Chloe Luo

Developed and maintained logmill, a log-based, real-time monitoring and latency-metric publishing platform using React.js, Flask, C++, and Python, parsing 10M+ daily GUTS log entries to correlate events into workflows and publish metrics to downstream systems.

Built contract monitoring mode with per-metric thresholds and GitHub PR–based JSON configs, reducing false positives by ~20%.

Designed long-term storage on AWS S3 like cloud storage to handle 1M+ weekly workflow and contract-monitoring events.

Deployed services via BPaaS (Kubernetes-based PaaS) with Jenkins-like CI/CD system; added C++ unit tests, Pytest suites, and static analysis checks, improving test coverage and reducing integration defects. 

Developed a highly scalable, fault-tolerant ETL pipeline using Python Django in collaboration with 2 engineers to aggregate data from multiple sources, including vendor APIs, Reddit, news outlets, using Agile methodologies.

Built scalable RESTful APIs with Redis on AWS ElastiCache, reducing response time for traders and enhancing data analysis performance.

Leveraged AWS EC2, Network Load Balancer (NLB), API Gateway, and MongoDB to achieve 99.99% uptime, ensuring high availability and fault tolerance of services.

Implemented a real-time data ingestion service using Kafka and MongoDB, optimizing the aggregation and processing of popular news topics from social media.

Developed REST APIs and integration gateways using Python (Flask) and Go to unify distributed data pipelines with cloud object storage (e.g., AWS S3) and analytics APIs, reducing API latency by ~40% via connection pooling. 

Built automation bots leveraging gRPC and enterprise messaging platforms for real-time notifications, task routing, and workflow deployment; applied Go concurrency (goroutines, channels) and file synchronization for high-throughput job execution. 

Designed and deployed automation and data integration solutions using RabbitMQ (topic-based routing, prefetch limits), OAuth2 (token caching, role-based permissions), and distributed logging middleware to improve traceability, enhancing operational efficiency by 30%+. 

Generated and presented industry reports and company pitches highlighting key trends to the firm

Sourced 50+ SMB targets within the cybersecurity, healthcare equipment, and healthcare services industry based on financial criteria of EBITDA between $2M and $10M; outreached to potential sellers for intro call

Conducted research on the cybersecurity vertical, evaluated the market size, and identified value creation initiatives

Spearheaded operational and financial due diligence on target companies; performed comparable company and precedent transaction analysis to determine valuation. 

Managed internal database and built lists of potential acquisition candidate companies; conducted initial due diligence, sourced 50+ businesses in given sub-verticals.

Focused on training a model to classify foliar diseases of apples given the photos of the apple leaves using natural language processing

Proposed technique including flipping, rotation, blurring and color/contrast lighting adjustment through data augmentation to tackle data insufficiency and imbalanced