# Amaan Mohammed

> AI Engineer | Applied ML @ University of Maryland | Ex-CNPC USA

Location: United States, United States
Profile: https://flows.cv/amaanmohammed

I’m a Master’s student in Applied Machine Learning at the University of Maryland with a B.E. in AI from VIT. During my CNPC USA internship, I tackled drilling optimization problems: synthesizing research, reviewing XGBoost/Random Forest baselines, and developing an ANN that enhanced ROP prediction on a 16k+-row, multi-well dataset. At UMD, I serve as a Graduate Course Aide for Data Science courses, guiding students through EDA, data cleaning, and model evaluation in Python/R.

I’m motivated by turning messy, domain-rich data into clear, actionable models; whether in energy or healthcare and I value collaborative workflows, reproducible pipelines, and rigorous metrics.

## Work Experience
### Graduate Course Aide @ University of Maryland
Jan 2025 – Present | College Park, Maryland, United States
Graduate Course Aide for INST452 – Health Data Analytics 
- Supported over 35+ students in applying real-world healthcare data to solve analytical and machine learning problems using R.
- Graded over 500+ assignments, providing detailed, constructive feedback to reinforce key concepts in data preparation, EDA, and model development.
- Held weekly office hours to support student learning through live Q&A, clarification of course material, and personalized guidance on assignments and projects.

### Machine Learning Intern @ CNPC USA
Jan 2025 – Jan 2025 | Houston, TX
- Designed and implemented a machine learning framework for Rate of Penetration (ROP) prediction, improving accuracy by 7 points (77% → 70%) over the existing XGBoost model.
- Conducted feature engineering and selection to reduce 52 to 21 key domain features, enhancing model interpretability and robustness across diverse drilling formations.
- Utilized state-of-the-art optimization techniques to conduct 60+ hyperparameter tuning trials, refining architecture depth, activation functions, and regularization to maximize model performance.
- Collaborated with domain experts to integrate machine learning insights into drilling optimization workflows, driving data-driven decision-making in operational planning.


## Education
### Master's degree in Applied Machine Learning
University of Maryland

### Bachelor of Technology - BTech in Artificial Intelligence
Vellore Institute of Technology

### High School Diploma
International Indian School Al-Jubail


## Contact & Social
- LinkedIn: https://linkedin.com/in/amaan-bari-mohammed
- GitHub: https://github.com/shr7q

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Source: https://flows.cv/amaanmohammed
JSON Resume: https://flows.cv/amaanmohammed/resume.json
Last updated: 2026-04-18