Megha Khosla

Assistant Professor, Delft University of Technology (TU Delft)

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Van Mourik Broekmanweg 6

2628 XE Delft

The Netherlands

I am an Assistant Professor in the Multimedia Computing Group in the Intelligent Systems Department at TU Delft. My primary area of research is machine learning on graph structured data. In particular, my research is dedicated to the development of cutting-edge algorithms that facilitate effective, interpretable, and privacy-preserving machine learning on graph data. I am currently spearheading a novel research line focused on exploring the intricate relationship between explainability and privacy in graph machine learning. Prior to my being a faculty member at TUD, I was a senior research at the L3S Research Centre, and the Leibniz University Hannover. I have also managed several collaborative projects both in Academia and in the industry where I spent a three-year stint after my PhD. I completed my PhD from the Max Planck Institute for Informatics (MPII), in Algorithms and the Complexity group, Saarbruecken, Germany.

news

Sep 22, 2023 Check out my tutorial (together with Luis Galárraga ) on Explainable GraphML presented at ECML 2023
Jul 13, 2023 My first PhD student Ngan Thi Dong defended her PhD thesis on Joint learning from multiple information sources for biological problems Check out her amazing work!

selected publications

  1. Multi-label Node Classification On Graph-Structured Data
    Zhao, Tianqi, Dong, Ngan Thi, Hanjalic, Alan, and Khosla, Megha
    Accepted for publication in Transactions on Machine Learning Research 2023
  2. Private Graph Extraction via Feature Explanations
    Olatunji, Iyiola E, Rathee, Mandeep, Funke, Thorben, and Khosla, Megha
    In Proceedings of Privacy Enhancing Technologies Symposium (PETS 2023) 2023
  3. A message passing framework with multiple data integration for miRNA-disease association prediction
    Dong, Thi Ngan, Johanna, Schrader, Mucke, Stefanie, and Khosla, Megha
    Nature Scientific Reports 2022
  4. ZORRO: Valid, Sparse, and Stable Explanations in Graph Neural Networks
    Funke, Thorben, Khosla, Megha, Rathee, Mandeep, and Anand, Avishek
    IEEE Transactions on Knowledge and Data Engineering 2022