About
I am a Ph.D. in Mechanical Engineering from Georgia Institute of Technology and recently a Research Scientist Intern at Meta Reality Labs Research. With over 10 years of experience spanning academia and industry, I specialize in ultrasound system development, from multi-channel transducer array design and beamforming optimization to GPU-accelerated wave propagation simulation and real-time signal processing. My work bridges nonlinear acoustics in heterogeneous media with practical wearable sensing and medical ultrasound therapy applications, combining rigorous computational modeling with hands-on hardware prototyping and embedded DSP.
Current Research
At Meta Reality Labs Research, I developed next-generation wearable ultrasound sensing technologies for AR/VR systems, working on compact transducer arrays for strain imaging, building real-time signal processing pipelines, and implementing Doppler-based motion rejection algorithms for robust sensing in dynamic environments. My doctoral research at Georgia Tech investigated transcranial focused ultrasound therapy using lensed as phased array based ultrasound systems, where I developed GPU-accelerated aberration correction algorithms, optimized acoustic hologram topologies through heterogeneous skull bone, and volumetric trasn-skull passive acoustic mapping and image reconstruction.
Research Areas
Wearable Ultrasound Sensing
Designing and prototyping wearable ultrasound transducer arrays for AR/VR platforms. Expertise in multi-channel system optimization, beamforming parameter tuning, Doppler-based motion rejection, and SVD-based clutter filtering for high-sensitivity physiological monitoring.
Transcranial Ultrasound Therapy
Developing acoustic hologram-based systems for non-invasive brain therapy using Verasonics multi-channel platforms. Implementing heterogeneous angular spectrum approaches for skull aberration correction, beamforming sequence optimization, and parametric array techniques for trans-skull monitoring.
Signal & Image Processing
SVD-based spatiotemporal filtering, strain imaging and elastography, Doppler flow processing, advanced beamforming (delay-and-sum, angular spectrum), and ultrafast high-frame-rate acquisition for transient event capture and volumetric image reconstruction.
Vibroacoustic Modeling & NVH
Experimental modal analysis, structural noise transfer path quantification, and active noise control with embedded DSP. Industry experience in powertrain NVH optimization, psychoacoustic tuning, and real-time adaptive filtering (FxLMS) on TMS320 platforms.
Professional Journey
Research Scientist Intern, Meta Reality Labs Research
May 2025 – Nov 2025
Developed and ehanced ultrasonic sensing modalitties for robotic and AR/VR platforms. Key contributions include:
- Beamforming parameter tuning and multi-channel ultrasound imaging system performance benchmarking
- Doppler-based motion rejection algorithms for robust wearable sensing
- SVD-based signal filtering for clutter suppression and SNR enhancement
- Cross-functional collaboration with hardware, firmware, and algorithm teams
Graduate Research Assistant, Georgia Tech
2020 – 2025
Ph.D. research in transcranial focused ultrasound therapy and neuromodulation biophysics. Key achievements:
- GPU-accelerated nonlinear wave propagation simulations for heterogeneous skull bone
- Fast aberration correction algorithms using differentiable acoustic propagators and Verasonics arrays
- Acoustic hologram topology optimization for improved spatial targeting and image reconstruction
- Ultrafast acquisition for capturing transient neural responses in neuromodulation studies
Graduate Research Assistant, Virginia Tech
2018 – 2020
M.S. research in vibroacoustic modeling and structural dynamics in the Vibration and Acoustics Laboratory. Key contributions:
- Experimental modal analysis of rolling tires using laser vibrometry and accelerometer arrays for structure-borne noise prediction
- Quantified structural noise and vibration transfer paths under realistic operating conditions
- Designed a semi-active tuned absorber to reduce rest tremors in Parkinson's patients
Senior Design Engineer, Bajaj Auto Ltd.
2015 – 2018
Led powertrain NVH optimization and active noise control system development with 3+ years of industry experience:
- Embedded DSP implementation (TMS320) for real-time active noise cancellation
- FxLMS adaptive filtering achieving 10 dB attenuation in engine intake noise
- End-to-end hardware prototyping, testing, and product validation
- Psychoacoustic tuning and pass-by-noise regulatory compliance
Technical Expertise
Ultrasound Systems
Multi-channel array systems (Verasonics) • Beamforming & sequence optimization • Transducer characterization • Aberration correction • Passive acoustic mapping • Ultrafast imaging
Signal & Image Processing
SVD-based spatiotemporal filtering • Strain imaging / elastography • Doppler processing & motion rejection • Digital Image Correlation (DIC) • Adaptive filtering (FxLMS)
Simulation & Analysis
GPU-accelerated nonlinear wave propagation • FEM (COMSOL, Ansys, HyperMesh) • Multiphysics modeling • MATLAB/Simulink
Programming & Hardware
Python • MATLAB • C/C++ (embedded DSP & hardware) • Simulink • Sensor prototyping • Signal acquisition (ADC/DAC) • CAD (SolidWorks, Siemens NX)
Vibroacoustic Modeling
Experimental modal analysis • Laser vibrometry • Structure-borne noise prediction • Noise transfer path analysis • Active Noise Control (ANC) • Psychoacoustic tuning • NVH optimization
Scientific Machine Learning
Data-driven acoustic modeling • Differentiable physics simulators • Machine vision • Imaging with learned models • Optimization techniques • Model order reduction
Research Philosophy
My research philosophy centers on bridging fundamental wave physics with practical engineering solutions. Whether developing wearable ultrasound sensors or advancing non-invasive brain therapies, I combine rigorous experimentation with high-fidelity computational modeling to tackle complex challenges. My interdisciplinary background — spanning nonlinear acoustics, multi-channel array systems, embedded DSP, and signal processing — enables me to approach problems end-to-end, from transducer design through algorithm development to system validation. I believe the most impactful research emerges at the intersection of disciplines, and I thrive in collaborative environments that bring together academic researchers and industry partners.
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