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Anika Rahman, M.Sc. in CSE

Lecturer

Department of Computer Science and Engineering

anikarahman37@gmail.com, anika.rahman@stamforduniversity.edu.bd

01942211865

Office: A/401, 51 Siddeswari Road, Dhaka

Profile

Anika Rahman is currently working as a Lecturer in the Department of Computer Science and Engineering of Stamford University Bangladesh. Prior to joining Stamford University Bangladesh, she worked as an Adjunct Lecturer in the Department of Computer Science and Engineering of Ahsanullah University of Science and Technology & Southeast University. She has completed her Bachelor of Science (Engg.) in Computer Science & Engineering from Ahsanullah University of Science and Technology and also completed her Masters of Science in Computer Science & Engineering from BRAC University.

Education

  • • M.Sc. in CSE from BRAC University • B.Sc. in CSE from Ahsanullah University of Science and Technology

Research Interest

• Artificial Intelligence • Machine Learning • Deep Learning • Natural Language Processing • Explainable AI • Data Science • Data Mining

Work Experience

  • • [2024-Present] – Lecturer, Stamford University Bangladesh. • [2023-2024] – Adjunct Faculty, Southeast University • [2022-2023] – Adjunct Faculty, Ahsanullah University of Science and Technology

Courses Taught

  • • Introduction to Computer Systems • Digital System Design • Numerical Methods and Computer Programming Lab • Numerical Methods Lab • Programming Language – I (C) • Graph Theory • Discrete Mathematics • Compiler (Theory & Sessional) • Computer Graphics (Theory & Sessional) • Computer Fundamentals

Publications

Conference Proceedings:

  1. A. Rahman and Mst. T. Khatun, “Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023),” International Journal on Cybernetics & Informatics, vol. 14, no. 1, pp. 27–40, Jan. 2025, doi: 10.5121/ijci.2025.140103.
  2. A. Rahman and M. T. Khatun, "Multivariate Analysis of Urban Air Pollution: Clustering and Patterns Across Major Asian Cities," 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), Sydney, Australia, 2024, pp. 462-467, doi: 10.1109/FMLDS63805.2024.00087.
  3. A. Rahman and M. G. Rabiul Alam, "Explainable AI based Maternal Health Risk Prediction using Machine Learning and Deep Learning," 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2023, pp. 0013-0018, doi: 10.1109/AIIoT58121.2023.10174540.
  4. M. H. K. Mehedi et al., "Automatic Bangla Article Content Categorization using a Hybrid Deep Learning Model," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 19-25, doi: 10.1109/R10-HTC54060.2022.9930103.
  5. K. O. Faruk et al., "Decentralized Neural Network Based Collaborative Filtering For Privacy Concern Recommendation Systems," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 432-437, doi: 10.1109/R10- HTC54060.2022.9929681.
  6. I. Nessa et al., "Recruitment Scam Detection Using Gated Recurrent Unit," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 445- 449, doi: 10.1109/R10-HTC54060.2022.9929928.

Book Chapter:

  1. Faruk, K.O. et al. (2023). K Nearest Neighbour Collaborative Filtering for Expertise Recommendation Systems. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham.

Research & Awards / Awards and Recognitions

  • Dean's list of Honor
  • Best Presenter Certificate

 

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