Danial Zendehdel

Research Engineer · Deep Reinforcement Learning

I build deep reinforcement learning systems for sequential decision making under uncertainty: multi-objective, constrained and multi-agent policies for battery dispatch, energy management and fair resource sharing. Before that I worked on 360° stereo depth estimation and real-time person tracking for robots at EPFL.

Available from November 2026.

CV Publications Projects

Recent

Stereo Depth Estimation on a Real, Unrectified 360° Dataset

Computer Vision Depth Estimation Datasets PyTorch

MSc thesis: recorded a new real-world dataset of 17,373 stereo 360° image pairs with LiDAR depth ground truth, and adapted deep stereo networks (PSMNet, 360SD-Net) to work on it without rectification.

Gesture-Triggered Person Following on a Loomo Robot

Computer Vision Robotics Tracking PyTorch

Real-time perception pipeline for EPFL’s human–robot tandem race: a Segway Loomo picks out the person who raises a hand using AlphaPose keypoints, then keeps them locked in with YOLOv5 + DeepSORT while it follows them.

Ship Classification and Localisation

Computer Vision Object Detection TensorFlow

Classify ships into five types (cargo, carrier, cruise, military, tanker) and localise them in the image, on ~6,200 hand-annotated images with bounding boxes. Compares a single multi-output MobileNetV2 against a split Xception + MobileNetV2 pair.

↑