Hi, I am a Master's student at Machine Learning and Artificial Intelligence (MLAI) Lab in Korea Advanced Institute of Science and Technology (KAIST), under the supervison of Prof. Sung Ju Hwang. My research interests include adversarial robustness, network compression, ensemble learning, and meta-learning. I am particularly interested in understanding the susceptibility of existing models to natural and synthetic distribution shifts. My current research focuses on developing algorithmic methods to enhance the confidence and generalizability of existing models while making them compact to deploy them in real-world safety-critical applications.
- [08/05/21] Our paper Learning to Generate Noise for Multi-Attack Robustness got accepted at ICML 2021.
- [30/10/20] Our paper Learning to Generate Noise for Multi-Attack Robustness got accepted at NeurIPS Meta-Learning Workshop 2020.
- [15/09/20] I received an ICML 2019 Outstanding Reviewer Award (top 33% of reviewers).
- [01/06/20] Our paper Adversarial Neural Pruning with Latent Vulnerability Suppression got accepted at ICML 2020. Code is available on GitHub.
- [09/11/20] Our paper Adversarial Neural Pruning got accepted at Neurips Safety and Robustness in Decision Making Workshop, 2019.
- [09/09/19] We upoloaded our paper Learning Sparse Networks Using Targeted Dropout on arXiv. We also published our blog post and code is available on GitHub.
- [26/08/19] I started my M.S. at KAIST under the supervision of Sung Ju Hwang.
- [21/08/19] Our paper VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting got accepted at GlobalSIP 2019. Code is available on GitHub.
- [30/09/18] We released our implementation for Adaptive Computation Time for Recurrent Neural Networks with improved performance. Code is available on GitHub.
- [14/05/18] I was selected as a mentor for Google Summer of Code 2018 for KDE.
- [23/10/17] I was selected as a mentor for Google Code-in 2018 for KDE.
- [08/09/17] I successfully completed Google Summer of Code 2017 for KDE.
- Conference reviewer
- NeurIPS (2020, 2021)
- ICML (2020 - Top 33%, 2021)
- AAAI 2021
- ACML 2020
- Workshop reviewer
- NeurIPS MetaLearning workshop 2020
- ICLR 2020
- ICML 2020
- NeurIPS 2020