I am PhD students at Malmo university, my backgrounds are:

• Edge AI: federated learning, split learning, distributed AI, intelligent IoT, distributed learning, intelligent mobile edge computing. • • IoT: Healthcare Internet of Things (HIoT), Unmanned Aerial Vehicles (UAVs), Vehicular Internet of Things (VIoT) and Autonomous Driving, Satellite Internet of Things (SIoT), and Industrial Internet of Things (IIoT), Smart city. • • Other: Extended reality (XR) (Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR)), Digital Twins, Blockchain. (Combination of Digital twins+blockchain+Federated learning+Extended reality) • • Data: Big data, Data mining, Process mining, Data stream, Real-time data, image processing, computer vision, video mining. • Machine learning: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning, Deep learning, Multi task learning. • • Computer network: wireless communications, Wireless sensor networks, Mobile network (6G/ 5G), LAN, MAN, WAN, Software-defined networking, NFV) • • Computing: distributed computing, parallel computing, cluster computing, High performance computing, edge computing (mobile edge computing), fog computing, cloud computing, GPU computing, accelerated computing. • Big data and Machine Learning tools: Hadoop (MapReduce, HDFS, NoSQL, Mahout, YARN, HIVE, Spark MLLib and…), spark, Keras, TensorFlow, Google colab, Pytorch, Caffe2, Spss, Python (scikit-learn, pandas, NumPy, Numba, scipy, and…). ✓ RAPIDS (Apache Arrow, Blazing SQL), (DASK + OPENUCX+ RAPID ✓ CUDA Python, CUDA Toolkit, CUDA-X AI, CUDA-X HPC, cuDNN, Thrust, OpenACC, Omniverse, Math Libraries, FLARE (Federated Learning Active Runtime Environment), data Stream. Now in Internet of Things and People projects we use different machine learning algorithms in order to date the anomaly detection. Also we work on data reduction, feature selection for big data and real time systems. Additionally, we work with different companies such as Sony and Sony Ericsson