A passionate software engineer at the intersection of machine learning, scientific computing, and real-world impact.
I am a software engineer with interests in developing deep-learning systems that are computationally efficient and privacy-preserving by leveraging numerical methods.
I have conducted several research projects in areas such as transfer learning for network security, context-driven fine-tuning for 3D body pose estimation at Carnegie Mellon University, and I am currently exploring graph neural networks as a preconditioner to solve ill-conditioned linear systems.
Aside from research, I have worked in industry as an IT intern, and have tutored 300+ students in advanced mathematics.
I'm a graduating 4th year at the California State Polytechnic University, Pomona, pursuing my B.S. in computer science with a minor in mathematics.
I aim to combine my research in deep learning and computational mathematics to augment real-time weighting solutions for efficient machine learning frameworks.
My goal is to leverage mathematical computing to contribute to the advancement of AI technologies which are ethical, efficient, and impactful across all industries whilst educating future innovators.
Recent work in machine learning, cybersecurity, and scientific computing.
Leveraging GNNs to implicitly solve ill-conditioned large-scale sparse linear systems found in physics and engineering applications.
Builds upon IMUPoser by integrating a context-driven fine-tuning approach with MotionGPT to enhance real-time 3D body pose estimation.
A novel framework for enhancing network intrusion detection in cross-domain evaluations and reducing dependence on large datasets.
Frameworks and platforms I've built to solve real-world problems.
A platform combining unbiased AI-driven proposal analysis with blockchain-verified voting to revolutionize democratic decision-making within organizations.
A machine learning model using convolutional neural networks and Keras VGG19 to detect malignant tumors in MRI scans on cross-domain datasets.
I'm always interested in hearing about new research opportunities and collaborations.
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