Vinay Chakravarthi Gogineni

Research areas

I conduct research in Artificial Intelligence (AI), focusing on three core pillars including foundational AI, responsible AI, and quantum AI, with applications in healthcare, the industrial internet of things, life sciences, and optics.

Within Foundational AI, I make efforts to advance Deep Learning and its subfields, including Graph-based and Generative models, with a focus on improving model efficiency, generalization, and handling multimodalities.

In Responsible AI, I develop privacy-preserving and trustworthy AI systems using Federated Learning to ensure data confidentiality while addressing challenges such as heterogeneity, personalization, communication efficiency, privacy and security. I also develop Machine Unlearning methods that help AI comply with GDPR and the AI Act, while promoting fairness, transparency, and unbiased decision-making. 

Verecently I started working in Quantum AI, where I co-design AI architectures to align with quantum requirements, enabling efficient implementation of classical AI models in the quantum domain.

In addition to fundamental research, my research in Applied AI has led to the development of a personalized cervical cancer screening recommender system to mitigate under- and over-treatment, and continues with AI-based early detection systems for colorectal cancer, cardio calcification, and dementia.

I am an IEEE Senior Member, Affiliated member of Pioneer Center for AI, Denmark, Member of IEEE Sensors Editorial board, recipient of the HC Ørsted Research Talent Award, 2024, Denmark, Best Poster Award from HAMLETS Conference/Workshop, 2025 in Copenhagen, Denmark, and Best Paper Award from APSIPA ASC, 2021 in Tokyo, Japan.

Currently, I am working on the following topics and their applications in healthcare, IIoT and Fusion energy:

• Machine Unlearning: Removing/Erasing the knowledge of specific data samples from already trained AI models to comply with data regulations.
• Fairness-Aware AI: Promoting unbiased and equitable decision-making in AI systems.
• Quantum Machine Learning: Reimaginign the classical AI systems to make them suitable for Quantum Domain.
• Personalized Federated Learning: Tailoring AI models to individual (or subgroups of the population)  needs while maintaining data privacy.
• Security and Privacy in Federated Learning: Enhancing the safety and confidentiality of distributed learning systems.
• Self-Supervised Learning: Leveraging abundant unlabeled data in the presence of very limited labelled data.
• Continual Learning: Enabling AI models to adapt and learn continuously from new data.
• Meta Learning: Developing AI models that can adapt swiftly to new tasks.
• Physics-Informed Neural Networks: Integrating physical laws into neural networks for more accurate predictions.
• AI for Healthcare, IIoT, and Optics: Applying advanced AI techniques to improve outcomes in these critical areas.