Neural Data Scientist at Alljoined Inc. • Brain–Computer Interface Researcher • San Francisco, CA
I'm a computational neuroscientist specializing in brain–computer interfaces, with 10+ years of experience in EEG decoding, multimodal data pipelines, and machine learning for neural signal processing. Currently developing next-generation BCI technologies for visual reconstruction and consciousness decoding.
Multi-subject EEG neural image reconstruction model. Submitted to CVPR 2026.
- Reduces new-user calibration from hours to ~15 minutes
- Uses shared neural patterns across individuals with lightweight latent alignment
- Cross-modal alignment between EEG embeddings and visual latent spaces (CLIP)
- GitHub • Blog Post
1.6 million visual stimulus trials for affordable BCI research. Published on OpenArxiv 2025.
- Dataset from 20 participants on consumer-grade hardware
- Demonstrates scaling laws apply to low-quality brain data
- Consumer EEG requires 20x+ the data volume of lab-grade systems
- GitHub • Blog Post
Cross-task EEG/ERP decoding framework with temporal generalization.
- Moving-window cross-validation across multiple ML models
- 134% speedup in analysis; novel signatures identified (VAP, inattentional blindness correlates)
- GitHub
Open-source EEGLAB plugin for high-speed EEG preprocessing and visualization.
- Parallelized MATLAB processing with streamlined GUI
- ~500% improvement in processing speed
- GitHub
Neural Data Scientist @ Alljoined (2025–Present)
- Designing end-to-end ML pipelines for multimodal datasets (EEG, behavioral data, eye-tracking)
- Developing preprocessing workflows improving model accuracy by 12%
- Scaling experiments across GPU clusters using SLURM
Visiting Scholar @ Chapman University (2025–Present)
- Computational models decoding conscious perception using EEG and ML
- Managing multi-lab EEG projects (>5 TB data, 200+ participants)
Programming & Tools: Python, PyTorch, TensorFlow, Scikit-Learn, MATLAB, SQL, JavaScript, R, C/C++
Neural Data & BCI: EEG/MEG Decoding, Lab Streaming Layer (LSL), Feature Extraction, Neural Signal Processing, Time–Frequency Analysis
Machine Learning: CNN, Transformer, RNN, Tree-Based Models, Time-Series Forecasting, Self-Supervised Learning
Infrastructure: Docker, AWS, SLURM, Git, Experiment Versioning
- "ENIGMA: A Unified Lightweight EEG-to-Image Model for Multi-Subject Visual Decoding" - CVPR 2026 Submission
- "Alljoined-1.6M: A Million-Trial EEG-Image Dataset for Evaluating Affordable BCIs" - OpenArxiv 2025
- "Two, Not One: Electrophysiological Correlates of Consciousness in a No-Report Paradigm" - ASSC 2025 Poster
- "Visual Awareness Positivity: A Novel Neural Correlate of Consciousness" - VSS 2025 Talk
- Ph.D. Cognitive Neuroscience - Southern Illinois University (2023)
- M.A. Cognitive Neuroscience - Southern Illinois University (2018)
- B.A. Psychology - Pontíficia Universidade Católica de São Paulo (2012)
Ask me about: EEG/BCI, Neural Decoding, Deep Learning for Neuroscience, Multimodal Data Pipelines
- Pronouns: He/Him/His
- Fun fact: Recent dad who loves spending time with my kid! Also a hardcore gamer who enjoys disconnecting to touch grass in the wild.
Check out my interactive portfolio for detailed project showcases, experience timeline, and publications!
Feel free to explore my repositories and reach out for collaborations or questions!