Mean Testing under Truncation beyond Gaussian Yuhao Wang, Roberto Imbuzeiro Oliveira, Themis Gouleakis
[ pdf]Arxiv preprint
About Me
I am an Applied Scientist at Amazon, working on machine learning, statistical inference, and data-driven decision-making under imperfect data. I received my Ph.D. in Computer Science from the National University of Singapore in 2025, where my doctoral research studied computational and statistical guarantees for algorithms on high-dimensional data.
My research lies at the intersection of machine learning, statistics, and causal inference. I am interested in reliable learning, testing, inference, and decision-making from imperfect data, particularly in settings involving selection bias, truncation, censoring, missing data, distribution shift, unmeasured confounding, and partial feedback. More recently, I have been interested in the gap between offline evaluation and online action: understanding what conclusions and decisions can be reliably justified from imperfect offline evidence.
My previous research experience includes an Applied Scientist internship at Amazon Germany, work at the Center for Integrative Artificial Intelligence at Mohamed bin Zayed University of Artificial Intelligence, a visiting researcher position in the Causality program at the Simons Institute, UC Berkeley , and research work at Eindhoven University of Technology on the Deep Learning in Core and Edge project. I received my master’s degree from the Hong Kong Polytechnic University and my bachelor’s degree from Northwest University, China.
I am always happy to connect with students and researchers interested in machine learning, statistics, causal inference, and reliable decision-making under imperfect data.
Get In Touch
Publications & Preprints
Optimal Structure Learning and Conditional Independence Testing Ming Gao, Yuhao Wang, Bryon Aragam
[ pdf]ICML 2026 Spotlight
Toward Universal Laws of Outlier Propagation Aram Ebtekar, Yuhao Wang, Dominik Janzing
[ pdf] [poster]The 41st Conference on Uncertainty in Artificial Intelligence (UAI), 2025
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
Thesis
Computational and Statistical Guarantees for Algorithms on High-Dimensional Data Yuhao Wang
[ pdf]Ph.D. Thesis, National University of Singapore, 2025.
Earlier Works
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.