EXPERIENCE
PhD Student
Machine Learning Group, Institute of Software Engineering and Theoretical Computer Science, Faculty IV, TU Berlin, Germany
November 2022 - present
Research on distributed and efficient machine learning.
Research student assistant
UMI-Lab, Machine Learning Group, Institute of Software Engineering and Theoretical Computer Science, Faculty IV, TU Berlin, Germany
May 2022 - present
Research on the topic of explainable artificial intelligence and bayesian machine learning.
Publication of the paper “Visualizing the diversity of representations learned by Bayesian neural networks”, submitted to “IEEE TMLR”.
Administration of the Lab’s compute nodes, including briefing new team members.
Supporting the Lab’s recruiting process.
Lecturer
TUBS GmbH, Berlin, Germany
January 2020 - January 2021
Lecturing the 4-week full time course “Data Science with Python”, and evaluating student assignments, projects and exams.
Lecturing the 4-week full time course “Machine Learning using Python”: Theory and Application”, and evaluating student assignments, projects and exams.
Data Science working student
Aperto an IBM company, Berlin, Germany
June 2018 - January 2019
Development of a Social Media Brand Monitor using Deep Neural Network Models and Word Embeddings for Sentiment Analysis.
Visualization of Analysis results using a Flask Dashboard app.
Setup of Hortonworks HDP Platform and integration of current apps into HDP environment.
Software Engineering intern
XAIN AG, Berlin, Germany
January 2018 - August 2018
Development of a distributed Access Control Service - used Stack: Solidity, Javascript Web3.JS, AWS, Docker.
Implementation of a Geth ETH Client to CAN-BUS Software installed in a Porsche Panamera.
Full Stack development of a Javascript WebApp - used stack: Javascript, React & Redux, PostgreSQL.
Data Science intern
Koneksys LLC, San Francisco, CA
May 2017 - December 2017
Research & Analysis on current Big Data technology, including: Cassandra, Hadoop, Spark, Blazegraph, in order to provide best fit solution for large-scale graph data processing.
Implementation of Apache Spark’s graph processing Framework (GraphFrames) on top of distributed HDFS cluster - used stack: HDFS, Apache Spark, Java.
Analysis of the internals of various query languages: SQL, SPARQL, Gremlin.
Java Development of a SPARQL-to-GraphFrames translator & RDF-to-GraphFrames compiler.
Evaluating assembled Big Data tool by querying large RDF Graph datasets(10B+ triples).