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cluebbers/README.md

Welcome to My GitHub Profile!

About Me

Hello! I'm Christopher L. Luebbers, a researcher specializing in deep learning, natural language processing (NLP), and artificial intelligence. I have a Master's degree in Applied Data Science from Georg-August-Universität Göttingen.

Research Interests

  • Deep Learning
  • Natural Language Processing
  • High-Performance Computing
  • Machine Learning

Featured Projects

HPDA_with_R

A comprehensive exploration of using R for high-performance data analytics, covering memory management, GPU computing, parallel processing, and comparative analysis with Python. Includes benchmarks, case studies, and code examples.

Meh-Tricks: Towards Reproducible Results in NLP

Investigates the reproducibility of METEOR, BLEU, and ROUGE scores in NLP research. Includes a systematic literature review, software validation testing, and code for analyzing evaluation protocols and packages.

DeepNLP_SS23

Project for the module "Deep Learning for Natural Language Processing" at Georg-August-Universität Göttingen. Implementations of BERT and sBERT for various NLP tasks, including sentiment analysis, paraphrase detection, and semantic textual similarity.

Skills

  • Programming Languages: Python, R
  • Frameworks: TensorFlow, PyTorch
  • Tools: Jupyter Notebooks, Docker, Git, GitHub
  • Data Analysis: Pandas, NumPy

Publications

  • Using R for High-Performance Data Analytics - A seminar report exploring the use of R for high-performance data analytics.
  • Meh-Tricks: Towards Reproducible Results in NLP - A research project investigating the reproducibility of evaluation scores in NLP.

Contact

Feel free to reach out to me for collaboration, questions, or any interesting discussions!

Get Involved

If you find my projects interesting and want to contribute, feel free to fork the repositories and submit pull requests. Let's collaborate and push the boundaries of AI and NLP research together!

Thank you for visiting my profile. Happy coding!

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  1. NLP_DeepLearning_Spring2023 NLP_DeepLearning_Spring2023 Public

    Implementing and fine-tuning BERT for sentiment analysis, paraphrase detection, and semantic textual similarity tasks. Includes code, data, and detailed results.

    Python

  2. Using_R_for_HPDA Using_R_for_HPDA Public

    Exploring R for high-performance data analytics, including memory management, GPU computing, parallel processing, benchmarks, case studies, and comparisons with Python.

  3. Reproducibility-METEOR-NLP Reproducibility-METEOR-NLP Public

    Investigates the reproducibility of METEOR scores in scientific papers. Includes a systematic literature review and validation of METEOR implementations.

    Jupyter Notebook 1