Research Assistant
HIV Project University of Wisconsin – Madison
August, 2015 to Present
August, 2015 to Present
- Working with genomic data science to predict interesting patterns in gene expression data.
- Performing statistical analysis in R and biological analysis on gene expression data to get better understanding of signaling pathway within HIV infected cells.
Research Interest
- Big Data
- Bioinformatics
- Machine Learning
- Data Mining
- Software Engineering
Undergraduate Thesis
Web Access Sequential Pattern Mining:
Much research has been done on discovering interesting and frequent user access patterns. WAP-mine algorithm is obviously a novel data structure in this field. However, WAP-mine requires re-constructing large numbers of intermediate condition WAP-trees during mining, which is also very costly. In my thesis work, an efficient single pass web access pattern mining algorithm has been proposed based on WAP-tree structure, known as iWAP (improved Web Access Pattern mining algorithm). To support interactive and incremental mining techniques, this algorithm eliminate finding frequent events from database and record the whole web access sequence database in WAP-tree. So, the key consideration is minimizing time requirement to build WAP-
tree and also how to facilitate dynamic mining techniques.
Publication
Tarannum Shaila Zaman, Nafisah Islam, Chowdhury Farhan Ahmed, Byeong-Soo Jeong “iWAP: A Single Pass Approach for Web Access Sequential Pattern Mining”, GSTF International Journal on Computing Vol.2 No.1, April 2012. [Link]