- Designed and implemented pipelines and augmented GNN models which achieved a significant classification performance: https://github.com/ChEB-AI/python-chebai-graph
- Built an agentic workflow for translating chemical descriptions into First-Order Logic (FOL) through iterative model-guided reasoning: https://github.com/ChEB-AI/chebai-NL2FOL
- Contributed extensively to the core framework, including redesigning data preprocessing pipelines, improving test coverage, enabling CI workflows, investigating issues, and developing proof-of-concept implementations https://github.com/ChEB-AI/python-chebai
- Contributions to AI-ensemble frameworks: https://github.com/ChEB-AI/python-chebifier
- Extending chebai framework to support proteins classification: https://github.com/ChEB-AI/python-chebai-proteins
- Enabled
LightningDataModulerestoration of concrete subclasses from checkpoints: Issue: Lightning-AI/pytorch-lightning#21477 and PR: Lightning-AI/pytorch-lightning#21478 - Fixed inconsistencies in saving model hyperparameters across inherited classes: Issue Lightning-AI/pytorch-lightning#21488 and PR Lightning-AI/pytorch-lightning#21490
- Implemented a Recurrent Neural Network (RNN) module for the framework: PR tracel-ai/burn#4460
- Added Shrink activation functions to the framework: tracel-ai/burn#4556
- Implemented Shrink activation support for ONNX integration in Burn: Issue: tracel-ai/burn-onnx#158 and PR tracel-ai/burn-onnx#200
- Added ONNX support for RNN modules in Burn: Issue tracel-ai/burn-onnx#11 and PR tracel-ai/burn-onnx#90
- Developed a custom LRU caching mechanism for AI ensemble systems to efficiently cache per-model inference results for each input: Issue: ChEB-AI/chebifier-web#9 and PR ChEB-AI/python-chebifier#15
- Improved error handling in chemical logic parsing by replacing index errors with clear, user-friendly exceptions for First-Order Logic formulas: PR sfluegel05/chemlog-peptides#14