Unique intersection: Clinical medicine, predictive analytics, and fundamental physics research. Leading the development of AI-driven healthcare solutions while advancing unified theories of the cosmos.

Data Science Team Leader - Centro de Operações de Emergências em Saúde de Santa Catarina

• 🎯 Mission: Unified universities, researchers, and specialists using predictive analytics and AI
• 🏆 Achievement: Santa Catarina state achieved best practices and strategic lockdowns in Brazil
• 📊 Created: Health situational awareness room connecting health data for predictive analysis and planning
• 🤝 Leadership: Coordinated interdisciplinary teams across academic and government institutions
• 📈 Impact: Evidence-based policy decisions saving thousands of lives

AI Agent Development for Proactive Patient Care

# Example: Proactive Care AI Pipeline
from medsuite.ai import ProactiveCareAgent
from medsuite.prediction import HealthRiskAnalysis

# Real-time patient monitoring with predictive intervention
agent = ProactiveCareAgent()
risk_model = HealthRiskAnalysis()

# Continuous health state assessment
patient_data = agent.collect_multimodal_data()
risk_score = risk_model.predict_deterioration(patient_data)

if risk_score > threshold:
    agent.trigger_proactive_intervention()

• 🤖 Developing: AI agents for proactive patient care
• 📱 Technology: Real-time health monitoring and predictive interventions
• 🔬 Innovation: Bridging emergency medicine expertise with cutting-edge AI

• 6D Camargo Metric: R₁ = 10⁻¹⁸ m, R₂ = 1.1×10⁻¹⁶ m, R₃ = 2×10⁻¹⁶ m
• α (R₂/R₁)²: 1.21×10⁴ | β (R₃/R₁)²: 4.00×10⁴
• Torsion coupling κ: 1.0 | Cutoff Λ_τ: 1.8 GeV
• H₀: 67.0 km/s/Mpc | Ω_m: 0.31

# Data Science Approach to Cosmological Analysis
import numpy as np
import pandas as pd
from goe.metric import CamargoMetric
from goe.analysis import BayesianInference

# Model comparison using AIC (Akaike Information Criterion)
models = ['LCDM', 'GoE']
aic_results = BayesianInference.compare_models(jwst_data, models)
delta_aic = aic_results['GoE'] - aic_results['LCDM']

# Result: ΔAIC = 33.24 (GoE strongly favored)
pbh_detections_forecast = 8_to_25  # JWST Cycle 3-4

# Time Series Analysis for SGWB Detection
from goe.signals import SGWBSpectrum
from scipy.signal import welch

sgwb = SGWBSpectrum(camargo_metric)
frequency, strain_density = sgwb.compute_spectrum()

# Peak detection algorithm
peak_freq = frequency[np.argmax(strain_density)]  # 100 μHz
lisa_snr = sgwb.calculate_snr('LISA')  # SNR = 12.4

print(f"LISA Detection: {peak_freq*1e6:.0f} μHz, SNR = {lisa_snr:.1f}")

# Linear Algebra Stability Analysis
from goe.quantum import StabilityAnalysis
import scipy.linalg as la

stability = StabilityAnalysis(metric)
kinetic_matrix = stability.compute_kinetic_matrix()
eigenvals = la.eigvals(kinetic_matrix)

# Statistical test for stability
ghost_count = np.sum(eigenvals < 0)  # Result: 0 ghost modes
stability_score = 1.0 if ghost_count == 0 else 0.0

print(f"Stability Status: {'STABLE' if stability_score == 1.0 else 'UNSTABLE'}")

My unique background allows me to apply data science rigor to both healthcare analytics and theoretical physics:

# Example: Applying healthcare analytics to cosmology
def bayesian_model_comparison(observational_data, theoretical_models):
    """
    Same statistical framework used in COES-SC for COVID-19 predictions
    now applied to cosmological model selection
    """
    aic_scores = {}
    for model in theoretical_models:
        likelihood = model.compute_likelihood(observational_data)
        n_params = model.parameter_count()
        aic_scores[model.name] = -2 * likelihood + 2 * n_params
    
    return aic_scores  # Lower AIC = better model

• 🎯 COES-SC Leadership: Best COVID-19 response in Brazil through data-driven strategy
• 📊 Situational Awareness: Created predictive health monitoring systems
• 🤖 AI Innovation: Developing proactive care agents at MedSuite
• 🏥 Emergency Medicine: 10+ years frontline experience

• ✅ 4 research phases completed with quantitative validation
• ✅ 0 ghost modes detected (quantum stability confirmed)
• ✅ Multi-messenger predictions across 3 observational channels
• ✅ Open science approach (100% reproducible, MIT License)

"Emergency medicine teaches pattern recognition under pressure. Data science provides the tools to quantify uncertainty. Theoretical physics demands both skills when deciphering fundamental reality."

