Applied Mathematician & Machine Learning Engineer
Bridging pure mathematics, computational science, and deep learning.

🌐 Personal Website & Blog • 🚀 Projects Portfolio •

• 🎓 Senior Mathematics Student at Universidad Yachay Tech (Ecuador).
• 🤖 Machine Learning Engineer trained via intensive bootcamps at Anyone AI.
• ☁️ Certified Cloud Application Developer candidate at AWS Cloud Institute.
• 🔬 Research focus: Scientific Machine Learning (SciML), Physics-Informed Neural Networks (PINNs), and Computer Vision for biomedical imaging.

• Physics-Informed Neural Networks (PINNs): Solving nonlinear partial differential equations (1D Viscous Burgers, 2D Heat Diffusion, Navier-Stokes) by embedding physical conservation laws directly into neural network loss functions.
• Biomedical Computer Vision: Isotropic 3D image reconstruction via generative models (CycleGAN) to resolve axial resolution anisotropy in confocal microscopy.
• Microplastics Detection: Environmental ML classification and spectral feature extraction using Fourier-Transform Infrared (FTIR) spectroscopy.
• Cloud & MLOps Architecture: Deploying containerized inference engines with FastAPI, Docker, and AWS infrastructure (EC2, S3).

A selection of in-depth essays combining mathematical precision, historical context, and scientific storytelling:

• 🌌 Cosmos: Alexander von Humboldt y la Red de la Vida — La unificación de las fuerzas naturales en los Andes y el nacimiento de la visión ecológica moderna.
• 🔭 Telescopio Espacial Nancy Grace Roman — Exploración de la energía oscura y exoplanetas desde el punto de Lagrange L2 con óptica infrarroja de gran campo.
• 📐 Puntos de Lagrange: Mecánica Celeste y Equilibrio Orbital — Dinámica del problema restringido de tres cuerpos, potenciales efectivos y estabilidad orbital.
• 🔬 PINNs: Redes Neuronales Informadas por la Física — Incorporación de operadores diferenciales y leyes de conservación fundamentales en grafos computacionales.
• 🌍 Pedro Vicente Maldonado: Geodesia y Vanguardia Matemática — La Misión Geodésica Francesa y la determinación de la forma de la Tierra en el siglo XVIII.

(Read all 20+ essays at jeffersonconza.github.io/blog)

• Core & Scientific Computing: Python, Julia, R, SQL, C++, Bash, LaTeX
• Deep Learning & SciML: PyTorch, DeepXDE, TensorFlow, scikit-learn, XGBoost, Hugging Face, Torchvision
• Math & Data Engineering: NumPy, pandas, SciPy, SymPy, Matplotlib, Seaborn, Manim
• MLOps & Cloud: Docker, FastAPI, Streamlit, AWS (EC2, S3, SageMaker), Git, Linux / Unix

import torch

# Embedding physical differential operators into the loss function
def physics_loss(u_net, x, t, nu=0.01 / torch.pi):
    x.requires_grad_(True)
    t.requires_grad_(True)
    
    u = u_net(torch.cat([x, t], dim=1))
    
    # Automatic differentiation (autograd) computes exact PDE derivatives
    u_t = torch.autograd.grad(u, t, grad_outputs=torch.ones_like(u), create_graph=True)[0]
    u_x = torch.autograd.grad(u, x, grad_outputs=torch.ones_like(u), create_graph=True)[0]
    u_xx = torch.autograd.grad(u_x, x, grad_outputs=torch.ones_like(u_x), create_graph=True)[0]
    
    # 1D Viscous Burgers' equation residual: f = u_t + u * u_x - nu * u_xx
    residual = u_t + u * u_x - nu * u_xx
    return torch.mean(residual ** 2)

"PyTorch turns mathematical intuition into tensors, autograd into differential equations, and neural networks into computational physics engines."