In this feature, the Editor-in-Chief of StartuptoEnterprise, Linda Ashok (LA), talks to Pakistani-American Data Scientist and Olympic Weightlifter Kulsoom Abdullah (KA) to present her talk on machine learning in the healthcare sector at the Digital Innovation Virtual Conference scheduled for Nov 5, 2020.
LA: It is a minority to be i) a Pakistani in America, ii) a woman in data science, and iii) an Olympic weightlifter. How do you bring all these identities together to overtake the majority?
KA: Being a minority doesn’t affect me personally but professionally when I don’t see many minorities equally represented in a group of people flock at conferences, or say, at the daily workplace.
LA: Kulsoom, I hear you. Would it be okay for you to share any experience you had as a Pakistani-American woman data scientist at work? And then also suggest how such a situation can be much better?
KA: It is tough to have a voice in a male-dominated environment, tougher to be a minority in that environment, and the toughest when the organization has no structured human resource team to stand up for the minority.
For example, I worked for a firm where I was not given structured tasks. I was experimenting, trying to figure things out. A male colleague got furious at me because I was slow in processing the ambiguity of the assigned task. Here is the thing, as data scientists, we use various coding languages, and it is not likely we know it all; however, we have the foundational readiness to pick up the new but with time. During this hour of preparation, the colleague got mad at me, which I deem is very unbecoming of a system not to allow enough learning curve. And it is not for a minority, irrespective of your nationality or technical brilliance; we all need time to level up.
In retrospection, the incident could have been prevented if there were any solid HR policies in place. I feel every company must have to be active in their efforts toward an inclusive and caring environment. Else, a lack of a strong HR framework allows mainstream toxicity to perpetuate the minority in terrifying ways that denigrate mental health and, consequently, business productivity.
A productive and high-performing workforce is achievable through diversity and inclusion. An organization has to be in charge of its workplace environment to steer clear of radicals corrosive to mental well-being. The moment someone like me feels that there’s no support available from the organization’s end, it is in the best interest to step down and focus on what can be done.
LA: Talking about mental health, Kulsoom, do you think you cope better with workplace stress and general socially induced anxiety as an Olympic weightlifter? Also, tell us of your journey with weightlifting so far, highlighting your motivation and challenges.
KA: I would say that Olympic weightlifting helped me physically and mentally. It still does today. And then, my experiences in competing added more to my life experiences.
I took Taekwondo in graduate school and reached the black belt level because of a German lady studying in the same graduate school and training in Taekwondo. She was already trained in Jujitsu and was a part of the German national team. On hearing my story, she motivated me to start the TKD class in graduate school, which eventually took me to the black belt.
Now, women were generally advised to do light weightlifting and several reps. In my case, I wanted to accelerate my training and make rapid progress. When I graduated, I learned about Olympic weightlifting—the elements of speed, timing, technique, and mobility interested me to get trained at a local CrossFit gym. My coaches pepped and prepared me over two years to finally get me to compete.
LA: Lovely. Now Kulsoom, do you think there is a gender gap in data science at both university and business levels? If yes, how do you think we can collectively bridge that divide?
KA: Yes, a gender gap in data science exists in both universities and businesses. Universities and businesses in data science must work actively to close this gap. It is promising to notice many elementary schools actively promoting STEM among young girls to build future unbiased leadership in this context.
LA: What are you currently working on?
KA: Currently, I am working on data models to detect fraud, abuse, overpayments, etc. in the health technology space concerning health insurance claims and electronic medical records of patients.
I am also working on several open-source projects of independent interest and as a volunteer Machine Learning engineer for Omdena, “a collaborative platform and social enterprise where a global community of changemakers builds innovative and ethical AI solutions to real-world problems.” Source
LA: Kulsoom Abdullah is one of the esteemed speakers at the Digital Innovation Virtual Conference by InnovationDigi on Nov 5, 2020. In this regard, do you think women in tech or women who code have adequate chances to voice their vision at large technology conferences? How do you think conference curators can consider having enough women and the nonbinary presented?
KA: I have heard the theory that it is a pipeline problem that there aren’t enough non-male tech people to connect and invite as speakers. In an incident I am aware of, the event organizers blatantly informed of not having many female presenters. It was then a list of high-performing women professionals was created and passed on to the organizers. This list was compiled by women themselves or their allies. Also, it helps to have i) mentors who are experienced speakers in preparing other women for a chance to speak, ii) make physical conferences more safe and secure for more women to join, and iii) have male allies who believe in equal opportunity for women and appreciates diversity in gender and critical thinking.
LA: At the Digital Innovation Virtual Conference 2020, what areas will you cover during your session?
KA: At the Digital Innovation Virtual Conference 2020, I will discuss how Artificial Intelligence (AI) through Machine Learning (ML) can nab healthcare fraud. In doing so, I will illuminate how the healthcare industry suffers due to consistent data fraudulence, how AI/ML can assist auditors with claims processing, streamlining pre-pay, post-pay, and adjudication, demonstrate the impact of ML in separate parts of claims processing; human in the loop option and other challenges. I will also talk about how data is labeled, explored, edited for inconsistencies, and measured for evaluation, among various other things.
LA: I really look forward to hearing you speak on Nov 5, 2020. I am sure it will be a great success for voices matter, and we have got you razor-sharp!