Sadegh Keshtkar


Since December 15, 2022, Mr. Sadegh Keshtkar has been an AI Developer in the “Computing” working group (AG C). He completed his second Master's in Computer Science at Tu Berlin and is now pursuing a Ph.D. in Computer Science at GWDG, under the supervision of Prof. Julian Kunkel. At Tu Berlin, his Master's thesis focused on studying how an automated red teaming agent learns using deep reinforcement learning. For his Ph.D., he's diving into federated learning's characteristics, exploring ways to improve this area of study with Prof. Kunkel's guidance.
ORCID: 0000-0002-4683-0136

  • Federated learning
  • Reinforcement learning
  • Application of machine learning methods

Improving Portability and Interoperability of Deep-Learning-Workloads using ONNXApply

We want to develop an AI model to predict the best suitable technical supporter for each new submitted question in a technical support system. We assume that every case has been solved by a single supporter totally independently in the past. Based on the historical communications of each case with respect to its supporter, we will use the attention mechanism to understand the context meanings of those conversations, so that we can solve this supervised classification NLP task like a normal classification task. After our model has been well implemanted, we will explore its best super-parameters for time and accuracy performance and export it as an ONNX file. In GPU and in CPU we attempt to execute our ONNX file for retraining with respect to time consumption variante and for inferencing with respect to accuracy variante in different ONNX runtimes for the portability and interoperability. Our task is to explore the maximum compatibility of our ONNX file within different ONNX runtime.

  • Learning to Attack: Automated Red Teaming Using Deep Reinforcement Learning, Sadegh Keshtkar (Master's Thesis), Advisors: Sahin Albayrak, 2022-05, BibTeX

All publications as BibTex

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