Ethan Young

Bachelor of Science in Computer Science

Relevant Coursework: Object Oriented Programming, Data Structures and Algorithms, Analysis of Algorithms, Intro to Machine Learning, Operating Systems, Cloud Computing.

Symphonix Systems

• Architected a multi-agent orchestration framework using dynamic routing to dispatch specialized domain models and a three-model consensus mechanism to ensure output consistency.
• Designed a hybrid SQL/vector persistence layer as a continuous learning knowledge base, reducing latency by ~90% by retrieving execution context and prior state (e.g., tmux sessions, error logs) to optimize recurring workflows.
• Engineered an automated evaluation harness utilizing 'LLM-as-a-Judge' patterns, replacing manual expert review with a scalable quantitative benchmark to accelerate the validation cycle for new agent deployments.

University of Nevada, Las Vegas (OIT)

• Managed the lifecycle decommissioning of a High-Performance Computing (HPC) cluster, documenting and auditing GPU compute servers and InfiniBand networking hardware to ensure secure asset recovery.
• Optimized data center infrastructure records by auditing fiber pathways across multiple facilities and synchronizing physical assets with Schneider Electric EcoStruxure DCIM software.
• Analyzed network security logs to identify unauthorized SNMP authentication patterns; utilized DNS/MAC lookup tools to programmatically trace source devices.

University of Nevada, Las Vegas (OIT)

• Collaborated within a 15-person engineering team to execute a high-impact, campus-wide infrastructure overhaul, orchestrating a phased migration from legacy Cisco stacks to Juniper hardware, including a full AP replacement over the summer and a comprehensive switch rollout through Spring.
• Accelerated the migration timeline by developing standardized configuration templates and automating IP management workflows, ensuring consistent VLAN and port-level logic across 660+ switches.
• Provisioned and validated ~4,500+ network assets, performing rigorous post-migration testing to ensure zero-downtime connectivity for campus operations.

Python, KNN, CNN Models

• Developed a geolocator and scene-labeling tool for UNLV, achieving 88% accuracy by performing data analysis on metadata from over 6,000 images to identify building proximity using a KNN algorithm and assigning scene labels (e.g., classroom, hallway) via a dynamically trained CNN model.