Mark Leznik

Mark Leznik joined the Institute of Resource Information Management in January 2018 as a research associate. He holds a M.Sc. degree in Computer Science from Ulm University, which he finished in 2017. The title of his Master's thesis was "Luminance Estimation of Colorized Near-Infrared Images", the work was performed at Daimler AG Research & Developement. Mark obtained his B.Sc. in Computer Engineering from Ulm University of Applied Science in 2013.


As part of my thesis, I am currently working on a broad range of topics related to time series data (cf. Research Topics Figure).

The time series analysis part (achieved by using both "classical" statistics and machine learning) includes forecasting, clustering, classification, anomaly detection and quality analysis. Hereby, I mainly focus on gathering more understanding into anomalies in time series data, as well as providing a set of tools to allow an obejctive analyis of the underlying time series data in terms of its quality (in regards to predictability).

Research Topics

The synthesis part refers to the artificial generation of time series data, mainly for the purpose of its anonymization. Such a mechanism allows for a privacy preserving data publication or exchange for publication or collaboration purposes respectively.

The storage aspect pertains to providing an easy access to time series data for data science purposes, ideally eliminating the need to wrangle huge data dumps around.

Additionally, I am working on intertwining data science methodologies with CI/CD DevOps principles. More precisely, this means adding a higher grade of automation to the developement and testing of data science components (i.e. model training) and better reproducibility across multiple environments (i.e. local workstations, HPC clusters).


  • AI Investments – an advanced investment tool based on machine learning and big data.
    Subproject: STOQS, Simple Timeseries Objective Quality Measurement Stack
    June 2019 - current
  • RECAP - Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications
    January 2018 - current

Theses & Student Projects

If you are interested in writing a thesis, seminar or doing a project on the topics described above, here are some ideas. If anything sounds interesting, or if you have your own idea, feel free to write me a mail or drop by my office.

Ongoing & Completed Topics

  • Synthesizing Cloud Server Workloads Using Generative Adversarial Networks (Master's Thesis)
  • Current Advances in Time Series Anomaly Detection (Bachelor's Thesis)
  • An LSTM Framework based on Tensorflow & Keras for Time Series Prediction Using Production and Artificial Data (Student Project)
  • Anomaly Detection in Time Series Data: An Overview (Seminar)
  • Is the Cloud ready for Autonomous Driving? (Seminar)
  • Deep Learning and Containers? What Gives? (Seminar)

Contact Information

Office Hours

Please arrange an appointment via mail.