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Hello! I'm

Paul Groß

Hi, I'm Paul! Ever since I got my hands on a book about app development in 9th grade, computer science hasn't let me go. I'm fascinated by the diversity of the field - from very technical, mathematical aspects to political-social topics.
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Paul Groß

My projects

Spacecraft Command

A casual mobile game made in Unity 3D
Omes

A combination of a planer and a messenger
Gympion

A draft for an app for gyms.
Hexplorers

A turn-based strategy game
Gympion Admin-App

The administrational component of Gympion
Heavy Landing

My first game released in 2014

Experience

2020
-
today
Philomatech UG (haftungsbeschränkt)

Founder and CEO
Freelance project-based contract work.

Software development, focus on mobile and web applications.
Game developement using the Unity Game Engine: Development of the turn-based 4x strategy game "Hexplorers".


2021
National Aeronautics & Space Administration (NASA)

C.S. Intern
Adapting a python-based data-analysis application for long term telemetry trending of the James Webb Space Telescope to integrate legacy data from an testing campaign.

Legacy telemetry data preparation, processing, and visualization.

Working in an international team remotly with a seven hour timeshift.
2018
-
2021
Airbus Defence & Space GmbH

Dual Student
Conceptualize a cloud migration strategy for existing applications. Developing an Angular based Web Client for a geographic information system (GIS) application and a microsevervice bases cloud-first backend adapter to intergrate the existing backend services.

Development of a gazetteer-search component for an miliary geographic information system (GIS).

Education

Baden-Wuerttemberg Cooperative State University (DHBW)

B. Sc. Computer Science – Information Technology

Awarded Volunteers Award 2021 of the Association of sponsors and alumni of the DHBW for "exemplary engagement for the scientific reputation of the study program". ECTS classification: A.

Physics Tutor, Member of the Local Senate and the Student Council and spokesperson of the computer science course.
2018 - 2021
Kreisgymnasium Riedlingen (KGR)

A-Level

School Award for "diligence and good performance", Award of the German Physical Society for "very good performance in physics", Award of the education partner Feinguss Blank GmbH for "diligence and good performance in the subjects math and physics".

Vice-chairman of the upper school association of the KGR, homework tutor.
2010 - 2018

Publications & Essays

Finding Clusters of Similar-minded People on Twitter Regarding the Covid-19 Pandemic

Conference Paper

Abstract: In this paper we present two clustering methods to determine users with similar opinions on the Covid-19 pandemic and the related public debate in Germany. We believe, they can help gaining an overview over similar-minded groups and could support the prevention of fake-news distribution. The first method uses a new approach to create a network based on retweet-relationships between users and the most retweeted characters (influencers). The second method extracts hashtags from users posts to create a “user feature vector” which is then clustered using a similarity matrix based on [1] to identify groups using the same language. With both approaches it was possible to identify clusters that seem to fit groups of different public opinion in Germany. However, we also found that clusters from one approach cannot be associated with clusters from the other due to filtering steps in the two methods.

Learn more
2021
Concept for the use of cloud technologies in existing [...] applications

Bachelor Thesis

No further information providable, sorry :(
2021
Sentiment analysis of Tweets for opinion analysis in relation to the Covid-19 pandemic

Student Research Project

Abstract: In this paper we present two clustering methods to determine users with similar opinions and common topics on the Covid-19 pandemic and the related public debate in Germany. We believe, they can help gaining an overview over similar-minded groups and could support the prevention of fake-news distribution. The first method uses a new approach to create a network based on retweet-relationships between users and the most retweeted characters. The second method extracts hashtags from users posts to create a user feature vector which is then clustered using a combination of the k-Means and DB-SCAN algorithm to identify groups using the same language. With both approaches it was possible to identify clusters that seem to fit groups of different public opinion in Germany. However, we also found that clusters from one approach can not be associated with clusters from the other due to the filtering steps in the two methods.
2020