My published research in machine learning, artificial intelligence, and related fields, contributing to the scientific community and advancing technological innovation.
Published June 2023
This paper presents a novel approach to neural network architecture adaptation for deployment in resource-constrained environments. We introduce a dynamic pruning methodology that automatically adjusts model complexity based on available computational resources while maintaining high accuracy.
Published May 2023
We propose a novel approach to cross-lingual transfer learning that significantly improves natural language understanding for low-resource languages. By leveraging shared linguistic features across related languages, our method achieves state-of-the-art performance on several benchmark tasks.
Published December 2022
This paper introduces a novel approach to self-supervised visual representation learning by exploiting geometric transformations. Our method enables models to learn robust visual features without labeled data, achieving comparable performance to supervised approaches on various downstream tasks.
Published October 2022
This research proposes practical frameworks for ensuring fairness, accountability, and transparency in deployed AI systems. We present a comprehensive methodology for auditing algorithms and mitigating bias, along with case studies demonstrating successful implementation in high-stakes domains.
Published December 2022
We introduce a probabilistic extension to graph neural networks that enables accurate uncertainty quantification in molecular property prediction. Our approach provides reliable confidence estimates alongside predictions, crucial for high-stakes applications like drug discovery and materials science.
Published November 2021
This study presents a comprehensive analysis of bias detection and mitigation techniques for large language models. We evaluate various debiasing approaches across different bias types and propose an ensemble method that outperforms existing techniques while maintaining model utility.
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