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Prof. Dr. Odej Kao


Technische Universität Berlin
School IV EECS, Distributed and Operating Systems Group

TEL 12-5, Ernst Reuter Platz 7, D-10587 Berlin

Prof. Dr. Odej Kao


Fellow | BIFOLD

Full Professor for Computer Science | Distributed and Operating Systems group at  Technische Universität Berlin

Chair | Einstein Center Digital Future

CIO | Technische Universität Berlin

1995 DAAD Award for Study Achievements

  • Cloud Computing
  • Big Data
  • IT Management
  • Resource Management
  • AI-supported IT Operations
  • HPC

  • Chairman Deutsches Forschugnsnetz (DFN)
  • State Advisory Board for Digitalization of the State of Berlin
  • TPC Parallel Computing
  • IEEE Transaction of Cloud Computing
  • IEEE Computer
  • IEEE Multimedia

Philipp Wiesner, Ramin Khalili, Dennis Grinwald, Pratik Agrawal, Lauritz Thamsen, Odej Kao

FedZero: Leveraging Renewable Excess Energy in Federated Learning

November 05, 2023

Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz Thamsen, Odej Kao

Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

August 01, 2022

Machine Learning| Mar 05, 2024

Sustainable Federated Learning

Federated Learning is expected to further increase the power consumption of machine learning, which is already recognized as one of the most energy-intensive computational applications today. However, due to its distributed nature, Federated Learning also offers new opportunities to align this demand with the availability of green energy.

BIFOLD Update| Dec 03, 2023

New open research positions

Join BIFOLD and collaborate with renowned experts on cutting-edge Machine Learning and Data Management research! Develop robust, trustworthy, and sustainable AI solutions with our team of international scientists. 

Data Management| Jun 10, 2022

Flexible adjustment of computing workloads could improve carbon footprint of data centers

To reduce their carbon footprint, more and more computing systems are connected to microgrids to gain direct access to renewable energy sources. However, the local availability of solar and wind energy is highly variable and requires consumers to timely adapt their consumption to the current supply. Researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) have developed a new admission control approach that accepts flexible workloads such as machine learning training jobs only if they can be computed relying solely on renewable excess energy.

Data Management| Nov 12, 2021

Scheduling computing tasks can reduce emission

To reduce the carbon footprint of cloud computing, researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) investigated the potential of shifting delay-tolerant compute workloads, such as batch processing and machine learning jobs, to times where energy can be expected to be green. Their publication “Let’s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud,” was now accepted at Middleware’21. 

Data Management| Nov 27, 2020

TUB distributed and operating systems researchers will offer multiple presentations at IEEE Big Data 2020

Three papers by researchers of the Distributed and Operating Systems group at TU Berlin, led by BIFOLD Principal Investigator Prof. Dr. Odej Kao, have been accepted for presentation at the 2020 IEEE International Conference on Big Data (Big Data 2020) and related workshops. The Conference will take place from December 10 – 13, 2020.