Research in Computer Science and Engineering

AI and Data Analytics

The school of computer science and Engineering has well established laboratory and team of researchers working in the area of Deep Learning, 3D reconstruction, video processing, vision applications and distributed computing.

Ongoing focused research areas are data analysis (modelling, predictive analysis, pattern recognition) to extract knowledge and insights for applications in the areas of innovative education Assessment (CEER), IHDS (Indian Heritage in Digital Space), Smart campus (Security and surveillance) and smart city, enhancing the performance of distributed computing systems.

The research group also has grants from DST sanctioned projects in the area of Indian Heritage in Digital Space and Underwater IP and 3D reconstruction.

Focused research themes:

  • Image and Video analytics.
  • 3D Data Representation and Analysis, Object Categorization, Super Resolution, Hole Filling and Inpainting.
  • Development of framework for filtering and categorization of crowd sourced data to provide 2D and 3D data processing methods/algorithms for the presentation of the crowd sourced data: Categorization, Filtering,
  • Super-Resolution, Object detection.
  • Optimization of AI algorithms for On Device.
  • Enhancing the performance of distributed computing using sharding techniques.

Network Engineering

The networking research group is working towards cross-layer optimization algorithms for joint routing, rate adaptation and power control for multi hop access networks like sensor and mesh. The group is also working in the in the areas of virtualization, cloud computing and software defined networks. Some of the current research topics are energy-efficient resource management in cloud data center networks and Intrusion detection in software defined networks.

The center for Cloud computing and Networking has a Cloud testbed of 4 blade servers, Juniper Networks switches/routers and Qualnet simulator for carrying out the research.

Research problems we are addressing include:

  • Cross-layer design for joint routing, scheduling and rate adaptation in multi-radio infrastructure wireless mesh networks.
  • Energy efficient resource management in data center networks.
  • Intrusion detection in software defined data center networks
  • SLA-violation detection in Cloud Environment using Blockchain
  • QoS Provisioning in 5G networks
  • Data traffic aware co-operative transmission for network resource optimization.

High Performance Computing

This research group focuses on the application of Parallel Computing, many-core computing and heterogeneous computing to solve large-scale scientific problems. The group has Center for High Performance Computing which has DGX-1 Server, Apple Mac systems and other multicore systems supporting the University as a Central facility for researchers, faculty and students.

Research problems we are addressing include:

  • A Framework for Power-Aware Scientific Application Development on Multicore Processor.
  • Speedup of Distributed Deep Learning Algorithm for Healthcare Application
  • A Distributed Deep Learning Framework for Detection of Deforestation
  • Automated Earned value tracking in 5D BIM models using Machine Learning Algorithms.
  • Diabetic Retinopathy Grading via Deep Convolution Networks

Collaborations with Other Universities

  • Volgograd State Technical University
  • Bennett University (Along with Royal academy of Engineering, Brunel University, London)