Christopher Dominguez
Performance-Focused Systems Engineer
Distributed Architectures · Applied Machine Learning
I design performance-aware distributed systems and applied ML pipelines that scale under real-world constraints.
B.S. Computer Science, May 2026 · Cal-Bridge Scholar · U.S. Citizen (Clearance Eligible)
ENGINEERING FOCUS
What I build and optimize
Performance Engineering
- MPI distributed-memory implementations
- OpenMP shared-memory optimization
- Runtime benchmarking and speedup analysis
- Scaling evaluation under real constraints
Applied Machine Learning
- USDA NIFA-funded research project
- MobileNetV2 fine-tuning (90% train / 87% validation)
- Data augmentation and generalization analysis
- Performance and reliability tradeoffs
Constraint-Based Systems
- Rule engine generating 18,000+ valid outputs
- Multi-constraint optimization modeling
- Dependency and prerequisite logic design
- Validation and correctness guarantees
FEATURED WORK
Selected projects and research
Parallel Computing Internship (MPI / OpenMP)
Performance benchmarking · scalability analysis · distributed vs shared memory
- Implemented and benchmarked parallel numerical workloads using MPI and OpenMP across multi-core and distributed configurations.
- Measured speedup and analyzed scalability ceilings under varying processor counts.
- Evaluated communication overhead, synchronization costs, and bottleneck behavior.
- Produced performance reports translating benchmark results into actionable engineering recommendations.
RESEARCH POSTER
Applied ML: MobileNetV2 Leaf Disease Classifier
Computer vision · fine-tuning · generalization · USDA NIFA-funded
- Led model fine-tuning and evaluation for a USDA NIFA-funded agricultural disease detection project.
- Achieved 90% training accuracy and 87% validation accuracy on a MobileNetV2-based CNN.
- Applied data augmentation and analyzed validation curves to reduce overfitting.
- Optimized training settings to balance accuracy, robustness, and inference efficiency.
RESEARCH POSTER
UCSD STARS: Constraint-Based Pathway Engine
Rule engine · dependency logic · optimization under constraints
- Designed a Python constraint engine generating 18,000+ valid multi-term academic plans for articulation agreement analysis.
- Modeled prerequisites and degree requirements as structured logical constraints to reflect real institutional rules.
- Engineered deterministic constraint validation and pruning to guarantee correctness across all generated outputs.
- Delivered structured decision-support outputs used in ongoing UCSD STARS research on transfer pathway equity.
PROJECT SCREENSHOTS
Come to PawPa (Godot): Systems-Driven Game Project
Gameplay systems · AI behaviors · physics tuning
- Collaboratively built a systems-driven gameplay prototype with a team of 3 using Godot and GDScript.
- Implemented AI enemy behaviors, player interactions, and resource/energy management systems.
- Tuned physics, collision, and responsiveness through iterative debugging and playtesting cycles.
- Designed all system interactions from scratch with a focus on reliability and extensibility.
CONTACT & RESUME