Niklas Grüner
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Hey, My name is Niklas Grüner

Hi, I’m a software engineer from Vienna. I like to tinker, build, and learn by doing. Always learning something new. Check out my projects!

Projects

About Me Here you will find more information about me, what I do, and my current skills mostly in terms of programming and technology

Get to know me!

Welcome to my portfolio! I'm a 22-year-old Computer Science student at TU Wien with a deep interest in software development and problem-solving. Over the years, I've worked on various projects, ranging from university coursework to personal and professional endeavors. Through my studies and hands-on work experience, I’ve had the chance to develop my skills, tackle real-world challenges, and continuously expand my knowledge. This website showcases a selection of my projects—take a look around and see what I’ve been working on!

I'm open to collaboration and new challenges. Thank you for visiting!. Feel free to contact me here.

Contact

My Skills

C
Java
Python
Scikit-Learn
PyTorch
Machine Learning
Computer Vision
JavaScript
TypeScript
HTML
CSS
SQL
Angular
ReactJS
Spring Boot
OpenGL

Projects Here, you'll discover a selection of personal projects I've created.

Software Screenshot

Multimodal Visual Life Detection

Multimodal visual life detection. During the process of writing my bachelor's thesis, I developed a system for Search and Rescue (SAR) situations utilizing a compact tri-modal camera system that simultaneously captures RGB, thermal, and depth imagery. Two life detection approaches were implemented: one for tracking large-scale body movements such as walking, crawling, and waving via object detection, and another for monitoring respiratory motion in the chest region through optical flow analysis. The thesis evaluates these approaches using recorded scenarios, highlighting the strengths and limitations of each method and modality, and offering insights into their potential applications for SAR operations.

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Software Screenshot

TutorMatch

Tutormatch. In collaboration with some of my university colleagues, we developed an online platform designed to connect students for collaborative learning. This platform, Tutormatch, facilitates students meeting up for study sessions, allowing them to teach and learn from each other effectively.

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Software Screenshot

Volume Visualization

This application is designed to process medical data, including MRI and CT scans, utilizing advanced direct volume rendering techniques. It leverages Three.js and the GLSL shader language for detailed visualization, enabling a comprehensive view of different parts and aspects of the scans. Additionally, D3.js is employed to display related data, enhancing the overall analysis and understanding of the medical information.

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Software Screenshot

Shazam - Audio Retrieval

This project is an implementation of the audio identification algorithm from the Original Shazam Paper (Wang, 2003). It uses audio fingerprinting and hash-based matching to enable efficient and accurate audio retrieval. By analyzing unique spectral features of an audio signal, the system generates compact hashes that allow for rapid identification of songs from a large database. Users can input a recorded audio sample, and the system will match it against the stored fingerprints to determine the song's name, even in noisy or distorted environments.

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Distributed Message Broker Service

An implementation of a distributed message broker service designed for reliable and scalable communication. Brokers register with a custom DNS for dynamic cluster management, routing messages through exchanges and queues. Automatic leader election using Raft, Ring, or Bully algorithms ensures high availability, seamlessly handling failures and maintaining uninterrupted operation.

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Rocket League - Trick Shot Classification

This project was part of a Kaggle competition at my university, where I developed a machine learning model to classify trick shots in Rocket League based on in-game metrics and player inputs. Using a dataset of 297 trick shots, I analyzed time-series data and summary statistics to train a classifier that recognizes different maneuver types. The project involved data preprocessing, feature engineering, model training, and evaluation. Check out the full implementation and results on GitHub!

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Software Screenshot

Exploratory Data Analysis - GDP vs. PISA

This project analyzes the relationship between GDP and PISA scores through exploratory data analysis. It examines trends in economic growth and education performance, identifying patterns across different countries. The study includes data cleaning, merging, and visualization to uncover insights into educational disparities and economic influence on learning outcomes. Regression modeling is used to analyze trends over time, while clustering techniques help group countries based on similar characteristics. Comparative analysis highlights top and bottom-performing countries in education relative to their GDP. The findings provide a deeper understanding of how economic factors impact student performance globally.

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