Ayşe Naz Erkan
San Francisco, California, United States
2K followers
500+ connections
Activity
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Congrats to Berkay Sebat and the whole Elevate Soho team!! LET'S F'IN GO!!!
Congrats to Berkay Sebat and the whole Elevate Soho team!! LET'S F'IN GO!!!
Liked by Ayşe Naz Erkan
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I’m thrilled to embark on my next adventure with a new startup. We are currently in stealth mode but stay tuned for more details in the coming…
I’m thrilled to embark on my next adventure with a new startup. We are currently in stealth mode but stay tuned for more details in the coming…
Liked by Ayşe Naz Erkan
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Great news from Sacramento—ten to zero with bipartisan support! AB 3211, the California Provenance, Authenticity and Watermarking Standards, a bill…
Great news from Sacramento—ten to zero with bipartisan support! AB 3211, the California Provenance, Authenticity and Watermarking Standards, a bill…
Liked by Ayşe Naz Erkan
Experience
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Education
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Licenses & Certifications
Projects
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LAGR
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LAGR is a project funded by the Defense Advanced Research Projects Agency (DARPA), which aims to promote the development of better learning systems for robot navigation in unconstrained outdoor environments. Several teams compete to get the best improvement to the baseline system developed by Carnegie Mellon University. The goal is to reach a given destination in the shortest time, using primarily two pairs of cameras as sensors, which makes obstacle avoidance in the robot’s course the main…
LAGR is a project funded by the Defense Advanced Research Projects Agency (DARPA), which aims to promote the development of better learning systems for robot navigation in unconstrained outdoor environments. Several teams compete to get the best improvement to the baseline system developed by Carnegie Mellon University. The goal is to reach a given destination in the shortest time, using primarily two pairs of cameras as sensors, which makes obstacle avoidance in the robot’s course the main challenge.
Our lab, in collaboration with Net-Scale technologies, Inc., developed a long range obstacle detection system that allows the robot to recognize obstacles at up to 35m. In this project, I implemented a self-supervised label propagation scheme that uses location correspondences and thus enables learning by using features gathered from various views of the same obstacle, i.e., from different orientations, scales, and lighting conditions.Other creators -
Languages
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Turkish
Native or bilingual proficiency
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French
Limited working proficiency
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English
Native or bilingual proficiency
More activity by Ayşe Naz
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My first MuleSoft Summit left me feeling even more energized and excited for the year ahead! 300+ attendees, 12 informative sessions, numerous demos,…
My first MuleSoft Summit left me feeling even more energized and excited for the year ahead! 300+ attendees, 12 informative sessions, numerous demos,…
Liked by Ayşe Naz Erkan
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After over a decade in the dynamic environment of Silicon Valley, I have decided to take the next step in my career journey by pursuing a Master of…
After over a decade in the dynamic environment of Silicon Valley, I have decided to take the next step in my career journey by pursuing a Master of…
Liked by Ayşe Naz Erkan
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