Display Jupyter Notebooks with Academic
Learn how to blog in Academic using Jupyter notebooks
from IPython.core.display import Image
Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')
print("Welcome to Academic!")
Welcome to Academic!
Install Python and JupyterLab
Install Anaconda which includes Python 3 and JupyterLab.
Alternatively, install JupyterLab with pip3 install jupyterlab.
Create or upload a Jupyter notebook
Run the following commands in your Terminal, substituting <MY-WEBSITE-FOLDER> and <SHORT-POST-TITLE> with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:
mkdir -p <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
cd <MY-WEBSITE-FOLDER>/content/post/<SHORT-POST-TITLE>/
jupyter lab index.ipynb
The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.
Edit your post metadata
The first cell of your Jupter notebook will contain your post metadata (front matter).
In Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:
---
title: My post's title
date: 2019-09-01
# Put any other Academic metadata here...
---
Edit the metadata of your post, using the documentation as a guide to the available options.
To set a featured image, place an image named featured into your post’s folder.
For other tips, such as using math, see the guide on writing content with Academic.
Convert notebook to Markdown
jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.
Example
This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter
Qifan Zhang
Senior Staff Researcher
Dr. Qifan Zhang (张起帆) is now a Senior Staff Researcher of Palo Alto Networks. His research focuses on safeguarding critical internet infrastructure and addressing emerging threats in networked systems. His work centers on Network Security, with deep expertise in the Domain Name System (DNS)—the backbone of internet communication. By combining protocol analysis, fuzzing techniques, and formal methods, he designs automated tools to uncover high-risk vulnerabilities in DNS implementations and standards.
One of his flagship projects, ResolverFuzz, is a novel testing framework that exposed critical flaws in widely deployed DNS resolvers, including protocol-level security gaps (e.g., cache poisoning) and implementation errors (e.g., memory corruption). These discoveries have directly strengthened cybersecurity practices for the industry, open-source communities, and public infrastructure providers, earning recognition from organizations like CERT/CC and CVE.
Beyond DNS, he also explores the intersection of AI and Security, investigating risks in real-world machine learning deployments. My research, published in ACSAC 2022, demonstrated the first practical model extraction attacks against autonomous vehicle (AV) systems, using gradient-descent-based methods to reverse-engineer proprietary AI models. This work underscores the urgent need for robust defenses in safety-critical AI applications.
Prior to Palo Alto Networks, he earned his Ph.D. in Computer Engineering from University of California, Irvine advised by Prof. Zhou Li in 2025, and B.Eng. in Computer Science and Technology from ShanghaiTech University in 2020, complemented by a summer session at the University of California, Berkeley in 2017.
Pronunciation of his name: Chee-Fan Jang.
His Curriculum Vitae (last updated on March 14, 2025)