Peilin Zhong
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Peilin Zhong, research scientist, |
About Me
I am a research scientist at Google NYC in the Algorithms and Optimization team lead by Vahab Mirrokni. I received my Ph.D. from Columbia University (Under supervision of Alex Andoni, Cliff Stein, and Mihalis Yannakakis). Previously, I was an undergraduate student at Institute for Interdisciplinary Information Sciences (Yao Class), Tsinghua University. I have broad interests in theoretical computer science, mainly in design and analysis of algorithms. Some particular interests include parallel and massively parallel algorithms, sketching, streaming algorithms, graph algorithms, machine learning, high dimensional geometry, metric embedding, numerical linear algebra, clustering, and other algorithms related to large-scale data computation.
Publications
(Authors are ordered alphabetically, except for papers marked by *)
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. In SODA 2022. Full version on pdf.
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Peilin Zhong.
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. In SODA 2022. Full version on pdf.
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Peilin Zhong.
Almost Linear Time Density Level Set Estimation via DBSCAN. In AAAI 2021. Conference version on pdf. Appendix on pdf. Codes on zip.
Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong.
Planning with General Objective Functions: Going Beyond Total Rewards*. In NeurIPS 2020. Conference version on pdf.
Ruosong Wang*, Peilin Zhong*, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang. (*joint first author)
Connected Components on a PRAM in Log Diameter Time. In SPAA 2020. Full version on pdf.
S. Cliff Liu, Robert E. Tarjan, Peilin Zhong.
See Chapter 4 of PhD thesis for the extension to COLLISION CRCW PRAM.
Parallel Approximate Undirected Shortest Paths Via Low Hop Emulators. In STOC 2020. Full version on pdf.
Alexandr Andoni, Clifford Stein, Peilin Zhong.
See Chapter 6 of PhD thesis for the extension to approximate SSSP tree and the dual problem of SSSP.
Enhancing Adversarial Defense by k-Winners-Take-All*. In ICLR 2020. Full version on pdf.
Chang Xiao, Peilin Zhong, Changxi Zheng.
Average Case Column Subset Selection for Entrywise L1-Norm Loss. In NeurIPS 2019. Full version on pdf.
Zhao Song, David P. Woodruff, Peilin Zhong.
Towards a Zero-One Law for Column Subset Selection. In NeurIPS 2019. Full version on pdf.
Zhao Song, David P. Woodruff, Peilin Zhong.
Rethinking Generative Coverage: A Pointwise Guaranteed Approach*. In NeurIPS 2019. Full version on pdf.
Peilin Zhong*, Yuchen Mo*, Chang Xiao*, Pengyu Chen, Changxi Zheng. (*joint first author)
Efficient Symmetric Norm Regression via Linear Sketching. In NeurIPS 2019. Full version on pdf.
Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong.
Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. In ICALP 2019. Full version on pdf.
Alexandr Andoni, Clifford Stein, Peilin Zhong.
See Chapter 5 of PhD thesis for the extension to open ear decomposition.
Relative Error Tensor Low Rank Approximation. In SODA 2019. Full version on pdf.
Zhao Song, David P. Woodruff, Peilin Zhong.
BourGAN: Generative Networks with Metric Embeddings*. In NIPS 2018 spotlight. Full version on pdf.
Chang Xiao, Peilin Zhong, Changxi Zheng.
Parallel Graph Connectivity in Log Diameter Rounds. In FOCS 2018. Full version on pdf.
Alexandr Andoni, Zhao Song, Clifford Stein, Zhengyu Wang, Peilin Zhong.
Subspace Embedding and Linear Regression with Orlicz Norm. In ICML 2018. Full version on pdf.
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong.
Low Rank Approximation with Entrywise L1-Norm Error. In STOC 2017. Full version on pdf.
Zhao Song, David P. Woodruff, Peilin Zhong.
Optimal Principal Component Analysis in Distributed and Streaming Models. In STOC 2016. Full version on pdf.
Christos Boutsidis, David P. Woodruff, Peilin Zhong.
Distributed Low Rank Approximation of Implicit Functions of a Matrix. In ICDE 2016. pdf
David P. Woodruff, Peilin Zhong.
Deep learning of feature representation with multiple instance learning for medical image analysis*. In ICASSP 2014. pdf
Yan Xu, Tao Mo, Qiwei Feng, Peilin Zhong, Maode Lai and Eric I-Chao Chang.
Manuscripts
Streaming Balanced Clustering. arXiv:1910.00788, 2019.
Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong.
Nearly Optimal Dynamic k-Means Clustering for High-Dimensional Data. arXiv:1802.00459, 2018.
Wei Hu, Zhao Song, Lin F. Yang, Peilin Zhong.
Theses
New Primitives for Tackling Graph Problems and Their Applications in Parallel Computing. PhD thesis. May, 2021.
Advisers: Alexandr Andoni, Clifford Stein, Mihalis Yannakakis.
Defense committee: Alexandr Andoni, Clifford Stein, Omri Weinstein, David P. Woodruff, Mihalis Yannakakis.
Selected Awards
Gold Medal, International Olympiad in Informatics, 2012.
Gold Medal, ACM/ICPC Asia Changchun Regional Contest, 2013.
Gold Medal, ACM/ICPC Asia Beijing Regional Contest, 2015.
Yao Award, Tsinghua University, 2015.
1st Place, ACM/ICPC Great New York Regional Contest, 2017.
31st Place, ACM/ICPC World Finals, 2018.
Google PhD Fellowship, 2019.