• Implemented various components of the JPEG2000 decoder standard (e.g. IDWT, data ordering, dequantization) in compliance with Digital Cinema System Specifications.

• Implemented multithreaded processing to increase application performance.

• Wrote C/C++ code in a Linux environment

• Used machine learning techniques to simulate and eliminate printing defects, with a focus on neural networks.

• Experimented with architectures such as convolutional neural networks and generative adversarial networks.

• Built a dataset for subsequent use for training, evaluation, and analysis with machine learning models.

• Designed and experimented with loss functions based on frequency analysis and contrast sensitivity functions.

• Wrote code to implement a recurrent neural network-based version of the CycleGAN image-to-image translation model.

• Developed a workflow for linking thermal video to print quality while working in a fast-paced R&D environment.

• Coded a data processing pipeline in order to transfer and process data from BitPlane Imaris.

• Analyzed relationships between thermal properties of TIJ printers and quality of print.

• Implemented various machine learning models in order to characterize cancerous and healthy patients’ blood metabolite composition through surface-enhanced Raman spectroscopy (SERS) data.

• Explored preprocessing and feature engineering techniques to enhance neural network performance.

• Wrote MATLAB code for cross-validation and optimizing network hyperparameters.

Tutor for students taking Introduction to Computer Science at Harvey Mudd College (course taught in Python). Grader for Introduction to Computer Science.