PhD researchers

xiaoshuai-zhang

Xiaoshuai Zhang is a PhD candidate and research assistant in the IoT2US Lab, 2016-2020, funded by the Chinese Scholarship Council (CSC) in China and QMUL. His PhD thesis topic is Enhanced Security and Privacy for Blockchain-enabled Applications in eHealth. He has 4 conference papers and 3 journal paper from his PhD to date. He has a B.S. Computer Science and Technology with Marine Science Minor (2009) and he has an M.Sc. Computer Technology (2016) both from the College of Information Science and Engineering, Ocean University of China, China.

raphael-kim

Raphael Kim is a PhD candidate and research assistant in the IoT2US Lab, 2016-2020, funded by the UK EPSRC QMUL Media Arts Technology (MAT) project. His research interests include bio-digital games and Speculative design. His current research explores the concept of biotic gaming, which integrates biological materials and processes in video games and Internet of Things (IoT) framework. His PhD thesis topic is Slow Biotic Games. He was a Researcher and visiting lecturer, Design Interactions, Royal College of Art (RCA), London. He has an MA in Design Interactions, from RCA (2012) and a Bsc Biotechnology, University College London (2004).

bangwu

Bang Wu is a PhD candidate and research assistant in the IoT2US Lab, 2017-2021, funded by the Chinese Scholarship Council (CSC) in China and QMUL. His PhD thesis topic is Human Activity Recognition Using a hybrid UWB based Low Power IoT model. He has 2 conference papers from his PhD to date. He has a BSc from the School of Geodesy and Geomatics, Wuhan University, China (2014) with a Major in Geodesy and Geomatics Engineering. He Has an MSc from the same place with a major in Geodesy and Geomatics (2016).

gyzhang-white-smal2l

Guangyuan Zhang is a PhD candidate and research assistant in the IoT2US Lab, 2018-2022, funded by the Chinese Scholarship Council (CSC) in China and QMUL. His PhD thesis topic is An Investigation of the Spatial and Temporal Distribution of People based on Indoor and Outdoor Positioning Data. His research interests are spatial-temporal big data mining, machine learning and deep learning, GIS and urban computing.

meng-xu

Meng Xu is a PhD candidate and research assistant in the IoT2US Lab, 2019-2023, funded by the Chinese Scholarship Council (CSC) in China and QMUL. Her PhD thesis topic is Leveraging Augmented Reality and Sensors to Enable Smart Buildings Services. Her research interests are spatial intelligence, machine learning and deep learning, augmented reality and urban computing.

zhaoliang-luan

Zhaoliang Luan is a PhD candidate and research assistant in the IoT2US Lab of QMUL EECS (2019-2023), funded by the Chinese Scholarship Council(CSC) and QMUL. His PhD topic is Robust Visual Localization Based on Learning Methods. His current research interests concentrate on Computer Vision, Simultaneous Localization and Mapping (SLAM) and Robotics.

zhao-huang

Zhao Huang is a PhD candidate and research assistant in the IoT2US Lab, 2020-2024, funded by the Chinese Scholarship Council (CSC) in China and QMUL. His PhD thesis topic is Intelligent Travel Route Recommendation Algorithm using Multi-source Data. His research interests are machine learning, deep learning, intelligence traffic system and urban computing.

yonglei-fan

Yonglei Fan is a PhD candidate and research assistant in the IoT2US Lab, 2020-2024, funded by the Chinese Scholarship Council (CSC) in China and QMUL. His PhD research interests contain Geographic Big Data Analysis, Geo-Information Analysis, Artificial Intelligence, Deep Learning and Indoor positioning.

qiqi-shu

Qiqi Shu is a PhD candidate in the IoT2US Lab, 2020-2024, funded by the Chinese Scholarship Council(CSC) in China and QMUL. His PhD topics are about the fusion algorithm of Lidar and camera stream in SLAM. His research interests are spatial intelligence, multiple source SLAM and machine learning.



YGX

Guangxia Yu is a PhD candidate in the IoT2US Lab, 2020-2024, funded by EECS, QMUL. Her research interests are environmental IoT, GIS and machine learning.