ISRT Alumni (ISRTAA) Reunion 2023 is on 18 February, 2023

ISRT Alumni (ISRTAA) Reunion 2023 will occur on February 18, 2023, on ISRT premises. All graduates are encouraged to attend the event. Details of the reunion event can be found on the website https://isrtalumni.org/reunion2023 and the reunion registration site (http://isrtalumni.org/reunion2023/reunion-2023-registration).

Seminar on Classification and Clustering for RNA-seq data with variable selection 

Speaker: Tanbin Rahman PhD, FDA, USA Title: Classification and Clustering for RNA-seq data with variable selection Abstract: Clustering and classification play an important role in identifying sub-types of complex diseases as well as building a predictive model in the field of medicine. In recent years, lowering of cost and high accuracy has made RNA-seq widely popular which

Applied Statistics Seminar on “Pairwise Accelerated Failure Time Models for Infectious Disease Transmission Within and Between Households”

isrt seminar room

Abstract: Pairwise survival analysis handles dependent happenings in infectious disease transmission data by analyzing failure times in ordered pairs of individuals. The contact interval in the pair ij is the time from the onset of infectiousness in i to infectious contact from i to j, where an infectious contact is sufficient to infect j if he

Seminar on “Data Science Product Development In the (AWS) Cloud” at 2 pm on July 24, 2023

Speaker: Sheikh Samsuzzhan Alam, Senior Data Science Developer for Operation, Novartis Pharma, Czech Republic Venue: ISRT seminar room Date and time: 2 pm on Monday, 24 July 2023 Title: Data Science Product Development In the (AWS) Cloud Abstract: Customer-facing software products are complex in nature and usually developed by multiple teams of engineers. On the other hand, software products

Applied Statistics and Data Science Seminar on Monday, August 21, 2023

Title: Hierarchical structural component models for pathway analysis of longitudinal categorical phenotypes Presenter:  Md. Kamruzzaman, PhD Associate Professor, Jagannath University, Email: kzaman1@isrt.ac.bd   Abstract: Several statistical methods for pathway analysis have been developed to test the association between pathways and phenotypes of interest. Since pathways are highly correlated, a hierarchical structural component models (HisCoM) was developed