| 2023 | ICML | Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting. | Jonathan Hehir, Daniel Ting, Graham Cormode |
| 2022 | SIGMOD | Statistical Schema Learning with Occam's Razor. | Justin Talbot, Daniel Ting |
| 2022 | SIGMOD | Adaptive Threshold Sampling. | Daniel Ting |
| 2021 | SIGMOD | Conditional Cuckoo Filters. | Daniel Ting, Rick Cole |
| 2020 | KDD | Data Sketching for Real Time Analytics: Theory and Practice. | Daniel Ting, Jonathan Malkin, Lee Rhodes |
| 2019 | SIGMOD | Approximate Distinct Counts for Billions of Datasets. | Daniel Ting |
| 2019 | SIGMOD | Learning to optimize federated queries. | Liqi Xu, Richard L. Cole, Daniel Ting |
| 2018 | KDD | Count-Min: Optimal Estimation and Tight Error Bounds using Empirical Error Distributions. | Daniel Ting |
| 2018 | SIGMOD | Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation. | Daniel Ting |
| 2016 | KDD | Towards Optimal Cardinality Estimation of Unions and Intersections with Sketches. | Daniel Ting |
| 2014 | KDD | Streamed approximate counting of distinct elements: beating optimal batch methods. | Daniel Ting |
| 2013 | INFOCOM | Mixture models of endhost network traffic. | John Mark Agosta, Jaideep Chandrashekar, Mark Crovella, Nina Taft, Daniel Ting |
| 2010 | ICML | An Analysis of the Convergence of Graph Laplacians. | Daniel Ting, Ling Huang, Michael I. Jordan |
| 2010 | UAI | Online Semi-Supervised Learning on Quantized Graphs. | Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting |