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EnglishWho Is Hong Jing (Jingles) and What Is Her Background?
Updated
Hong Jing, who publishes as "Jingles," is described on her site as a data scientist and an Alibaba PhD student who writes about machine learning and neuroscience. That is the full extent of the biographical detail the site itself provides; for anything beyond it, the site's About page is the place to look. This summary is for readers trying to gauge whether her writing is worth their time, not for anyone seeking a formal academic or professional profile.
What the site states about her
The site's own description gives three facts:
- Name: Hong Jing, published under the name "Jingles"
- Role: Data scientist
- Affiliation: PhD student at Alibaba
Her writing is framed around two fields: machine learning and neuroscience. The site presents her as someone who learns by doing and documents the process — the homepage tagline is literally "Learning by Doing."
What she writes about
The article list on the homepage shows the range of topics, and it is broad within those two fields rather than narrowly specialized:
| Theme | Example articles |
|---|---|
| Reinforcement learning | Coordinating multiple RL agents on Overcooked |
| 3D vision | Introducing Objectron: "ImageNet" to advance 3D object understanding |
| Optimization | Why learned optimizers outperform standard optimizers like Adam |
| Deep learning fundamentals | How convolutional layers work; Illustrated guide to Transformer |
| Language models | State-of-the-art language models in 2020; 3 types of sequence prediction problems |
| Neuroscience and AI | The relationship between AI and neuroscience; decoding brain signals with ML and neuroscience |
| Applied and societal topics | Computer vision for COVID-19 diagnosis; realistic deepfakes on Colab; AI's role in the pandemic |
Two patterns stand out. First, a lot of the content is explanatory — "how X works" and "illustrated guide" pieces aimed at readers building intuition. Second, the neuroscience thread is genuine rather than decorative: brain-signal decoding and the AI–neuroscience relationship appear as recurring subjects, which is consistent with the site's stated focus.
There are also practical, hands-on pieces — setting up LaTeX in Atom, running deepfakes on Colab, improving a holiday itinerary with machine learning — which fit the "learning by doing" framing.
How to judge whether to trust the content
The site supports a few reasonable inferences and one thing it does not support:
- Supported: She presents herself as an active practitioner (data scientist) and a doctoral student, and the article mix is consistent with someone working across ML research and neuroscience.
- Supported: The writing is largely tutorial and explainer style, so it is most useful as a learning resource rather than as a source of original research claims.
- Not supported: The site does not list a university, department, supervisor, publication record, or years of experience. Do not assume credentials beyond "data scientist" and "Alibaba PhD student" — those are the only claims made.
If you need verifiable credentials — for citation, hiring, or collaboration — treat the site as a pointer, not proof. Check the About page first, then follow the linked Medium, LinkedIn, and GitHub profiles, which are the channels the site itself directs readers to for published work and contact.
Where to go next
- About page — for whatever fuller background she chooses to share
- Published Work — for papers and external publications
- Projects / Showcase — for code and demos
- Medium, LinkedIn, GitHub — linked from the site for ongoing writing, professional profile, and repositories
The practical takeaway: if you want clear, example-driven explanations of ML concepts with an occasional neuroscience angle, the site is built for that. If you need to confirm her academic standing or publication history, the homepage won't tell you — go to the About and Published Work pages, and cross-check on LinkedIn.