Hi there! I’m Rafeed Chaudhury.

I’m a bioengineer living in San Francisco currently working on a stealth project at Google ATAP.




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About me

I am a Bioengineer / Project Manager on a stealth project at Google ATAP in Mountain View, CA. Currently, I am leading research, development, prototyping, and user testing of new hardware and software interfaces for Google’s future products and features. I love design, technology, and finding hacks to make our lives better and more efficient. I’m passionate about developing new technologies to advance human health and challenging and changing the status quo.

Prior to living in San Francisco, I lived in Scottsdale, Arizona and Atlanta, Georgia while pursuing degrees in Biomedical Engineering. I'm a Sun Devil from Arizona State and a Ramblin' Wreck from Georgia Tech.

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My Research Projects

My research focuses heavily on experimental cardiovascular fluid dynamics. The goal of this research is to improve current experimental techniques for design and improvement of pre-surgical planning. Pre-surgical planning allows clinicians and surgeons to plan surgeries before going into the ER, reducing hospital costs, patient time under anesthesia, and patient mortality.

Start-up Pipe Flows

In this study, we examine the simultaneous entrance flow development and transient response of starting flow in a finite length tube when a constant volume flux is imposed suddenly on a resting flow. PIV measurements of the transient, spatially developing flow are used to verify the analytical solution for fully developed start-up and the numerical simulation of developing start-up flow. Numerical solutions at many Reynolds numbers are used to develop a simple, complete description of the simultaneous development of the flow in space and time. Based on these results, we formulate simple, approximate rules for achieving fully developed, steady state flow in starting flows driven by piston pumps.

Read the journal article

Prediction of Downstream Flow and Velocity Waveforms in Piston Pump Systems

In this project, we use linear and time-invariant (LTI) system theory to predict the response to a piston-driven pump in order to establish velocity waveforms at a phantom test section downstream. Specifically, mass control flux and the step response of the pump are used to find the transfer function of the entire system with respect to flow conditions at locations downstream of the pump. We use stereographic particle image velocimetry (PIV) and MRI to compare the pump output to the pump input and predicted velocity waveform. Experimentalists may use this to validate data, and computational groups may leverage these results to minimize the computational domain, especially when using patient-specific geometries to reduce computation time.

Read the conference poster

A High Performance Aortic Flow Pump System

We designed a newly capable piston-based pulsatile flow pump system that can generate high volume flow rates, replicate physiologic waveforms, and pump high viscosity fluids against large impedances. The system is also compatible with a broad range of fluid types, and is operable in magnetic resonance imaging environments. Performance of the system was validated using image processing-based analysis of piston motion as well as particle image velocimetry. The new system represents a more capable pumping solution for aortic flow experiments than other available designs, and can be manufactured at a relatively low cost.


Read the journal article

Pre-Surgical Planning for Coarctation of the Aorta (CoA)

Coarctation of the Aorta (CoA) occurs in 10% of all congenitcal heart defect patients, resulting in life-threatening conditions. The surgical approach is dictated by the severity of the narrowing, by which the method of treatments is divided between minimally invasive and extensively invasive procedures. Modern diagnostic procedures allude to many disadvantages of invasive interventions, which make it difficult for clinicians to deliver an optimal form of care. Computational Fluid Dynamics (CFD) addresses these issues by providing new forms of diagnostic measures that is non-invasive, inexpensive, and more accurate compared to other evaluative devices.

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Last updated: October 10, 2017

Last updated: October 10, 2017

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