Rate My Professor Ashiq Anjum

Anjum's research specializations include data intensive distributed systems for high performance analytics, distributed and scalable machine learning models, self-adapting federated networks of digital twins, and physics-informed neural networks for trustworthy digital twins emulating cyber-physical systems, with applications in telecoms, aerospace, earth observation, digital manufacturing, healthcare, rail, automobile, and space sectors. His research has been funded through grants including EPSRC projects EP/Y00597X/1 on AI-driven digital twins for net zero and EP/Y018281/1 for clinical care, as well as EU-funded projects such as Health-e-Child (IP, FP6), neuGrid (STREP, FP7), and TRANSFORM (IP, FP7). For the last twenty years, he has collaborated with CERN Geneva on distributed analytics platforms for LHC data comprising hundreds of petabytes. He serves as AI lead for the £60 million METEOR flagship project at Space Park Leicester. Key publications include "Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics" (IEEE Transactions on Sustainable Computing, 2023), "Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks" (Pattern Recognition, 2022), "A Deep Learning based Explainable Control System for Reconfigurable Networks of Edge Devices" (IEEE Transactions on Network Science and Engineering, 2022), "RES: Real-time Video Stream Analytics using Edge Enhanced Clouds" (IEEE Transactions on Cloud Computing, 2020), and "Deep Learning Hyper-parameter Optimisation for Video Analytics" (IEEE Transactions on Systems, Man and Cybernetics: Systems, 2018).