Peilin Zhong
Scholar

Peilin Zhong

Google Scholar ID: l4ffN_QAAAAJ
Google Research
parallel algorithmstreaming algorithmsketching algorithmmachine learningnumerical linear algebra
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Citations & Impact
All-time
Citations
1,860
 
H-index
22
 
i10-index
34
 
Publications
20
 
Co-authors
0
 
Contact
CVOpen ↗
Publications
12 items
Memory Caching: RNNs with Growing Memory
2026
Cited
0
TNT: Improving Chunkwise Training for Test-Time Memorization
2025
Cited
0
Differentially Private Synthetic Data Release for Topics API Outputs
2025
Cited
0
ATLAS: Learning to Optimally Memorize the Context at Test Time
2025
Cited
0
Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
2025
Cited
0
Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures
2025
Cited
0
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
2025
Cited
0
PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts
2025
Cited
0
Resume
Academic Achievements
  • Published multiple papers, including 'Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches' (SODA 2022), 'Massively Parallel and Dynamic Algorithms for Minimum Size Clustering' (SODA 2022), 'Almost Linear Time Density Level Set Estimation via DBSCAN' (AAAI 2021), etc.
Research Experience
  • Research scientist at Google New York in the Algorithms and Optimization team led by Vahab Mirrokni.
Education
  • Ph.D. from Columbia University (Advisors: Alex Andoni, Cliff Stein, Mihalis Yannakakis). Undergraduate from Institute for Interdisciplinary Information Sciences (Yao Class), Tsinghua University.
Background
  • Research scientist at Google NYC in the Algorithms and Optimization team. Main research 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.