Di WangPHD candidate
Department of Computer Science and Engineering
State University of New York at Buffalo
Email : dwang45 "at" buffalo.edu, shao3wangdi "at" berkeley.edu
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Department of Computer Science and Engineering at The State University of New York (SUNY) at Buffalo under supervision of Dr. Jinhui Xu . Before that I got my Master degree in Mathematics at University of Western Ontario in 2015, and I got my Bachelor degree in Mathematics and Applied Mathematics at Shandong University in 2014.
- January'19 One paper has been accepted to CISS 2019!
- December'18: One paper has been accepted to ALT 2019!
- December'18: I have revieved UB CSE Best Graduate Research Award 2018!
- September'18: I will be a Visiting Graduate Student in the Data Privacy program at Simons, Berkeley during Spring 2019.
- My most recent resume (last updated in January, 2019) can be found here.
- Spring 2019: Visiting Graduate Student, Data Privacy: Foundations and Applications, Simons Institute for the Theory of Computing, University of California, Berkeley.
- Summer 2018: Graduate Research Intern,Harvard University Privacy Tools Project, Harvard University, Mentor: Adam Smith.
- Estimating High Dimensional Robust Mixture Model via Trimmed Expectation-Maximization Algorithm. Abstract▼ Di Wang*, Xiangyu Guo* and Jinhui Xu . (* equal contributions)
- Lower Bound of Sparse Covariance Matrix Estimation in the Local Differential Privacy Abstract▼ Di Wang and Jinhui Xu .
- Differentially Private Empirical Risk Minimization with Non-convex Loss Functions Abstract▼ Di Wang, Changyou Chen and Jinhui Xu .
- Locally Differentially Private Principal Component Analysis Abstract▼ Di Wang and Jinhui Xu .
- On the Sparse Linear Regression Under Local Differential Privacy Abstract▼ Di Wang and Jinhui Xu .
- Privacy-aware Synthesizing for Crowdsourced Data Abstract▼ Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu , Aidong Zhang.
- Differentially Private High Dimensional Sparse Covariance Matrix Estimation Abstract▼ Di Wang and Jinhui Xu. Submitted to Theoretical Computer Science.
- On the Emprical Risk Minimization In the Non-interactive Local Differential Privacy Model Abstract▼ Di Wang, Marco Gaboardi, Adam Smith and Jinhui Xu . Submitted to Journal of Machine Learning Research (JMLR).
- Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning Abstract▼ Di Wang and Jinhui Xu . Submitted to ACM Transactions on Intelligent Systems and Technology (TIST).
- Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization Abstract▼ Di Wang and Jinhui Xu . Submitted to ACM Transactions on Privacy and Security (TOPS).
- Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding Abstract▼ Di Wang, Jinhui Xu and Yang He. 53rd Annual Conference on Information Sciences and Systems (CISS 2019).
- Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations Abstract▼ Di Wang, Adam Smith and Jinhui Xu . The 30th International Conference on Algorithmic Learning Theory (ALT 2019).
- High Dimensional Sparse Linear Regression under Local Differential Privacy: Power and Limitations Abstract▼ Di Wang, Adam Smith and Jinhui Xu . NIPS 2018 Workshop on Privacy Preserving Machine Learning.
- Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View. Abstract▼ Di Wang and Jinhui Xu . Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019). Selected as Oral Presentation.
- Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. Abstract▼ Di Wang, Marco Gaboardi and Jinhui Xu . Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018.
- Differentially Private Sparse Inverse Covariance Estimation. Abstract▼ Di Wang, Mengdi Huai and Jinhui Xu . 2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip). Selected as Oral Presentation.
- Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. Abstract▼ Di Wang and Jinhui Xu . Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018). Selected as Oral Presentation.
- Differentially Private Empirical Risk Minimization Revisited: Faster and More General. Abstract▼ Di Wang, Minwei Ye and Jinhui Xu . Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017.
- CSE 437/537 Introduction to Machine Learning, Summer 2019 @SUNY at Buffalo.
- Teaching assistant:
- CSE 437/537 Introduction to Machine Learning, Spring 2018 @SUNY at Buffalo.
- CSE 431/531 Analysis of Algorithm, Fall 2017, Spring 2017, Fall 2016, Spring 2016 @SUNY at Buffalo.
- CSE 115 Introduction to Computer Science for Majors I, Fall 2015 @ @SUNY at Buffalo.
- MATH 1229A Methods of Matrix Algebra, Summer 2015, Spring 2015 @ UWO.
- ATH 1225B Methods of Calculus, Fall 2014 @ UWO.
NeuIPS2019, ICDCS 2019, ICCV 2019, CVPR 2019, ICML 2019, AISTATS 2019, KDD 2018, AAAI 2017 2018, CompIMAGE 2018, IWCIA 2017
ACM Computing Surveys, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Pattern Analysis and Machine Intelligence, Theoretical Computer Science, Information Processing Letters
- Best CSE Graduate Research Award in 2018, SUNY at Buffalo
- NIPS Travel Award, 2018, 2017
- Western Graduate Research Scholarship, Western University, 2014-2015
- Algebraic Geometry Summer School Scholarship, ENCU, Shanghai, 2013