Amirarsalan Rajabi

Amirarsalan Rajabi

AI, Machine Learning

Amirarsalan Rajabi’s Homepage

Bio

I am a Senior Machine Learning Engineer at Integral Ad Science, where I build and deploy large-scale LLM and vision-language systems — spanning distributed inference infrastructure, automated labeling pipelines, and production ML frameworks. I earned my Ph.D. from the University of Central Florida in December 2022, where I worked as a graduate research assistant at CASL.

Publications

You can also find my articles on my Google Scholar profile.

Fair Bilevel Neural Network (FairBiNN): On Balancing Fairness and Accuracy via Stackelberg Equilibrium

Published in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024

This paper addresses the persistent challenge of bias in machine learning models, proposing a bilevel optimization approach that balances fairness and accuracy.

Recommended citation: Yazdani-Jahromi, M., Khodabandeh Yalabadi, A., Rajabi, A., Tayebi, A., Garibay, I., & Garibay, O. (2024). Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium. Advances in Neural Information Processing Systems, 37, 105780-105818.

Through a fair looking-glass: mitigating bias in image datasets

Published in International Conference on Human-Computer Interaction, 2023

Recommended citation: Rajabi, A., Yazdani-Jahromi, M., Garibay, O. O., & Sukthankar, G. (2023, July). Through a fair looking-glass: mitigating bias in image datasets. In International Conference on Human-Computer Interaction (pp. 446-459). Cham: Springer Nature Switzerland.

Distraction is all you need for fairness

Published in arXiv , 2022

Recommended citation: Yazdani-Jahromi, M., Rajabi, A., Tayebi, A., & Garibay, O. O. (2022). Distraction is all you need for fairness. arXiv preprint arXiv:2203.07593.