Faculty Profile

Azim, Riasat

Assistant ProfessorMobile : 01796619200Email : riasat@cse.uiu.ac.bd

Ph.D. Computer Science & Technology, Hunan University, Changsha, Hunan, China.(Ongoing).

M.Sc. Engg. Computer Science and Technology,  Wuhan University of Technology, Wuhan, Hubei, China.

B.Sc. Engg. Computer Science and Engineering,  Khulna University, Bangladesh.

Higher Secondary Certificate: Cantonment Public School & College, Parbatipur, Dinajpur.

Secondary School Certificate: Cantonment Public School & College, Parbatipur, Dinajpur.

  • Azim, S. Wang, “CDSImpute: An Ensemble Similarity Imputation method for single-cell RNA sequence dropouts,” Computers in Biology and Medicine, Elsevier, vol. 146, 105658, Aug. 2022, doi: 10.1016/j.compbiomed.2022.105658. (SCIE, Q1, IF: 6.698)


  • Azim, S. Wang, S. Zhou, and X. Zhong, “Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data,” Cell Cycle, Taylor & Francis, vol. 19, no. 16, pp. 2028–2039, Aug. 2020, doi: 10.1080/15384101.2020.1789315. (SCIE, Q2, IF: 5.173)


  • Azim and S. Wang, “Cell-specific gene association network construction from single-cell RNA sequence,” Cell Cycle, Taylor & Francis, pp. 1–16, Sep. 2021, doi: 10.1080/15384101.2021.1978265. (SCIE, Q2, IF: 5.173)


  • Zhou, S. Wang, Q. Wu, R. Azim, and W. Li, “Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression,” Comput. Biol. Chem., Elsevier, vol. 85, p. 107200, Apr. 2020, doi: 10.1016/j.compbiolchem.2020.107200. (SCIE, Q2, IF: 3.737)


  • Elahe MF, Kabir MA, Mahmud SMH, Azim. Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle. Electronics. 2023; 12(1):55.(SCIE, Q2, IF: 2.69)


  • R Azim, W Rahman, MF Karim,” Bangla hand-written character recognition using support vector machine”, 2016, International Journal of Engineering Works 3 (6), 36-46.


  • R Azim, AM Rahman, S Barua, I Jahan,” Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach”, 2016, Journal of Data Analysis and Information Processing 4 (03), 101.


  • R Azim, S Barua, J Rahman, ABMM Rahman,” Enterprise Knowledge Collaboration for Decision Making Based on Cloud Big Data Using HDFS”, 2016, INNOVATION AND MANAGEMENT, 1801.

Research Interest
Single-Cell RNA-Sequence, Genetics, Biological Networks, Genomics, Medical Computing, DNA Methylation, Deep Neural Networks, Cancer, Belief Networks, Bioinformatics, Diseases, Evolutionary Computation, Genetic Algorithms, Optimization, Probability, Tumors, Pattern Classification, Molecular Biophysics, Support Vector Machines, Proteins, Cellular Biophysics.

Ongoing Projects

  • Deep Neural Network based Cancer Prediction from RNA-seq.
  • Cellular Heterogeneity and Single-cell lineage Analysis using single-cell RNA-seq.
  • Spatial transcriptome profiling and Heterogeneity Analysis.

• August 2012 – September 2014 : Software Engineering, SmartAspects, Dhaka, Bangladesh.

• June 2015 – March 2016 Teaching Assistant, Wuhan University of Technology, Wuhan, Hubei, P.R. China.

• July 2016 – August 2017: Data Analyst & software engineer, S3Innovate Pte. Ltd., Singapore.

• May 2021 – December 2022: Lecturer, Computer Science & Engineering Northern University Bangladesh, Bangladesh.



Introduction to Bioinformatics, Statistical Analysis in Bioinformatics, Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays, Genome data science. Experience with next-generation sequencing tools and techniques, statistics and bioinformatics, relational databases, and data mining. Also, have expertise on

  • miRNA disease Association.
  • Single Cell RNA-Sequence analysis.
  • Methylation analysis.
  • Differential gene expression analysis.
  • Spatial Transcriptome.

Programming Languages & Systems

  • R, Python, .Net, C#, Java SE, Java EE, Angular JS, C, Java Script, Ajax, C++, LATEX,
  • SQL, MySQL.
  • Windows, Linux, Ubuntu.


Good understanding of biostatistical methodology and tools. Excellent statistical analysis skills of high-throughput biological data, including RNA-seq, protein array and Illumina Methylation Assay.

Artificial Intelligence & Machine Learning

Extensive working knowledge of working with Artificial Intelligence and Machin learning algorithms such as deep convolutional network, artificial neural network, support vector machine, component analysis, attribute reduction, regression, clustering and classification algorithms.  Experience with Algorithms development and Scientific computing. Hands-on experience in developing Bioinformatics Analysis Algorithms for sequencing data analysis using Artificial Intelligence and Machin learning algorithm.

Application & Tools

  • David: Functional Annotation Bioinformatics Microarray Analysis
  • GSEA: Gene Set Enrichment Analysis(GSEA)
  • Cytoscape: complex network visualization.
  • G Profiler.
  • Kaplan Meier plotter.
  • DrugMap

General Capabilities

Algorithm development with a particular focus on performance; automated, database-assisted or custom bioinformatics pipeline-controlled evaluation of complex biological data; data visualization; automated statistical evaluation.