fu-Liang Yang, “90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c”, Sensors 2021, 21(23), 7815.
• Bitewulign Kassa Mekonnen, Tung-Han Hsieh, Dian-Fu Tsai, Shien-Kuei Liaw, Fu-Liang Yang, and Sheng-Lung Huang, “Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation”, Diagnostics 2021, 11(4), 685.
• Hsiao-Mei Wu, Tse-Ang Lee, Ping-Liang Ko, Wei-Hao Liao, Tung-Han Hsieh, and Yi-Chung Tung, “Widefield frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) for accurate measurement of oxygen gradients within microfluidic devices”, Analyst, 2019, 144, 3494.
• Hsien-San Hou, Kuang-Li Lee, Chen-Hung Wang, Tung- Han Hsieh, Juan-Jie Sun, Pei-Kuen Wei, and Ji-Yen Cheng, “Simultaneous assessment of cell morphology and adhesion using aluminum nanoslit-based plasmonic biosensing chips”, Sci. Rep. 9, 7204 (2019).
• Wing Kiu Yeung, Huai-Yi Chen, Juan-Jie Sun, Tung-Han Hsieh, Mansoureh Z. Mousavi, Hsi-Hsien Chen, Kuang-Li Lee, Heng Lin, Pei-Kuen Wei, and Ji-Yen Cheng, “Multiplex detection of urinary miRNA biomarkers by transmission surface plasmon resonance”, Analyst, 2018, 143, 4715.

• Bitewulign Kassa Mekonne, Wei-Ru Lu, Tung-Han Hsieh, Justin Chu and Fu-Liang Yang, “Accurate and 30-plus days reliable cuffless blood pressure measurements with 9-minutes personal photoplethysmograph data and mixed deduction learning”, Scientific Reports 14, 23722 (2024).
• Wei-Ru Lu, Wen-Tse Yang, Justin Chu, Tung-Han Hsieh, and Fu-Liang Yang, “Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training”, Sci. Rep. 12, 6506 (2022).
• Wei-Che Huang, Chin-Dian Wei, Shimshon Belkin, Tung-Han Hsieh, and Ji-Yen Cheng, “Machine-learning assisted antibiotic detection and categorization using a bacterial sensor array”, Sensors and Actuators B: Chemical, 355 (2022) 131257.
• Bitewulign Kassa Mekonnen, Tung-Han Hsieh, Dian-Fu Tsai, Shien-Kuei Liaw, Fu-Liang Yang, and Sheng-Lung Huang, “Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation”, Diagnostics 2021, 11(4), 685.
• Hsien-San Hou, Kuang-Li Lee, Chen-Hung Wang, Tung-Han Hsieh, Juan-Jie Sun, Pei-Kuen Wei, and Ji-Yen Cheng, “Simultaneous assessment of cell morphology and adhesion using aluminum nanoslit-based plasmonic biosensing chips”, Sci. Rep. 9, 7204 (2019).