Svetlana Pavlitskaya: Unterschied zwischen den Versionen
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{{Mitarbeiter | {{Mitarbeiter | ||
|Vorname=Svetlana | |Vorname=Svetlana | ||
− | |Nachname= | + | |Nachname=Pavlitska |
|Akademischer Titel=M.Sc. | |Akademischer Titel=M.Sc. | ||
|Forschungsgruppe=Angewandte Technisch-Kognitive Systeme | |Forschungsgruppe=Angewandte Technisch-Kognitive Systeme | ||
|Stellung=FZI-Mitarbeiter | |Stellung=FZI-Mitarbeiter | ||
|Ehemaliger=Nein | |Ehemaliger=Nein | ||
− | |Email=svetlana. | + | |Email=svetlana.pavlitska@kit.edu |
|Hinweis DE='''FZI Forschungszentrum Informatik'''<br />Haid-und-Neu-Straße 10-14<br />76131 Karlsruhe | |Hinweis DE='''FZI Forschungszentrum Informatik'''<br />Haid-und-Neu-Straße 10-14<br />76131 Karlsruhe | ||
|Hinweis EN='''FZI Research Center for Information Technology'''<br />Haid-und-Neu-Straße 10-14<br />76131 Karlsruhe | |Hinweis EN='''FZI Research Center for Information Technology'''<br />Haid-und-Neu-Straße 10-14<br />76131 Karlsruhe |
Aktuelle Version vom 3. Februar 2023, 09:48 Uhr
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M.Sc. Svetlana Pavlitska
- FZI-Mitarbeiter
- Email: svetlana pavlitska∂kit edu
- Forschungsgruppe: Angewandte Technisch-Kognitive Systeme
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FZI Forschungszentrum Informatik
Haid-und-Neu-Straße 10-14
76131 Karlsruhe - vCard
I studied computer science at RWTH Aachen University from 2011 to 2018 with a focus on deep learning and computer vision. Since November 2018, I am a research scientist at the FZI Research Center for Information Technology in the Technical Cognitive Systems (TKS) department and a PhD student at AIFB. My research focus is on the inherent insufficiencies of deep neural networks, such as their sensitivity to random corruptions and adversarial attacks, lack of generalization, and interpretability. I have a strong interest in applying ideas from fundamental DL research to improve the robustness of camera-based perception in an autonomous vehicle and intelligent infrastructure for autonomous driving.
Open Bachelor/Master Theses and Hiwi Positions
- Increasing the robustness of deep learning approaches for perception in autonomous driving
- Combining expert DNNs to improve the security and interpretability of perception tasks
- Accelerating deep neural networks for real-time perception in automated driving
- Testing perception algorithms on a mobile development platform for infrastructure-based automated driving.
- Bachelor thesis, Master thesis: Send me an email with your interest or idea
If you are interested in a student position in one of these fields, just send me an e-mail with your CV, your grades, and two sentences, why you are interested in the position.
Publications