Machine Intelligence Signal And Network (MISN Lab)
Recent technological advancements in the internet, biology, basic sciences, engineering, and finance have produced large and complex data sets. By using techniques from optimization, signal processing, high-dimensional statistics, graph theory, and machine learning, we develop data-driven methods to solve optimization and learning problems arising from these new technologies. More specifically our impetus is on graph machine learning theory and applications, optimization theory, federated learning, wireless, networks, and machine learning for computational neuroscience, medicine, and earth sciences.
Recent Updates
● Congratulations to Pooja on being selected for the NSF-EMBS-Google Sponsored Young Professional NextGen Scholar Recognition.
● A historic and proud moment for MISN Lab Hon'ble President of India, Smt. Droupadi Murmu, addressed and appreciated our pioneering initiative, Adi Vaani, the world's first AI-powered platform for Janjatiya (tribal) languages. [Rashtrapati Bhavan Press Releases][PIB Press Releases]
● It is a proud moment for MISN Lab as we have officially launched the Beta version of Adi Vaani the world's first AI-powered platform dedicated to Janjatiya (tribal) languages. [PIB Press Releases]
● Our paper "Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus " got accepted to [Empirical Methods in Natural Language Processing (EMNLP 2025)] .Congratulations to Pooja, Shashwat and Vaibhav.
● Our paper ""REFINE: Enabling Efficient and Trustworthy Modeling of Financial Networks via GNN-to-MLP Knowledge Distillation" " got accepted to [12th IEEE International Conference on Data Science and Advanced Analytics (DSAA-2025)] .Congratulations to Vipul and Jyotismita.
● Our paper "CrossMed: A Multimodal Cross-Task Benchmark for Compositional Generalization in Medical Imaging" got accepted to [ International Conference on Biomedical and Health Informatics (BHI-2025)] .Congratulations to Pooja and Siddhant.
● Our paper "Coarse-and-learn: Efficient online node labeling " got accepted to [32nd International Conference on Neural Information Processing(ICONIP-2025)] .Congratulations to Subhanu and Manoj.
● We are pleased to announce that Prof. Sandeep Kumar was recently featured on [DD News] for a special segment on Artificial Intelligence
● Our paper "Modularity Aided Consistent Attributed Graph Clustering via Coarsening " got accepted to [Transactions on Machine Learning Research(TMLR)] .Congratulations to Samarth, Yukti and Manoj.
● Our paper "An Efficient Framework for Epidemiological Parameter Estimation via Graph Reduction and Graph Neural Networks " got accepted to [ACM Transactions on KDD] .Congratulations to Manoj and Md Alfaf.
● Our paper "GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision " got accepted to [Transactions on Machine Learning Research(TMLR)] .Congratulations to Aditya.
● Our paper "Multi-Component Coarsened Graph Learning for Scaling Graph Machine Learning " got accepted to [Companion Proceedings of the ACM on Web Conference 2025] .Congratulations to Subhanu and Manoj.
● Our paper "Evidence-based Epileptic Seizure Detection" got accepted to [47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC-2025)] .Congratulations to Siddhant and Vivek.
● Our paper "A novel coarsened graph learning method for scalable single-cell data analysis" got accepted to [Computers in Biology and Medicine] .Congratulations to Mohit Kataria, Ekta Srivastava and Kumar Arjun.
● Our paper "HyperDefender: A Robust Framework for Hyperbolic GNN" got accepted to [AAAI'25] .Congratulations to Nikita Malik and Rahul.
● Our paper "A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks" got accepted to [IEEE Transactions on Signal Processing] .Congratulations to Vipul Kumar.
● Our paper "CoRE-BOLD: Cross-Domain Robust and Equitable for BOLD Signal Analysis" got accepted to [ML4H] .Congratulations to Vipul Kumar and Jyotismita Barman.
● Congratulations to Manoj Kumar on Successfully Defending His PhD. [Photo].
● Our paper "PPDA: A Privacy Preserving Framework for Distributed Graph Learning" got accepted to [ICONIP'2024] .Congratulations to Nikita Malik.
● Our paper "UGC: Universal Graph Coarsening" got accepted to [NeurIPS24] .Congratulations to Mohit Kataria.
● Our paper "Optimization Framework for Semi-supervised Attributed Graph Coarsening " got accepted to [UAI-2024] .Congratulations to Manoj and Subhanu.
● 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 Mohit, 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].
● Congratulations to Vipul and Nikita for receiving the prestigious Prime Minister Research Fellowship (PMRF).
● Congratulations to Aditi and Rocktim for winning the best B.Tech Thesis award. They worked on developing a feature-aware graph coarsening algorithm for scaling graph machine learning.
● Congratulations to Bharat and Vivek for getting the best student paper award at theIEEE SPCOMM conference. [Photo]..
Reach out to MISN Lab
CONTACT US
MISN Lab, Block III, Department of Electrical Engineering, IIT Delhi, Hauz Khas ,110016
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