I’m currently working on various data science, machine learning and computer vision applications/initiatives in public transport. I’m also working on building demand models for urban transportation and mobility planning. I never miss a change to sneak in a bit of functional data analysis to analyze real time bus routes data since I worked on functional data analysis for four years during my doctoral research :)

Current technologies I’m working with include: Python, R, MLflow, Scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, Google Cloud Platform, Nivida Jetson Orin Platform, Nvidia Tao Training Toolkit, Nvidia DeepStream SDK and GStreamer.

Waki Community Exchange LLC is the developer of Waki, a cross-platform mobile application designed to facilitate the donation, upcycling, and swapping of physical objects. The aim is to focus on supporting communities and enhancing environmental awareness, while offering an exceptional user experience.

I supported my friend in founding the company and building the app, contributing to the development of the app’s backend and frontend using Dart, Flutter, and Firebase.

Waki app is currently published on the Google Play Store and Apple App Store with 2000+ downloads. Do check it out to support our work!

IBIDAT is a research institute of the Carlos III University of Madrid (UC3M). It was originally founded as a colaboration between UC3M and Santander Bank, although tt is now an independent research institute. The research institute takes on research and industry projects in the field of data science and machine learning. I spent 2 years there as a postdoctoral fellow working on a variety of data science and machine learning projects. The following are some of my achievements at IBIDAT:

• Developed and deployed large‑scale LLM‑based content classification system processing 25,000+ documents (35GB data) for a Spanish market intelligence firm.

• Led end‑to‑end data science pipeline for a major Spanish retail group’s seasonal promotion analysis project, processing multi‑year sales data to optimize promotional ROI (€100M+ annual promotional spending).

• Researched and developed fair machine learning model prototypes for a major Spanish bank’s credit decision systems to ensure equitable outcomes across customer applications.

• Contributed to interpretable machine learning initiatives at a major Spanish bank.

IMDEA Networks Institute is a research institute of the Madrid Autonomus Community of Spain. The institute does cutting edge research in telecommunication, networks and computer science. I worked as a researcher at IMDEA Networks Institute from 2018 to 2022, under the supervision of Antonio Fernández Anta. My research at IMDEA was focused on scalable anomaly detection techniques for big data streams.

• Developed three novel anomaly detection algorithms for big data analysis, published in Advances in Data Analysis and Classification and Stat journals. Check my google scholar profile for more details.

• Translated research into production‑grade open‑source software (fdaoutlier R package, published on CRAN) implementing algorithms in R and C++ with 86% test coverage.

• Co‑authored comprehensive review on AI/ML models for solar irradiance prediction published in Scientific Reports (2022), evaluating deep learning architectures (LSTM, CNN) and traditional ML approaches across different temporal scales.

• Contributed to developing data processing pipeline in R and Python for COVID‑19 prevalence estimation project (CoronaSurveys) using machine learning and network scale‑up methodology. Project gained international media coverage (50+ outlets across Spain, Portugal, Germany, US) and secured €300K funding from Spanish Ministry of Sciences and Innovation (TED2021‑131264B‑I00).

• Conducted large‑scale blockchain network analysis processing 30GB+ Ethereum transaction data (millions of transactions) using network science and graph analytics. Revealed centralization patterns in mining ecosystems, identifying dominant pools and preferential attachment behaviors in distributed systems through end‑to‑end graph analytics quantifying node connectivity and edge strength distributions.

• Successfully delivered 47 data science and statistical analysis projects as independent consultant across healthcare, finance, and education sectors, maintaining 100% client satisfaction rating.

• Projects spanned experimental design, advanced statistical modeling, predictive analytics, custom statistical software development, and training sessions.