Mandeep Rathee

I'm a PhD student at L3S Research Center, Hannover

About

My research interests lies in the application of graph-based machine learning to information retrieval and Web tasks.

Interests

  • Interpretability in Graph Neural Networks
  • Interpretable Search and Recommandation

Publications

BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

Mandeep Rathee, Thorben Funke, Avishek Anand, and Megha Khosla

Private Graph Extraction via Feature Explanations

Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke, and Megha Khosla

Learnt sparsification for interpretable graph neural networks

Mandeep Rathee, Zijian Zhang, Thorben Funke, Megha Khosla, and Avishek Anand

ZORRO: Valid, Sparse, and Stable Explanations in Graph Neural Networks

Thorben Funke, Megha Khosla, Mandeep Rathee, and Avishek Anand

Resume

Overview

Mandeep Rathee

I am Mandeep. Currently I am working on Interpretability in Graph Neural Networks under the supervision of Dr. Avishek Anand at L3S Research Center. I completed my masters degree in Mathematics and Computing from Indian Institute of Technology, Patna, India. For my master thesis, I got DAAD fellowship under the “Combined Study and Practice Stays for Engineers from Developing Countries (KOSPIE) with Indian IITs 2019” from September 2019 to May 2020. I did my master thesis form L3S Research Center, Hannover. Also, I have learned German language up to A1 level.

Education

Ph.D.

2020 - Current

L3S Research Center, Hannover

Interpretability in Graph Neural Networks.

Masters of Technology

Mathematics and Computing
2018 - 2020

Indian Institute of Technology, Patna(India)

I did my master's thesis in L3S Research Center of Leibniz University Hannover. My topic for master's thesis was Graph Augmentation and Learning using Graph Neural Networks.

Knowledge

Courses Attended

Machine Learning
Deep Learning
Natural Language Processing
Data Mining
Data Structure and Algorithm
Probability and Statistics
Network Science
Numerical Optimization
Graph Theory
Linear Algebra

Technical

C
C++
Python
R
Pytorch
Tensorflow
Keras

Teaching

TA for Deep Learning Course, Summer Semester, 2021

Leibniz University Hannover

  • Machine Learning Basics
  • Neural Net Basics
  • Convolutional Neural Networks
  • Optimization and Regularixation
  • Unsupervised Approaches: PCA, Autoencoders, GANs
  • Attention Mechanism
  • Deep Learning for Graphs
  • Interpretable Deep Learning
  • Deep Learning for Language
  • Contact

    Location:

    L3S Research Center, Appelstrasse 4b, 30167, Hannover

    Telephone:

    Not Available

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