Professional Logbook

This logbook answers a simple question:

What have I actually built, worked on, and deployed?

It is a structured, traceable record of my work across applied AI, research, engineering systems, and teaching.
Rather than a traditional CV, it documents projects, systems, and outcomes, with links to publications, tools, and blog entries that capture the process behind the work.

This page complements:

• Applied AI & Engineering — overview of deployed systems
• Teaching — courses and academic activity

Some linked blog posts are written in Spanish and reflect real-time work, experiments, and events.

2026 — Experimental Platforms and Behavioral AI

Interdisciplinary experimental system — Animal behavior analysis (Veterinary Medicine, UDLA)

What was built:
A complete experimental platform combining hardware, software, and computer vision for behavioral analysis.

Key contributions:

• Designed and integrated a mobile experimental platform
• Implemented IoT modules (smart feeders, LEDs, interaction buttons)
• Developed software for device control and data acquisition
• Integrated top-view vision system for real-time tracking
• Calibrated instrumentation for experimental protocols
• Enabled real-time measurement of behavioral variables (impulsivity)

Traceable work:

• Blog: Implementación de plataforma experimental para el estudio de impulsividad canina

2019 – 2025 · AI for Healthcare and Neuroscience

Data Scientist — Faculty of Medicine, University of Chile
Neurosystems · SCIANLab · AudioBrain · Tech4Medics-Lab

Predictive modeling in neuroscience (EEG)

What was built: Machine learning systems for predicting postoperative risk using physiological signals.

Key contributions:

• Designed predictive ML models for clinical outcomes
• Applied spectral analysis to EEG signals
• Modeled combined anesthetic effects using clinical + electrophysiological data

Traceable work:

• Blog: EEG research activities
• Projects: FONDEF ID19I10345, ID20I10371

Automated diagnostics (glaucoma)

What was built: A machine learning pipeline for clinical diagnosis using physiological signals.

Key contributions:

• Developed ML pipeline based on chromatic pupilometry
• Integrated signal processing with predictive modeling

Traceable work:

• Blog: Retina–Brain workshop
• Thesis: Glaucoma diagnosis
• Poster: Glaucoma diagnosis

Deep learning for biomedical imaging

What was built: Deep learning systems for microscopy and biomedical image analysis.

Key contributions:

• Developed RaViTT (Vision Transformer)
• Built pipelines for 3D nuclei segmentation (top-3, Cell Tracking Challenge)
• Applied DL to microscopy classification tasks

Traceable work:

• Paper: IEEE CAI 2023
• Paper: RaViTT (arXiv)
• Blog: Student projects in AI

Scientific software and open tools

What was built: Research-oriented software tools for biomedical image analysis and quantitative interpretation of complex biological data.

Key contributions:

• Co-developed VolumePeeler (FIJI plugin) for 3D visualization and geometric analysis
• Improved visualization and quantification of volumetric biological datasets
• Focused on interpretability of complex image structures in microscopy data
• Applied image analysis methods to:
  • Membrane deformation quantification
  • Embryo development tracking in live imaging data

Traceable work:

• Publication: VolumePeeler — BMC Bioinformatics (2023)

Early biomarkers of cognitive decline

What was built: Signal-processing-based system for early detection of cognitive decline.

Key contributions:

• Designed audiological test (AudioBrain)
• Investigated otoacoustic emissions as biomarkers

Traceable work:

• Paper: Alzheimer’s & Dementia
• Blog: Congreso Futuro

Impact:

• The resulting publication is among the Top 10 most cited articles (2024)

Population-level studies and mental health

What was built: Data-driven analysis of social and psychological factors.

Key contributions:

• Analyzed loneliness and isolation in older adults
• Applied statistical modeling to large datasets

Traceable work:

• Frontiers in Psychology (2026)
• Alzheimer and Dementia (2025)

2009 – 2018 · Computer Vision in Industry

Applied Research Engineer — University of Chile

Smart mining systems

What was built: Computer vision and machine learning pipelines for geological analysis in mining environments.

Key contributions:

• Designed machine learning pipelines for automated lithology classification
• Developed video-based pattern recognition methods for rock type estimation
• Applied computer vision techniques under real-world industrial constraints

Traceable work:

• Paper: Rock Lithological Classification (2015)
• Poster: Lithological Classification

• Poster: Rock Type Estimation

Retail analytics

What was built: Vision-based systems for supermarket monitoring.

Key contributions:

• Developed stock breakage detection systems
• Applied feature extraction and optimization

Traceable work:

• Project: INNOVA 13IDL2-23589

IoT and geotechnical monitoring

What was built: Real-time telemetry systems for mining environments.

Key contributions:

• Designed Bluetooth-based monitoring systems
• Built hardware–software pipelines for real-time data

Traceable work:

• Collaboration: CSIRO Chile
• MPES 2019

Education

• PhD in Electrical Engineering — University of Chile (2020)
  Focus: color–texture classification using multiscale features and SVM ensembles

• Diploma in Electrophysiology — University of Chile (2019)
  Focus: bioelectric signal processing

• Electrical Engineering — University of Chile (2010)
  Focus: iris detection using Particle Swarm Optimization

Cross-cutting and Independent Projects

These projects reflect complementary skills in systems thinking, creativity, and execution.

Independent film production

• Produced a full-length independent film
• Managed team, logistics, and production workflow
• Delivered crowdfunded project under constraints

Urban documentation and cultural heritage

• Fieldwork + photography + urban analysis
• Study of historic street plaques in Santiago

Virtual environments

• Built a virtual university campus (Gather Town)
• Enabled interaction during pandemic conditions

Independent development

• Developed DeloreanCamara (image alignment app)
• Real-time comparison of historical vs current images

Media and event photography

• Accredited photographer (radio media)
• Produced real-time visual content
• Strong alignment with visual analysis (CV mindset)

Teaching and Mentorship

What I do:

• Teach Machine Learning, Computer Vision, and Engineering
• Supervise theses in AI, CV, and biomedical systems

Focus:

• Full development cycle: problem → implementation → evaluation → communication

Academic and Community Engagement

• Reviewer — ISICS 2024, Intelligent Computing Systems Symposium
• Speaker in AI and interdisciplinary research events
• Participation in international conferences

Technical Stack

• Languages: Python, R, MATLAB
• Frameworks: PyTorch, TensorFlow, scikit-learn, OpenCV, FIJI
• Core areas:
  • Machine Learning & Deep Learning
  • Computer Vision
  • Signal Processing (EEG)
• Infrastructure: Docker, Google Cloud, Huawei Cloud

Final note

This logbook documents what I have worked on over time.

Each section includes references to projects, systems, or publications when available.