Applied AI & Engineering
AI Engineer & Data Scientist focused on designing and deploying real-world AI systems across healthcare, industry, and applied research.
My work centers on translating machine learning, computer vision, and signal processing methods into deployable systems, operating under real-world constraints and integrated into practical workflows.
Selected Projects & Impact
Clinical AI & Biomedical Imaging
- Cell Tracking Challenge (2nd place): Developed deep learning models for 3D nuclei segmentation
- RaViTT: Vision Transformer architecture for biomedical image classification
- Glaucoma Diagnosis Pipeline: Integrated physiological signals with predictive modeling for clinical use
- VolumePeeler: FIJI plugin for 3D visualization and quantification of biological data
Industrial AI & IoT
- IoT Behavioral Analysis Platform: Real-time integration of hardware, computer vision, and control systems
- Industrial Automation (Mining/Retail): Computer vision systems for process optimization and monitoring
Signal Processing & Cognitive Health
- Postoperative Risk Prediction: EEG-based predictive modeling for clinical outcomes
- Auditory Biomarkers: Developed signal-processing methods for early detection of cognitive decline
→ Resulted in a Top 10 most cited article (2024) —
Professional Experience
Data Scientist — Faculty of Medicine, Universidad de Chile Neurosystems, SCIANLab & AudioBrain (2019–2025)
- Led development and deployment of AI systems integrated into clinical workflows
- Designed ML and computer vision solutions for diagnostics and biomedical applications
Applied Research Engineer — University of Chile (2009–2018)
- Developed computer vision and automation systems in industrial environments
- Contributed to industry-driven R&D and technology transfer
Core Capabilities
- End-to-end AI systems (data → modeling → deployment)
- Computer vision (segmentation, detection, real-world systems)
- Signal processing (EEG, physiological data, time-series)
- Deep learning (CNNs, Transformers, biomedical imaging)
Open-Source & Tools
- Modular pipelines for biomedical imaging and microscopy
- Industrial computer vision systems
- Focus on reproducibility and interdisciplinary usability
Collaboration & Impact
- Multidisciplinary collaboration (engineering, clinical, research teams)
- Translation of technical results into actionable decisions
- Delivery of deployable systems under real-world constraints
- Bridging academic research and industry applications
Context
For a detailed and traceable view of projects and technical work, see the Logbook.
For teaching and academic activities, see Teaching.