Data Scientist & Computational Materials Physicist based in Malang, Indonesia. Passionate about applying machine learning, deep learning (ANN, GNN/CGCNN, LSTM), and data analytics to solve real-world industry problems and materials science challenges.

Data Scientist with hands-on experience in data analysis, machine learning model development, and data visualization gained through virtual internships and applied academic research. Proficient in Python and SQL, with strong command of Pandas, NumPy, Scikit-learn, PyTorch for predictive modeling and data preprocessing, alongside Power BI and Streamlit for building interactive dashboards.

• M.Sc. in Computational Materials Physics (Jan 2025 – Present)
  Brawijaya University — Malang, Indonesia
• B.Sc. in Computational Physics (Sept 2020 – Jun 2024)
  Brawijaya University — Malang, Indonesia

Brawijaya University (Computational Physics Lab) — Malang, Indonesia

• Developed a hybrid PID and Artificial Neural Network (ANN) controller to improve temperature regulation accuracy in a PCR (Polymerase Chain Reaction) system.
• Designed and trained the ANN model to optimize PID parameters, enhancing thermal stability and control response speed.
• Validated system performance against conventional PID controllers via simulation models.

British Airways — London, UK

• Conducted sentiment analysis on customer reviews to surface key drivers of passenger satisfaction.
• Applied Natural Language Processing (NLP) and machine learning models to classify customer sentiment.
• Visualized data using Pandas, Matplotlib, and Seaborn to deliver actionable customer experience insights.

BCG X (Boston Consulting Group) — Boston, USA

• Performed customer churn analysis for a client simulation (XYZ Analytics) using Pandas and NumPy.
• Engineered and optimized a Random Forest classification model, achieving 85% accuracy in predicting customer churn.
• Delivered an executive summary with strategic data-driven recommendations for the Associate Director.

• 📈 Bitcoin Price Prediction Using LSTM (Feb – Mar 2025)
  Built a Long Short-Term Memory (LSTM) deep learning model for time-series forecasting of Bitcoin price movements.
• ⚖️ Analysis of Obesity Factors Using Machine Learning (Dec 2024 – Jan 2025)
  Evaluated lifestyle and demographic factors predicting obesity risk using machine learning classifiers.
• 💊 Analysis of Drug Types for Diabetes Management (Nov – Dec 2024)
  Developed a K-Nearest Neighbors (KNN) model to recommend diabetes drug treatments based on patient health profiles.
• 🧪 AMARUS: CGCNN Multi-Property Screening Pipeline
  Accelerated multi-property prediction (Band Gap, Formation Energy, Bulk/Shear Modulus, Adsorption Energy) for Li-S battery cathode hosts via PyTorch CGCNN and Streamlit 3D visualization.
• 🏆 Conqueror Project — Pattern Recognition & ANN (Oct – Nov 2024)
  Compared Classification, Clustering, and ANN models to optimize pattern recognition performance.
• 🛍️ Alchemist Project — eCommerce Sales Dashboard (Aug – Sep 2024)
  Built an interactive dashboard to track revenue, orders, and customer trends.

• AI Powered Data Science — Digital Skola (Dec 2024)
• BCG Data Science Job Simulation — Forage (Jan 2025)
• British Airways Data Science Job Simulation — Forage (Jan 2025)

• Programming & Querying: Python (Pandas, NumPy, Scikit-learn, PyTorch), SQL
• Data Visualization & Web Apps: Power BI, Streamlit, Matplotlib, Seaborn, Plotly
• Machine Learning & Deep Learning: Predictive Modeling, Classification, Clustering, ANN, GNN/CGCNN, LSTM, NLP
• Scientific Computing & Tools: Git, Jupyter Notebook, ASE, GPAW, Pymatgen, Py3Dmol

• 💼 LinkedIn: linkedin.com/in/muhammadamarussalam
• 📧 Email: salamamar02@gmail.com
• 💻 GitHub: github.com/amar-codes02