Deep-n-Cheap: An Automated Efficient and Extensible Search Framework for Cost-Effective Deep Learning. - Sourya Dey; Sara Babakniya; Saikrishna C. Kanala; Marco Paolieri; Leana Golubchik; Peter A. Beerel; Keith M. Chugg (SN Comput. Sci., 2021)

Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge. - Sara Babakniya; Souvik Kundu 002; Saurav Prakash; Yue Niu; Salman Avestimehr (CoRR, 2022)

Defending against Poisoning Backdoor Attacks on Federated Meta-learning. - Chien-Lun Chen; Sara Babakniya; Marco Paolieri; Leana Golubchik (ACM Trans. Intell. Syst. Technol., 2022)

A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. - Sara Babakniya; Zalan Fabian; Chaoyang He 001; Mahdi Soltanolkotabi; Salman Avestimehr (NeurIPS, 2023)

Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory. - Sara Babakniya; Zalan Fabian; Chaoyang He 001; Mahdi Soltanolkotabi; Salman Avestimehr (CoRR, 2023)

SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models. - Sara Babakniya; Ahmed Roushdy Elkordy; Yahya H. Ezzeldin; Qingfeng Liu; Kee-Bong Song; Mostafa El-Khamy; Salman Avestimehr (CoRR, 2023)

Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? - Sara Babakniya; Souvik Kundu 002; Saurav Prakash; Yue Niu; Salman Avestimehr (Trans. Mach. Learn. Res., 2023)

AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design. - Asal Mehradfar; Xuzhe Zhao; Yue Niu; Sara Babakniya; Mahdi Alesheikh; Hamidreza Aghasi; Salman Avestimehr (CoRR, 2024)

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights. - Asal Mehradfar; Xuzhe Zhao; Yue Niu; Sara Babakniya; Mahdi Alesheikh; Hamidreza Aghasi; Salman Avestimehr (CoRR, 2025)

Escaping Collapse: The Strength of Weak Data for Large Language Model Training. - Kareem Amin 002; Sara Babakniya; Alex Bie; Weiwei Kong; Umar Syed; Sergei Vassilvitskii (CoRR, 2025)

Clustering and Median Aggregation Improve Differentially Private Inference. - Kareem Amin 002; Salman Avestimehr; Sara Babakniya; Alex Bie; Weiwei Kong; Natalia Ponomareva 001; Umar Syed (CoRR, 2025)