I'm a theoretical Machine Learning Ph.D. at Department of Computer Science, Johns Hopkins University. I am fortunate to be advised by Raman Arora.
My research interest focuses on Robust Adversarial Learning, Deep Learning Theory. I'm passionate about understanding the fundamental question of machine learning and deep learning, as well as using ML algorithms and data-driven techniques to solve real-world problems.
Designed practical defense algorithm against adversarial attack for unsupervised domain adaptation that outperform competitive baselines and along with theoretical guarantees.
Designed Importance Weighted Subset Selection algorithm (IWeS) that outperforms competitive baselines on large datasets and along with theoretical guarantees.
Responsible for designing defense algorithms in DARPA project on AI Robustness against deception.
Explored the foundation of robust adversarial learning.
Investigate bandit problem under Fatigue-aware Online Recommendation System.
EN.601.475/675 Introduction to Machine Learning
EN.601.779 Machine Learning: Advanced Topics
Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva, Alexey Kurakin, Ibrahim Hammoud, Raman Arora
in submission
Yunjuan Wang, Kaibo Zhang, Raman Arora
in submission
Gui Citovsky, Giulia DeSalvo, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang (alphabetical order)
International Conference on Learning Representations (ICLR), 2023
Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora
Advances in Neural Information Processing Systems (NeurIPS), 2022
Yunjuan Wang, Poorya Mianjy, Raman Arora
International Conference on Machine Learning (ICML), 2021
Yunjuan Wang, Theja Tulabandhula
Workshop on Information Technologies and Systems (WITS), 2021
Yunjuan Wang, Kezhen Zhang, Yong Deng
Journal of Ambient Intelligence and Humanized Computing, 2018
GPA: 4.0/4.0
GPA: 4.0/4.0
GPA: 3.89/4.3
