CV

Education

• M.S. in Electrical and Computer Engineering, The Johns Hopkins University, 2014
• M.S. in Applied Mathematics and Statistics, The Johns Hopkins University, 2019
• Ph.D in Electrical and Computer Engineering, The Johns Hopkins University, 2019

Work experience

• 2020-present: AI healthcare scientist
  • Philips Research North America
• Summer-Winter 2019: Algorithm Engineer
  • Healthcare startup
  • Duties included: Algorithm innovations to address class imbalance and intra-class variants
• Summer 2018: Deep Learning Intern
  • Siemens Healthineers
  • Duties included: Deep learning framework on scoring motion-corrupted MRI scansdeep learning framework on scoring motion-corrupted MRI scans

Skills

• Artificial Intelligence, Machine Learning, Deep Learning, Signal Processing
• Complex problem solving
• Effective Communication

Publications

Segmentation of Thalamus from MR Images via Task- Driven Dictionary Learning

Partial face recognition

Compressive coding via random replicate mirror

Reconstruction-free deep convolutional neural networks for partially observed images

Sub-bagging in sparse optimization

Collaborative ensambles in sparse optimization

Optical coherence tomography using physical domain data compression to achieve MHz A-scan rates

MSAR: Identify comorbidities associated with recurrent ED and in-patient visits

LLL. (2022). "Identify comorbidities associated with recurrent ED and in-patient visits." Journal 1. 1(3).

Improvements of LACE+ 30-day Readmission Risk Score

Predict unit-level LoS using time-series modelling

Teaching

Wavelets

Introduction to Electrical and Computer Engineering

Compressed Sensing

Service and leadership

• reviewers in multiple IEEE conferences and journals