Rakib Hasan hails from the picturesque city of Barishal in Bangladesh and brings over five years of industry experience to his Ph.D. research in Mechanical Engineering at the University of Texas at San Antonio (UTSA). With a foundation in computer-aided design and manufacturing, Rakib has led research and development teams in Bangladesh, gaining expertise in wearable sensors, machine learning, and image processing along the way.
Currently, Rakib works as a research and teaching assistant in the Medical Design and Innovation Laboratory at UTSA, focusing his efforts on medical robotics, devices, and fabrication. He has contributed to a state-of-the-art review paper on breast cancer diagnosis using convolutional neural networks, published in the Journal of Healthcare Informatics Research. Additionally, he has presented research on a novel liquid flow-loop testing methodology for suction device evaluation at a poster session.
Outside of the lab, Rakib indulges his passion for music and audiobooks. Rakib’s LinkedIn profile, showcasing his background and accomplishments in further detail, is available at http://www.linkedin.com/in/rakib-hasan–/fr.
Harrison, P., Hasan, R. & Park, K. State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs). J Healthc Inform Res 7, 387–432 (2023). https://doi.org/10.1007/s41666-023-00144-3
Hasan, R., Saketh R. Peri, Maria J. Londoño-Jaramillo, R. Lyle Hood. A Novel Liquid Flow-Loop Testing Methodology for Comprehensive Evaluation of Airway Suction Device Design Standards.
Hasan, R., Ali, S.S. and Bhowmick, D.T., 2014. Simulation of a workpiece in CNC milling machine on the basis of G and M code based NC programming. In International Conference on Mechanical, Industrial and Energy Engineering (pp. 25-26).