27.03.2024What made you decide to pursue a career in IT research?
I chose to pursue a career in IT because, from a young age, I was inherently drawn to puzzles and the challenge of uncovering underlying rules within complex environments.
I realized during my BSc studies that I possessed strong research abilities. I enjoyed every subject that involved some combination of math and experimentation. My fascination with unravelling intricate systems and discovering patterns naturally led me to the dynamic and ever-evolving field of AI, where I find joy in exploring the intersections of technology, science, and innovation.
Throughout my academic journey, I became a staunch advocate for the hypothesis-experiment-conclusion framework, which aligns seamlessly with the iterative nature of AI research. This methodology not only fuels innovation but also fosters a deeper understanding of the technologies we develop.
As an AI enthusiast and advocate, I find immense fulfilment in pushing the boundaries of what is possible in the field.
Briefly, what is your current research at IDEAS NCBR about?
My research interests lie in utilizing AI to advance medical image analysis, with a specific focus on representation learning and robustness. Currently, I’m working on two projects: developing new biomarkers for cardiovascular disease from mammograms and improving follicular lymphoma detection. Engaging in research in AI within the field of medicine offers a profound sense of fulfillment as it allows me to merge my passion for technology with the noble pursuit of improving healthcare outcomes (in the vein of AI for Good movement).
Do you think there is a gender disproportion in the number of AI/ML experts?
Certainly, there is. It’s even more evident when you look for female leaders in AI/ML. I don’t think there is any current study on the state of the AI/ML field, but I would expect a 15-85 female-male proportion.
What could be the reason for so few women to decide to pursue a scientific career in AI/ML?
If I were to list the top three factors, I’d emphasize historical bias, unconscious bias in IT culture, and, to a lesser extent, inequality and barriers in education.
The tech industry, including AI and machine learning, has historically been male-dominated, with societal biases and stereotypes that dissuade women from pursuing careers in STEM fields from an early age. These biases may manifest in various forms, such as a lack of encouragement and gendered expectations, ultimately resulting in fewer women entering AI/ML professions.
Furthermore, IT culture is strongly masculinized, perpetuating stereotypes such as the typical IT guy being unable to communicate with the outside world, which might discourage young girls from engaging in any IT-related activities.
What, in your opinion, could change that situation?
To address the gender disparity in IT, several key strategies could be implemented.
Firstly, promoting diversity and inclusion initiatives within the tech industry is crucial. This involves actively recruiting and supporting women in AI/ML roles, creating inclusive work environments, and providing opportunities for career advancement.
Implementing policies that support parental leave, flexible work arrangements, and childcare support can help alleviate the burden on women in the workforce, enabling them to maintain career continuity and advancement opportunities in AI/ML roles.
Additionally, fostering a culture that values work-life balance and recognizes the importance of caregiving responsibilities for both men and women can contribute to creating a more inclusive and supportive environment for women.
Secondly, it’s essential to break cultural stereotypes perpetuated by e.g. the comedy series, such as The IT crowd (I enjoyed it though!) by showcasing diverse role models and leaders in the field. Strong leader figures who are women can serve as inspiring role models, challenging stereotypes and encouraging more women to pursue careers in AI/ML.
Lastly, fostering early interest and engagement in STEM fields among young girls through educational programs and initiatives can help broaden the pipeline of future female AI/ML experts. By collectively addressing systemic barriers, promoting inclusivity, and work-life balance and highlighting diverse voices and perspectives, we can work towards a more equitable and diverse AI/ML community.
How can women contribute to the development of this area of science?
I don’t believe that women have any other development path or other voice/perspective than men themselves. As researchers we are equal.
If you were to point out one character trait that describes a good scientist, what would it be?
I’m certain about the top two: curiosity and determination. A perfect combination for any success when applied with caution.
Aleksandra Osowska-Kurczab, researcher on AI in medicine at IDEAS NCBR, recently defended her PhD thesis in Computer Science at Warsaw University of Technology. She’s also ML tech lead at Allegro.pl. She gained expertise in successful ML applications in medicine during her PhD studies, NCN grant, and a few research exchange programs (most recently at Radboud UMC, NL). She has more than 5 years of commercial experience in deploying Machine Learning solutions at Samsung R&D Poland and more recently at Allegro.pl, where she leads a research team on large-scale recommendation systems. Her research interests include robust deep learning and representation learning. In her spare time – pianist, geek and tennis enthusiast.
