Rehman Ali received the B.S. degree in biomedical engineering from Georgia Institute of Technology in 2016. He is currently an NDSEG fellow, completing a M.S. in Computational & Mathematical Engineering and pursuing a Ph.D. in Electrical Engineering at Stanford. His research interests include signal processing, inverse problems, computational modeling of acoustics, and real-time beamforming algorithms. His current research is developing accurate and spatially resolved speed-of-sound imaging in tissue based on phase aberration correction, spatial coherence, and computed tomography
Honors & Awards
National Defense Science and Engineering Graduate (NDSEG) Fellowship, Department of Defense (DoD) (2017)
Henry Ford II Scholar Award, Georgia Institute of Technology (2015)
AP State Scholar Award, College Board (2012)
Education & Certifications
B.S., Georgia Institute of Technology, Biomedical Engineering (2016)
Graduate Course Assistant for Intro to Imaging and Image-based Anatomy (RAD/BioE 220), Stanford University Department of Radiology and Bioengineering (1/9/2017 - 3/17/2017)
Developed homework problems and designed problem sets related to the physics-portions of the course. Provided supplementary review sessions and hosted office hours to offer additional assistance to students.
Systems Engineering Intern for Siemens Ultrasound Innovation Team, Siemens Ultrasound (5/23/2016 - 9/23/2016)
Developed a high-speed beamforming simulator with the ability to emulate system-specific architectures and non-idealities. Performed simulations and parameter optimization for retrospective transmit focusing for implementation in ultrasound system. Worked on automated-testing software for characterizing the accuracy of beamforming in ultrasound system.
Mountain View, CA
Undergraduate Teaching Assistant for Mathematics Department at Georgia Tech, Georgia Institute of Technology (8/25/2014 - 12/11/2015)
Planning, teaching, and grading for differential equations recitation section in conjunction with professor. Providing supplementary handouts, review sessions, and office hours to students who need additional assistance.
- Open-Source Gauss-Newton-Based Methods for Refraction-Corrected Ultrasound Computed Tomography SPIE-INT SOC OPTICAL ENGINEERING. 2019
Local speed of sound estimation in tissue using pulse-echo ultrasound: Model-based approach.
The Journal of the Acoustical Society of America
2018; 144 (1): 254
A model and method to accurately estimate the local speed of sound in tissue from pulse-echo ultrasound data is presented. The model relates the local speeds of sound along a wave propagation path to the average speed of sound over the path, and allows one to avoid bias in the sound-speed estimates that can result from overlying layers of subcutaneous fat and muscle tissue. Herein, the average speed of sound using the approach by Anderson and Trahey is measured, and then the authors solve the proposed model for the local sound-speed via gradient descent. The sound-speed estimator was tested in a series of simulation and ex vivo phantom experiments using two-layer media as a simple model of abdominal tissue. The bias of the local sound-speed estimates from the bottom layers is less than 6.2m/s, while the bias of the matched Anderson's estimates is as high as 66m/s. The local speed-of-sound estimates have higher standard deviation than the Anderson's estimates. When the mean local estimate is computed over a 5-by-5mm region of interest, its standard deviation is reduced to less than 7m/s.
View details for PubMedID 30075660
Distributed Phase Aberration Correction Techniques Based on Local Sound Speed Estimates
View details for Web of Science ID 000458693001161
Regularized Inversion Method for Frequency-Domain Recovery of the Full Synthetic Aperture Dataset From Focused Transmissions
View details for Web of Science ID 000458693001220
- Pattern formation in oscillatory media without lateral inhibition PHYSICAL REVIEW E 2016; 94 (1)