Thiviyan Thanapalasingam
Research Scientist
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolThiviyan Thanapalasingam is an AI research scientist with nine years of experience bridging machine learning research and practical engineering, currently based in Amsterdam. He has led and contributed to neuroscience-inspired and neurosymbolic generative model initiatives during a PhD at the University of Amsterdam and supported AI-for-Science projects at SonyAI, combining pipeline engineering, research data management, and project roadmapping. His hands-on work includes open-source PyTorch implementations for relational reasoning and improving dataset generation and training instrumentation, reflecting a focus on robust, reproducible ML experimentation. Comfortable moving between full-stack prototyping and academic publication, he also organises workshops and chairs conference tracks, demonstrating leadership in community-building. Colleagues describe him as a learner-problem-solver who pairs deep technical rigor from computational chemistry and AI with practical delivery across research and engineering domains.
9 years of coding experience
4 years of employment as a software developer
PhD, Artificial Intelligence, PhD, Artificial Intelligence at University of AmsterdamMaster of Chemistry (MChem), Computational & Physical Chemistry, First-Class (honours), Master of Chemistry (MChem), Computational & Physical Chemistry, First-Class (honours) at University of Leicester
Github Skills (5)
pytorch10
deep-learning10
python10
machine-learning9
tensorboard8
Programming languages (6)
Github contributions (5)
kimhc6028/relational-networks
Mar 2020 - Mar 2020
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)
Role in this project:
ML Engineer
ML EngineerContributions:25 commits in 14 days
Contributions summary:Thiviyan primarily focused on implementing and refining a PyTorch implementation of a relational network. Their contributions included modifying the `sort_of_clevr_generator.py` file to improve the dataset generation process and address issues in object counting and line detection. Furthermore, the user integrated tensorboard logging, added a command to run the ternary RN model, and fixed a deprecation warning in the model's softmax function. These modifications suggest an effort to improve model performance and training process.
pytorchrelationalsimple-neural-networkdeep-learningneural-network
thiviyanT/IntelliGraphs
Jun 2023 - Feb 2025
IntelliGraphs is a collection of graph datasets for benchmarking generative models for knowledge graphs.
Contributions:17 releases, 4 PRs, 79 pushes in 1 year 8 months
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