MD HASIBUZZAMAN

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Hasib's Bio

Md Hasibuzzaman is a biomedical researcher and engineer currently working at the National Cancer Center (NCC), Republic of Korea, focusing on biomedical convergence, diagnostic and treatment technologies. His work involves artificial intelligence, biomedical imaging, computational fluid dynamics (CFD), ultrasound simulation, and signal processing. At NCC, he uses ANSYS, COMSOL Multiphysics, Field II Pro, Verasonics Vantage systems, and MATLAB/Python for advanced modeling, simulation, and biomedical data analysis.

He previously worked at the National University of Singapore (NUS) on flexible wearable devices and biomaterials, and was a research assistant in the Biomedical Information and Signal Lab at Gwangju Institute of Science and Technology (GIST), South Korea, collaborating with Seoul National University.

Hasibuzzaman holds a Master of Science in Biomedical Engineering from GIST, an upper second-class Bachelor’s degree in Biomedical Engineering from Universiti Teknologi Malaysia (UTM), and a Diploma in Biomedical Engineering from a public polytechnic in Bangladesh. His primary and secondary education were in science.

He has completed four internships in the field of biomedical engineering, including:

A 3-month research internship at TU Ilmenau, Germany.
One year of research experience at E-life Solution Plt, Malaysia.
A 3-month internship at Siemens Healthineers.
A one-year part-time role at Medirome Bangladesh Limited.

His research expertise includes AI in healthcare, instrumentation, neural disease diagnosis, rehabilitation, EEG, EMG, and speech signal processing using machine learning, deep learning, MATLAB, PRAAT, and 3D printing. He also has strong experience in CFD blood flow simulations, biomechanical modeling, robotics, wearable devices, biomaterials, and drug delivery.

He is passionate about exploring new research methods, enhancing his skills, and contributing to innovative biomedical solutions.

“I never exhaust myself in trying.”

Hasib's MS thesis is "Voice-based Amyotrophic Lateral Sclerosis Classification Using Deep Learning Methods and Comparisons with Classical Machine Learning Models. Healthy vs ALS classification accuracy: 99.25% using CNN (MFCC features). Healthy vs ALS with dysarthria vs ALS without dysarthria classification accuracy: 98.36% using CNN (MFCC features). His undergraduate thesis, which was part of his PhD research, was focused on CFD simulation. Hasib's biomedical understanding has expanded significantly as a result of his participation in two research projects with E-life Solution Plt and ILMENAU, Germany. He worked in faculty laboratories and participated in a number of research activities during his academic career. In addition, he is honing his skills in Python, TensorBoard, PyTorch, LaTex, MatLab, Praat speech analysis, and Solid Works. Aside from that, he has exceptional ideation capabilities, technical expertise, the capacity to solve challenges in potentially tough settings, and strong communication skills. He is also a bright individual, multitasker, active, and enthusiastic leader who desires to be an academician, biomedical researcher, and biomedical device innovation specialist for the future of healthcare