Experience + Projects
AI Engineer
ASMLAI Foundation & Platform
Developed PyTorch models to predict part performance and failure modes at the design stage, using simulation feedback and hyperparameter tuning to improve accuracy 18%.
Built a gen AI agent framework with modular workflows and pipelines that automated 80% of documentation tasks for my team while maintaining internal standards.
Created a multi-agent blueprint for building end-to-end AI agents, including a cost framework for tokens, tool calls, and execution. An early use case was a risk-assessment agent that reviewed 37 risk events and recommended a vendor for the supply-chain team.
- June 2025 - Present
- San Diego, CA
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Bioengineering Intern, Data Analytics
AquilliusBioTech Startup
Ran extensive statistical analysis on genomic datasets in Python and SQL, improving predictive model accuracy by 15% through targeted feature engineering such as aggregating mutation frequency by gene pathway.
Built validation frameworks with stress tests and redundancy checks to model biological system reliability and reduce experimental error.
- June 2024 - Sept 2024
- San Diego, CA
Data Analyst Intern, Financial Analytics
Advanced Trustee Strategy Wealth ManagementFinance and Investment Management
Analyzed portfolios in Python and SQL, applying regression models to uncover risk exposures and investment opportunities that informed allocation decisions.
Built interactive Power BI dashboards to visualize key financial metrics, cutting manual reporting time and giving stakeholders live insight for faster decisions.
- June 2023 - Sept 2023
- San Diego, CA
Agentic AI Blueprint & Evaluation Framework
ProjectPython, LLMs, Agent Orchestration
Building a modular blueprint that assembles end-to-end AI agents from reusable components so teams can stand up task-specific agents quickly.
Designing an evaluation framework that scores agents on accuracy, reliability, and task completion to benchmark and improve agent designs.
- June 2026 - Present
Audio Keyword Classification
Neuromorphic Computing GroupPython
Built a model that classifies keywords in audio clips by pairing signal processing with neural networks, reaching 87% accuracy on the Google Speech Commands dataset.
- Jan 2024 - June 2024
- Santa Cruz, CA
Robotic Arm
SlugboticsHardware
Built a robotic arm with servo motors and sensor feedback for steady, accurate movement, integrating five motor controllers for multi-axis control.
- Jan 2024 - June 2026
- Santa Cruz, CA
Research
Accepted to the Neuromorphic Computing Group Research Lab Under Professor Eshraghian
The Neuromorphic Computing Group (NCG) at UC Santa Cruz, led by Professor Jason Eshraghian,
is pioneering the intersection of neuroscience and artificial intelligence by developing
cutting-edge neuromorphic computing systems.
These systems mimic the brain's structure and function,
enabling more efficient and intelligent processing capabilities for a range of applications,
from autonomous systems to advanced robotics. Professor Eshraghian's research focuses on creating
hardware that closely emulates neural networks, allowing for faster and more energy-efficient computation
compared to traditional digital processors.
This innovative approach not only advances our understanding
of neural networks but also pushes the boundaries of how machines can process and interpret information,
making his work a vital contribution to the future of AI and computing.
As part of my research within the Neuromorphic Computing Group at UC Santa Cruz, I developed an Audio Keyword Classification
system aimed at enhancing the efficiency and accuracy of real-time speech recognition.
This project focused on creating a machine learning model capable of identifying specific spoken keywords within audio streams,
leveraging the power of Python and key libraries like Librosa and TensorFlow. The system was designed to process and analyze
audio data, extracting relevant features such as Mel-frequency Cepstral Coefficients (MFCCs) to improve the model's precision.
