Research Topics
Research Topics
Have you ever imagined how Artificial Intelligence can transform the way we treat diseases, create art, or revolutionize sports? My goal is to explore these possibilities and go beyond what we already know. But one essential point: I am NOT interested in replacing human work with AI or robots. My research aims to develop technologies that assist, complement, and enhance human capabilities, never to replace them.
Furthermore, I believe that theory and practice must go hand in hand. We do not study theory just out of curiosity - we propose new methods because real-world problems need solutions, and existing approaches are not always sufficient. If there is not yet a suitable model to solve a real-world challenge, we need to develop one. Thus, my research involves both creating new theoretical approaches and applying them in different domains, always with the goal of transforming knowledge into real impact.
If you also want to innovate, solve real problems, and build technologies that empower people, let's work together!
My research is divided into two main areas: AI theoretical development and practical applications to solve real-world problems. Theory guides practice, and practical challenges drive the need for new theories.
For AI to advance, we need new models, techniques, and ways of looking at challenges. My focus is on developing ideas that help AI become more efficient, accurate, and explainable, always with the goal of enhancing human work, not replacing it.
π How can we teach AI models to better understand data?
π Is it possible to make AI more reliable, fair, and interpretable?
These questions are not just theoretical. They arise from the need to solve real problems and improve existing technologies. Some of the areas I study include:
Multi-label classification: Modeling label correlations to create more accurate classifiers and find optimal partitions.
Explainability and transparency: Developing ways to make models less opaque and more understandable.
Bias reduction and fairness improvement: How can we create more impartial models?
These studies do not remain just in theory, they serve as the foundation for concrete applications.
AI should not be just an abstract concept. I want to apply it to solve real problems and impact different areas, always with the purpose of assisting and strengthening human capabilities. Many of the techniques developed in theoretical research can and should be applied here.
1. Brain Cancer: AI to Save Lives, Not to Replace Doctors
What if we could detect brain cancer before the first symptoms appear? My goal is to develop models that help doctors and healthcare professionals make more precise decisions, save time, and offer better treatments, always as support, never as a replacement. For this, techniques such as explainable neural networks and advanced classification models, developed in the theoretical part of the research, are essential to ensure reliable diagnoses.
2. Art and Entertainment: Creativity and AI Hand in Hand
AI can be a powerful tool for creators! From automatically generated scripts to intelligent animations, I want to explore how technology can enhance human creativity, but without replacing artists, screenwriters, or directors. Generative models and deep learning studied in theory are applied to create tools that expand artistic possibilities without compromising human authorship.
3. Voice and Vocal Performance: AI to Care for Voices, Not to Create Artificial Singers
As a singer, I know that the voice is a delicate instrument. I want to develop tools that help singers avoid vocal injuries, improve singing techniques, and even create new sound effects with AI, without ever trying to replace human artistry with machines. Audio processing techniques and learning vocal patterns, studied in theory, are fundamental to creating these tools.
4. Orthodontics: Improving Diagnoses, Not Removing Professionals
Jaw and maxillofacial problems affect millions of people. How can we use AI to help dentists and orthodontists perform faster diagnoses and more efficient treatments? Here, AI is an ally, not a substitute. The theoretical development of computer vision algorithms and medical image analysis enables these systems to be applied in practice.
5. Sports: AI for Athletes, Not to Replace Players
As a volleyball player, I want to develop AI tools that help athletes improve their performance. This includes video analysis to predict plays, advanced training statistics, and even personalized recommendations for nutrition and recovery. Many of the statistical and machine learning techniques developed in theory are directly applicable to sports analysis.
6. Eye Health: AI to Predict and Support Vision Care, Not to Replace Doctors
As someone deeply interested in ocular health, I aim to develop AI tools that help predict and monitor the progression of eye conditions such as myopia, astigmatism, and presbyopia. By using advanced predictive models, we can identify individuals at risk of developing these conditions over time, enabling early interventions. The goal is to provide personalized recommendations, from corrective lenses for near and far vision to multifocal solutions, always in collaboration with eye care professionals. Many of the classification, prediction, and recommendation techniques developed in theory can be directly applied to improve vision care.Β Β
π‘ If you love programming, data science, and want to apply AI to solve real problems without dehumanizing processes, this is the right place for you!
The technology we create reflects the society we live in. I want to study and address the challenges faced by women in computing and exact sciences, promoting a more inclusive and diverse environment.
π If you are interested in research with social impact, there is a lot we can explore together!
Whether suggesting a movie, a training strategy, or even a personalized medical treatment, recommendation systems have enormous potential. I want to develop intelligent systems that truly understand users and provide precise and useful recommendations, always as support and not as an algorithmic imposition that replaces human decision-making.
π How about creating the next generation of recommendation systems together?
Not all AI exists only on computers! Many of the challenges I study may require intelligent devices, such as:
π€ Medical machines with embedded AI for faster and more accurate diagnoses, but always with doctors in control.
π Smart sensors for athletes that analyze movement and prevent injuries, assisting coaches and physiotherapists.
π¨ AI-powered creative tools for artists, musicians, and filmmakers, serving as a complement to their artistic vision.
Here, technology exists to empower, not to replace.
If you are curious, love learning, and want to work on innovative projects, there is a place for you here. Whether in theoretical research or in creating practical solutions, there is a lot we can build together.