Dipankar Sarkar

My research lies at the intersection of machine learning, distributed systems, and decentralized technologies, with a focus on developing practical solutions that bridge theoretical advances with real-world applications.

Machine Learning & Privacy-Preserving AI — In federated learning, I've pioneered techniques for handling imbalanced data classification (Fed-Focal Loss) and optimizing communication efficiency (CatFedAvg), making distributed machine learning more practical for real-world deployments. My work addresses fundamental challenges in privacy-preserving AI: how to train powerful models across distributed data without compromising individual privacy or model performance. Current research explores federated learning for Web3 applications, deepfake detection networks, and copyright-compliant generative AI systems.

Blockchain & Decentralized Systems — My Web3 research focuses on infrastructure-level innovations: developing protocols for Decentralized Physical Infrastructure Networks (DePIN), designing mechanisms for fair value distribution and MEV mitigation in Ethereum, and solving atomic composability challenges in multi-rollup environments. Through projects like Tesseract, FairFlow, and generalized DePIN frameworks, I work to make decentralized systems more scalable, fair, and practical.

With 25+ provisional patent applications and publications in leading venues, I actively collaborate on projects that advance both ML and blockchain technology while maintaining a focus on real-world applicability and ethical deployment.