What an AI agent reads on Daniel Ngo
An assistant's crawler fetches the page once, without running JavaScript, and reads the text, the structured data and the links. Below is what it gets from dtngo.com, taken from the copy the scanner kept.
3,087
characters of text without JavaScript
none
structured data for the business
0
of 3 AI crawlers turned away at the edge
The words an agent gets
Title: Daniel Ngo · language en. First 1,800 of 3,087 characters:
Daniel Ngo Daniel Ngo AI Research Scientist / J.P. Morgan Chase AI Research About Research Contact CV · Email 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 R
What it can say about the business
| A business type in JSON-LD | no |
| Name | no |
| Telephone | no |
| Address | no |
| Opening hours | no |
| Menu or price list | nothing |
| Cuisine in structured data | no |
| JSON-LD blocks | 0 |
The links it would act on
No booking or ordering link found. An agent has nothing to act on.
Everything here comes from fetching the homepage once as a crawler. The report turns it into a score and a list of fixes, with code to paste.