Machine Learning & Artificial Intelligence
Regression
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Classification
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Clustering
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Dimensionality Reduction
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Deep Learning
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Neural Networks
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Reinforcement Learning
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Genetic Algorithms
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Regression · Classification · Clustering · Dimensionality Reduction · Deep Learning · Neural Networks · Reinforcement Learning · Genetic Algorithms ·
AREAS OF
EXPERTISE
01
03
SUPERVISED ML
Design and optimization of supervised Machine Learning to solve complex problems efficiently, with expertise in Regression Models (OLS Regression, LASSO Regression, Regression Trees, Support Vector Regression), Ensemble Models (Random Forest, Gradient Boosting…) and Classification Models (Classification Trees, Support Vector Classification, K-nearest Neighbor).
DEEP LEARNING
NEURAL NETWORKS & DEEP LEARNING
I work with Machine Learning and AI as practical tools for solving real-world, high-stakes problems. My experience spans the full modeling pipeline — from problem formulation and model selection to validation, optimization, and deployment — with a particular focus on robustness, interpretability, and performance in noisy, data-constrained environments.
While my core expertise lies in classical and statistical machine learning, I maintain working knowledge across modern deep learning and AI methodologies, applying each technique where it adds genuine value rather than novelty.
Practical experience with core Deep Learning Architectures, applied selectively where non-linear representation learning is justified. These include ANNs, MLPs, CNNs and recurrent models (RNN, GRU, LSTM), alongside foundational knowledge of Transformers, LLMs and Retrieval-augmented systems.
MACHINE LEARNING (UNSUPERVISED)
02
UNSUPERVISED ML
Application of unsupervised Machine Learning algorithms for structure discovery, Dimensionality Reduction (PCA, Factor Analysis), Clustering (K-means, Hierarchical Clustering, DBSCAN…), and Regime Identification (Gaussian Mixture Models) in complex datasets.
REINFORCEMENT LEARNING
04
REINFORCEMENT LEARNING
Understanding of Reinforcement Learning frameworks for sequential decision-making under uncertainty, including exposure to both model-based and model-free approaches.
MACHINE LEARNING (SUPERVISED)
05
OPTIMIZATION & HEURISTICS
AI OPTIMIZATION & (META)HEURISTICS
Experienced working with different heuristic and metaheuristic optimization methods for high-dimensional, non-convex problems, including Single Solution Based Techniques (Local Search, Simulated Annealing, Tabu Search…), Evolutionary Algorithms (Genetic Algorithms), and Swarm Based Algorithms (Particle Swarm Optimization, Artificial Bee Colony…).
MY ML & AI PROJECTS
6
PROJECTS
+2k
HOURS OF WORK
+20k
LINES OF CODE
SEE MORE PROJECTS
MATH, PHYSICS & ENGINEERING PROJECTS
COMPUTER SCIENCE PROJECTS
QUANT FINANCE PROJECTS
Contact
I'm always open to discussing quantitative research opportunities, collaborative projects, or interesting technical problems. Feel free to reach out using the form below.
Álvaro Sánchez
Quantitative Researcher
Email:
alvarosf07@gmail.com