ACM-W Rising Star Award Recipient: Dr Eva Tuba
ACM-W Rising Star Award Recipient
ACM-W would like to announce Dr. Eva Tuba as this year’s recipient of the ACM-W Rising Star Award! The ACM-W Rising Star Award recognizes a woman whose early-career research has had a significant impact on the computing discipline.
Dr Tuba is currently an Assistant Professor at Trinity University, Texas, in the Department of Computer Science.
Congratulations on winning the ACM Women Rising Star Award. Can you tell us about your journey in the field of computer science and technology? What inspired you to pursue this field, and what challenges did you face along the way?
Thank you very much. I am incredibly honored and thrilled to be the recipient of this year’s ACM Women Rising Star Award. I feel very humbled to be recognized alongside the remarkable recipients from previous years. It is a huge recognition and motivation to continue with my research and work. I also want to thank my mentor and friend, Dr. Paul Myers who encouraged me to apply and supported my nomination.
My journey in the field of computer science started very early, even though at that time, I was not aware that it would be my career one day. Having a computer sounds like an obvious thing nowadays, but 30 years ago that was not the case (just to say, it was the time of dial-up internet) My father is a university professor, teaching mathematics, electrical engineering, and computer science for over 45 years, so I do not remember the time without multiple PCs in the house. Due to his profession, me and my five siblings were introduced to computers from a young age, and not just how to use them, but how they work, how to code, etc. The more I learned, the more fascinated I was by how such simple concepts result in such powerful machines. With a great professor as he is, everything seemed so simple and logical yet you could be creative. Considering all this, I feel that choosing to pursue computer science in higher education was a natural step. I always wanted to be a professor, so as time approached, I became aware of a whole different aspect of a professor’s life – research. Once I joined one of the most successful research groups in the region, my life changed completely. It was very demanding at first but I was thrilled by the whole process. I remember at the beginning, I could not get enough of discovering and experimenting with new tools and knowledge that I was gathering. I would run simulations all day and night, set the music to play as the last line of the code, and turn the speaker’s volume up, so it would wake me up to see the results and run new tests. Needless to say, not everyone in the house shared my enthusiasm when the music blasted in the middle of the night.
I finished my education in a region of great minds but where you do not have a lot of opportunities. The situation has improved in recent years, but when I was a graduate student, there was no new funding, or calls for projects, so it took more effort to achieve things and be recognized worldwide compared to peers from other countries. I was very blessed to have my father as a support and mentor. I can thank him for everything I achieved and for who I am today. His guidance, mentorship, and scientific and financial support, enabled me and other members of that research team to become the best in the region and be recognized by leading names in our research field.
You are included in Stanford University’s list of 2% of the most influential scientists in the world in the field of Artificial Intelligence and Image Processing in the years 2020, 2021, and 2022. According to you, what are the most pressing problems in your field today? How do you think your research makes a difference in addressing these problems and their impact?
With the revolutionary changes that happened recently in the field of AI and by extension image processing and other fields, there are a lot of issues that have to be addressed. Computer science is a fast-developing field, like no other and there is a repetitive pattern through history where other fields take time to catch up with the advances in computer science. Nowadays, due to AI, specifically machine learning development, we have a lot of excitement, advances, questions, problems, worries, and concerns.
On one side there are concerns about the effect of using AI. For example, one of the important issues with AI is to ensure ethical and fair use. Additionally, privacy concerns are paramount, especially in image processing where sensitive personal data is involved. On the other hand, there are technical concerns that are usually the focus of computer scientists. The scalability and efficiency of AI models have been a continuous subject of research. Robustness and generalization of AI models are also critical areas of concern. I would also like to add that with all these advances, specifically generative AI, it became easier to get an illusion of skills and knowledge while ignoring the importance of fundamental understanding. Without having an understanding of basic concepts, it is impossible to make further progress.
Some of my research includes adopting optimization algorithms for tuning AI models and finding lightweight models that reduce computational demands while preserving quality. Also, my research aims to strengthen AI models against vulnerabilities and improve their reliability and applicability across various tasks through transfer learning and domain adaptation techniques. Besides addressing these technical issues, I tend to teach my students the importance of all points of view, to be aware of the impact of their work beyond technical aspects.
In one of your most cited papers, “Brain image segmentation based on firefly algorithm combined with k-means clustering”, which was written in 2019, you and your co-authors presented an algorithm for brain image segmentation. How have the insights you presented in this paper changed or held up since then?
