Sedat OZER home

[Archive] Call for papers: CFP I organized the Southern California AI and Robotics Symposium (SCAIR 2025), on October 29–30, 2025. Check on CPPNews.

[Archive] Call for papers: CFP: I organized the special session on “Deep learning applications for autonomous systems” at the IEEE SIU 2022 Conference.

Dataset announcement: A new UAV dataset from the Ozer Lab: SkyData Challenge. The dataset will be available there. If you are interested, feel free to email Dr. Ozer.

Dr. Sedat Ozer is a researcher, a machine learning scientist, an Assistant Professor and a data analysis expert. His work focuses on robot vision, deep learning, and AI for autonomous systems.

Dr. Ozer was a PI and recipient of TÜBİTAK’s prestigious 2232 International Fellowship for Outstanding Researchers (2020–2023), hosted by the Department of Computer Science at Özyeğin University (OzU). Other post-PhD roles include CSAIL, MIT (with Daniela Rus in the Distributed Robotics Laboratory); and the Virginia Image and Video Analysis (VIVA) Lab at UVa with Scott Acton. He received his PhD in the ECE Department at Rutgers (advisor: Deborah Silver; dissertation: Activity Detection in Scientific Visualization).

He is a scientist by nature and an (electrical and computer) engineer by education. He enjoys studying and discussing the theory behind our daily world applications, their validity and applicability in general (in other fields). We have the tendancy of believing any "theory" presented to us, if it is provided with "some" logical evidence. However, while mostly true theories, sometimes a theory is confused with an hypothesis and (possibly due to a misuse of the term theory) such an hypothesis is also presented to us as a theory. These type of "theories" usually work only under certain assumptions which limits their applicability in general. This is where he likes doing research at, by extending the generality and robustness of what is available. Specifically his research interests overlap the following fields:

Research interests:

  • Drone Vision
  • Robot sensing
  • Data analysis
  • Visual object tracking & detector design
  • Signal, image, and video processing & computer vision
  • 3D object/group tracking & scientific visualization
  • Activity detection and path planning for autonomous systems
  • Theory of machine learning, deep learning, learning to learn, meta‑learning