SOS Team Members

Kai Cui

PhD student


work +49 6151 16-57 238
fax +49 6151 16-57 241

Work S3/06 230
Merckstrasse 25
64283 Darmstadt

Kai Cui joined the Lab as a PhD student in December 2019 and is part of the emergenCITY project funded by Hesse's research promotion programme LOEWE, research area KOM. There, he is interested in applying reinforcement learning and mean field methods to optimize network configurations and drone communication networks.

Prior to that, he received his M.Sc. degree in Computer Science, and in Electrical Engineering and Information Technology with focus on automation systems from Technische Universität Darmstadt in 2019.

His research topics includes reinforcement learning, game theory and mean field games. In particular, he is interested in new, scalable solutions for multi-agent control problems via mean-field frameworks. Possible applications could include learning drone swarm behaviour such as indoor exploration, or analyzing vaccination strategies.


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Student Projects

Zhihao Lu
(Master thesis)
Robust Control of Crazyflie Quadcopters 12/2022
Caio Victor Gouveia Freitas
(Bachelor thesis)
Visually Localizing, Approaching and Carrying Objects using Magnetic MAVs 11/2022
Yao Zhang
(Master thesis)
Bio-inspired Communication in Reinforcement Learning for Foraging Tasks 10/2022
Julian Dobler
(Bachelor thesis)
Optimal Object Transportation via Drone Swarms 08/2022
Ruiyao Wu
(Master thesis)
Visual Self-Localization for UAVs 08/2022
Yao Zhang
(Master thesis)
Collective Intelligence and Symmetry Breaking via Reinforcement Learning 07/2022
Lin Qu
(Master thesis)
Viral Reconfiguration of Network Topologies using Drone Swarms 07/2022
Roman Leon Coura
(Bachelor thesis)
3D Modular Drone Structures for Manipulation of Objects 06/2022
Elvis Majushi
(Master thesis)
Drone Swarms for Communication and Exploration in Disaster Scenarios 06/2022
Abdelrahman Azzam
(Master thesis)
Adaptive Routing via Reinforcement Learning and Mean Field Control 06/2022
Mengbing Zhou
(Master thesis)
Online System Identification and Robust Control for Quadrotors 04/2022
Amos Newswanger
(Master thesis)
Indoor Visual Navigation on Micro-AerialDrones without External Infrastructure 04/2022
Tilman Sachs
(Bachelor thesis)
Decentralized Pattern Formation via Reinforcement Learning 04/2022
Marvin Joos
(Master thesis)
FPGA-based Deep Neural Networks for UAVs 04/2022
Wencheng Zhang
(Master thesis)
Collaborative Visual Exploration of Unknown Environments Using UAV Swarms 02/2022
Rafael Davila Sosa
(Bachelor thesis)
Control of Hexagonal Multi-Drone Structures 02/2022
Lalith Kishore Sai Macherla Sathish
(Master thesis)
Optimal Sequential Object Transportation via Reinforcement Learning 12/2021
Ruikang Sun
(Master thesis)
Model-free and Model-based Mean-Field Reinforcement Learning 11/2021
Dongxu Wang
(Master thesis)
Multi-agent Reinforcement Learning and Voting Algorithms 11/2021
Simon Schwab
(Bachelor thesis)
Control of Modular Drone Structures using Multi-AgentReinforcement Learning 10/2021
Davi Klein Spindola
(Bachelor thesis)
End-to-End Reinforcement Learning for Low-Level Control of Quadcopters 09/2021
Alexandre Knihar
(Bachelor thesis)
Depth Estimation and 3D Reconstruction for Micro Aerial Vehicles 06/2021
Tizian Claus Dege
(Bachelor thesis)
Neural Face Detection on Micro Aerial Vehicles 04/2021
David Askari-Badouee
(Bachelor thesis)
Decentralised Assembly of Drone Morphologies for Large Object Transportation 03/2021
Berk Calabakan
(Bachelor thesis)
Mid-Air Self-Assembling Drone Swarms 03/2021
Jiean Yan
(Master thesis)
Performance Comparison of Quadcopter Collision Avoidance Algorithms 01/2021
Shouran Mu
(Master thesis)
Potential and Rotational Flows for Collision-Free Drone Swarm Maneuvers 12/2020
Ramzi Ourari
(Master thesis)
Multiagent Collision Avoidance in Drone Swarms via Deep Reinforcement Learning 11/2020
Hongjing Tang
(Master thesis)
Low-Level Control of Quadcopters via Reinforcement and Imitation Learning 11/2020
João Victor Stahl Mosz
(Master thesis)
Performance Evaluation of Different Low-Level Quadcopter Control Algorithms 09/2020

Possible Student Projects you can find here .