Projects

Table of Contents

• Surgical Instruments Detection with Custom Trainning
• Patient’s Clinical Outcome Prediction
• Model aggregation to forecast reservoir production
• Car suspension design

Surgical Instruments Detection with Custom Trainning (2024 –)

• Automated inventory checks via a phone camera, reducing task time by 99%, from ~20minutes to seconds.
• The model identified missing components.
• Captured and manually annotated images to build a custom labeled dataset for model training.
• Fine-tuned YOLO using transfer learning to detect missing components.
• Identified screws by box position using a custom clustering algorithm.
• Handled low-contrast aluminum parts by testing lighting, angles, and OpenCV preprocessing.

(a) Playing with opencv and distortion correction.	(b) - Initial setup to control: opencv + led lights.	(c) - Opencv transforms for blob detection.
(e) - AI: natural light condition.	(f) - Detections with a segmentation model.	(d) - AI + clustering algorithm: color and labels by same row/column.

Patient’s Clinical Outcome Prediction (2023 –)

• Objective: Accelerate treatment for high-risk patients by leveraging real-time data.
• Developed, evaluated and monitored models for patient’s clinical outcome prediction. Deployed APIs for integration with the app.
• EHR (Electronic Health Record) data is continuously updated before the patient’s next scheduled consultation, potentially indicating risks in real-time. The model operates 24/7, delivering instant alerts to physicians and performing risk stratification, enabling faster interventions and improving patient outcomes.

Model aggregation to forecast medium-term reservoir production (2020 - 2021)

• Scientific initiation project, with a published paper in on of the biggest energy congress of the world, Rio Oil and Gas.
• I worked at Fortaleza’s research group, STORMS. We used realizations of a benchmark resevouir model “OLYMPUS” as models to forecast production.
• We associated weights to each model and tested different optimization alogirthms to minimize a developed loss function.
• Particle Swarm optimization and others Bio-Inspired optimization algorithms.
• This strategy was validated with an average prediction error below 5%;

Car suspension design - Apuama Racing - FSAE (2018 - 2019)

• Used mathematical models to create vehicle simulations (point-mass-model, biclycle-model).
• Used Tire Models to interpolate new data points from the tire’s data.
• Developed a Suspension distribution forces program in Matlab. We would use the maximum forces obtained in braking and cornering simulations to dimension and reduce mass of the suspension.
• Project to reduce steering torque for better handling of the vehicle.
• Method to measure the car’s center of gravity and validate design.