Lukas Pfannschmidt

PhD Candidate

CITEC Machine Learning Group

Biography

Finishing PhD candidate with 5 years of experience dedicated to efficient and maintainable solutions to interesting problems. Studies of bioinformatics and machine learning with focus on feature selection. Very familiar with handling and representing diverse data.

Interests

Machine Learning
Software Engineering
Decentralized Applications
Mobility Solutions

Education

PhD in Machine Learning, planned 2020
Bielefeld University
B.Sc & M.Sc. in Bioinformatics and Genome Research, 2014 & 2016
Bielefeld University

Experience

Research Associate

CITEC, Prof. Hammer’s Machine Learning group

Oct 2018 – Present Bielefeld, Germany

Responsibilities include:

Research ( Modelling & Analysis )
Development and Deployment
Scientific writing and presentation

Guest Researcher

SFU, Prof. Ester’s Datamining group

May 2018 – Sep 2018 Vancouver, Canada

Responsibilities include:

Research ( Feature Representation, Non-linear Models)
Scientific writing and presentation

Research Associate

International Research Training Group GRK 1906

Oct 2015 – Apr 2018 Bielefeld, Germany

Responsibilities include:

Research & Development
Scientific writing and presentation
Teaching

Teaching Assistant

Genome Informatics group

Apr 2014 – Oct 2016 Bielefeld, Germany

Responsibilities include:

Teaching

Technical Lead

Endoscope App Team Competition - Winner Team

Apr 2013 – Nov 2013 Bielefeld, Germany

Project Goal: Android App for medical professionals handling and washing endoscopes in a clinical setting.

Developed in competitive setting with the endoscope manufacturer (Miele) as client. Focus on agile development with changing requirements.

Responsibilities include:

Software architecture
Task delegation
DevOps
Development

Publications

Lukas Pfannschmidt, Barbara Hammer (2020). Sequential Feature Classification in the Context of Redundancies.

PDF Code Experiments

Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.

PDF

Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer (2019). FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

PDF DOI

See all publications