I am a biomedical engineer and AI researcher, currently a Senior AI Engineer at Quantivly Inc., where I develop AI methods for radiology operations. Previously, I was an AI Engineer and then an AI Scientist at Philips, working on AI-enabled intravascular ultrasound (IVUS), and a Postdoctoral Research Fellow at the Lerner Research Institute, Cleveland Clinic, in Dr. Xiaojuan Li and Dr. Kunio Nakamura's lab, where I worked on accelerated quantitative MRI of the knee.
I completed my PhD in Biomedical Engineering at Case Western Reserve University under the supervision of Dr. David L. Wilson. My dissertation, 3D Ultrasound Imaging of the Eye, covered the design of 3D ultrasound imaging systems for the anterior segment and the whole eye, together with deep learning methods for image enhancement, segmentation and volumetric assessment.
Before that, I worked as a computer vision researcher at Semion. I completed my Bachelors and Masters degrees in Electrical and Electronic Engineering at Bangladesh University of Engineering and Technology, where I was advised by Dr. Celia Shahnaz.
PhD in Biomedical Engineering, 2024
Case Western Reserve University
MSc in Electrical and Electronic Engineering, 2018
Bangladesh University of Engineering and Technology
BSc in Electrical and Electronic Engineering, 2016
Bangladesh University of Engineering and Technology
Senior AI Engineer, Oct 2026 – present
Quantivly Inc., Somerville, MA
Development of AI methods for radiology operations.
AI Scientist, Aug 2026 – Oct 2026
Philips, Innovation & Design, Cambridge, MA
AI Engineer, Jun 2025 – Aug 2026
Philips Image Guided Therapy Devices, Plymouth, MN
Data readiness, validation and regulatory compliance for AI-enabled intravascular ultrasound (IVUS) products, as part of the algorithm development team.
Postdoctoral Research Fellow, Aug 2024 – Jun 2025
Program of Advanced Musculoskeletal Imaging, Lerner Research Institute, Cleveland Clinic
Deep learning for segmentation, image enhancement and radiomics in musculoskeletal and neuroimaging; accelerated whole-knee T1ρ and T2 mapping with automated analysis.
Graduate Research Assistant, Aug 2018 – May 2024
Department of Biomedical Engineering, Case Western Reserve University
3D ultrasound biomicroscopy and whole-eye 3D ultrasound systems; GAN-based deconvolution of depth-dependent blur; deep learning segmentation and volumetric assessment of the ciliary body; deep learning for non-contrast ultrasound microvessel imaging.
Computer Vision Researcher, Dec 2016 – Feb 2018
Semion, Dhaka, Bangladesh
Deep learning methods for abnormality detection and localization in chest radiographs.
We developed clinically feasible whole-knee T1ρ and T2 mapping in under 3 minutes, combining accelerated imaging with deep learning based automated analysis.
We developed a 3D ocular ultrasound system (3D-Ultrasound BioMicroscopy or 3D-UBM) capable of imaging ocular structures, applying deep learning segmentation and GAN-based image enhancement, and automating biometric measurements to investigate disease and trauma-related changes and treatment outcomes.
We proposed an end-to-end deep learning approach for microvessel imaging using non-contrast ultrasound, which efficiently removes tissue clutter, extracts blood signals, and reconstructs microvessels, demonstrating promising results and potential for adaptation to various scenarios.
We developed speech enhancement methods based on wavelet domain features using decision directed approach and generative models.
We explored the performance of different convolutional neural network architectures in disease classification, organ segmentation and localization in chest x-ray images.
We developed a sleep apnea detection approach from spectral features of EEG signals.
An up-to-date list of publications can be found on Google Scholar.