Christopher G. Brinton, PhD

For an exhaustive list of publications, see my CV.

Books

• C. Brinton, M. Chiang. The Power of Networks: Six Principles That Connect Our Lives. Princeton University Press, Dec. 2016.
• C. Brinton, M. Chiang. Networks Illustrated: 8 Principles Without Calculus. EdWiser Scholastic Press, June 2013.

10 Representative Papers (since 2016)

• J. Kim, T. Kim, D. Love, C. Brinton. Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning. International Conference on Machine Learning (ICML), 2023.
• R. Sahay, M. Zhang, D. Love, C. Brinton. Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders. IEEE Transactions on Cognitive Communications and Networking, 2023.
• S. Azam, S. Hosseinalipour, Q. Qiu, C. Brinton. Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? International Conference on Learning Representations (ICLR), 2022.
• J. Kim, T. Kim, M. Hashemi, D. Love, C. Brinton. Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks. IEEE/ACM Transactions on Networking, 2022.
• F. Lin, S. Hosseinalipour, S. Azam, C. Brinton. N. Michelusi. Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations. IEEE Journal on Selected Areas in Communications, Vol. 39, No. 12, p. 3851-3869, 2021.
• S. Wang, Y. Ruan, Y. Tu, S. Wagle, C. Brinton, C. Joe-Wong. Network-Aware Optimization of Distributed Learning for Fog Computing. IEEE/ACM Transactions on Networking, Vol. 29, No. 5, p. 2019-2032, 2021.
• S. Hosseinalipour, C. Brinton, V. Aggarwal, H. Dai, M. Chiang. From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks. IEEE Communications Magazine, Vol. 58, No. 12, 2020.
• C. Brinton, S. Buccapatnam, L. Zheng, D. Cao, A. Lan, F. Wong, S. Ha, M. Chiang, H.V. Poor. On the Efficiency of Online Social Learning Networks. IEEE/ACM Transactions on Networking, Vol. 26, No. 5, 2018.
• T. Yang, C. Brinton, C. Joe-Wong, M. Chiang. Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks. IEEE Journal of Selected Topics in Signal Processing, Vol. 11, No. 5, Aug. 2017.
• C. Brinton, S. Buccapatnam, M. Chiang, H.V. Poor. Mining MOOC Clickstreams: Video Watching Behavior versus In-Video Quiz Performance. IEEE Transactions on Signal Processing, Vol. 64, No. 14, Jul. 2016.

10 Recent Papers

• A. Das, A. Ramamoorthy, D. Love, C. Brinton. Coded Matrix Computations for D2D-Enabled Linearized Federated Learning. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
• S. Wang, S. Hosseinalipour, V. Aggarwal, C. Brinton, D. Love, W. Su, M. Chiang. Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks. IEEE Communications Magazine, 2023.
• E. Ruzomberka, D. Love, C. Brinton, A. Gupta, C. Wang, H. V. Poor. Challenges and Opportunities for Beyond-5G Wireless Security. IEEE Security and Privacy, 2023.
• B. Ganguly, S. Hosseinalipour, K. Kim, C. Brinton, V. Aggarwal, D. Love, M. Chiang. Multi-Edge ServerAssisted Dynamic Federated Learning with an Optimized Floating Aggregation Point. IEEE/ACM Transactions on Networking, 2023.
• S. Wang, R. Sahay, C. Brinton. How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers? International Conference on Communications (ICC), 2023.
• Z. Zhou, S. Azam, C. Brinton, D. Inouye. Efficient Federated Domain Translation. International Conference on Learning Representations (ICLR), 2023.
• R. Sahay, S. Nicoll, M. Zhang, T. Yang, C. Joe-Wong, K. Douglas, C. Brinton. Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach. IEEE/ACM Transactions on Networking, 2023.
• D. Han, D. Kim, M. Choi, C. Brinton, J. Moon. SplitGP: Achieving Both Generalization and Personalization in Federated Learning. IEEE International Conference on Computer Communications (INFOCOM), 2023.
• S. Wang, S. Hosseinalipour, M. Gorlatova, C. Brinton, M. Chiang. UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach. IEEE Transactions on Network and Service Management, 2023.
• Y. Chu, S. Hosseinalipour, E. Tenorio, L. Cruz, K. Douglas, A. Lan, C. Brinton. Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. Conference on Information and Knowledge Management (CIKM), 2022.

Patents

• C. Brinton, M. Chiang, S. Ha, W. Ju, R. Rill, J. Walker, E. Tenorio. Systems and Methods to Assist an Instructor of a Course. U.S. Patent #17/029,707. Issued Aug 2022.
• C. Brinton, W. Chen, M. Chiang, S. Ha, R. Rill. System and Method for Automated Course Individualization via Learning Behaviors and Natural Language Processing. U.S. Patent #10,339,822. Issued Jul 2019.