# Predictive health analytics (MedSuite approach)
from medsuite.models import RiskStratification
from medsuite.monitoring import RealTimePatientData

# Multi-modal data fusion
patient_stream = RealTimePatientData()
risk_model = RiskStratification(
    vitals=True, 
    labs=True, 
    imaging=True,
    clinical_notes=True
)

# Continuous prediction loop
for patient_data in patient_stream:
    risk_score = risk_model.predict(patient_data)
    if risk_score > 0.8:  # High risk threshold
        trigger_proactive_intervention(patient_data.id)

# Same statistical rigor applied to cosmology
from goe.analysis import CosmologicalInference
from goe.data import JWSTObservations, LISAProjections

# Bayesian model comparison
jwst_data = JWSTObservations.load_cycle_2()
models = ['LCDM', 'GoE_minimal', 'GoE_full']

inference = CosmologicalInference()
evidence = inference.compute_bayesian_evidence(jwst_data, models)
model_odds = inference.bayes_factors(evidence)

print(f"GoE vs ΛCDM Bayes Factor: {model_odds['GoE_full']['LCDM']:.1f}")

• 🤖 Proactive Care Agents: Real-time patient monitoring with predictive interventions
• 📊 Health Analytics Platform: Integrating multi-modal health data streams
• 🔬 Clinical AI Research: Bridging emergency medicine with machine learning

• 📈 Full Tensor Analysis: Extending beyond scalar perturbations
• 🔍 6×6 Kinetic Matrix: Complete stability verification
• 📊 Data Pipeline: Automated analysis for observational validation

• 🔄 Cross-Domain AI: Applying healthcare ML to cosmological data analysis
• 📚 Educational Content: Teaching data science for physics research
• 🌐 Open Science: Expanding reproducible research methodologies

# Core technical competencies
languages = ['Python', 'R', 'SQL', 'Mathematica']
ml_frameworks = ['TensorFlow', 'PyTorch', 'Scikit-learn', 'XGBoost']
data_tools = ['Pandas', 'NumPy', 'SciPy', 'Matplotlib', 'Plotly']
physics_tools = ['SymPy', 'Astropy', 'Cosmological packages']
health_tech = ['FHIR', 'HL7', 'Medical imaging', 'Real-time monitoring']

• 📊 Version Control: Git/GitHub with full reproducibility
• 📓 Documentation: Jupyter notebooks with comprehensive analysis
• 🚀 Deployment: Docker containerization for consistent environments
• 📈 Monitoring: Real-time dashboards for both health and physics data

• 🔬 Research: guilherme@medsuite.com.br
• 📊 ORCID: 0009-0004-8913-9419
• 💻 GitHub: @Infolake
• 🏥 MedSuite: medsuite.com.br

• Healthcare AI: Predictive analytics and proactive care systems
• Theoretical Physics: Multi-messenger cosmology and unified theories
• Data Science: Cross-domain methodology development
• Open Science: Reproducible research and education
• Emergency Medicine: Clinical decision support systems

The intersection of three critical domains:

• 🏥 Clinical Expertise: 10+ years emergency medicine + COES-SC leadership
• 📊 Data Science Mastery: Predictive analytics + AI development + statistical modeling
• 🌌 Theoretical Physics: Novel cosmological frameworks + quantitative validation

"From saving lives in emergency rooms to predicting cosmic evolution - data science provides the bridge between clinical intuition and fundamental understanding."

"Where healthcare data science meets theoretical cosmology - developing unified approaches to understanding complex systems."

Last updated: July 10, 2025 | Current Status: Phase 3.5 Complete ✅ | MedSuite AI Development 🚀

A combinação única de medicina de emergência + ciência de dados + física teórica realmente se destaca! Quer algum ajuste específico?

English:
"Time is not a dimension we move through, but a multidimensional manifold we exist within. Understanding its true nature is the key to unifying all of physics."

Português:
"O tempo não é uma dimensão pela qual nos movemos, mas uma variedade multidimensional dentro da qual existimos. Compreender sua verdadeira natureza é a chave para unificar toda a física."