Community Connections Events for Faculty
“One Developer, Two Decades and Thirty Million Users Later: How Python Tutor Became a Long-Lasting and Widely Used Academic Software Project”
Since 2009, Philip Guo has been developing Python Tutor, a tool that lets users write code in a web browser and see what happens step-by-step as the computer runs it. The tool visualizes variables, objects, pointers, data structures and stack frames. These visuals help students to debug their homework, teachers to explain concepts and programmers to prepare for job interviews. Despite its name, this tool visualizes code written in Python, Java, C, C++ and JavaScript, which are five of the most popular languages used in both teaching and industry.
Throughout the past two decades, over 30 million students and teachers from over 180 countries have used Python Tutor to visualize over 500 million pieces of code. Guo will reflect upon the design decisions (and non-decisions) he made to turn an off-the-cuff prototype into one of the most widely used pieces of academic software. He will cover three eras of Python Tutor development:
- Act I – Inception, the unique conditions that birthed this idea as a grad student side project.
- Act II – Acceleration, sustaining a decade’s worth of user growth as he began his career as a professor.
- Act III – Disruption, when Generative AI both threatens the health of this project and may also be what revitalizes it.
About the Speaker
Philip Guo is a professor of cognitive science at UC San Diego. His research spans human-computer interaction, computing education and human-centered AI for education. He studies how people learn computer programming and data science, and he builds tools to help people better understand code and data. For instance, his Python Tutor educational tool has been used by 30 million students and teachers from over 180 countries throughout the past two decades. Python Tutor received a UIST Best Paper award and the ACM SIGCSE Test of Time award for long-term impact. His other lines of research have received 10-year long-term impact awards from VL/HCC and ISSTA, along with Best Paper awards at CHI, UIST, SIGCSE, VL/HCC, ICSE and ISSTA.
Large language models are now trusted with sensitive data, embedded in consequential decision pipelines, and are hosted on shared, expensive hardware. These factors make them an increasingly attractive target, and not just at the level of what you type into a chat box. In this talk, we’ll go on a tour of LLM security and privacy threats, organized around a simple question, looking at different classes of attacks based on attackers’ access points.
Nael Abu-Ghazaleh starts where most people think LLM security lives: at the prompt itself, showing how attackers with malicious intent can bypass the model’s safety training. A second group of attacks targets training time, where an attacker who controls even a small slice of a model’s training data can plant hidden behaviors that surface only later, including those that enable leaking private information or undermining the safety alignment of the model. Finally, Abu-Ghazaleh goes below the model entirely, to the hardware it runs on: his recent work shows the GPU infrastructure powering today’s LLMs leaks information across users who are supposed to be strongly isolated from each other. Taken together, these attacks show that LLM security isn’t one problem but several, spread across different layers of the stack, including inference time, training time, and hardware and system vulnerabilities. Abu-Ghazaleh closes with where he thinks the most promising defenses lie, and the open questions.
Speaker
Nael Abu-Ghazaleh is a professor in the Computer Science and Engineering Department at the University of California, Riverside. His research is in architecture and system security, and security for emerging systems. He has published over 250 papers in these areas, several of which have been recognized with best paper awards or nominations. His offensive security research has resulted in the discovery of several new attacks on CPUs and GPUs that have been disclosed to companies including Intel, AMD, ARM, Apple, Microsoft, Google and Nvidia, and resulted in patches and modifications to products, and coverage from technical news outlets. He is a member of the Micro Hall of Fame, an ACM distinguished member, an IEEE distinguished speaker and an IEEE Computer Society Distinguished Contributor.
Join us for the free show featuring Fri. las Chorizeras as part of the 7C Story Exchange.
Learn about the power of story-telling via music.
Come share your story and connect with the 7C community.
Free to attend plus free lunch (first come, first served).