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roberval1994/README.md

Hi, I'm Roberval Filho 👋

Data Scientist | Operational Research Analyst | PhD candidate (UNIFESP/ITA)

📄 Curriculum Vitae / Currículo

🌐 English | Português


🚀 About me

Data Scientist and Operational Research researcher with 10+ years of experience in Operational Research, Mathematical Modelling, Metaheuristics, and Artificial Intelligence applied to real-world business problems. I work at the interface between academic research and corporate application, combining exact formulations (MCLP, Linear & Integer Programming), metaheuristics (GRASP), and Quantum Computing (QUBO, QAOA) with Data Science and Machine Learning for logistics, geoprocessing, and decision support.

  • 🎓 PhD candidate in Operational Research at UNIFESP / ITA
  • 💼 Former Data Scientist / Optimization Analyst (Dom Rock)
  • 🏆 3rd Place – Best Paper Award, ICPR-A 2026
  • 🎯 Open to Data Science and Optimization Analyst roles

🧰 Tech stack

Python SQL Java Pandas NumPy scikit-learn SciPy GeoPandas Gurobi Qiskit

Operational Research: PuLP · HiGHS · CBC · Gurobi · CPLEX Data Science / ML: pandas · NumPy · SciPy · scikit-learn · Matplotlib · GeoPandas Quantum: Qiskit · QAOA · QUBO Data & Cloud: PostgreSQL · MySQL · AWS · Git · Power BI

📌 Featured projects

Project Topic Stack
PhD Research — Operational Research Hub connecting the three doctoral studies GeoPandas, PuLP, Qiskit
Spatial Interpolation of Precipitation IDW vs Kriging vs Splines in sparse networks PyKrige, GeoPandas
Multiobjective Rain-Gauge Optimization MCLP, K-Means, greedy & hybrid heuristics PuLP/CBC, Folium
Quantum Rain-Gauge Optimization QUBO / QAOA facility location Qiskit, IBM Quantum
ML Demand Forecasting (end-to-end) Full ML pipeline, time series scikit-learn, SHAP, XGBoost
PySpark Taxi Demand & Fleet Optimization Big-data ML + exact optimization PySpark, Spark MLlib, PuLP

📈 A progression of growing complexity

My projects are meant to be read as a journey — from estimating a signal, to optimizing a decision, to doing both at scale:

  1. Estimate — Spatial interpolation of precipitation: classical geostatistics (IDW, Kriging, Splines).
  2. Optimize (classical) — Multiobjective rain-gauge networks: exact MCLP, heuristics and hybrids.
  3. Optimize (quantum) — QUBO / QAOA facility location: the same decision, explored on quantum hardware.
  4. End-to-end ML — Demand forecasting: a full, tested ML pipeline with explainability.
  5. ML + optimization at scale — PySpark taxi demand & fleet optimization: distributed ML feeding an exact optimizer (forecast → decide).

Different data sources, different stacks, increasing complexity — ending where ML and Operational Research meet on big data.


🇧🇷 PT-BR

Cientista de Dados e pesquisador em Pesquisa Operacional com mais de 10 anos de experiência em Pesquisa Operacional, Modelagem Matemática, Metaheurísticas e Inteligência Artificial aplicadas a problemas reais de negócio. Atuo na interface entre a pesquisa acadêmica e a aplicação corporativa, combinando formulações exatas (MCLP, Programação Linear e Inteira), metaheurísticas (GRASP) e Computação Quântica (QUBO, QAOA) com Ciência de Dados e Machine Learning para logística, geoprocessamento e suporte à decisão.

  • 🎓 Doutorando em Pesquisa Operacional na UNIFESP / ITA
  • 💼 Ex-Cientista de Dados / Analista de Otimização (Dom Rock)
  • 🏆 3º Lugar – Best Paper Award, ICPR-A 2026
  • 🎯 Aberto a vagas de Ciência de Dados e Analista de Otimização

📈 Uma progressão de complexidade crescente

Meus projetos foram pensados para serem lidos como uma jornada — de estimar um sinal, a otimizar uma decisão, até fazer os dois em escala:

  1. Estimar — Interpolação espacial de precipitação: geoestatística clássica (IDW, Krigagem, Splines).
  2. Otimizar (clássico) — Redes multiobjetivo de pluviômetros: MCLP exato, heurísticas e híbridos.
  3. Otimizar (quântico) — Localização via QUBO / QAOA: a mesma decisão, explorada em hardware quântico.
  4. ML ponta a ponta — Previsão de demanda: pipeline de ML completo, testado e com explicabilidade.
  5. ML + otimização em escala — PySpark: demanda de táxi & alocação de frota: ML distribuído alimentando um otimizador exato (prever → decidir).

Fontes de dados distintas, stacks distintas, complexidade crescente — terminando onde ML e Pesquisa Operacional se encontram em big data.

📍 Grossos, Rio Grande do Norte, Brasil · Disponível para mudança 📫 roberval.researcher.or@outlook.com 🔗 Currículo Lattes

Pinned Loading

  1. PhD-Research-Operational-Research PhD-Research-Operational-Research Public

    PhD research in Operational Research (UNIFESP/ITA): optimization of rainfall monitoring networks, from geostatistics to quantum computing. Hub for the three studies.

  2. ml-demand-forecasting ml-demand-forecasting Public

    End-to-end machine learning pipeline for hourly demand forecasting (EDA, feature engineering, tuning, evaluation, explainability).

    Jupyter Notebook

  3. pyspark-taxi-demand-fleet-optimization pyspark-taxi-demand-fleet-optimization Public

    Big-data ML (PySpark/Spark MLlib) + Operational Research: forecast NYC taxi demand per zone, then allocate a fleet with an exact ILP.

    Jupyter Notebook

  4. Multiobjective-Optimization-of-Urban-Rain-Gauge-Networks Multiobjective-Optimization-of-Urban-Rain-Gauge-Networks Public

    Multiobjective reallocation of rain-gauge stations integrating geoprocessing and operational research (MCLP, K-Means, greedy and hybrid heuristics).

    HTML

  5. Quantum-Optimization-Rain-Gauge-QUBO-QAOA Quantum-Optimization-Rain-Gauge-QUBO-QAOA Public

    Quantum optimization of rain-gauge networks: QUBO formulation solved with QAOA (Qiskit), benchmarked against classical solvers. PhD research, UNIFESP/ITA.

  6. Spatial-Interpolation-of-Precipitation-in-Sparse-Rainfall-Networks Spatial-Interpolation-of-Precipitation-in-Sparse-Rainfall-Networks Public

    Comparative evaluation of IDW, Kriging and Splines for precipitation interpolation in sparse rainfall networks (South America). PhD research, UNIFESP/ITA.

    Jupyter Notebook