Luis Hernandez Sanchez

Luis Hernandez Sanchez

Computer Science · Columbia University

LH10171@gmail.com / LAH2240@columbia.edu · (631) 525-1803

GitHub: luis10171 · LinkedIn

Software engineering & applied ML

Building software.
Expanding access.

I’m Luis, a computer science student at Columbia. I build practical tools for learning, healthcare, and everyday challenges — with a focus on making technology more accessible for all, especially underserved areas.

Explore my work My experience

Columbia University B.S. Computer Science, 2029

PORTFOLIO & EXPERIENCEA selection of what I do

01 / Selected work

Ideas put into practice.

A few projects, and the thinking
that made them happen.

EDUCATION / FULL-STACK
Explore AtlasOne
A clearer path through college.

OCT 2025 — FEB 2026

AtlasOne

An AI-powered academic planning platform that helps students map four years of coursework and track graduation requirements.

  • Python
  • Django
  • Gemini API

$1,000 competition award

Inside the project

The challenge

Give students additional academic guidance, particularly in schools with limited counseling staff.

What I built

A full-stack Django platform with relational data models, authentication, form validation, personalized dashboards, and a PDF transcript parser for automated progress tracking.

I integrated Google’s Gemini API to tailor guidance to student profiles, coursework, and academic progress.

The outcome

Won $1,000 in a Shark Tank-style competition for technical implementation and educational impact.

GitHub ↗
HEALTHCARE / COMPUTER VISION
Explore STEP
Recognition without the cloud.

OCT 2022 — MAR 2023

Healthcare facial recognition

An offline prototype for patient identification and record retrieval, designed for a single device in resource-constrained settings.

  • Python
  • OpenCV
  • face_recognition

1st place · New York STEP Science Fair

Inside the project

The challenge

Enhance patient identification and record retrieval without depending on internet access or cloud services.

What I built

A Python prototype using OpenCV and face_recognition, optimized for offline operation on a single, low-resource device.

The outcome

Achieved approximately 85% identification accuracy with a simulated database of 25 patients. This was a research prototype evaluated on simulated data.

Won 1st place statewide across all divisions at the New York STEP Science Fair.

GitHub ↗
ENERGY / MACHINE LEARNINGPractical insight. Smaller footprint.

AUG 2024 — MAY 2025 · BNL RESEARCH

Offline energy forecasting

A machine learning model that uses five years of household electricity data to predict energy consumption with fully offline operation.

  • Python
  • scikit-learn
  • Machine learning

Designed for low-resource environments

Inside the project

The challenge

Make energy-consumption prediction accessible without relying on cloud services, internet connectivity, or high-end hardware.

What I built

Developed a model in Python and scikit-learn using five years of household electricity data during my research at Brookhaven National Laboratory.

The outcome

Engineered the system for fully offline operation and low-resource, cross-platform environments, reducing hardware and connectivity dependencies.

02 / Experience

Some of my hands-on experience...

Download résumé ↓
JAN — JUN 2026Remote

Curious Cardinals

Engineering Mentor & Programming Tutor

  • Mentored three middle and high school students through individualized, project-based instruction in computer science and software engineering.
  • Designed and taught a C# game development curriculum covering multiplayer architecture, object-oriented programming, debugging, and software development practices.
AUG 2024 — MAY 2025Brookhaven, NY

Brookhaven National Laboratory

Student Researcher

  • Developed a Python and scikit-learn model using five years of household electricity data to predict energy consumption with fully offline operation.
  • Engineered the system for low-resource, cross-platform environments, minimizing hardware, internet, and cloud dependencies to improve accessibility.

03 / Beyond the code

People first.
Always learning.

My work connects software engineering with education, research, and community. I’m drawn to practical problems where thoughtful technology can make a difference. Growing up, I didn't have access to the same resources as many of my peers, and I want to help others avoid that same struggle.

That same interest carries into mentoring students and supporting Spanish-speaking families through my church community.

Education

Columbia University

B.S. in Computer Science · Sophomore

New York, NY · Sep 2025 — May 2029

C.P. Davis Scholar
Columbia University Scholars Program

Relevant coursework
Data Structures in Java, Advanced Programming in C, Software Quality and Design

My toolkit

Languages
Python, Java, C, C++, C#, HTML/CSS
Frameworks & libraries
Django, scikit-learn, Pandas, OpenCV, face_recognition
Tools & platforms
Git, GitHub, Gemini API, OpenAI API, Linux/UNIX

Community

Youth Leader

Iglesia Apostolica De La Fe En Cristo Jesus

Jan 2020 — Present

Attend weekly services and uphold the community’s mission to serve and support families in need. Helped implement and maintain live-streaming services to keep the community connected during the COVID-19 pandemic.

04 / Let’s connect

Good things start
with a conversation.

Have a project, an opportunity, or an interesting problem?
I’d love to hear about it!

Email is the best way to reach me.