Biography

I’m a Berlin-based PhD student interested in and researching machine learning (ML) topics. My special interests are data quality and data cleaning problems. Currently, I’m employed at the Berlin University of Applied Sciences and Technology (BHT) and working on research projects about applying ML to medical, health care, and sustainability problems.

Download my CV .

Interests
  • Data Quality
  • Data Cleaning
  • Conformal Predictors
Education
  • MSc in Data Science, 2020

    Berlin University of Applied Sciences and Technology (BHT)

  • BSc in Computer Science, 2018

    Karlsruhe University of Applied Sciences

Experience

Work Experience

 
 
 
 
 
PhD Student
November 2020 – Present Berlin

Current research projects:

Previous research projects:

 
 
 
 
 
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

Machine Learning Summer School in Health, Care and Biosciences
The curriculum covered a broad range of cutting-edge topics including a variety of machine learning applications on imaging, omics and sensor signals delivered by senior executives, leaders and top researchers from MIT, Harvard Medical School, ETH Zürich, Roche, Bayer, L’Oreal, Apple, and Novartis, among others.
See certificate
Ethics and Governance of Artificial Intelligence for Health
Artificial intelligence (AI) has enormous potential for improving health outcomes and helping countries achieve universal health coverage. However, for AI to have a beneficial impact on people’s health, ethical considerations and human rights must be placed at the centre of its design, development and use. Adapted from the core contents of the Guidance on Ethics & Governance of Artificial Intelligence for Health, this course introduces entry-level knowledge to policymakers, AI developers and designers, and health care providers who are involved in designing, developing, using and regulating AI technology for health.
See certificate
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

Contact

Consider using encrypted e-mail-communication via PGP and the following address.
E-Mail: message@sebastian-jaeger.me
Public Key: OpenPGP
Fingerprint: 51ED 1EC4 FBD5 1C01 418C 89BF 52EE EBCB 8DAD 014D