Sandeep Kumar is an assistant professor in the Department of Electrical Engineering and School of Artificial Intelligence (ScAI)  at the Indian Institute of Technology Delhi (IIT Delhi). He heads the Machine Intelligence Signals and Networks (MISN) lab at IIT Delhi. His current research focuses on graphical models, manifolds, semi-supervised learning, and large-scale optimization. He received his M.Tech and Ph.D. degrees from the Indian Institute of Technology (IIT), Kanpur, and completed his postdoc from the Hong Kong University of Science and Technology.

Research: I supervise the Machine Intelligence Signals and Networks (MISN) Lab at IIT Delhi. The overarching theme of our research efforts lies in the coherent interaction between optimization, machine learning, signal processing, and graphical models.  Specific problems of interest include:

• Graph Compression

• Machine Learning over non-Euclidean Spaces

• Fair Federated Personalized Graph Neural Networks

• Applications of Graph ML in Earth science, Neuroscience, Wireless communication, and Circuit Design

• Our paper "No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation "got accepted to AAAI 2024. Congratulations to Nimesh and Anuj for their great efforts!

• Our paper " Linear Complexity Framework for Feature-Aware Graph Coarsening via Hashing", got accepted to NeurIPS Workshop in Frontiers in Graph Learning. Congratulations to Moohit, Aditi, and Rocktim. 

• Congratulations to Hemanthika and Subhanu for receiving the prestigious PMRF doctoral fellowship 

• Our paper "Graph of Circuits with GNN for Exploring the Optimal Design Space" got accepted to [NeurIPS 2023], Congratulations to Aditya, Sapna, and Ankesh!

• Congratulations to Jyotismita for receiving the prestigious TCS doctoral fellowship.

• Our Paper "Featured Graph Coarsening with Similarity Guarantees" got accepted to [ICML 2023], Congratulations to Manoj, Anurag, and Shaswat!

• Congratulations to Raghav on his work "Decodability of Eigenspace of EEG Graph for Motor Imagery Task" to the Organization for Human Brain Mapping (OHBM 2023) in a poster session.

• Congratulations to Aditya on his work "Graph Machine Learning assisted Analog Circuit Designing" being accepted to the Design Automation Conference (DAC 2023) in the work-in-progress category. 

• Our Paper "A Unified Framework for Optimization-Based Graph Coarsening" got accepted to the Journal of Machine Learning (JMLR 2023)  Congratulations to Manoj and Anurag. [Paper]