Tara Esmaeilbeig

Real-time Range-Angle Estimation and Tag Localization for Multi-static Backscatter Systems

Tara Esmaeilbeig, Kartik Ramesh Patel, Traian E. Abrudan, John Kimionis et autres

Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and …

Real-time Range-Angle Estimation and Tag Localization for Multi-static Backscatter Systems

Tara Esmaeilbeig, Kartik Ramesh Patel, Traian E. Abrudan, John Kimionis et autres

Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and …

Linearization Explains Fine-Tuning in Large Language Models

Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian, Mesrob I. Ohannessian

Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlying their training performance and generalization remain underexplored. In this paper, we provide several insights into such fine-tuning …

Linearization Explains Fine-Tuning in Large Language Models

Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian, Mesrob I. Ohannessian

Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlying their training performance and generalization remain underexplored. In this paper, we provide several insights into such fine-tuning …

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Next-Generation Backscatter Networks for Integrated Communications and RF Sensing

Traian E. Abrudan, John Kimionis, Tara Esmaeilbeig, Eleftherios Kampianakis et autres

This paper provides a comprehensive analysis and theoretical foundation for next-generation backscatter networks that move beyond communication and integrate RF location sensing capabilities. An end-to-end system model for wideband OFDM backscatter systems is derived, including detailed characterization of propagation channels, receiver chain …

RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates

Md. Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, Chen Chen et autres

Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, Chen Chen, Mojtaba Soltanalian, Niloofar Yousefi. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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Linearization Explains Fine-Tuning in Large Language Models

Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian, Mesrob I. Ohannessian

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Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?

Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian

Reconfigurable intelligent surfaces (RIS) offer unprecedented flexibility for smart wireless channels. Recent research shows that RIS platforms enhance signal quality, coverage, and link capacity in integrated sensing and communication (ISAC) systems. This paper explores the use of fully-connected beyond diagonal RIS (BD-RIS) …

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RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates

Md. Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, Mojtaba Soltanalian et autres

We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and RoBERTa, and larger LMs like Bloom-7B, …

Deep Learning-Enabled One-Bit DoA Estimation

Farhang Yeganegi, Arian Eamaz, Tara Esmaeilbeig, Mojtaba Soltanalian

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We …

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Deep Learning-Enabled One-Bit DoA Estimation

Farhang Yeganegi, Arian Eamaz, Tara Esmaeilbeig, Mojtaba Soltanalian

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We …

Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?

Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian

Reconfigurable intelligent surface (RIS) have introduced unprecedented flexibility and adaptability toward smart wireless channels. Recent research on integrated sensing and communication (ISAC) systems has demonstrated that RIS platforms enable enhanced signal quality, coverage, and link capacity. In this paper, we explore the …