About Me
I am joining UCSB’s ECE department starting Fall 2022.
My research area includes information theory, distributed computing, and machine learning.
Recent Publications
- H. Jeong, M. D. Wu, M. Médard, N. Dasgupta, and F. P. Calmon “Who Gets the Benefit of the Doubt? Racial Bias inMachine Learning Algorithms Applied to Secondary School Math Education”, NeurIPS 2021 Workshop on Math AI for Education (2021)
- H. Jeong, H. Wang, and F. Calmon, “Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values“, To appear in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2022).
- H. Jeong, V. Cadambe, F. Calmon, and A. Devulapalli, “ε-Approximate Coded Matrix Multiplication is NearlyTwice as Efficient as Exact Multiplication”, JSAIT Special Issue on Coded Computing (2021)
- H. Jeong, Y. Yang, C. Engelmann, V. Gupta, T.M. Low, P.Grover, V. Cadambe, and K. Ramchandran, “3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication” (Euro-par 2020)
News
- (Mar 2022) The code for FairMIPForst is now available on Github.
- (Feb 2022) We presented “Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values” in the oral session at AAAI 2022. (Top 5% of submitted papers!)
- (Dec 2021) Our paper on “Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values” got accepted to AAAI 2022.
- (Oct 2021) Our paper on “Who Gets the Benefit of the Doubt? Racial Bias in Machine Learning Algorithms Applied to Secondary School Math Education” is accepted at NeurIPS 2021 Workshop on Math AI for Education (MATHAI4ED)
- (Oct 2021) I gave an invited talk at ITW 2021 on our work on approximate coded computing.
- (July 2021) Ateet Devulapalli gave a talk on our work on approximate coded matrix multiplication with Viveck Cadambe, and Flavio Calmon at the ISIT 2021.
- (July 2021) The Edu+ML research was featured on the Harvard SEAS website. Read more here.
- (June 2021) We now have an amazing intern, Michael Wu on the Edu+ML team, who also runs a start-up for a social donation app!
- (May 2021) My research on ML for more diversity in STEM education with Flavio Calmon (Harvard), Nilanjana Dasgupta (UMass Amherst), and Muriel Medard (MIT) won the 2021 Harvard Data Science Initiative Postdoctoral Research Fund!
- (Mar 2021) I gave a talk at SIAM CSE 21 on ‘Coding Theory Meets High-Performance Computing –3D Coded SUMMA and More’.