My research interests include information theory, fault-tolerance in high-performance computing, distributed machine learning, and fairness in machine learning with its application to education.

Recent Publications

[5] Haewon Jeong, Yaoqing Yang, Christian Engelmann,  Vipul Gupta, Tze Meng Low, Pulkit Grover, Viveck Cadambe, and Kannan Ramchandran, “3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication” (Euro-par 2020)

[4] Quang Minh Nguyen, Haewon Jeong, and Pulkit Grover “Coded QR Decomposition” (ISIT 2020, Full Version)

[3] Haewon Jeong*, Sanghamitra Dutta, Yaoqing Yang*, Viveck Cadambe, Tze Meng Low and Pulkit Grover, “Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing,” (Proceedings of the IEEE, 2020)

[2] Haewon Jeong*, Sanghamitra Dutta*, Mohammad Fahim*, Farzin Haddadpour*, Viveck Cadambe and Pulkit Grover, “On the Optimal Recovery Threshold of Coded Matrix Multiplication” (IEEE Transactions on Information Theory, 2020)

[1] Haewon Jeong and Pulkit Grover “Energy-adaptive Error Correcting For Dynamic and Heterogeneous Networks” (Proceedings of the IEEE, 2019)