Hello! I'm a machine learning engineer based in San Jose, California. I'm currently studying Data Science at UC Berkeley looking for New Grad 2026 Roles. I have prior experience in medical imaging research, autonomous parking development, and developing software for compilers.
You can check out a pdf verison of my resume here
Education
University of California, Berkeley
Master of Science in Data Science
August 2024 – May 2026 · GPA: 3.95
University of California, Merced
Bachelor of Science in Applied Math
August 2020 – May 2024 · GPA: 3.71
Technical Skills
Programming Languages
Python
C++
Bash
SQLLibraries & Frameworks
PyTorch
TensorFlow
Scikit-learn
NumPy
SciPy
Pandas
Matplotlib
OpenCV
Transformers
PytestTechnologies & Tools
Git
AWS
Docker
ONNX
TensorRT
SLURM
Conda
Linux
Spark
Hadoop
KubernetesExperience
Advanced Micro Devices, Inc. (AMD)
AI Software Engineer Intern
May 2025 – August 2025 · San Jose, CA
- Built an internal ONNX manipulation CLI that unblocked compilation and enabled operator substitution at scale.
- Enabled new compiler support for unsupported operators by decomposing Conv3D to Conv2D, and GRU to LSTM.
- Resolved dynamic shapes via robust shape inference, unblocking compiler passes and stabilizing model builds.
- Unlocked full model offloading to AIE, enhancing performance and eliminating the dependency on CPU for inference.
Valeo
Machine Learning Software Engineer Intern
July 2024 – May 2025 · San Mateo, CA
- Developed and filed a patent on deep learning monocular simultaneous localization and mapping technology.
- Reconstructed and merged 3D point clouds using deep learning-enhanced keypoint detection and feature matching.
- Quantized and distilled semantic segmentation networks, achieving a 50% reduction in memory usage.
- Converted PyTorch/Tensorflow models to ONNX and TensorRT, reducing inference time by 40%.
- Wrote and submitted a first-author paper on deep learning techniques for localization and mapping to ICRA 2026.
Computational Optimization Group
Machine Learning Researcher
February 2023 – May 2024 · Merced, CA
- Invented a two-stage weakly supervised framework for classifying high-resolution medical whole slide images.
- Performed self-supervised pre-training and transfer learning of a ResNet encoder on 80+ GB of image data.
- Processed images into patches and trained an attention-based classifier for cost-effective and interpretable results.
- First-authored a conference paper accepted for publication and presentation at IEEE EMBC 2024.
National Science Foundation (REU)
Machine Learning Researcher
June 2023 – August 2023 · Merced, CA
- Trained a generative adversarial network to generate synthetic images addressing data scarcity and class imbalance.
- Engineered an iterative refinement pipeline that evaluated and selected high-quality synthetic images for retraining.
- Boosted classification performance, increasing F1 score by 0.05 and AUROC by 0.03.
Lawrence Livermore National Laboratory
Data Science Intern
July 2023 – August 2023 · Livermore, CA
- Engineered long short-term memory-based models for irregular heartbeats and delivered a presentation on findings.
- Built and optimized convolutional neural networks for precise reconstruction of cardiac transmembrane potentials.