Philip Guo - UC San Diego

Philip Guo is an associate professor of Cognitive Science at UC San Diego. His research spans human-computer interaction, data science, programming tools, and online learning. He studies how people learn programming and data science, and he builds tools to help people better understand code and data.

FAQ | Google Scholar | Curriculum Vitae

Students

Ian Drosos (Ph.D., Cognitive Science)
Sean Kross (Ph.D., Cognitive Science)
Sam Lau (Ph.D., Cognitive Science)
Julia Markel (undergrad, Computer Engineering)
Kendall Nakai (undergrad, Cognitive Science)

Publications

2021

Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs. Julia M. Markel and Philip J. Guo. ACM Technical Symposium on Computer Science Education (SIGCSE), 2021.

2020

Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists. Ian Drosos, Titus Barik, Philip J. Guo, Robert DeLine, Sumit Gulwani. ACM Conference on Human Factors in Computing Systems (CHI), 2020.
The Design Space of Computational Notebooks: An Analysis of 60 Systems in Academia and Industry. Sam Lau, Ian Drosos, Julia M. Markel, Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2020.
Learnersourcing at Scale to Overcome Expert Blind Spots for Introductory Programming: A Three-Year Deployment Study on the Python Tutor Website. Philip J. Guo, Julia M. Markel, Xiong Zhang. ACM Conference on Learning at Scale (work-in-progress), 2020.
Designing the Future of Experiential Learning Environments for a Post-COVID World: A Preliminary Case Study. Julia M. Markel and Philip J. Guo. Symposium on the New Future of Work, 2020.
Papercode: Generating Paper-Based User Interfaces for Code Review, Annotation, and Teaching. Priyan Vaithilingam, Julia M. Markel, Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST) (poster), 2020.
Towards a Dynamic Multiscale Personalized Information Space. Amy Rae Fox, Philip J. Guo, Clemens Nylandsted Klokmose, Peter Dalsgaard, Arvind Satyanarayan, Haijun Xia, James D. Hollan. Convivial Computing Salon (workshop at the <Programming> conference), 2020.
The Essence of Program Semantics Visualizers: A Three-Axis Model. Josh Pollock, Grace Oh, Eunice Jun, Philip J. Guo, Zachary Tatlock. Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU), 2020.
Data Theater: A Live Programming Environment for Prototyping Data-Driven Explorable Explanations. Sam Lau and Philip J. Guo. Workshop on Live Programming (LIVE), 2020.

2019

Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges. Sean Kross and Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2019.
Improv: Teaching Programming at Scale via Live Coding. Charles Chen and Philip J. Guo. ACM Conference on Learning at Scale, 2019.
Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires. Carrie J. Cai and Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2019.
End-User Programmers Repurposing End-User Programming Tools to Foster Diversity in Adult End-User Programming Education. Sean Kross and Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2019.
Bespoke: Interactively Synthesizing Custom GUIs from Command-Line Applications By Demonstration. Priyan Vaithilingam and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2019.
Mallard: Turn the Web into a Contextualized Prototyping Environment for Machine Learning. Xiong Zhang and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2019.

2018

Non-Native English Speakers Learning Computer Programming: Barriers, Desires, and Design Opportunities. Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2018.
Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmers. April Y. Wang, Ryan Mitts, Philip J. Guo, Parmit K. Chilana. ACM Conference on Human Factors in Computing Systems (CHI), 2018.
Codemotion: Expanding the Design Space of Learner Interactions with Computer Programming Tutorial Videos. Kandarp Khandwala and Philip J. Guo. ACM Conference on Learning at Scale, 2018.
Students, Systems, and Interactions: Synthesizing the First Four Years of Learning@Scale and Charting the Future. Sean Kross and Philip J. Guo. ACM Conference on Learning at Scale, 2018.
Porta: Profiling Software Tutorials Using Operating-System-Wide Activity Tracing. Alok Mysore and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2018.
Fusion: Opportunistic Web Prototyping with UI Mashups. Xiong Zhang and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2018.
The Impact of Culture on Learner Behavior in Visual Debuggers. Kyle Thayer, Philip J. Guo, Katharina Reinecke. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2018.

2017

Older Adults Learning Computer Programming: Motivations, Frustrations, and Design Opportunities. Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2017.
CodePilot: Scaffolding End-to-End Collaborative Software Development for Novice Programmers. Jeremy Warner and Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2017.
Hack.edu: Examining How College Hackathons Are Perceived By Student Attendees and Non-Attendees. Jeremy Warner and Philip J. Guo. ACM International Computing Education Research conference (ICER), 2017.
DS.js: Turn Any Webpage into an Example-Centric Live Programming Environment for Learning Data Science. Xiong Zhang and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2017.
Omnicode: A Novice-Oriented Live Programming Environment with Always-On Run-Time Value Visualizations. Hyeonsu Kang and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2017.
Torta: Generating Mixed-Media GUI and Command-Line App Tutorials Using Operating-System-Wide Activity Tracing. Alok Mysore and Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2017.
HappyFace: Identifying and Predicting Frustrating Obstacles for Learning Programming at Scale. Ian Drosos, Philip J. Guo, Chris Parnin. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2017.

