Dr. Arjumand Younus: Leveraging AI to foster Social Good

Dr. Arjumand Younus is an Assistant Professor (Computational Social Science) at UCD School of Sociology. Formerly, she was a Research Scientist at Afiniti AI and a part-time lecturer at 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 Ph.D. in Computer Science from the 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). 

She is the recipient of the Google Women Techmakers scholarship for Europe, the Middle East, and Africa region. 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.

Dr. Arjumand, in this engaging conversation, tells us about how data science can be used for social good. 

First of all, can you please tell us about your educational background? 

My bachelors was in computer science from Karachi University (KU) after which I moved to South Korea, KAIST, also known as Asian MIT, for a Masters in computer science. Afterwards, I came to Ireland to do my PhD in Computer Science, which was a joint program between the University of Milano, Bicocca, and the University of Galway. 

What is your current place of work and what do you do?

I’m currently working at University College Dublin (UCD) which is one of the top universities in Ireland as an assistant professor in Computational Social Science in the School of Sociology. My job is to teach computer science to social science people.

Well, that’s some crossover. Can you please tell us what got you interested in data science and data analysis in the first place? What was the one point where you realized you should start exploring data science?

It was all a coincidence. When I was doing my intermediate back in 2004, there wasn’t a lot of guidance and especially since I belong to a family where there weren’t a lot of educated people. I was good at Maths, very good in fact so engineering was a natural choice. I applied to the NED computer science program, but my high school percentage was not enough to get in. I got admission in civil engineering but according to my father, it was not a good career choice for girls and that there was no scope of civil engineering whatsoever. So I took his advice reluctantly and applied to the KU Computer Science test and cleared it. I dropped the NED and went to KU, and it turned out to be the best decision. 

There was not much awareness nor were there many platforms where you could learn much. When my fourth year began at KU, I started developing an interest in writing research papers. My interest developed in search engines and information retrieval, and that was the beginning of data science, as we know it now. After that, I moved to  South Korea where I got familiar with machine learning, databases, and the like. And so that’s how I started developing interest in data science.

So Arjumand can you tell us what are the biggest areas of opportunities or questions that you want to tackle or generally which data science can cater to in the future?

I think a particularly untapped area is humans and society. For example, how data science can help social good and this is something which we, in Pakistan and the countries in the Global South, even India, are not working on currently. We hear about India getting ahead of us in technology, but that is limited to the economic arena. We are not developing our own solutions for our own people and not thinking of ways in which data science can help solve the problems of our society. I think that bringing ideas from interdisciplinary fields like sociology and merging them with data science is going to be the next thing. And this is what I believe is the next hot opportunity to explore.

That’s very interesting. What do you think are the essential skills that data scientists need in order to excel in the big data field?

First of all, there should be no fear and there should be willingness to learn new concepts. Don’t limit yourself. Secondly, keep a keen eye to read through huge amounts of data because even Andrew Ng said that your machine learning or your data science algorithm is only going to be as good as the data you have. So the first step is to look into the data manually and see the insights. You need to have a qualitative sense or sense of being able to extract the insights from data yourself to be a good data scientist. 

Would you like to give any piece of advice for data science students or practitioners who are just starting out and are just in the initial stages of exploring the field of data science?

I would say, develop a habit of reading and that too from areas other than data science. The best ideas come when you’re exploring and talking to people who are not in your field. For example, if you think about politics, examine how politics and data science can merge. 

In terms of the technologies, I would say tools like ChatGPT can be explored more in detail. Interestingly, the idea of ChatGPT started four years back when transformer models came into picture and a Google researcher, Ashish Vaswami, published an article called Attention is All You Need that pioneered it. Hardly anyone paid attention at that time since no one reads journal articles. But that is exactly where you get a glimpse of what’s happening around the technological world. So to get ahead of the curve, you need to keep an eye on the big-time researchers in the field of data science.