Diversifier les STEM ; l’importance de la représentation, de l’équité et de l’inclusion

Pouvez-vous vous présenter et nous parler de votre parcours en biophysique, neurosciences et science des données biomédicales ?

Je m’appelle Paula Petrone et je suis argentine. J’ai d’abord étudié la physique, puis mon parcours s’est orienté vers la biophysique et les sciences de la vie. Depuis une dizaine d’années, je me consacre en grande partie à la science des données biomédicales, qui est un domaine que j’affectionne. Chaque jour est différent et chaque projet offre une variété de défis et d’opportunités d’apprendre. Pendant l’afflux de COVID, j’ai lancé ma propre startup dans l’analyse de données biomédicales, et j’ai également lancé mon propre laboratoire universitaire à ISGlobal. Dans mon laboratoire, nous avons plusieurs projets qui traitent de différents problèmes biomédicaux tels que le COVID, le paludisme, Chagas et la santé mentale, traitant de différentes données biomédicales, y compris l’imagerie.

Pendant SLAS Europe 2022, vous participez à une session spéciale intitulée « Diversité, équité et inclusion ». Pouvez-vous nous donner un aperçu de ce que les téléspectateurs doivent attendre de cette conférence ?

Nous essayons de discuter – et de sensibiliser – la question de l’inclusion sur le lieu de travail. Nous pensons que c’est un sujet très important, particulièrement à cette conférence.

Essentially, we want to make people think about their work-life balance and the inclusion of different genders and backgrounds. I think that is very important. I am very proud of the organizers for having that session and that panel.

What does each part of diversity, equity, and inclusion in the sciences look like to you personally?

I work in STEM, which encompasses data analytics, mathematics, and physics. Unfortunately, we do not get to see a lot of women in these careers. I also dedicate time to mentoring and influencing women in starting and ultimately leading in these careers because it is very important that we have that gender perspective in data science and STEM. That diversity in the workplace is important because we get to analyze data from a different viewpoint and interpret results diversely.
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When we are talking about medicine, women are usually ignored in datasets. When you think about capturing data at the hospital, we hardly ever capture things like menopause or menstrual periods, so that is an area in which I think women can really contribute, particularly to the type of questions that we ask and the way that we analyze the data, as well as the decision-making that we do.

According to the United States Census Bureau, 27% of the STEM workforce in 2019 were women, an increase of 19% since 1970. Why is it important that we continue to encourage diversity in the STEM fields?

First and foremost, it is important to note that 27% is not that much, especially when you think that today, many STEM university careers are increasing the number of female students, reaching 50% in some programs. The question is, then, what happens after education: where does the inequality come from?

One of the main things I think about is the coincidence between one of the most productive stages of a woman’s career and their reproductive cycle: women between the ages of 30 to 40 are often in a stage when they get to learn a lot and escalate in the leadership ladder, but it might also coincide with a time when many women think about beginning a family and often need or want to take time off work. This means that there can be challenges for women who want to progress in both.

The other reason I think women are often denied leadership roles is the prevalence of gendered stereotypes and how that impacts workplace roles. From the time we are children, women are encouraged not to misbehave and to be obedient in every aspect of our lives. 

This means that ambition and a healthy drive to succeed professionally can be misconstrued by society or interpreted negatively – which can detrimentally impact how women progress. 

Traits like this are essential for anyone to succeed in a leadership position – so it is important that we create an environment that fosters and influences female behavior to allow women to obtain those higher up-positions. To be a game-changer, sticking to the status quo is not always productive. 

Why do you believe there is ongoing under-representation and inequity in STEM subjects during higher education, as well as for individuals wanting to enter the STEM workforce?

This can be answered in two halves. The first half is the absence of role models for many women. Science – specifically, leadership in STEM careers  – is dominated by white men. This means that many people do not get to see themselves represented in this area.

The more women that we see in leadership positions, the better the diversity of leadership roles will be for the new generations to come. That is why, in Barcelona, where I live, I am an organizer for the Barcelona chapter of Women in Data Science, an annual conference for women in data science and STEM careers. Women in Data Science or WiDS is a conference that aims to inspire, educate and support women in data science all over the world. Organized for the first time by Stanford University in 2015, it turned into a worldwide conference in more than 60 countries.

The other part is inclusive job positions. What happens is that usually, people advertise for jobs using non-inclusive language. That is a problem because research shows that women do not tend to want to apply for positions that request ‘proficiency’ in this topic or ‘high expertise’ in other topics. Historically, women are inclined to apply for jobs that emphasize ‘nurture,’ ‘cultural development,’ ‘personal developments,’ ‘work-life balance’ and ‘opportunities to learn.’

This can be attributed to the fact that, as I said, women have not, by tradition, been encouraged to be ambitious or to think that they can do everything. This means that those people tend to have a lower level of self-confidence when it comes to applying for a job.

Positions that actively encourage women to apply – even explicitly in the text – are a good step to increasing the diversity and the gender balance in many STEM workplaces.

