Ambarish Moharil

Ambarish Moharil MSc

PhD Candidate at SAFEGUARD lab

  • Research profile: Geometric Deep Learning and XAI
  • Research expertise: Automated Machine Learning/Deep Learning, Machine/Deep Learning for Spatio-Temporal Multimodal Data, Model-Based Bayesian Optimization, Advanced Statistical Analysis.

Biography

Ambarish Moharil is a PhD Candidate at Safeguard Lab, working with the Data Governance unit at JADS in collaboration with Deloitte. His research focuses on the explainability and transparency of deep learning models, with a specific interest in exploring geometric symmetries in neural parameter spaces, such as scale-invariance. Ambarish is developing methods to analyze dynamic representations of neural networks in high-dimensional spaces while retaining important symmetries like permutation invariance. He has published extensively in top-tier venues such as ICSE-MSR, NLBSE, ECML PKDD, and KDD. Recently, he designed a hybrid AutoDL tool for synthesizing multimodal pipelines using pre-trained transformers and created the TABASCO toolkit to assist in disambiguating amorphous nouns in software requirements documents. His research aims to enhance human-AI collaboration by examining the impact and behavior of AI systems.

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Research Projects

JADS also participates in several (international) projects, which contribute to grand societal challenges like health, food security, smart transport and secure societies. Together with companies, government, NGO’s and other knowledge institutions, JADS works on solutions by using data.

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