Lately, it seems that all the focus has been on artificial intelligence, but let us not forget that some of the most important technological breakthroughs have actually come from people who are really good at what they do.
IEEE Fellow, professor and researcher Karen Panetta has spent decades applying engineering techniques to problems that extend far beyond computing to solve some of the world’s most pressing issues.
Her work has ranged from underwater imaging systems for search-and-rescue to wildlife monitoring systems and low-cost methods for detecting harmful pathogens – thus her work has had a really profound effect on life on Earth.
Many of Panetta’s projects actually emerged from identifying real-world problems, rather than pursuing technology for technology’s sake, and that’s a clear differentiator between the people who have the biggest impact on scientific development, and those that don’t.
STEM education and AI literacy should be a priority
Today, research is one of the sectors most impacted by AI, and the likes of Panetta are now able to push their work even further with projects like computer and human vision.
She is also the 2026 winner of the IEEE Mildred Dresselhaus Medal, acknowledged for her “contributions to computer vision and simulation algorithms, and leadership in developing programs to promote STEM careers.”
Now, Panetta is a prominent voice on AI literacy and access to STEM education – as virtually every sector is rushing to deploy AI, she argues that public understanding of the technology has failed to keep pace, and the risks could soon emerge.
To explore the future of AI and innovation, I spoke with Karen Panetta about the technologies and influences that inspire her work, the challenges that face STEM education, and why solving humanity’s toughest problems requires more than just technical expertise.
- A cursory look at your work and career shows the wide spread of your interests in science and technology. What makes you such a keen polymath?
I am constantly motivated by social issues and the technical challenges I see in the media.
For instance, disasters at sea motivated me to develop better underwater vision for search and rescue. The loss of life among first responders who put their lives at risk to keep us safe led me to work on imaging techniques to see through smoke and darkness.
Recently, we have seen more food-borne illnesses causing deaths and hospitalisations. This led me to develop a low-cost method for detecting pathogens like E.coli without a microscope or culturing.
- Your work has had the greatest influence on computer vision. In our quest to simulate the human senses, what remains to be done to give an artificial device, human vision?
One of my most recognised works showed that humans judge image quality according to human vision, yet there were no metrics being used by computer algorithms that were analogous to human perception of quality.
So, I created these quantitative metrics which allows autonomous systems to judge what images humans would see pertinent information more readily. Human vision also has depth perception and can extrapolate scenes with obstructed objects.
AI can help us combine depth perception and resolve some of the issues we have recognising objects that are partially occluded or clustered together.
As an example, we have these issues when we are trying to determine the health and size of animals. We take images from different distances and groups of animals clustered together. We had challenges with baby chimpanzees being segmented from scenes when they were on their mother’s back.
The goal was to use images rather than human/animal interactions to gather size and health data. Touching animals causes stress and potential health hazards to humans and we wanted to avoid this.
We needed to use depth estimation and other sophisticated algorithms to solve these challenges. In summary, we need more types of sensors to give us more information about a scene or objects in a scene.
In the future, we may even use audio and chemical sensors to help us really provide accurate analysis of ‘what we see’.
- You have worked extensively on an image recognition database that helped researchers discover bias in AI. What’s your perspective on AI and its impact on society at large?
AI has the potential to be a very powerful tool, and everyone is rushing to push out AI products and services without fully understanding the limitations or putting in safeguards that guarantee that AI does not cause harm.
The general public’s education level often does not include AI literacy, leading them to believe that AI responses are always correct. This is concerning and it is difficult to change public perception once false information is widely disseminated. It’s also very expensive to ‘undo’ and correct misinformation.
- What do you think are the biggest challenges to the advancement of STEM globally? How has IEEE helped address this?
Access to education across different cultures and languages needs to be improved. It takes generations for mindsets to change and unfortunately, the STEM professions have misinformation that has long served to filter out participation in these disciplines.
For instance, “You need to be good at math and science”. This has been turning young people away from pursuing STEM for decades.
We need to focus more on the creativity and ability to make an impact on solving real-world challenges, whether it is in our own community or elsewhere to get youth to see these fields as open to them.
The IEEE is addressing this by creating global educational content that is accessible to everyone, leveraging the power of its 500k members to create subject matter that meets the needs of their own communities.
IEEE is also providing role models and providing mentors to young professionals to help engage and encourage these young people. IEEE has several affinity groups including IEEE Women in Engineering (WIE), IEEE Young Professionals and IEEE Life members, who all work together to make one big community.
Through this, IEEE creates a cohesive welcoming environment to support people, no matter what stage of their career they are in or where they are from.
- What cultural item (movie, book, song) do you think influenced you – and your work – the most?
My only role models were the TV shows like “I dream of Jeannie” or “Bewitched” and the Harry Potter Movies. They all featured women with power (magic) but they had to hide their power because others couldn’t accept they could do amazing things other people couldn’t. In a way, engineering is my magic.
- I came across an interview you did in 2022 where you said that “That was groundbreaking at the time, and especially for somebody who was a computer architect coming into this field where other people had been in this for decades”. How important is this sort of cross-pollination in your opinion, and what can be done in organisations to encourage this?
I found new directions in well-established fields of imaging because I had come with fresh perspectives from a totally different area (computer architecture and simulation).
Systems today can’t be successful if we don’t look at designs from every discipline and usage scenario. I have multiple appointments as a professor in the Tufts School of Dental Medicine and departments of Mechanical Engineering, Computer Science and Electrical and Computer Engineering.
This is because my work spans so many different areas and is applied across different disciplines. If I don’t work with professionals/experts in other disciplines, how can I possibly create technology that gets at the heart of solving their greatest challenges?
- Can you tell me about the significance of the IEEE Mildred Dresselhaus Medal and what this recognition means to you?
I was so fortunate to know Millie Dresselhaus. She was a wonderful mentor. I often say, she was small in stature, but a giant in brilliance and heart.
Receiving this medal named after her is a profound recognition and a lasting testament to her life. She impacted so many people, including myself and I get to carry her legacy forward to impact others.
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desire.athow@futurenet.com (Desire Athow)