How do you believe society can improve the inclusivity and representation of marginalized groups in STEM, and what can we do to further diversify the sciences as a whole?

I think it is essential to have the conversation in a balanced way. There is a prevalent culture now – termed ‘cancel’ culture – which means that when there is a great deal of discourse about something, people become tired of it and begin to reject the topic.

Inclusion and diversity also include the important aspects of men wanting to have work-life balance and be present at home, particularly when it comes to child-rearing. It is important to have a new gender-neutral conversation that is not just focused on women and different ethnic groups but also focuses on men and their role in the workplace as a whole. Having a well-balanced conversation where everybody is included is the path to improving inclusion and diversity.

There have been enormous breakthroughs in the life sciences in the last decade. How do you believe technology has transformed research in the life sciences during this time, and in what areas do you believe the biggest advances have been?

My field is in data science and artificial intelligence, so I will focus primarily on the promise of AI in the life sciences. There is a huge amount of opportunity, but at the same time, we have to also consider that these technologies are a little over-hyped. I am not sure that the public actually knows the limitations of AI in the life sciences – we tend to perceive that computers think by themselves.

There are a lot of unsolved challenges, and I think there is a gap between what we develop in the academic sector and what is actually deployed at the clinic or in the industry sector. This means that there is a gap in our research and a disparity as to how that research is translated into technology. I think there is a lot we have to do. The potential is huge, but also we have to manage our expectations and be realistic as to what we can do and what we will do.

Do you believe there are any limitations to the use of technology in the life sciences? How can we combat these challenges?

AI and data science at this moment in time is very well developed. The algorithms that we have and that we are working on are very much advanced, but the huge challenge comes from the availability of data. This is something that people do not discuss enough. Data access and data generation is huge, but the quality of data and data access is not always at hand.

I think when we want to develop algorithms on patient data or clinical data, we do not usually have very good data sets. Companies now realize that they have to keep their data in good condition. So, my advice to companies and startups is that they should really work on how they will acquire the data and how they share that data.

Looking ahead, are there any areas of technology you’re excited to see excel in the next ten years?

When it comes to the topic of AI and deep learning, my particular area of interest is focused on biomedical imaging. I think that there are a lot of images out there that come from patients from microscopy. In my lab, for instance, we are developing a very strong line of research to analyze and get the best out of these imaging data.

What’s next for you? Are you currently involved in any exciting upcoming projects?

There are several projects at the lab which are related to the analysis of images to understand why we age. We are looking at stem cells with microscopy and understanding the differences between young cells and old cells to really understand how we can reverse the aging process in the lab.

We are also working with neglected diseases, like malaria and Chagas. I think there is a huge opportunity in the academic sector to work on these largely forgotten diseases of the developing world.

Qu’est-ce qui vous enthousiasme pour SLAS ? Qu’attendez-vous le plus des prochains jours ?

Je suis très enthousiaste à l’idée de rencontrer des gens, de partager des idées et d’établir de nouvelles relations. C’est l’une des premières conférences auxquelles j’ai assisté après la pandémie. Je suis vraiment excité. C’est comme revenir à la vie après s’être remis de ces deux années.

Où les lecteurs peuvent-ils trouver plus d’informations ?

À propos de Paula Petrone

Paula Petrone est professeure associée à ISGlobal et dirige l’équipe de science des données biomédicales dont l’objectif est le développement d’algorithmes appliqués au diagnostic précoce, à l’évaluation des risques et au traitement des maladies chroniques, de la santé mentale et de la neurodégénérescence, hl’informatique de la santé, les dispositifs de santé portables et l’imagerie médicale. Elle a obtenu son diplôme de premier cycle en physique à l’Institut Balseiro en Argentine. Elle est titulaire d’un doctorat. en biophysique de l’Université de Stanford.

Son domaine d’expertise est l’analyse de données à l’intersection de la chimie, de la biologie et de la médecine, et de l’apprentissage automatique. En tant que chercheuse postdoctorale chez Novartis NIBR, puis en tant que scientifique principale des données chez Roche, elle a développé plusieurs modèles d’apprentissage automatique appliqués à la découverte de médicaments et au criblage à haut débit.

Son travail postdoctoral en neurosciences au Barcelona Brain Research Center combine imagerie et apprentissage automatique pour prédire la maladie d’Alzheimer avant l’apparition de troubles cognitifs.

Ces dernières années, elle a été conseillère en science des données pour des sociétés pharmaceutiques et des startups biotechnologiques. En 2020, elle fonde la startup Phenobyte Life Sciences spécialisée dans l’application de l’intelligence artificielle dans les secteurs des biotechnologies et de la santé numérique.

Co-ambassadrice de WiDS (Women in Data Science, Stanford, 2021-2022) pour Barcelone, la Dre Petrone est une ardente défenseure de la diversité dans les carrières STEM.

Elle est également engagée dans la sensibilisation du public aux opportunités et aux limites de l’application de l’IA et de la technologie dans les soins de santé. Elle est auteur de plusieurs publications scientifiques, conférencière, mentor et mère de deux enfants.