Activation Density Driven Efficient Pruning in Training. - Timothy Foldy-Porto; Yeshwanth Venkatesha; Priyadarshini Panda (ICPR, 2020)

Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks. - Karina Vasquez; Yeshwanth Venkatesha; Abhiroop Bhattacharjee; Abhishek Moitra; Priyadarshini Panda (DATE, 2021)

PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks. - Youngeun Kim; Yeshwanth Venkatesha; Priyadarshini Panda (CoRR, 2021)

Federated Learning With Spiking Neural Networks. - Yeshwanth Venkatesha; Youngeun Kim; Leandros Tassiulas; Priyadarshini Panda (IEEE Trans. Signal Process., 2021)

PrivateSNN: Privacy-Preserving Spiking Neural Networks. - Youngeun Kim; Yeshwanth Venkatesha; Priyadarshini Panda (AAAI, 2022)

MIME: adapting a single neural network for multi-task inference with memory-efficient dynamic pruning. - Abhiroop Bhattacharjee; Yeshwanth Venkatesha; Abhishek Moitra; Priyadarshini Panda (DAC, 2022)

Neural Architecture Search for Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Priyadarshini Panda (ECCV (24), 2022)

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Ruokai Yin; Priyadarshini Panda (ECCV (12), 2022)

Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? - Youngeun Kim; Hyoungseob Park; Abhishek Moitra; Abhiroop Bhattacharjee; Yeshwanth Venkatesha; Priyadarshini Panda (ICASSP, 2022)

Addressing Client Drift in Federated Continual Learning with Adaptive Optimization. - Yeshwanth Venkatesha; Youngeun Kim; Hyoungseob Park; Yuhang Li 001; Priyadarshini Panda (CoRR, 2022)

Exploring Temporal Information Dynamics in Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Anna Hambitzer; Priyadarshini Panda (AAAI, 2023)

Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing. - Abhiroop Bhattacharjee; Abhishek Moitra; Youngeun Kim; Yeshwanth Venkatesha; Priyadarshini Panda (ACM Great Lakes Symposium on VLSI, 2023)

Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems. - Yeshwanth Venkatesha; Youngeun Kim; Hyoungseob Park; Priyadarshini Panda (Neural Networks, 2023)

HaLo-FL: Hardware-Aware Low-Precision Federated Learning. - Yeshwanth Venkatesha; Abhiroop Bhattacharjee; Abhishek Moitra; Priyadarshini Panda (DATE, 2024)

Rhychee-FL: Robust and Efficient Hyperdimensional Federated Learning with Homomorphic Encryption. - Yujin Nam; Abhishek Moitra; Yeshwanth Venkatesha; Xiaofan Yu 001; Gabrielle De Micheli; Xuan Wang; Minxuan Zhou; Augusto Vega; Priyadarshini Panda; Tajana Rosing (DATE, 2025)

Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges. - Amit Ranjan Trivedi; Sina Tayebati; Hemant Kumawat; Nastaran Darabi; Divake Kumar; Adarsh Kumar Kosta; Yeshwanth Venkatesha; Dinithi Jayasuriya; Nethmi Jayasinghe; Priyadarshini Panda; Saibal Mukhopadhyay; Kaushik Roy 001 (DATE, 2025)

Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits. - Yeshwanth Venkatesha; Souvik Kundu 009; Priyadarshini Panda (Trans. Mach. Learn. Res., 2025)

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection. - Yeshwanth Venkatesha; Souvik Kundu 009; Priyadarshini Panda (CoRR, 2025)