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. Previously, I worked as a postdoctoral scholar at Globus Labs led by Prof. Ian Foster in the Department of Computer Science at the University of Chicago since November 2022. I am also the founder and elected chair of the SPEC RG Predictive Data Analytics Working Group.

In a nutshell, my research aims to expand the potential of data science in scientific computing. In other words, my research contributes to key aspects of developing intuitive, efficient, and sustainable data science solutions across disciplines and domains. In addition, my research is inherently interdisciplinary, and I apply a translational approach as I work to develop, apply, and evaluate methods and techniques in various domains. In the long term, I expect that my research will contribute to the creation of dynamic data science ecosystems that will inherently accelerate scientific computing applications.

My primary research interests focus on the following areas integrating experience and expertise from performance engineering and data science, but is not limited to:

  • Data science: My focus is on data analytics, clustering, and imputation. I am also interested in benchmarking and developing data analytics methods. In addition, my research is inherently interdisciplinary, and I apply a translational approach to transfer methods and techniques in various domains.
  • Data science clouds: I am interested in the development, autonomous management (i.e., autonomous scaling of resources), benchmarking of various building blocks of such clouds, and runtime prediction and scheduling of data analytic tasks.
  • Data management: The focus is on FAIR (findable, accessible, interoperable, and reusable) data management and the promotion of publicly available research data.
  • Data privacy: The idea here is to exchange data with third parties, preserving the privacy of the data. I am interested in synthetic data generation and homomorphic encryption.
  • Sustainable data science: Specifically, this involves the development of an energy efficiency benchmark for Deep and Machine Learning.

Most Recent News

Sep 17, 2024 Our article, “Network impact analysis on the performance of Secure Group Communication schemes with focus on IoT”, has been accepted for publication in the International Journal of Discover Data. Secure and scalable group communication is vital for IoT applications like smart cities and healthcare, especially in Wireless Sensor Networks with low-capacity sensors. Choosing the right Secure Group Communication (SGC) scheme is tough due to varied options. While past research focused on server/client runtimes, we are the first to analyze network-based performance. Using ComBench, we tested SKDC, LKH (centralized), and G-DH (decentralized) schemes under different network conditions. Our findings? Packet loss and delay significantly affect execution times, more so than group size.
Sep 12, 2024 I accepted the invitation as program committee member at the 14th International Conference on Data Science, Technology and Applications (DATA).
Sep 7, 2024 Our research paper, “Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing”, has been accepted for presentation at the International Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS). Scientific research increasingly depends on distributed resources from HPC and cloud systems to edge devices. Event-driven architecture (EDA) enhances applications targeting these infrastructures by managing communication, processing, and security of distributed events. Enter Octopus—a hybrid cloud-to-edge event fabric designed to scale with demand, enable resilient applications, and enforce fine-grained access control. Supporting up to 9.6M events/second, Octopus is tailored for use cases like self-driving labs, data automation, and dynamic workflows.
Aug 12, 2024 I am thrilled to start my journey today as an Assistant Professor at the Illinois Institute of Technology! Excited for the challenges and opportunities ahead.
Jul 18, 2024 Our research paper, “An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions”, has been accepted for presentation at the 20th IEEE International Conference on e-Science (eScience). Serverless computing abstracts infrastructure, letting developers focus on code. Yet, “cold start” latency, the cost to deploy environments, can hinder scientific computing with its sporadic demands. Our study tackles this by pre-installing Python packages in container images. Analyzing data from Globus Compute and Binder, we evaluate four container strategies. Pre-installed packages reduce cold start time but need more storage, while dynamic installs save space but add delays. Our simulator shows moderate warm times can cut cold starts without heavy overhead.

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
The list of all publications can be found here.