Amazon (June 2025 – September 2025)

Software Development Engineering Intern

• Developed a network traffic prediction system and service dependency visualizer to anticipate user traffic during peak events, allowing engineers to scale services seamlessly and reducing a previously manual effort by 80% (20 days to 2).

Systems Energy and Efficiency Lab @ UCSD (Oct 2023 – Present)

Machine Learning Research Intern

• First author in an IEEE-IoTJ'25 journal publication (under review) proposing a continual learning framework for improving classifier performance in real-world data streaming.
• First author in an AAAI'25 AICS research paper proposing a novel masked autoencoder architecture and probabilistic mappings for anomaly detection in network intrusion scenarios.
• Co-author in an IEEE-SafeThings'25 paper on an adversarial defense selection algorithm for network intrusion.
• Second author in an IEEE-CSR'24 publication evaluating adversarial attack impacts on intrusion detection models.

Scripps Institution of Oceanography (Sept 2023 – June 2024)

Natural Language Processing Research Intern

• Applied statistical natural language processing and fine-tuned large language models on climate corpora to build document classifiers for regional climate trends.

Code in Place @ Stanford University (April 2023 – June 2023)

Teaching Assistant

• Volunteered to lead weekly sections for Stanford's CS106A, hosting a cohort of 15 students to learn Python programming fundamentals.

Triton NeuroTech

Machine Learning Engineer

• Developed and validated an LSTM model for the Prosthetics team, achieving over 90% accuracy in classifying EMG time-series signals into finger movement commands for a prosthetic arm.