Diogo Ribeiro DiogoRibeiro7

Hi there 👋, I'm Diogo Ribeiro

Welcome to My GitHub Profile!

I'm a veteran Data Scientist with over two decades of experience, proudly hailing from the picturesque country of Portugal. My journey through diverse fields such as computer science, economy, management, medicine, natural sciences, engineering, pure mathematics, and applied mathematics has been a continuous source of fascination and inspiration. Welcome to my GitHub profile!

🔗 Connect with me

GitHub Stats

🧠 Areas of Expertise

🔍 Data-Driven Decision Making

Supply Chain & Logistics: Optimizing operations to enhance efficiency and reduce costs.
Sustainability: Promoting environmental responsibility through data-driven strategies.
Finance & Health: Leveraging data to improve financial models and healthcare outcomes.

📊 Machine Learning & Statistics

Health Applications: Advancing healthcare solutions and outcomes using machine learning and statistical analysis.
Mathematical Research: Focusing on differential equations and partial differential equations to solve complex problems in epidemiology, economics, and sociology.
Graph Theory: Uncovering patterns and connections in social networks to provide valuable insights into human interactions.
Big Data Analytics in Marketing: Decoding customer behaviors and preferences to enhance business strategies.
Statistics & Probability: Developing predictive models and risk assessment tools crucial for finance, insurance, and public policy.
Sustainability Algorithms: Creating algorithms that promote renewable energy use and reduce carbon footprints.

🛠️ Technical Skills

Programming Languages:
- Python: Expertise in data manipulation, statistical modeling, and machine learning, using libraries such as Pandas, NumPy, SciPy, and Scikit-Learn.
- SQL: Proficient in writing complex queries for data extraction, manipulation, and analysis, with experience in optimizing queries for large datasets.
- R: Skilled in statistical analysis and data visualization, leveraging packages such as dplyr, ggplot2, and caret for specialized analytics in health and logistics.
- Bash/Zsh Scripting: Knowledgeable in scripting for automation tasks, particularly in data processing pipelines and deployment workflows.
- Additional Languages: Familiar with Fortran, Ruby, Rust, and TypeScript for specialized applications, including numerical methods, IoT, and web development.
Data Science & Machine Learning Tools:
- Scikit-Learn and TensorFlow: Experienced in developing and deploying machine learning models, including supervised and unsupervised learning for classification, regression, and clustering tasks.
- XGBoost and LightGBM: Specialized in gradient boosting methods for high-performance predictive modeling, particularly in structured datasets.
- Time Series Analysis Libraries: Proficient with Statsmodels, Prophet, and custom implementations in Python for forecasting and anomaly detection.
- Visualization: Expertise with Matplotlib, Seaborn, and Plotly for creating comprehensive visual reports that aid decision-making.
Big Data and Real-Time Processing:
- Apache Flink and Apache Kafka: Skilled in setting up real-time data streaming and analytics pipelines, especially for IoT and industrial applications.
- Apache Iceberg: Experience building a data lake house to manage big data efficiently, reducing costs and improving access and query performance.
- Hadoop Ecosystem: Familiarity with HDFS and Spark for handling large datasets and parallel processing, especially for batch processing in ETL pipelines.
IoT & Automation:
- Raspberry Pi & Sensors: Experience integrating hardware and software for IoT applications, automating data collection, and monitoring in logistics and environmental control.
- Edge Computing and Embedded Systems: Implementing data processing on edge devices for real-time insights, utilizing Python and C for embedded system applications.
- MQTT and HTTP Protocols: Knowledgeable in IoT protocols to manage sensor data flow, ensuring reliable and efficient communication between devices and analytics platforms.
DevOps & Automation:
- GitHub Actions: Proficient in developing CI/CD workflows for automated testing, deployment, and monitoring of machine learning and data processing pipelines.
- Docker and Kubernetes: Skilled in containerization and orchestration, facilitating scalable deployments of ML models and data processing systems.
- Jenkins: Experienced in setting up CI/CD pipelines for continuous integration, testing, and deployment.
Database & Data Warehousing:
- PostgreSQL, MySQL, and DynamoDB: Expertise in relational and NoSQL databases, proficient in database design, indexing, and query optimization.
- Apache Iceberg: Using Iceberg for data lake storage, allowing for efficient, schema-evolving, and real-time analytics solutions.
- AWS S3 and Redshift: Experience with cloud storage and data warehousing solutions, ensuring scalable data handling for analytics and machine learning applications.
Statistical & Analytical Skills:
- Survival and Cohort Analysis: Statistical modeling for understanding customer retention, product lifespan, and behavior over time.
- Optimization: Linear and nonlinear programming for optimizing industrial and supply chain processes.
- Predictive Modeling and Forecasting: Time-series analysis and demand forecasting in logistics, supply chain, and health.
- Health Outcomes Modeling: Experience developing models to analyze patient outcomes and healthcare intervention efficacy.

