Daniel Coblentz

ML Research Assistant @ Hood College

Pursuing a B.S. in Computer Science and
a minor in Mathematics

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/ About Me

Hello! Im Daniel Im a current rising senior at Hood College with a strong background in Computer science and mathematics. My primary interests are in generative AI and large scale databases.

I enjoy working with complex datasets, building efficient systems, and exploring how AI can be used to create new solutions. I like finding practical ways to apply what I learn, whether it's optimizing database performance or experimenting with AI models I’m always looking for opportunities to work on interesting projects and collaborate with others who share similar interests.

Here are some tech tools I've been working with recently:

MongoDB Firebase JavaScript
Python Scikit-learn Pandas

Outside of my academic life, I enjoy traveling to new places, going to the gym, and teaching others.

Picture of Daniel Coblentz

/ Experience

Berkeley Lab

Hood College

Hood College

CodePath

International Help

/ Projects

Spell Checker

A text processing tool that uses a trie data structure and binary search to detect and correct spelling errors. Designed to efficiently scan input text, retrieve valid words, and suggest accurate replacements using the Levenshtein Distance algorithm.

Java, Git

Venture

A full-stack event management system with user authentication, permissions, and dynamic content updates. Built to streamline the listing, creation, and deletion of events within a relational database.

HTML, CSS, JS, Python, SQL, GCP, Git

Huffman Encoder

A file compression tool that implements the Huffman encoding algorithm to generate efficient binary codes based on character frequency. It compares the original and compressed file sizes to illustrate the space savings. Built to demonstrate core concepts in data compression, tree structures, and priority queues.

Java, Git, file I/O

CCSE Programming Competition

A set of algorithmic problems solved during the CCSE Eastern Conference Programming Competition. Showcases efficient solutions using recursion, sorting, graphs, and dynamic programming under time constraints.

Python

ASL Classifier

ASLConnect combines convolutional neural networks with real-time analysis to recognize and interpret ASL digits (0-9) with high accuracy.

Python, Tensorflow, NumPy, Scikit-Learn, OpenCV, Matplotlib, Git

Hand Gesture Recognition with CNN

Implemented a convolutional neural network in Python using Keras to classify and identify hand gestures from a custom dataset. Applied data preprocessing, model training, and evaluation processes, achieving a 99.07% accuracy rate and maintaining an average loss of 0.0141.

Python, TensorFlow, Keras, Google Colab