Mohammad Ehsan Shahmi Chowdhury | Green University of Bangladesh

Mohammad Ehsan Shahmi Chowdhury

Lecturer & Program Coordinator

  • Masters of Science (M. Sc.), Department of Computer Science and Engineering, University of Dhaka
    • 2018 – 2020
    • CGPA 3.77
    • Ranked 3rd
  • Bachelor of Science (B. Sc.), Department of Computer Science and Engineering, University of Dhaka
    • 2013 – 2017
    • CGPA 3.57
    • Ranked 6th
  • Higher Secondary Certificate (HSC), Notre Dame College
    • 2010 – 2012
    • GPA 5.00
  • GCE O Level Examination, European Standard School
    • 2006 – 2010
    • SSC equivalent GPA 5.00
  • January 2020 – Present
    • Lecturer
    • Department of Computer Science and Engineering
    • Green University of Bangladesh

Journals:

  • Mohammad Ehsan Shahmi Chowdhury, Chowdhury Farhan Ahmed, and Carson K. Leung. 2021. “A New Approach for Mining Correlated Frequent Subgraphs”, ACM Trans. Manage. Inf. Syst. 13, 1, Article 9 (March 2022), 28 pages. DOI: https://doi.org/10.1145/3473042

Conferences:

  1. M. E. S. Chowdhury, N. Ahmed and L. Jamal, “A New Perspective in Designing an Optimized Fault Tolerant Reversible Multiplier,” 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2019, pp. 274-279, doi: 10.1109/ICIEV.2019.8858530.
  2. “Conventional Multiplier” part of the Honors Thesis has achieved the “Best Paper Award” under the category Computing and Statistics for the paper “A New Perspective in Designing an Optimized Fault Tolerant Reversible Multiplier” in Students Conference of Science and Engineering (SCSE) 2017 hosted by IEEE branch of Dhaka University
  • Best Paper Award achieved
    • Paper “A New Perspective in Designing an Optimized Fault Tolerant Reversible Multiplier“
    • Category of Computing and Statistics
    • Students Conference of Science and Engineering (SCSE) 2017
    • hosted by IEEE Branch, Dhaka University
  1. Postgraduate Thesis : Mining Correlated Frequent Subgraphs
    • Extract and mine important association rules from frequent subgraphs
    • Proposed new useful measures and methods to generate useful rules as a complete framework for all types of graphs
    • Generated impressive results tested on benchmark graph datasets
  2. Undergraduate Thesis : Efficient Approaches to Design Fault Tolerant Reversible Multipliers
    • Proposed a new method to carry out normal conventional multiplication, using separate reversible circuitries
    • Designed new and better modular multipliers and hence optimize them using fault tolerant reversible logic gates
  • Skills
    • Programming Languages     : C, JAVA, Python
    • Software Packages     : Microsoft Visio, GIT (Version control Software), MS-Office, NetBeans, Eclipse
    • Sub-fields     : Data Mining (Graph Mining), basic Machine Learning algorithms, simple deep learning tools like Neural Network
    • Database     : Oracle 10g XE, SQLite
  • Personal Interests
    • Music                                     : Singing and also just listening to music for my attraction towards mind-soothing or any types of melody; music creates my heart
    • Sci-fi movies and series        : Watching movies for entertainment and new revolutionary & complex ideas
    • Travelling                               : Touring to different countries with the family and friends