Rik Sarkar

Rik Sarkar serves as a Reader (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh and is Deputy Director of the Laboratory for Foundations of Computer Science. His work bridges theoretical computer science with practical applications in data science, focusing on trustworthy machine learning systems and algorithmic foundations.

His research spans Machine Learning Theory, Topological Data Analysis, Network Analysis, Generative AI, and Privacy/Fairness frameworks. Key investigations include SGD optimization in neural networks, data valuation economics, differential privacy mechanisms, and biomedical applications like Alzheimer's detection through topological modeling. Application domains extend to IoT sensor networks, drug design, and mobility data analysis using geometric and topological methods.

Scientific Awards: No specific awards, fellowships, or medals were documented in the source materials.

Dr. Sarkar actively supervises PhD candidates and postdoctoral researchers, with current openings in machine learning theory, generative AI, and topological data analysis. He leads the FAIRCROWD II project (funded by REPHRAIN) developing decentralized data valuation systems, emphasizing practical implementations of theoretical frameworks for real-world impact.

His research group operates within the Laboratory for Foundations of Computer Science, combining geometric topology, optimization theory, and machine learning to address challenges in biomedical engineering (e.g., 3D tongue surface modeling with global media recognition), chemical property prediction, and privacy-preserving spatial analytics.