I'm pursuing a Master's degree in Artificial Intelligence for Connected Industries at Universitat Politècnica de Catalunya (UPC) in Barcelona, Spain.
My research interests lie at the intersection of machine learning, wireless networks, and network measurement. I'm particularly interested in understanding how models behave when network conditions change and how the quality and timing of real-world measurements affect predictive performance.
Before Learning from Cellular Traces: Auditing Throughput Semantics and Measurement Timing in a Public 5G/LTE Dataset
Accepted — ACM CoNEXT 2026 Student Workshop (CoNEXT-SW '26)
This paper investigates the consistency of throughput measurements in public 5G/LTE cellular traces. It examines the relationship between reported throughput and received-byte counts, identifies measurement discrepancies, and highlights the importance of validating data semantics before using network traces for machine learning.
Status: Accepted; publication forthcoming.
📄 Read the accepted paper and research details
A machine-learning project using real-world cellular network measurements to predict downlink throughput under changing network conditions.
Key components:
- Temporal alignment of network measurements
- Radio and mobility-related features
- Session-aware walk-forward validation
- Comparison of Linear Regression, Random Forest, XGBoost, and LightGBM
- Analysis of prediction errors and changing network conditions
Alongside my research projects, I have worked on Java programming, software engineering exercises, and unit testing.
- Java programming and object-oriented design
- JUnit testing
- Software engineering coursework
These projects are available in my GitHub repositories.
Programming: Python, Java, SQL
Data Analysis: Pandas, NumPy, Matplotlib, Jupyter
Machine Learning: Scikit-learn, XGBoost, LightGBM, Random Forest
Networking: 5G/LTE, Wireless Networks, Network KPIs, Mobility Analysis
Research Methods: Temporal Validation, Feature Engineering, Measurement Auditing, Performance Evaluation
- Machine learning for wireless and mobile networks
- Reliable prediction under changing network conditions
- Time-series analysis and temporal validation
- Network measurement and data quality
- Intelligent and adaptive networked systems
Interested in research collaborations and funded PhD opportunities in machine learning, wireless networks, and intelligent networked systems.