Dr. Md Ashikuzzaman (Preferred name: Ashik, he/him) is an Assistant Professor of Electrical and Computer Engineering at the University of Missouri–Kansas City (UMKC) and the Founding Director of the MIGHTY Lab (Medical Imaging for Global Health TechnologY). His research focuses on computational medical imaging, especially ultrasound imaging, where he blends analytic optimization with deep learning to develop advanced algorithms that improve diagnostic accuracy, robustness, and efficiency, particularly in resource-constrained and complex clinical settings.
Dr. Ashik earned his Ph.D. and M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal, Canada, and his B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET). Before joining UMKC, he was a Postdoctoral Fellow at Johns Hopkins University, where he contributed to translational research at the intersection of signal processing, biomechanics, and medical imaging.
His work has been published in leading venues such as IEEE TMI, IEEE TUFFC, IEEE TBME, IEEE ISBI, IEEE IUS, and IPCAI, and has been recognized with several competitive fellowships and awards in North America and internationally.
Dr. Ashik was drawn to UMKC for its vibrant research environment, strong culture of interdisciplinary collaboration, and commitment to community-focused innovation. These values closely align with his vision for developing accessible and equitable healthcare technologies.
He actively contributes to the academic community as an Associate Editor for Medical Physics and has served as an organizing committee member, session chair, and pitch competition judge at IEEE IUS 2023. He is also a frequent reviewer for leading journals and conferences in medical imaging and signal processing.
Outside academia, he enjoys spending time with his family, exploring nature, reading books, listening to music, watching movies and series, and traveling.
LinkedIn: https://www.linkedin.com/in/md-ashikuzzaman-/
Google Scholar: https://scholar.google.com/citations?user=BWGxcsUAAAAJ&hl=en&oi=ao
Lab Website: Under construction, stay tuned!
Postdoctoral Fellowship, Johns Hopkins University, Baltimore, MD, USA (2023-2025)
Ph.D., Concordia University, Montreal, QC, Canada (2023)
M.A.Sc., Concordia University, Montreal, QC, Canada (2019)
B.Sc., Bangladesh University of Engineering and Technology, Dhaka, Bangladesh (2015)
Areas of expertise:
Medical imaging, Image processing, Computational ultrasound imaging, Analytic optimization, Machine learning and deep learning
Work experience:
Assistant Professor, University of Missouri-Kansas City, Kansas City, MO, USA (2025-present)
Postdoctoral Fellow, Johns Hopkins University, Baltimore, MD, USA (2023-2025)
Graduate Research Assistant, Concordia University, Montreal, QC, Canada (2017-2023)
Graduate Teaching Assistant, Concordia University, Montreal, QC, Canada (2018-2021)
Selected publications:
Md Ashikuzzaman et al. (2024). Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification. IEEE Transactions on Biomedical Engineering (IEEE TBME), in press.
Md Ashikuzzaman et al. (2024). MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (IEEE TUFFC), vol. 71, no. 11, pp. 1389-1405.
Md Ashikuzzaman et al. (2024).Displacement Tracking Techniques in Ultrasound Elastography: From Cross-Correlation to Deep Learning. IEEE TUFFC, vol. 71, no. 7, pp. 842-871.
Marcelo Lerendegui et al. (2024). ULTRA-SR: Assessment of Ultrasound Localisation and TRacking
Algorithms for Super Resolution Imaging. IEEE Transactions on Medical Imaging (IEEE TMI), vol. 43, no. 8, pp. 2970-2987. (Winner of the ULTRA-SR Challenge)
Md Ashikuzzaman, Ali K. Z. Tehrani, and Hassan Rivaz (2023). Exploiting Mechanics-Based Priors
for Lateral Displacement Estimation in Ultrasound Elastography. IEEE TMI, vol. 42, no. 11, pp. 3307-3322.
Ali K. Z. Tehrani, Md Ashikuzzaman, and Hassan Rivaz (2023). Lateral Strain Imaging using
Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography. IEEE TMI, vol. 42, no. 5, pp. 1462-1471.
Md Ashikuzzaman and Hassan Rivaz (2022). Second-Order Ultrasound Elastography with L1-norm
Spatial Regularization. IEEE TUFFC, vol. 69, no. 3, pp. 1008-1019.
Md Ashikuzzaman, Timothy J. Hall and Hassan Rivaz (2022). Incorporating Gradient Similarity for
Robust Time Delay Estimation in Ultrasound Elastography. IEEE TUFFC, vol. 69, no. 5, pp. 1738-1750.
Md Ashikuzzaman, Ali Sadeghi-Naini, Abbas Samani, and Hassan Rivaz (2021). Combining First and
Second Order Continuity Constraints in Ultrasound Elastography. IEEE TUFFC, vol. 68, no. 7, pp. 2407-2418.
(a-MEM Challenge-winning algorithm for shear wave particle tracking, employed by Tehrani et al.)
Md Ashikuzzaman et al. (2020). Low Rank and Sparse Decomposition of Ultrasound Color Flow Images for
Suppressing Clutter in Real Time. IEEE TMI, vol. 39, no. 4, pp. 1073-1084.
Selected awards:
Best Poster Award, Next Generation Computational Bio-Imaging Conference, Rice University, Houston, TX (2024)
The Verasonics Joint Winner Award, ULTRA-SR Challenge, IEEE IUS 2022, Venice, Italy (2022)
Best Poster Award (Third Place), Graduate Student Research Conference (GSRC), Concordia University, Canada (2022)
GSA Academic Excellence Award, Concordia University, Canada (2021)
FRQNT Doctoral Research Fellowship (PBEEE, V1 category), Government of Quebec, Canada, ranked first (2021)
FRQNT Doctoral Research Fellowship (B2X), Government of Quebec, Canada, ranked first (2021)
International Tuition Award of Excellence, Concordia University, Canada (2020)
Quebec Bio-Imaging Network (QBIN) PhD Scholarship, Government of Quebec, Canada (2019)
Gina Cody School of ENCS Graduate Scholarship, Concordia University, Canada (2019)
Mitacs Globalink Research Award, Mitacs, Canada (2019)
QBIN Conference Travel Award, Government of Quebec, Canada (2019)
Concordia Merit Scholarship, Concordia University, Canada (2017)