Mr. Sounak Banerjee | Resource Management in Cloud | Best Researcher Award

Mr. Sounak Banerjee | Resource Management in Cloud | Best Researcher Award

Mr. Sounak Banerjee, Jadavpur University, India

Sounak Banerjee holds a Ph.D. in Computer Science & Engineering from Jadavpur University, focusing on resource management in cloud computing environments. Prior to that, he completed his M.Tech. in Computer Science & Engineering at the University of Calcutta, where his thesis centered on Customer Purchase Behaviour Analysis using Machine Learning techniques. His academic journey includes achievements such as qualifying in GATE CSE and UGC NET JRF. He has also undergone project-based training in J2EE and ASP.NET from CMC Academy and Simoco Educational Development & Application Initiative, respectively.

Professional Profile:

Scopus

๐Ÿ“š Education:

  • Ph.D., Computer Science & Engineering, Jadavpur University, Research Area: Resource Management in Cloud Computing Environment.
  • M.Tech. in Computer Science & Engineering, University of Calcutta, Thesis Title: Customer Purchase Behaviour Analysis using Machine Learning.

๐Ÿ’ป Skills:

  • Languages: Bengali, English, Hindi.
  • Coding: C, C++, Java, PHP, Python, SQL, XML, Dart, LaTeX.
  • Software: MySQL, Weka, Visual Studio, Anaconda, Android Studio.
  • Operating System: Windows, Linux, MacOS.

๐Ÿ† Awards and Achievements:

  • Qualified in GATE CSE.
  • Qualified in UGC NET JRF.

๐Ÿ“œ Certification:

  • Project-Based Training on J2EE, CMC Academy.
  • Project-Based Training on ASP.NET, Simoco Educational Development & Application Initiative.

Publication Top Notes:

  1. Sensitive Electrochemical Detection of Carvacrol using Carbon Paste Electrode
    • Published: 2022
    • Journal: 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022)
    • Cited by: 2
  2. SLA-aware Stochastic Load Balancing in Dynamic Cloud Environment
    • Published: 2021
    • Journal: Journal of Grid Computing
    • Cited by: 5
  3. Efficient resource utilization using multi-step-ahead workload prediction technique in cloud
    • Published: 2021
    • Journal: Journal of Supercomputing
    • Cited by: 14