Yiding (Sophie) Hao
· Moorman-Simon Assistant Professor of Linguistics and Data ScienceNew York University · Center for Data Science
Active 2009–2025
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About
Yiding (Sophie) Hao is the Moorman-Simon Assistant Professor of Linguistics and Data Science at Boston University. Her research focuses on the intersection of linguistics and data science, contributing to the development of innovative approaches in these fields. As a faculty fellow at the NYU Center for Data Science, she has been involved in advancing research agendas that span academia, industry, and government, fostering interdisciplinary collaborations and peer-reviewed publications. Her work exemplifies a commitment to building an environment where independent research can thrive, and many of her colleagues have gone on to hold faculty positions at prestigious institutions or to contribute significantly to applied research teams in various sectors.
Research topics
- Artificial Intelligence
- Computer Science
- Biology
- Computational biology
- Genetics
- Information Retrieval
- Data Mining
- Medicine
- Cell biology
- Bioinformatics
Selected publications
Integrated analysis of multimodal single-cell data
Cell · 2021 · 15754 citations
1st authorCorrespondingThe simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear c…
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Nature Biotechnology · 2023 · 4568 citations
1st authorCorrespondingIntegrated analysis of multimodal single-cell data
bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 484 citations
1st authorCorrespondingAbstract The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset…
Niche-Selective Inhibition of Pathogenic Th17 Cells by Targeting Metabolic Redundancy
Cell · 2020 · 110 citations
The future of rapid and automated single-cell data analysis using reference mapping
Cell · 2024-05-01 · 71 citations
reviewOpen access
Frequent coauthors
- 78 shared
Rahul Satija
- 36 shared
Dan R. Littman
Howard Hughes Medical Institute
- 29 shared
Saket Choudhary
- 29 shared
Madeline H. Kowalski
New York Genome Center
- 27 shared
Stuart M. Brown
ExxonMobil (United States)
- 27 shared
Hans‐Hermann Wessels
- 24 shared
Isabella Mascio
New York University
- 21 shared
Zhiheng Pei
New York University
Awards & honors
- Moorman-Simon Assistant Professor of Linguistics and Data Sc…
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