Lisbon, Portugal | diogodebastos18@gmail.com | LinkedIn | GitHub | Google Scholar 👉 try talking with my CV
Hello world! I'm Diogo, a Ph.D. physicist and currently a senior data scientist at EDP. My passion for physics, data, and AI drives my work with Python, C++ , machine learning, quantum computing, and hardware development. I thrive on solving complex challenges and continually learning from my peers. In addition to technical expertise, I value leadership and mentorship, believing that sharing knowledge fosters innovation and growth.
2022/09 - current
Price Prediction: Improved short-term price forecasting by reducing error margins from 15% to 5% using advanced ML techniques - LSTM for time-series predictions and CNN for noise reduction. Implemented solutions in Python with PyTorch and managed data via SQL (Oracle/MySQL). Currently working with TFT (Temporal Fusion Transformer) models.
Portfolio Optimization: Enhanced EDP’s gross margin by 30% through energy commodities portfolio optimization using quadratic programming in Python with the Gurobi optimizer.
Algorithmic Trading: Developed an algorithmic trading framework and proprietary strategies for energy assets, boosting profits.
Workflow Transition: Migrated computational workflows from MATLAB and R to Python, increasing code efficiency, scalability, and team productivity.
Cloud Deployment: Deployed predictive models and analytical tools on Azure Cloud Services, ensuring high availability and scalability.
LLM Integration: Pioneered LLM-based applications to streamline report generation, summarization, and data interpretation by leveraging Azure OpenAI’s API, Llama2, Retrieval Augmented Generation, Weaviate, and LangChain.
GenAI: Launched a proof-of-concept translation app using OpenAI’s API with integrated speech-to-text, translation, and text-to-speech to improve global communication, despite challenges with latency and Latin-based language accuracy.
Team Leadership: Mentored colleagues and promoted MLOps best practices, ensuring robust ML pipelines, while guiding the team through complex projects to drive innovation and excellence.
Product leader: for ML applications, portfolio optimization and AI-driven trading.
Thesis supervision: Supervisioning a thesis on deep learning for price forecasting.
2016/01 - 2017/06
Startup Launch: Co-founded a startup with 4 entrepreneurs.
Product Strategy: Managed market segmentation, validation, pricing, and client acquisition.
IoT Development: Created an IoT solution for cold chain monitoring across 24 devices.
PCB Design: Designed a low-power embedded system PCB using Altium.
Prototyping: Developed prototypes with Arduino and Raspberry Pi on GNU/Linux.
Wireless Integration: Sensor-to-gateway communication via 2.1 GHz and 433 MHz.
Data Analytics: Stored sensor data in the cloud (SQLite) and analyzed it with MATLAB.
Testing Initiation: Launched the testing phase for the sensor network.
2014/09 - 2016/02
Organizational Growth: Led the non-profit, growing membership from 10 to 30.
Recruitment Process: Redesigned the recruitment procedure.
Rebranding: Managed the complete rebranding.
Educational Initiative: Launched a summer academy for high-school students to explore programming, web development, and hardware.
Networking Event: Initiated .works to connect the college community with national tech startups.
Hackathon Oversight: Oversaw ShiftAPPens, a hackathon for college students.
2018/01 - 2023/07
Advanced Research: Pushed the state-of-the-art of high energy physics by measuring the most stringent limits for top squark pair production cross section at low masses. This was accomplished by analyzing the proton-proton collision at the Large Hadron Collider detected by the Compact Muon Solenoid at CERN. I built the code framework using C++ due to the size of the samples and boosted decision trees (ROOT TMVA) and neural networks (Tensorflow) as a machine learning method to classify simulated data. Python and batch scripting were also used. A statistical analysis is performed in the end using Bayesian statistics to test the hypothesis of supersymmetry and dark matter in the context of the four-body decay of the top squark.
Monte Carlo Enhancement: Improved Standard Model simulations by analyzing lepton-neutrino kinematics from W boson decays.
Detector Algorithm: Developed an algorithm to estimate fake leptons due to detector inefficiencies.
Quantum Classification: Applied Variational Quantum Classifiers with Python and PennyLane, matching classical methods in supersymmetry searches.
Quantum GANs: Reduced Monte Carlo data augmentation time from months to days using Quantum Generative Adversarial Networks with qubit encoding.
Quantum Circuit Design: Utilized genetic algorithms in Python and Qiskit to build quantum circuits.
CMS Collaboration: Contributed to open-source code within the CMS collaboration (4000+ members).
ASIC Calibration: Tested and calibrated ASICs for the CMS Timing Detector upgrade targeting a 30–40 ps resolution.
Operations Management: Managed computer operations for the Portuguese CMS site.
System Debugging: Debugged systems at the Laboratório de Instrumentação e Física Experimental de Partículas FARM using Slurm.
Mentorship: Developed and mentored a summer internship program utilizing high-energy physics data and machine learning.
Outreach & Representation: Organized outreach events for students, created a lab student council, and represented CMS-Portugal at the European Committee for Future Accelerators Early-Career Researchers Panel.
2018/01 - 2023/07
Ph.D. in Physics at Instituto Superior Técnico hosted by Laboratório de Instrumentação e Física Experimental de Partículas, in collaboration with the Compact Muon Solenoid collaboration, CERN: “Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at the Large Hadron Collider” (Mark: Pass with Distinction) supervised by Dr. Pedrame Bargassa and Prof. João Varela
2011/09 - 2016/07
Thesis: “Automated monitoring and diagnosis of cold chains” (Mark: 18/20), supervised by Prof. Francisco Cardoso and Whitesmith.co CEO Rafael Jegundo
2023/06/12 - The CMS Collaboration, “Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at the Large Hadron Collider” JHEP06(2023)060
Bastos, D. “Using Variational Quantum Algorithms and Quantum Generative Adversarial Networks for Supersymmetry in High‑Energy Physics” (internal paper)
The CMS Collaboration, “Experimental characterization of the BTL Front-end Board based on TOFHIR1” (internal note)