🔭 Current Research Interests

Machine Learning in Health
- Exploring machine learning techniques, such as neural networks, clustering, and anomaly detection, to enhance diagnostic accuracy, early disease detection, and patient monitoring. Special focus on real-time data analysis from wearable sensors and IoT devices to track and predict health events in non-hospital settings.
Mathematics & Differential Equations
- Ordinary differential equations (ODEs) and partial differential equations (PDEs) with applications across physics, biology, and epidemiology. Emphasis on numerical methods for solving high-dimensional PDEs and non-linear ODEs, which model complex, dynamic systems in both natural and social sciences.
Graph Theory in Social Networks
- Utilizing graph theory to analyze large-scale social networks for understanding community structure, information flow, and influence patterns. Interest in dynamic networks and how graph-based metrics can capture the evolution of social interactions over time, with applications in public health, misinformation detection, and sociological studies.
Big Data Analytics in Marketing
- Applying predictive modeling, clustering, and sentiment analysis on big data sources (e.g., social media, transaction logs) to find trends in customer behavior. The focus is on optimizing customer segmentation, personalized recommendations, and lifetime value predictions, which inform targeted marketing and strategic decision-making.
Sustainability & Renewable Energy
- Developing and deploying machine learning models and optimization algorithms that facilitate sustainable practices in energy production, consumption, and conservation. This includes work on predictive maintenance for renewable energy sources, energy demand forecasting, and optimization of renewable energy grids to maximize efficiency and reduce environmental impact.
Precision Medicine & Genomics
- Investigating the use of machine learning in genomics to personalize treatment plans based on individual genetic profiles. Focusing on predictive algorithms for assessing disease susceptibility, optimizing drug dosages, and minimizing adverse drug reactions.
Health Economics & Cost Optimization
- Using statistical modeling and cost-benefit analysis to evaluate the economic impact of healthcare interventions. Research focuses on optimizing resource allocation in healthcare systems and assessing cost-effectiveness of preventive versus treatment-based approaches.
Mental Health Prediction Models
- Developing models that analyze behavioral, physiological, and environmental data to predict and detect early signs of mental health disorders. Emphasis on using wearable sensor data and natural language processing of social media to assess mental well-being and provide timely interventions.
Chronic Disease Management & Monitoring
- Building AI models to assist in the long-term monitoring and management of chronic diseases like diabetes, hypertension, and heart disease. Focusing on predictive algorithms for disease progression and remote monitoring tools for personalized healthcare management.
Public Health & Epidemiology
- Applying data science to public health for modeling infectious disease outbreaks, understanding social determinants of health, and designing interventions to improve population health outcomes. Special interest in network-based models for tracking disease spread and optimizing vaccination strategies.
Behavioral Economics & Consumer Decision-Making
- Analyzing how cognitive biases and psychological factors influence consumer behavior and economic decision-making. Research includes creating models to simulate decision-making processes and designing interventions to encourage better financial choices.
Macroeconomic Forecasting Using Big Data
- Leveraging big data from diverse sources (social media, transaction data, global events) to improve macroeconomic forecasting models. Focus on using real-time data for more accurate predictions of economic indicators like GDP growth, inflation rates, and unemployment.
Inequality & Wealth Distribution
- Investigating the mechanisms behind income and wealth inequality across different populations and regions. Utilizing machine learning to analyze complex, multi-generational economic data and identify structural barriers to equitable wealth distribution.
Environmental Economics & Sustainable Development
- Developing economic models that integrate environmental sustainability and natural resource management. Focus on assessing the impact of climate policies on economic growth, as well as market-based approaches for carbon reduction, such as cap-and-trade systems.
Labor Economics & Future of Work
- Exploring the impacts of automation, gig economy, and remote work on labor markets. Emphasis on predictive models for job displacement risk, skill transition paths, and the economic implications of workforce trends on income distribution and employment patterns.
Financial Risk Modeling & Crisis Prediction
- Building models to assess and predict financial risks, particularly in volatile markets. Focus on stress testing and scenario analysis for financial institutions, as well as early warning systems for economic crises and stock market crashes using advanced statistical techniques.

🌟 Highlights

Extensive Experience: Over 20 years in data science, spanning multiple disciplines and industries.
Interdisciplinary Approach: Combining knowledge from computer science, mathematics, economics, and natural sciences to solve complex problems.
Hands-On Projects: Practical experience with IoT, automation, and environmental monitoring using Raspberry Pi and sensors.
Advanced Research: In-depth exploration of machine learning applications, mathematical modeling, and quantum computing.

📈 Let's Connect and Collaborate!

Thank you for visiting my profile. I'm always open to connecting with fellow data enthusiasts, researchers, and professionals. Whether you're interested in collaborating on a project, discussing innovative ideas, or exploring new opportunities, feel free to reach out!

Happy Coding and Collaborating!

Area	Tool
OS
Languages
Databases
Datalake
Infrastructure
Command Line
Cloud Services
Typesetting Tools
Streaming
DevOps Tools
Data Analysis and Visualization
Data Science
Machine Learning
Data Engineering

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