I'm an AI researcher with a background in robotics and deep learning, driven by the challenge of making intelligent systems actually behave intelligently in the real world. During my M.S. at Bilkent University, I worked on Task and Motion Planning (TAMP) in the LIRA Lab, where I focused on bridging the gap between high-level reasoning and low-level motion—basically, helping robots think and move more like living systems. My work was inspired by how biological systems adapt and structure behavior in complex settings. Before that, I built a strong foundation in electrical engineering and machine learning during my undergrad at Bilkent. I'm always up for discussions. Feel free to reach out.

Latest News

Paper accepted at Pattern Recognition!
Excited to share that our paper, "Locally Adaptive One-Class Classifier Fusion with Dynamic lp-Norm Constraints for Robust Anomaly Detection" has been accepted for publication in Pattern Recognition.

In this work, we introduce a novel ensemble anomaly detection framework that dynamically adapts classifier fusion weights using localized lp-norm constraints, enabling improved robustness to data imbalance and distributional shifts. This approach achieves significant computational gains with an interior-point optimization method and excels across both benchmark datasets and real-world temporal sequences.

A key highlight of this work is the introduction of LiRAnomaly, a novel robotics anomaly detection dataset based on pick-and-place tasks using a Franka EMIKA robot. The dataset captures both static and temporal anomalies—from sensor occlusions to trajectory obstructions—making it a valuable benchmark for safety-critical robotic manipulation.
Paper accepted at IEEE RA-L!
Excited to share that our paper, "H-MaP: An Iterative and Hybrid Sequential Manipulation Planner" has been accepted for publication in IEEE Robotics and Automation Letters (RA-L).

In this work, we present a hybrid approach that reduces configuration space dimensionality by decoupling object trajectory planning from manipulation planning, enabling robots to handle complex tasks involving tool use and bimanual coordination.