Scholar
Lingxiao Wang
Google Scholar ID: VPyxd6kAAAAJ
NJIT, TTIC, UCLA
Citations & Impact
Citations
2,226
H-index
23
i10-index
30
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- - Published a paper titled 'Efficient Privacy-Preserving Stochastic Nonconvex Optimization' at NeurIPS 2023
- - Published a paper titled 'Federated Online and Bandit Convex Optimization' at ICML 2023
- - Published a paper titled 'Differentially Private Matrix Completion through Low-rank Matrix Factorization' at AISTATS 2023
- - Published a paper titled 'Towards Optimal Communication Complexity in Distributed Non-convex Optimization' at NeurIPS 2022
- - Published a paper titled 'Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the US' at PNAS 2022
- - Published a paper titled 'Variance-reduced First-order Meta-learning for Natural Language Processing Tasks' at NAACL 2021
- - Published a paper titled 'Revisiting Membership Inference Under Realistic Assumptions' at PET 2021
Research Experience
- - Assistant Professor in the Department of Data Science at New Jersey Institute of Technology
- - Research Assistant Professor at Toyota Technological Institute at Chicago
Education
- - Ph.D. in Computer Science from the University of California, Los Angeles, advised by Professor Quanquan Gu
- - MS in Statistics from the University of Washington
Background
- Currently an Assistant Professor in the Department of Data Science at New Jersey Institute of Technology. Research interests broadly include privacy and security in machine learning (e.g., privacy risks in large models), collaborative learning (distributed and federated learning), optimization for machine learning, low-rank matrix recovery, and high-dimensional statistics.
