Manuscripts Under Review

• Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J Love and Christopher G Brinton, ‘‘Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning’’, 2025.
• Anindya Bijoy Das and Shahnewaz Karim Sakib, ‘‘Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness’’, 2024.
• Satyavrat Wagle, Anindya Bijoy Das, David J Love and Christopher G Brinton, ‘‘Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning’’, 2024.
• David Nickel, Anindya Bijoy Das, David Love and Christopher G. Brinton, ‘‘Multi-Agent Hybrid Soft Actor-Critic for Joint Spectrum Sensing and Dynamic Spectrum Access in Cognitive Radio Networks’’, 2024.
• Byunghyun Lee, Anindya Bijoy Das, David Love, Christopher G. Brinton and James Krogmeier, ‘‘Constant Modulus Waveform Design with Interference Exploitation for DFRC Systems: A Block-Level Approach’’, 2023.
• Junghoon Kim, Taejoon Kim, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J. Love, and Christopher G. Brinton, ‘‘Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches’’, 2023.

Selected Journals

• Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton, ‘‘Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning’’, in IEEE Jour. on Selected Areas in Communications, 2024. Paper.
• Anindya Bijoy Das, Aditya Ramamoorthy, David J Love and Christopher G Brinton ‘‘Sparsity-preserving encodings for straggler-optimal distributed matrix computations at the edge’’, in IEEE Internet of Things Journal, 2024. Paper.
• Ashwin Natraj Arun, Anindya Bijoy Das, Christopher G Brinton, David J Love, James V Krogmeier ‘‘Do Small Cells Make Sense for Simple Low Cost LPWANs?’’, in IEEE Wireless Communications Letters, 2024. Paper.
• Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love and Christopher G. Brinton, ‘‘Distributed Matrix Computations with Low-weight Encodings’’, in IEEE Jour. on Selected Areas in Info. Theory, vol. 4, pp. 363-378, 2023. paper.
• Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love, Christopher G. Brinton, ‘‘A Decentralized Pilot Assignment Methodology for Scalable O-RAN Cell-Free Massive MIMO’’, in IEEE Jour. on Selected Areas in Communications, 2023. Paper.
• Anindya Bijoy Das and Aditya Ramamoorthy, ‘‘A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation’’ in IEEE Jour. on Selected Areas in Info. Theory, vol. 3, pp. 241-256, 2022. paper.
• Anindya Bijoy Das and Aditya Ramamoorthy, ‘‘Coded sparse matrix computation schemes that leverage partial stragglers’’, in IEEE Trans. on Info. Theory, vol. 68, pp. 4156-4181, 2022. paper.
• Anindya Bijoy Das, Aditya Ramamoorthy, Namrata Vaswani, ‘‘Efficient and Robust Distributed Matrix Computations via Convolutional Coding’’, in IEEE Trans. on Info. Theory, vol. 67, pp. 6266-6282, 2021. paper.
• Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang, ‘‘Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing’’ in IEEE Signal Proc. Magazine, vol. 37, pp. 136-145, 2020. paper.
• Anindya Bijoy Das and Mohammed Imamul Hassan Bhuiyan, ‘‘Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain’’, in Biomedical Signal Proc. and Control, Elsevier, vol. 29, pp. 11-21, 2016. paper.
• Anindya Bijoy Das, Mohammed Imamul Hassan Bhuiyan and S M Shafiul Alam, ‘‘Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection’’, in Signal, Image and Video Proc. , Springer, vol. 10, pp. 259-266, 2016. paper.

Selected Conference Papers

• Shahnewaz Karim Sakib and Anindya Bijoy Das, ‘‘Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations’’, in IEEE International Conference on Big Data (BigData), 2024. paper.
• Shahnewaz Karim Sakib and Anindya Bijoy Das, ‘‘Explainable Vertical Federated Learning for Healthcare: Ensuring Privacy and Optimal Accuracy’’ in IEEE International Conference on Big Data (BigData), 2024. paper.
• Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle, Christopher G Brinton, ‘‘Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning’’, IEEE International Conference on Communications (ICC), 2024. paper.
• Anindya Bijoy Das, Aditya Ramamoorthy, David J Love, Christopher G Brinton, ‘‘Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations’’ in Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023. paper.
• Satyavrat Wagle, Anindya Bijoy Das, David J Love, Christopher G Brinton, ‘‘A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning’’, IEEE Global Communications Conference (GlobeCom), 2023. paper.
• Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love and Christopher G. Brinton, ‘‘Coded Matrix Computations for D2D-Enabled Linearized Federated Learning’’ in IEEE Intl.Conf. on Acoustics, Speech and Signal Proc. (ICASSP), 2023. paper.
• Anindya Bijoy Das and Aditya Ramamoorthy, ‘‘An Integrated Method to Deal With Partial Stragglers and Sparse Matrices in Distributed Computations’’, IEEE Intl. Symp. on Info. Theory (ISIT), 2022. paper.
• Anindya Bijoy Das and Aditya Ramamoorthy, ‘‘Distributed matrix-vector multiplication: A convolutional coding approach’’, in IEEE Intl. Symp. on Info. Theory (ISIT), 2019. paper.
• Anindya Bijoy Das, Aditya Ramamoorthy and Li Tang, ‘‘C3LES : Codes for Coded Computation that Leverage Stragglers’’, in IEEE Info. Theory Workshop (ITW), 2018. paper.