Portfolio Evaluation
Engineering Evidence
| Area | Evidence |
|---|---|
| Control Systems | Not demonstrated directly |
| Dynamic Systems | Supporting datasets only |
| Robotics | Supporting feature-extraction data only |
| Mechanical Design | Not demonstrated |
| Manufacturing | Not demonstrated |
| Numerical Methods | Strong: optimization, gradient descent, Newton’s method |
| Simulation | Moderate |
| Heat Transfer | Not demonstrated |
| CAD/CAM Integration | Not demonstrated |
| Optimization | Strong |
| Scientific Computing | Strong |
Scores
| Criterion | Score |
|---|---|
| Technical depth | 7/10 |
| Code quality | 7/10 |
| Documentation quality | 8/10 |
| Academic value | 8/10 |
| Industry value | 7/10 |
| Graduate admissions value | 7/10 |
Resume Bullet
- Completed a computational-engineering machine-learning portfolio covering
numerical optimization, regression, binary classification, neural networks,
feature extraction, and reproducible dataset validation; documented a
hadron/gamma challenge with
10,700training and2,676test observations.
LinkedIn Bullet
- Built a reviewable scientific-computing portfolio around optimization, regression, classification, neural networks, feature extraction, dataset inventory tooling, and reproducible prediction validation.