Anna Chau - Software Engineer & ML Researcher

About Me

Greetings! My name is Anna. I'm a Software Engineer and ML Researcher pursuing my Master's in Electrical and Computer Engineering at the University of Southern California. I earned my Bachelor's in Electrical and Computer Engineering from Temple University in Philadelphia.

With expertise spanning theoretical foundations and practical applications, I specialize in developing innovative solutions that translate cutting-edge research into real-world impact. I'm driven by the challenge of making software more accessible, efficient, and transformative across diverse domains. As I approach closer to my graduation from USC, I find myself compelled to join in on various opportunities that support my growth as an engineer.

Education

MS in Electrical & Computer Engineering

University of Southern California, Los Angeles, CA

2024 - 2026

GPA: 3.7

BS in Electrical & Computer Engineering

Temple University, Philadelphia, PA

2020- 2024

GPA: 3.63

Experience

2 Years Research

1 Year Industry

Experience

Machine Learning Researcher

Temple University, Computer Fusion Laboratory Aug 2023 — May 2024

Developed computer vision solutions for maritime infrastructure assessment and corrosion detection systems.

Optimized a ResNet-based CNN to achieve >92% accuracy in multi-class corrosion severity classification for real-time detection of ship hull images to enhance safety and reliability in maritime operations
Maintained an interactive corrosion-detection web application with React front end and Flask/MongoDB backend to streamline image visualization and data management
Collected and manually annotated ship hull images under varying environmental conditions to label corrosion regions and severity levels to build a rich supervised training dataset

Python PyTorch React Flask MongoDB

Test Engineering Intern

Vicor Corporation May 2023 — Aug 2023

Conducted comprehensive performance analyses on power conversion systems while contributing to Six Sigma quality improvement initiatives.

Collected and analyzed performance data of power units post-production, evaluating voltages, efficiency, load regulation, and thermal performance across varying load conditions
Utilized SQL and Excel-based statistical analysis to assess unit compliance with engineering specifications and applied Six Sigma methodologies to identify and quantify variation across multiple production runs
Optimized measurement acceptance ranges and test sequence configurations and maintained safety margins while reducing false-reject rates and decreasing average test time by 43 seconds per unit

SQL Excel Six Sigma Data Analysis

Featured Projects

SocialCycle

Sep 2025 — Present

Developing a cross-platform application (React Native/TypeScript) that centralizes local cycling and running club events into a unified, data-driven calendar accessible on web and mobile applications.

Implemented secure OAuth2 integration with the Instagram Graph API to ingest structured and unstructured social data through a compliant data ingestion pipeline built in FastAPI
Engineering a scalable backend deployed on AWS (EC2, S3, RDS) with Docker-based microservices and asynchronous workers to handle media ingestion, ML inference, and event-verification workflows
Designing ML models utilizing NLP (caption parsing, hashtag analysis) to extract ride details and auto-populate calendar data

React Native TypeScript FastAPI AWS Docker

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Autism Severity Detection

Aug 2025 — Present

Researching multi-modal learning to predict DSM-5 autism support needs from T1 MRI embeddings with possible genomic variant-level features.

Building a reproducible neuroimaging pre-processing pipeline that includes NIfTI I/O, skull-stripping (HD-BET/BET), bias-field correction, MNI152 registration, resampling, and intensity normalization (WhiteStripe/z-score)

Python Neuroimaging Machine Learning

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Cardiovascular Disease Detection

Aug 2023 — May 2024

Optimized a convolutional neural network to detect the presence of six cardiovascular diseases from raw electrocardiogram (ECG) signals.

Raised F1-score from 0.83 to 0.89 to improve diagnostic automation and model generalization
Processed and standardized dataset of ~2.3 million ECG traces to normalize scaling variations across 12-lead recordings from heterogeneous ECG machines
Investigated feature importance across precordial leads and mitigated multicollinearity in input signals for optimal channel configurations

Python PyTorch Signal Processing

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Micromouse Autonomous Robot

Feb 2022 — Dec 2022

Built an autonomous maze-solving robot with computer vision and cloud computing capabilities.

Tailored the functionality of a robot by managing ROS2 packages designed for three-dimensional movement in Gazebo simulated maze
Implemented programs within AWS Cloud server to enable robot to autonomously explore a maze using LiDAR 3D point cloud vision and maze solving algorithms

Python ROS2 AWS Gazebo LiDAR

Meet Zinnie

In my downtime, I am usually being supervised by my cat, Zinnie. She has her own blog where she documents her very important life. Please check it out!

Visit Zinnie's Blog →|