Amin Beheshti

Affiliation

School of Computing, Macquarie University

Topic

Graph Neural Networks,Neural Network,Deep Learning,Anomaly Detection,Domain Experts,Machine Learning Models,Big Data,Convolutional Neural Network,Graph Convolutional Network,Graph Data,Internet Of Things,Machine Learning,Nodes In The Graph,Real-world Datasets,Variational Autoencoder,Artificial Intelligence Systems,Attention Mechanism,Generative Adversarial Networks,Graph Structure,Internet Of Vehicles,Knowledge Base,Latent Representation,Learning Algorithms,Learning Models,Named Entity Recognition,Professional Knowledge,Recurrent Neural Network,Representation Learning,Social Media,Subject Matter Experts,AUC Score,Activation Function,Anomaly Detection Methods,Anomaly Score,Business,Business Process Management,Complex Graph,Data Augmentation,Data Instances,Data Management,Data Space,Decision-making Process,Deep Learning Models,Deep Models,Expert Knowledge,Fake News,Federated Learning,Graph Attention Network,Graph Convolution,Graph Summarization,

Biography

Amin Beheshti received the bachelor’s and master’s degrees (Hons.) in computer science and the Ph.D. degree in computer science and engineering from UNSW Sydney, Sydney, NSW, Australia, in 2013.
He is a Postdoc with UNSW Sydney. He is the Director of AI-enabled Processes (AIP) Research Centre and the Head of the Data Analytics Research Laboratory, Department of Computing, Macquarie University, Sydney. He is also a Senior Lecturer in data science with Macquarie University and an Adjunct Academic in computer science with bbreak UNSW Sydney.