Adelphi University

Built to Learn

Driven by curiosity to understand how complex worlds connect, I'm crossing AI and mathematics to build a deep, foundational understanding of intelligent systems and the principles that power them.

4.0
Cumulative GPAAdelphi University
Artificial IntelligenceMajor
MathematicsMinor
Santiago Rodriguez, computer science and AI student at Adelphi University
Santiago Rodriguez, AI student at Adelphi University
Academic Focus & Philosophy

Why I Study What I Study

01 // Major — Bachelor's

Artificial
Intelligence

“AI is the most transformative technology of our lifetime.”

Core Conviction

Initially, I was a CS major, but when I saw Adelphi had a standalone AI major, I immediately switched.

The Rationale

Why? Because I believe wholeheartedly in the potential this technology has to do good. Because I've read extensively about its possibilities and built projects that put those ideas into practice. Because I understand the math and technology behind it and believe it can be improved even more.

Studying AI so deeply gives me a new perspective on things — by seeing so many different facets of this technology, I understand it, how it works, and how it can be used. I'm constantly motivated to learn more, as I want to be one of the leaders shaping the field and contributing to it.

02 // Minor

The Language of Mathematics

The Spark & Origin

“Math has always been my favorite subject.”

Some of my earliest memories are my dad drawing out the Pythagorean theorem on a piece of paper, or practicing multiplication tables with my whole family. It was never something I dreaded — it was something I genuinely looked forward to.

Higher-Order Depth

That same love carried into higher math as I grew up. Calculus, Statistics, Linear Algebra, ODEs — each course was more demanding, but the pull was always the same. Learning higher mathematics is like deepening your own understanding of the universe, and that is fulfilling in itself.

I was already diving deep into math courses, and after a professor noticed how much I enjoyed it, she suggested making it official with a minor. Looking at how naturally it paired with my AI major, it was an easy decision.

Academic Foundation

Strongest Coursework

The courses that shaped my foundation — spanning the theoretical mathematics that powers AI and the computer science that brings it to life.

Mathematics

Calculus I

Differential Calculus & Limits

Calculus II

Integral Calculus & Series

Multivariable Mathematics

Vectors, Gradients & 3D Calculus

Ordinary Differential Equations

Dynamical Systems & SIR Modeling

Statistics & Data Analytics

Probability & Statistical Inference

Discrete Structures

Mathematical Logic & Combinatorics

Computer Science & AI

Data Structures

Algorithms, Complexity & Memory

Intro to Machine Learning

Supervised, Unsupervised & Losses

Artificial Intelligence

Heuristics, Search & Neural Systems
Core Capabilities

Technical Skills

Organized by capability — from classical statistical learning and rigorous validation methodology to empirical data science and modern deep architectures.

Machine Learning

Predictive Modeling & Validation

Supervised Learning

Classification & regression algorithms for structured prediction.

Linear & Logistic Regression
K-Nearest Neighbors
Decision Trees & Random Forests
Binary Classification

Unsupervised Learning

Discovering latent geometry and density structures.

K-Means Clustering
PCA / Dimensionality Reduction

ML Practice

Rigorous evaluation, generalization, and data hygiene.

Feature Engineering
Train/Validation/Test Splits
Cross-Validation
Model Evaluation
Data Leakage Detection & Prevention
Hyperparameter Tuning

Data Science

Exploratory Analysis & Inference

Extracting actionable signal from complex distributions through exploratory analysis, feature curation, and statistical rigor.

Exploratory Data Analysis
Statistical Analysis
Data Cleaning & Preprocessing
Feature Selection
Visualization

Deep Learning

Neural Architectures & Optimization

Architecting deep representations through gradient backpropagation, convolution kernels, and multi-head attention mechanisms.

Neural Networks
Backpropagation
Gradient Descent
CNNs
Transformers
Cool Stuff I've Done Beyond the Traditional Path

Academic Highlights

Hands App Prototype

Adelphi Scholarship & Creative Works Conf.

Presented with one of my best friends. We showcased 'Hands,' an app meant to solve the paradox of students not being able to get a job because they lack experience, and not being able to get experience because they don't have a job. Though just a work in progress, it received a great reception and sparked amazing interactions.

Disease Spread Modeling

Adelphi Scholarship & Creative Works Conf.

For my second presentation, I worked with classmates to model the spread of disease using Ordinary Differential Equations and an SIR model. This project gave me first-hand experience in taking theoretical math from academia and applying it directly to a real-world situation.

Teaching Gradient Descent

Selected by Professor

My professor, knowing how deeply interested I am in Gradient Descent, gave me the opportunity to explain it to my classmates. I explained everything from the formula, what it meant, its applications, and why it mattered in the greater scheme of things. It was a great experience and everyone understood it perfectly.

Never Done
Learning

I'm focused on developing a deep understanding of the logic behind what I'm building. Every class, every late night, every concept I wrestle with is building toward something bigger. The way I see it, the sharper I get now, the more I'll be able to build, contribute, and give back when it counts.