Medical Image Computing & Computed Assisted Intervention (MICCAI).

International Conference on Learning Representations (ICLR).

IEEE International Symposium on Biomedical Imaging (ISBI).

IEEE Transactions on Medical Imaging (TMI).

Deep Learning for Computer Vision (DLCV).

MICCAI Deep Learning in Medical Image Analysis (DLMIA) 2018 MICCAI Machine Learning in Medical Imaging (MLMI) 2019ДЄutomatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network Semi-Supervised Multi-Task Learning With Chest X-Ray Images Medical Imaging Meets NeurIPS (Med-NeruIPS) 2019ДЇast and Automatic Segmentation of Pulmonary Lobes From Chest CT Using a Progressive Dense V-NetworkД¬omputer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualization 2019 Self-supervised Semi-Supervised Multi-Context Learning for the Combined Classification and Segmentation of Medical ImagesДЄAAI Conference on Artificial Intelligence 2020Д«ipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images

IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS) 2020 International Conference on Pattern Recognition (ICPR) 2020ДЇully-Automated Analysis of Scoliosis from Spinal X-Ray Images Progressive Adversarial Semantic Segmentation International Conference on Machine Learning and Applications (ICMLA) 2020 Multi-Adversarial Variational Autoencoder Nets for Simultaneous Image Generation and ClassificationДeep Learning Applications, Volume 2, 2021 MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images IEEE International Symposium on Biomedical Imaging (ISBI) 2021 SSIQA: Multi-Task Learning for Non-Reference CT Image Quality Assessment With Self-Supervised Noise Level Prediction MICCAI Machine Learning in Medical Imaging (MLMI) 2021 Window-Level is a Strong Denoising Surrogate International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021 Personalized CT Organ Dose Estimation from Scout Images MS Thesis 2016 at Delaware State University, Dover, Delaware, USAДЄutomatic Extraction of Road Networks From High Resolution Satellite ImagesД«S Thesis 2012 at the Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh PhD Thesis 2020 at the University of California, Los Angeles, California, USAД®stimation of Breast Anatomical Descriptors From Mastectomy CT Images I am also interested in visual representation learning and domain generalization for medical image analysis tasks.ДЇrom Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis Recently, I focus on semi-supervised learning with multi-task learning, self-supervised learning, and generative modeling. I am particularly interested in developing advanced AI-powered medical imaging tools for clinical applications. My research is primarily centered around artificial intelligence, computer vision, and medical imaging.

Jan 2021: Two papers accepted at ISBI 2021!.Feb 2021: Gave a guest lecture to the Advanced Data Mining class at Utah State University.Feb 2021: Received the Stanford Bio-X Travel award!.

Apr 2021: Presented our SSIQA (oral) and MultiMix (poster) papers at ISBI 2021.

Apr 2021: Our article won the CMBBE: Imaging & Visualization Best Paper Award for the 2019-20 biennium!.

May 2021: Paper (early) accepted at MICCAI 2021! (Top 13%).

May 2021: Manuscript accepted at MELBA!.

Jul 2021: Abstract accepted at RSNA 2021 as an oral paper.

Aug 2021: Paper accepted at MLMI 2021.

Sep 2021: Presented our CT organ dose prediction paper at MICCAI 2021.

Oct 2021: Review article on Upstream Machine Learning published in RCNA.

I have also spent time at Philips Corporate Research NA at Cambridge, MA and Tencent Medical AI Lab at Palo Alto, CA. I also received an MS in Computer Science degree from Delaware State University (DSU) in 2016 and a Bachelor in Computer Science and Engineering (CSE) degree from the Rajshahi University of Engineering and Technology (RUET), Bangladesh in 2012.Дuring my MS at the DSU Medical Imaging and Simulation (MEDIS) Laboratory, in collaboration with the Penn XPL Lab, I worked on an NIH-funded breast cancer research project, jointly advised by Professor David Pokrajac and Professor Predrag Bakic. My PhD research was focused on effective deep learning, with a particular focus in semi-supervised learning and multitask learning, applied to computer vision and medical imaging. Prior to Postdoc, I obtained my PhD in Computer Science from the University of California, Los Angeles (UCLA) Graphics and Vision Laboratory in 2020, advised by Distinguished Professor Demetri Terzopoulos. I work with Professor Adam Wang and Professor Evan Zucker on upstream AI for improved medical imaging. I am a Postdoctoral Research Scholar in the Wang group in the Radiological Sciences Laboratory (RSL) at Stanford University.