Anabia Alam

Anabia Alam

Class of 2018
BS Computer Science

Core Skills

  • Analytical Skills
  • Predictive Modeling
  • Algorithms
  • Python
  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Deep Learning
  • Artificial Intelligence (AI)
  • SQL, R

Academic Awards / Achievements

  • Merit Scholarship, Aug 2014 - May 2018

Experience

Leadership / Meta-curricular

  • Member WiCSE, Nov 2015 - April 2018
  • Participant Data Science and Machine Learning Workshop, July 2017
  • Outreach Officer, Brain.Hack(), Nov 2015 - March 2017
  • Secretary - Society for Sustainability, Nov 2015 - Sept 2017

Internship / Volunteer Work

  • Admin Assistant, HU EHSAS Centre, July 2017 - Dec 2017

Final Year Project

Project Title

Characterization of Genetic Variations in two Local Varieties of Mango: A Multidisciplinary Approach

Description

Mango is one of the world’s major fruit crops. In Pakistan, mango is the most abundant fruit crop with production exceeding millions of tons. This project takes a multidisciplinary approach involving techniques from genomics, systems biology, and bioinformatics to understand the genomic architecture of mango. Two local varieties/cultivars of mangoes, Kaa’ala Chaunsa and Sindhri, were chosen because of their respective physical traits, amount of fibre content, and shelf life. The project identifies genetic variations in the selected mango variety at a genome-wide level and further characterizes them into different regions of the genes. It also identifies variations shared between or unique to sequenced cultivars. This project stemmed from a workshop on Computational Biology held at Habib University by LUMS professor Dr Aziz Mithani, who is also Anabia’s external advisor.

Project Pictures