Yuhao Wang was born in China. She's currently a Ph.D. student under the supervision of Divesh Aggarwal and Arnab Bhattacharyya , in school of computing, National University of Singapore working on computational and statistical guarantees for algorithms on high-dimensional data. From 2024 to 2025, she served as an applied scientist intern at Amazon, Germany, worked with Dominik Janzing in the AWS Group on causality research for outlier propagation and with Betty Mohler Tesch in the Fashion and Fitness Group on economic data analysis and customer behavior insights. Since summer 2023, she's as a research intern at Center for Integrative Artificial Intelligence, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, working with Kun Zhang 's group on topics related to reasoning and inference with causal knowledge. In spring 2022, she served as a visiting researcher with the Causality program at the Simons Institute, UC Berkeley . Between 2018 and 2020, funded by KPN, she worked as a researcher in Mykola Pechenizkiy 's group on the Deep Learning in Core and Edge (DLCE) project at the Eindhoven University of Technology. She received her master's degree under the supervison of Ivan Wang-Hei Ho at the Hong Kong Polytechnic University, and her bachelor’s degree in Electronics Science and Technology from Northwest University in China.

Yuhao is broadly interested in the statistical and computational challenges that arise in data science and machine learning. Her research focuses on developing methods for learning, testing, and inference reliable insights from data, with a particular emphasis on causality—understanding causal relationships from observational data despite challenges like unmeasured confounders, outlier propagation, missing data such as truncation, censoring with finite sample guarantees. She's also excited about exploring other fundamental problems in data science and their real-world applications. Her research works are available at her Google Scholar.

Recent Works

Toward Universal Laws of Outlier Propagation
Aram Ebtekar, Yuhao Wang, Dominik Janzing

[ pdf]

Arxiv preprint

Gaussian Mean Testing under Truncation,
Clement L. Canonne, Themis Gouleakis, Joy Qiping Yang, Yuhao Wang

[ pdf] [poster]

The 28th International Conference on Artificial Intelligence and Statistics, 2025

Learning High Dimensional Gaussian from Censored Data
Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis, Yuhao Wang

[ pdf] [poster] [code]

The 28th International Conference on Artificial Intelligence and Statistics, 2025

PAC Style Guarantees for Doubly Robust Generalized Front-Door Estimator,
Yuhao Wang, Arnab Bhattacharyya, Jin Tian, N. V. Vinodchandran

[ pdf] [presentation]

Causal@UAI2024 Oral, 2024

Optimal Estimation of Gaussian (Poly)Trees
Yuhao Wang, Ming Gao, Waiming Tai, Bryon Aragam, and Arnab Bhattacharyya

[ pdf] [poster] [code]

The 27th International Conference on Artificial Intelligence and Statistics, 2024

Learning Sparse Fixed-Structure Gaussian Bayesian Networks
Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, and Yuhao Wang

[ pdf] [presentation] [code]

The 25th International Conference on Artificial Intelligence and Statistics, 2022

Identifiability of AMP Chain Graph Models
Yuhao Wang and Arnab Bhattacharyya

[ pdf] [presentation] [code] [Video demo]

The Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Causal Discovery from Incomplete Data: A Deep Learning Approach
Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy

[pdf ] [presentation ] [poser]

AAAI StarAI Workshop, 2020.

VANET Meets Deep Learning: The Effect of Packet Loss on the Object Detection Performance
Yuhao Wang, Vlado Menkovski, Ivan Wang-Hei Ho, Mykola Pechenizkiy

[pdf ] [presentation ]

IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019.

Joint Deep Neural Network Modelling and Statistical Analysis on Characterizing Driving Behaviors
Yuhao Wang and Ivan Wang-Hei Ho

[pdf ] [poster ]

IEEE 29th Intelligent Vehicles Symposium (IV), 2018.

On-Road Feature Detection and Fountain-Coded Data Dissemination in Vehicular Ad-hoc Networks
Yuhao Wang and Ivan Wang-Hei Ho

[pdf ] [video demo ] [presentation ]

IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017.