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Senior Research Scientist

Daniel Ting

About

My interests lie in developing novel statistical methods. In particular, I am interested in data sketching and sampling which lies in the intersection of statistics and databases. These are methods to summarize big data into memory efficient summarizations that can still answer a broad set of questions. I also have strong interests in the analysis and design of experiments and machine learning. My visualization oriented ML research is in manifold learning and non-linear dimensionality reduction where I study the mathematical limit operators implied by existing methods and how to design new operators and, hence, new methods.

I received my PhD in Statistics at UC Berkeley under the supervision of Michael Jordan. My PhD work focused on non-parametric Bayesian cluster models and semi-supervised/manifold learning. I also worked on data privacy for my MSc at Carnegie Mellon under Stephen Fienberg. Before Tableau, I was a core data scientist at Facebook primarily working on experimentation.

Focus

  • Statistics
  • Data sketching
  • Experimentation
  • Machine Learning

Papers

Statistical Schema Learning using Occam’s Razor

Justin Talbot, Daniel Ting
SIGMOD ’22, June 12–17, 2022, Philadelphia, PA, USA
PDF

Conditional Cuckoo Filters

Daniel Ting, Rick Cole
ACM SIGMOD 2021, June 20-25, 2021, Xi'an, Shaanxi, China

Simple, Optimal Algorithms for RandomSampling Without Replacement

Daniel Ting
Arxiv
PDF

Conditional Cuckoo Filters

Daniel Ting, Rick Cole
arXiv:2005.02537 [cs.DS] 5 May 2020
PDF

Learning to Optimize Federated Queries

Liqi Xu, Richard L. Cole, Daniel Ting
aiDM at SIGMOD 2019 (Amsterdam, The Netherlands, June 30 - July 5, 2019)
PDF

Approximate Distinct Counts for Billions of Datasets

Daniel Ting
SIGMOD 2019 (Amsterdam, The Netherlands, June 30 - July 5, 2019)
PDF

Optimal Sub-sampling with Influence Functions

Daniel Ting, Eric Brochu
NeurIPS 2018
PDF

Count-Min: Optimal Estimation and Tight Error Bounds

Daniel Ting
KDD 2018
PDF

Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation

Daniel Ting
SIGMOD 2018
PDF

Family learning: nonparametric statistical inference with parametric efficiency

Will Fithian, Daniel Ting
Arxiv
PDF

Adaptive Threshold Sampling and Estimation

Daniel Ting
Arxiv
PDF

Towards optimal cardinality estimation of unions and intersections with sketches.

Daniel Ting
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD), 2016.
PDF

Streamed approximate counting of distinct elements: Beating optimal batch methods.

Daniel Ting
ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2014
PDF

Online Semi-Supervised Learning on Quantized Graphs

Michal Valko, Branislav Kveton, Daniel Ting, Ling Huang
26th Conference on Uncertainty in Artificial Intelligence (UAI) , July 2010
PDF

An Analysis of the Convergence of Graph Laplacians

Daniel Ting, Ling Huang, Michael Jordan
International Conference on Machine Learning (ICML), June 2010
PDF

Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.

Daniel Ting, Guoli Wang, Maxim Shapovalov, Rajib Mitra, Michael I. Jordan, Roland L. Dunbrack, Jr.
PLoS Computational Biology vol. 6, no. 4 (2010)
PDF
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