Welcome!

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Dr. rer. nat. André Bauer

I am an Assistant Professor in the Department of Computer Science at the Illinois Institute of Technology and the founder and elected chair of the SPEC RG Predictive Data Analytics Working Group.

The overarching goal of my research is to expand the potential of data science in scientific computing by designing robust, efficient, and sustainable system solutions tailored to the evolving needs of data-driven science. As scientific progress increasingly depends on the effective use of data science ecosystems, the diversity of hardware architectures, application demands, and usage patterns poses significant challenges. My work addresses these complexities through a focus on systems and performance engineering, leveraging interdisciplinary expertise to optimize and adapt scientific computing infrastructures for emerging data science applications.

In particular, I see the following key challenges that need to be addressed:

  • Resource Optimization: Efficiently allocating and managing resources across diverse hardware platforms to accommodate changing, data-intensive workloads.
  • Adaptive Systems: Developing systems that can dynamically adapt to evolving data and model requirements.
  • Data Security and Privacy: Safeguarding sensitive data while enabling collaborative data science.
  • System-Level Optimization: Optimizing the complex interplay of components within data science ecosystems for maximum performance.
  • Sustainable Computing: Minimizing the environmental impact of data science practices.

Most Recent News

Dec 10, 2024 Our research paper, “Object Proxy Patterns for Accelerating Distributed Applications”, has been published in IEEE Transactions on Parallel and Distributed Systems. Distributed applications benefit from workflow and serverless frameworks, but advanced data flow optimization is often left to developers—especially as data scales. We propose three high-level proxy-based programming patterns—distributed futures, streaming, and ownership—to simplify and optimize distributed programming. Using benchmarks and scientific applications, we demonstrate significant improvements in runtime, throughput, and memory usage, showcasing the power of these patterns for complex distributed systems.
Dec 10, 2024 Two of our research papers have been accepted for publication: “Interatrial block is an independent risk factor for new-onset atrial fibrillation after cardiac surgery” in the Journal of Thoracic and Cardiovascular Surgery Open and “Preoperative interatrial block is associated with postoperative atrial fibrillation after cardiac surgery” in Interdisciplinary CardioVascular and Thoracic Surgery.
Nov 18, 2024 I accepted the invitation as program committee member at the 11th International Conference on Time Series and Forecasting (ITISE).
Oct 18, 2024 I am happy to announce that our Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf 2025) has been accepted to be co-located with the 16th ACM/SPEC International Conference on Performance Engineering (ICPE).
Oct 1, 2024 Our paper, “Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques”, has been accepted in Journal of Sports Science and Medicine. We analyzed 5402 training sessions & 732 matches using 174 parameters (heart rate, GPS, accelerometer, etc.). We compared different techniques and deep learning achieved top perceived exertion ratings predictions, with max heart rate, acceleration & distance covered as key predictors.

Selected Publications

  1. The Globus Compute Dataset: An Open Function-as-a-Service Dataset From the Edge to the Cloud
    André Bauer, Haochen Pan, Ryan Chard, Yadu Babuji, Josh Bryan, Devesh Tiwari, Ian Foster, and Kyle Chard
    Future Generation Computer Systems, Apr 2024
  2. An Empirical Study of Container Image Configurations and Their Impact on Start Times
    Martin Straesser, André Bauer, Robert Leppich, Nikolas Herbst, Kyle Chard, Ian Foster, and Samuel Kounev
    In Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), May 2023
  3. Methodological Principles for Reproducible Performance Evaluation in Cloud Computing
    Alessandro V. Papadopoulos, Laurens Versluis, André Bauer, Nikolas Herbst, Jóakim Kistowski, Ahmed Ali-Eldin, Cristina Abad, J. Nelson Amaral, Petr Tuma, and Alexandru Iosup
    IEEE Transactions on Software Engineering (TSE), Aug 2021
  4. Libra: A Benchmark for Time Series Forecasting Methods
    André Bauer, Marwin Züfle, Simon Eismann, Johannes Grohmann, Nikolas Herbst, and Samuel Kounev
    In Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE), Apr 2021
  5. Time Series Forecasting for Self-Aware Systems
    André Bauer, Marwin Züfle, Nikolas Herbst, Albin Zehe, Andreas Hotho, and Samuel Kounev
    Proceedings of the IEEE, Jul 2020
  6. Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field
    André Bauer, Marwin Züfle, Nikolas Herbst, Samuel Kounev, and Valentin Curtef
    In Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE), Apr 2020