About

I am an AI Research Scientist at J.P. Morgan Chase AI Research. My research interests lie in the intersection of machine learning, algorithmic game theory, and econometrics. I am particularly interested in online machine learning and exploration–exploitation tradeoffs, and their impact in socioeconomic environments. My current work focuses on bandit algorithms, differential privacy, federated learning, strategic learning, and uncertainty quantification.

I received a Ph.D. in Computer Science at the University of Minnesota, co-advised by Professors Steven Wu and Maria Gini. During my undergraduate study, I received a B.S. in Computer Science and Mathematics from Dickinson College, where I worked on a Genetic Algorithm honors project with Professor Grant Braught.

I am happy to talk about incentive-aware machine learning and uncertainty quantification. Feel free to reach out.

Interests

  • Bandit Algorithms
  • Differential Privacy
  • Federated & Strategic Learning
  • Uncertainty Quantification

Education

  • PhD in Computer Science University of Minnesota
  • BS in Computer Science & Mathematics Dickinson College

Research

  • Discretization-free Multicalibration through Loss Minimization over Tree Ensembles [arXiv]

    NeurIPS 2025

    Hongyi Henry Jin, Zijun Ding, Daniel Ngo, Zhiwei Steven Wu

  • Toward Breaking Watermarks in Distortion-free Large Language Models [arXiv]

    Shayleen Reynolds, Hengzhi He, Daniel Ngo, Saheed Obitayo, Niccolò Dalmasso, Vamsi K Potluru, Manuela Veloso

  • Adaptive and Robust Watermark for Generative Tabular Data [arXiv]

    UAI 2026

    Daniel Ngo, Archan Ray, Akshay Seshadri, Daniel Scott, Saheed Obitayo, Niraj Kumar, Vamsi K. Potluru, Marco Pistoia, Manuela Veloso

  • Reconciling Model Multiplicity for Downstream Decision Making [arXiv]

    ICLR 2025

    Ally Du, Daniel Ngo, Zhiwei Steven Wu

  • Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration [arXiv]

    TMLR

    Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu

  • Federated Learning as a Network Effects Game [arXiv]

    Shengyuan Hu, Daniel Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu

  • Incentivizing Combinatorial Bandit Exploration [arXiv]

    NeurIPS 2022

    Xinyan Hu, Daniel Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu

  • Improved Regret for Differentially Private Exploration in Linear MDP [arXiv]

    ICML 2022

    Daniel Ngo, Giuseppe Vietri, Zhiwei Steven Wu

  • Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses [arXiv]

    ICML 2022

    Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu

  • Incentivizing Compliance with Algorithmic Instruments [arXiv]

    ICML 2021

    Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu

  • Attentional autoencoder for weighted implicit collaborative filtering

    Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems

    Hoang-Vu Dang, Dung Ngo

Contact

tuandungngo207 [at] gmail.com

Curriculum Vitae (PDF)