Dr. Riasat Azim

Assistant Professor, Dept. of CSE

ROOM: 337 (1-3) A

PABX: 5114

Email: riasat@cse.uiu.ac.bd


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

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.

Research Interest

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.

Professional Experiences

• 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.


Technical Expertise


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.


Journal Papers


A patient-specific functional module and path identification technique from RNA-seq data

Computational BiologyNetworking

Publication: Computers in Biology and Medicine

Author List: Dr. Riasat Azim,

Conference Papers


Deep Multi-Modal Approach for Protein Function Prediction and Classification

Computational BiologySignal Processing for Biomedical Applications

Conference: 2024 6th International Conference on Electrical Engineering and Information & Communication Technology

Author List: Riasat Azim,

ENRNN-AU-Net: A Hybrid Deep Learning Model to Classify and Segment Histopathology Images of Breast Cancer

Conference: Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS)

Author List: Riasat Azim,