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Spotlight: Hermenegild Arevalo
Portrait of Hermenegild Arevalo by B. Gudim.

Spotlight: Hermenegild Arevalo

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We recently interviewed Hermenegild Arevalo, Chief Research Scientist at Simula, discussing his background & career as a researcher.

What is your background? 

I have a major in biomedical engineering from Tulane University. My interest in healthcare initially led me towards medical training. However, while taking classes in biomedical engineering, I discovered computational physiology. I was captivated by the idea of representing life through mathematical equations. This fascination led me to do my PhD on modeling the electrophysiology of the heart under the guidance of Professor Natalia Trayanova at her lab at Johns Hopkins in 2006.

I’ve been in the same field since 2006, and it’s been exciting and motivating to see how it has developed so much. Seeing how the complex equations were modeled on our computers 20 years ago, when simulations would take weeks, compared to that same simulation now taking a couple of minutes on my laptop. I’ve had the privilege to witness these technological advancements first hand, which have made it possible to bring this technology from research to use in society. This is the whole direction of my research. 

What are some defining moments in your career? 

The first defining moment of my career was joining Professor Trayanova’s group when doing my PhD. She has had quite an impact in the field, and being under her training was an important part of making science a career to me. 

Another defining moment in my career was when I started to work more closely with clinicians and saw the potential of applying my work to clinical settings. This has caused me to stay in the field because I could see how my work was useful to patients and society. For example, around 2010 we were able to do studies on modelling actual human hearts, and this is some of the first works where we predicted arrhythmias in actual patients with very good results. These results were also part of what motivated me to stay in the field. It resulted in a well-cited Nature Communications paper and has opened up the field, as one of the first clinical applications of the technology. 

Can you describe what you’re currently working on? 

All my research projects are within the vein of trying to bring computer modeling of the heart to applicability in the clinic. 

One ongoing project, SimCardioTest, involves collaboration with nine other institutions to develop this technology for creating in-silico trials - the use of computer simulations to check how medical treatments or drugs might work. This could make the development of devices and drugs more cost-effective and accelerate their availability to the public.

In another project, led by PhD student Julie Uv, we generate computer models of pregnant women which includes the fetal heart. To improve the diagnosis and monitoring of fetal heart development. A project like this requires a thorough validation and verification process for our software where we review every line of code to ensure that first, the equations we're solving are what we're actually solving (because these are very complex codes), and second, that our predictions match what we have in the real world. This is another one of the big advantages of computer simulations because we can have virtual populations that represent all these underrepresented populations that are not usually enrolled in clinical trials and include them in our virtual trials, also called in silico clinical trials. 

What’s an analogy or metaphor to help others understand your work? 

We currently use computer models in pretty much everything we have around us. For example, virtual versions are created for buildings and cars before they are built. Extensive and costly tests are being conducted on these virtual models before actually building them in real life. This is what we are aiming to also have for the human body. So that we can test on highly accurate models that one would never try on a real person due to how invasive they would be, but that are safe to conduct on a computer model. 

What are some challenges in your field? 

Perhaps the biggest challenge is protecting patient privacy. AI and machine learning are quickly becoming very useful and powerful components of computer models. While AI and ML require huge amounts of data, these can make it possible to identify associations, risks, and we can predict patient outcomes in ways not possible otherwise. That’s both exciting and revolutionary. Yet, that data must be protected, as it is very sensitive and personal. Luckily, all hospitals I work with are very protective of their data. So one of the biggest challenges right now is keeping this balance between improving the models and preserving patient privacy. 

Norway is a particularly interesting place to perform this research partly because of the advanced medical practice here, where everything is digitized and everything is to a certain extent centralized. But there are also very strong laws in place to protect that patient data and it's not freely accessible to researchers. Patients give very explicit consent that their data can be used for research. And that consent has to be very, very specific. So that's very good as far as protecting patient data.

What advice would you give someone getting into your scientific field? 

To make impactful research you need to collaborate with people. When you tackle big problems such as introducing computer modeling into health care it requires the expertise of many people, and not one person can do this. My best work has always emerged from collaborations involving people from various backgrounds. Hence, my advice would be to maintain an open dialogue with people from different fields and stay curious.

Thanks to Hermenegild for contributing to this researcher profile.

At Simula, we take pride in our people, with over 150 scientific researchers, fostering a collaborative and innovative environment for science research.

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