Arjumand Younus

Dr Arjumand Younus is a Research Scientist in Afiniti AI, and a part-time lecturer in Technological University Dublin. Before this appointment, Arjumand has contributed to SFI funded projects during her different post-doctoral positions at CONSUS-UCD and INSIGHT-UCD. She is also serving in the capacity of co-director for Women in Research Ireland which is a volunteer-run registered charity working for better representation of women and under-represented groups in academia.

Arjumand received a joint PhD in Computer Science from National University of Ireland Galway (Ireland) and University of Milano-Bicocca (Italy), MS degree in Computer Science from Korea Advanced Institute of Science and Technology (South Korea), and BS in Computer Science from the University of Karachi (Pakistan). Her research focuses on Machine Learning, Natural Language Processing, and Data Science for Social Good. Arjumand is passionate about the value of artificial intelligence technology to make society better, and at the moment is involved as an academic partner in various AI for Social Good projects.


Sessions

07-15
15:30
30min
Python for Arts, Humanities and Social Sciences
Arjumand Younus, Dr. Muhammad Atif Qureshi

Computational methods particularly those involving data analytics are now taking root in various humanities disciplines. However, students and researchers working in these disciplines lack the necessary programming proficiency and coding experience . The need then arises to make Python-based computational methods accessible – we present case-studies of how to do this via various Python modules being taught at College of Business in Technological University Dublin and by means of walkthrough of an interdisciplinary social good project called InEire. It comes down to complementing existing quantitative and qualitative methods with methods based on analysis of various types of data specific to the social science problem being solved. We essentially go through the process of building curiosity-driven exploration in social science students via a theoretically driven research question rather than the Python technique itself, and then focusing on the various steps involved in solving that question; and finally boiling it down to a concrete Python-based data analytics methodology. This project-based teaching methodology helped us develop Python skills in newbies eventually leading to a Python-based data analytic skills in students of disciplines other than Computer Science.

Education, Teaching & Further Training
Liffey A