• Prototyped and tested GNSS/RTK positioning algorithms with centimeter level accuracy using extended Kalman filter• Built a graph neural network based safety model

• Developed and shipped multitouch algorithms for 2020 and 2021 iPhones with new touch sensing technologies. Leveraged signal processing and machine learning techniques and solved technical challenges in image processing, feature extraction, and classification due to low signal-to-noise ratio of hardware. Served as algorithm tech lead.• Participated in the architecture of sensing algorithm infrastructure for future Apple sensing technologies. Served as key contributor of all-new development and production code bases.

My research interests centered around low-dimensional and parsimonious structures in signal processing and machine learning. My PhD dissertation discusses the fundamental identifiability and efficient algorithms for blind deconvolution, blind calibration, and other bilinear inverse problems under sparsity or low-dimensional subspace constraints. I also worked on learning adaptive sparsifying transforms, and leveraging sparsity and low-rankness properties for image and video restoration.

ECE 513: Vector Space Signal Processing

ECE310: Digital Signal Processing

ECE551: Digital Signal Processing, II (Graduate Level DSP Course)