It is very interesting to see how the field of image processing drastically changed in the last 5-10 years. Namely, this paper was written around 6 years ago, and at that time this was a hot research topic. One of the crucial steps in any application that required the detection or recognition of different objects (e.g. tumors, bleedings, vessels, faces, buildings, handwritten characters) was feature extraction. Features represent numerical descriptions of objects of interest. We, humans, try to figure out what attributes tell us that this is a cat or dog, so from a matrix of pixel values, we find edges, texture, we calculate color contrast, roundness of objects, etc., and then use machine learning to learn the pattern in these features and how to differentiate objects. This was a large part of research at that time, finding appropriate features. Just a few years later, this is a non-existing step. Recent models such as convolutional neural networks automatically extract features that are more appropriate for computers (less for humans) and significantly better accuracy is achieved. So the focus in the field has completely changed from image manipulation toward AI model tuning and design.
How do you think your research is making a difference in addressing the problems within AI and image processing, and what impact do you hope it will have?
My research changed a lot along with the drastic change in the field. Currently, there are a lot of ongoing projects and different directions for my research. All these directions are trying to answer some of the previously mentioned challenges and hopefully make an impact on developing the field while protecting a wider audience from malicious use. One direction of research is further improving and adjusting AI models for specific problems that involve digital images and sequential data such as tumor classification and detection or DNA sequence analysis. In this case, it is important to understand the basics in order to know how to tune a model for a specific problem. This part of research also focuses on creating reliable models, resistant to adversarial attacks.
Another direction is related to the problem of authentication and protection of digital content. With my students, I am working on research about AI methods that integrate data poisoning algorithms to distort the original art used by unauthorized AI art generators, thereby preserving the originality of artists’ work. Another way of protecting digital content is by using a diffusion model training approach that applies source citation embeddings as a degradation operator, allowing for training dataset content attribution during inference. Unrelated to images, there is research done on the usage of AI for automatic optimization algorithm design. Instead of manually adjusting optimization algorithms for individual problems, we use AI models. In the near future, I would like to do some work on explainable AI, something that has been overlooked for too long.
Based on your experiences studying and working at esteemed academic institutions, what advice would you give to aspiring researchers looking to maximize their potential and make the most of their time in academia?
I noticed that a lot of students and faculties focus on specific job positions, satisfying the minimum requirements, inventing the next great thing while looking down and ignoring “smaller progress”, etc. While we should always be aware of the objectives for achieving our goals, pure pursuit of satisfying norms for one and only one thing is not the best way to navigate life and career. I remember years ago, I was at a conference where the keynote speaker talked about his career path. On the one hand, he had great achievements behind him and led a very exciting and happy life, but on the other hand, almost nothing ever went as he planned. He took every opportunity given to him and made the best of it, and the final results were incredible. I dare to give my students advice, especially when they complain about how they are not interested in some topic because they plan to do something else, why they need some courses, etc.: Have clear goals, but keep your minds open and take every opportunity that comes your way. Many times in your life you would be surprised, that something you thought was pointless and useless, nothing you are interested in would be the thing you enjoy extremely, something that would differentiate and raise you above the others and help you go further than your dreams. Having a broad knowledge is equally important to in-depth knowledge of narrow topics.
Eva, as a role model for young women in technology, your insights are invaluable. How do you believe we can inspire and support more women to pursue careers in this field?
Inspiring and supporting more women to pursue careers in technology requires a multifaceted approach. I was raised in a home where gender did not play any role. My brothers and sisters and I did almost everything together, from dance lessons to judo and wrestling, science competitions, etc. This enabled me to confidently do whatever I chose to do. Taking that into account, I would say that first and foremost, we need to challenge and change the stereotypes, in this case, stereotypes that suggest technology is a male-dominated field. This should start from an early age, in the home, school, playgrounds, and everywhere. Early exposure to technology is crucial. We should encourage girls to explore coding and tech-related activities from a young age through workshops, camps, and school programs. Here, at Trinity University, we organize a TECH (Trinity Encourage Computing for Her) camp for middle school girls every year. Supporting strong STEM education and ensuring that schools have the necessary resources can help spark and sustain their interest. Mentorship, networking, visibility, and representation matter greatly. Building supportive networks and communities where women can share experiences and resources, and connecting young women with experienced female technologists can provide invaluable guidance and career advice.
The most common advice I give to female students is to be confident and courageous, to believe in themselves, and to be their own best friend, not the worst enemy. Too often, I witness extraordinary female students who doubt and harshly judge themselves. Together, we can create an environment where women feel empowered to pursue and excel in careers in technology.