Vinay Chakravarthi Gogineni

Research areas

My research focuses on Deep Learning, Federated Learning, and Data Analysis on Graphs, with applications in the industrial internet-of-things, healthcare, and fusion energy. I have advanced Federated Learning by addressing communication load, privacy, security, and personalization. I have developed a personalized cervical cancer screening recommender system to address under- and over-treatment and have been working on AI-driven early detection systems for colorectal cancer, cardio calcification, and dementia. My work in Machine Unlearning guides AI to comply with data regulations like GDPR and the AI Act, thereby protecting individual privacy while promoting unbiased decision-making.

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

• 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.
• Machine Unlearning: Removing/Erasing th 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.
• AI for Healthcare, IIoT, and Fusion Energy: Applying advanced AI techniques to improve outcomes in these critical areas.

I am an IEEE Senior Member and a member of the editorial board for the IEEE Sensors Journal. I  was a recipient of the ERCIM Alain Bensoussan Fellowship in 2019 and the Best Paper Award at APSIPA ASC-2021, Tokyo, Japan.  I was also a recipient of the HC Ørsted Research Talent Award, 2024, Denmark.