Biography

I’m a Berlin-based PhD student. Researching and working on the Green Consumption Assistant research project at the Berliner Hochschule für Technik (BHT).

Download my CV.

Interests
  • Machine Learning
  • Data Quality
  • Automated Pipelines
Education
  • MSc in Data Science, 2020

    Berliner Hochschule für Technik (formerly Beuth University of Applied Sciences)

  • BSc in Computer Science, 2018

    Karlsruhe University of Applied Sciences

Experience

Work Experience

 
 
 
 
 
PhD Student
November 2020 – Present Berlin
Research project: Green Consumption Assistant
 
 
 
 
 
Thesis Student
March 2020 – September 2020 Berlin
Title: Compressing BERT - An Evaluation and Combination of Methods
 
 
 
 
 
Working Student
March 2018 – February 2020 Berlin

Kubernetes-based data science platform: Internal used platform to speedup and scale data science projects.

Responsibilities:

  • Implementation and security of the Kubernetes cluster environment
  • Selection and combination of components and tools
  • Implementation of a Go-based CLI tool to easily interact with the platform

Metadata management system for 3D mass spectroscopy data: Research project in collaboration with the Mannheim University of Applied Sciences.

Responsibilities:

  • Leading and coordinating a team of five students
  • Communicating and presenting the project process internally and to public
 
 
 
 
 
Thesis Student
October 2017 – February 2018 Karlsruhe
Title: Horizontales Skalieren von Deep Learning Frameworks
 
 
 
 
 
Working Student
August 2017 – September 2017 Karlsruhe

Kubernetes-based data science platform: Internal used platform to speedup and scale data science projects.

Responsibilities:

  • Implementation and security of the Kubernetes cluster environment
  • Selection and combination of components and tools
  • Implementation of a Go-based CLI tool to easily interact with the platform

Recent Publications

Accomplish­ments

Coursera
Sequence Models

This course aims to:

  • understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
  • be able to apply sequence models to natural language problems, including text synthesis.
  • be able to apply sequence models to audio applications, including speech recognition and music synthesis.
See certificate
Coursera
Convolutional Neural Networks

This course aims to:

  • understand how to build a convolutional neural network, including recent variations such as residual networks.
  • know how to apply convolutional networks to visual detection and recognition tasks.
  • know to use neural style transfer to generate art.
  • be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
See certificate
Coursera
Structuring Machine Learning Projects

This course aims to:

  • understand how to diagnose errors in a machine learning system, and
  • be able to prioritize the most promising directions for reducing error
  • understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
  • know how to apply end-to-end learning, transfer learning, and multi-task learning
See certificate
Coursera
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This course aims to:

  • understand industry best-practices for building deep learning applications.
  • be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
  • be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
  • understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
  • be able to implement a neural network in TensorFlow.
See certificate
Coursera
Neural Networks and Deep Learning

This course aims to:

  • understand the major technology trends driving Deep Learning
  • be able to build, train and apply fully connected deep neural networks
  • know how to implement efficient (vectorized) neural networks
  • understand the key parameters in a neural network’s architecture
See certificate