2016

Paradise Unplugged: Identifying Barriers for Female Participation on Stack Overflow. Denae Ford, Justin Smith, Philip J. Guo, Chris Parnin. ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE), 2016.
Understanding Conversational Programmers: A Perspective from the Software Industry. Parmit K. Chilana, Rishabh Singh, Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2016.

2015

Codeopticon: Real-Time, One-To-Many Human Tutoring for Computer Programming. Philip J. Guo. ACM Symposium on User Interface Software and Technology (UIST), 2015.
Codechella: Multi-User Program Visualizations for Real-Time Tutoring and Collaborative Learning. Philip J. Guo, Jeffery White, Renan Zanelatto. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2015.
Codepourri: Creating Visual Coding Tutorials Using A Volunteer Crowd Of Learners. Mitchell Gordon and Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2015.
Perceptions of Non-CS Majors in Intro Programming: The Rise of the Conversational Programmer. Parmit K. Chilana, Celena Alcock, Shruti Dembla, Anson Ho, Ada Hurst, Brett Armstrong, Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2015.
Toward a Domain-Specific Visual Discussion Forum for Learning Computer Programming: An Empirical Study of a Popular MOOC Forum. Joyce Zhu, Jeremy Warner, Mitchell Gordon, Jeffery White, Renan Zanelatto, Philip J. Guo. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2015.
How High School, College, and Online Students Differentially Engage with an Interactive Digital Textbook. Jeremy Warner, John Doorenbos, Bradley N. Miller, Philip J. Guo. International Conference on Educational Data Mining (EDM), short paper, 2015.
Wait-Learning: Leveraging Wait Time for Second Language Education. Carrie J. Cai, Philip J. Guo, James Glass, Robert C. Miller. ACM Conference on Human Factors in Computing Systems (CHI), 2015.
OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller. ACM Transactions on Computer-Human Interaction (TOCHI), 2015.

2014

Data-Driven Interaction Techniques for Improving Navigation of Educational Videos. Juho Kim, Philip J. Guo, Carrie J. Cai, Shang-Wen (Daniel) Li, Krzysztof Z. Gajos, Robert C. Miller. ACM Symposium on User Interface Software and Technology (UIST), 2014.
A Direct Manipulation Language for Explaining Algorithms. Jeremy Scott, Philip J. Guo, Randall Davis. IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), short paper, 2014.
Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos. Juho Kim, Phu Nguyen, Sarah Weir, Philip J. Guo, Robert C. Miller, Krzysztof Z. Gajos. ACM Conference on Human Factors in Computing Systems (CHI), 2014.
Demographic Differences in How Students Navigate Through MOOCs. Philip J. Guo and Katharina Reinecke. ACM Conference on Learning at Scale, 2014.
How Video Production Affects Student Engagement: An Empirical Study of MOOC Videos. Philip J. Guo, Juho Kim, Rob Rubin. ACM Conference on Learning at Scale, 2014.
Understanding In-Video Dropouts and Interaction Peaks in Online Lecture Videos. Juho Kim, Philip J. Guo, Daniel T. Seaton, Piotr Mitros, Krzysztof Z. Gajos, Robert C. Miller. ACM Conference on Learning at Scale, 2014.

2013

Online Python Tutor: Embeddable Web-Based Program Visualization for CS Education. Philip J. Guo. ACM Technical Symposium on Computer Science Education (SIGCSE), 2013.

2012

Characterizing and Predicting Which Bugs Get Reopened. Thomas Zimmermann, Nachiappan Nagappan, Philip J. Guo, Brendan Murphy. ACM/IEEE International Conference on Software Engineering (ICSE), Software Engineering In Practice (SEIP) track, 2012.
Burrito: Wrapping Your Lab Notebook in Computational Infrastructure. Philip J. Guo and Margo Seltzer. USENIX Workshop on the Theory and Practice of Provenance (TaPP), 2012.
Software Tools to Facilitate Research Programming. Philip J. Guo. Ph.D. dissertation, Department of Computer Science, Stanford University, 2012.
HAMPI: A Solver for Word Equations over Strings, Regular Expressions and Context-free Grammars. Adam Kiezun, Vijay Ganesh, Shay Artzi, Philip J. Guo, Pieter Hooimeijer, Michael D. Ernst. ACM Transactions of Software Engineering Methodology (TOSEM), 2012.
CDE: A Tool for Creating Portable Experimental Software Packages. Philip J. Guo. IEEE Computing in Science and Engineering, 2012.

2011

Proactive Wrangling: Mixed-Initiative End-User Programming of Data Transformation Scripts. Philip J. Guo, Sean Kandel, Joseph M. Hellerstein, Jeffrey Heer. ACM Symposium on User Interface Software and Technology (UIST), 2011.
Using Automatic Persistent Memoization to Facilitate Data Analysis Scripting. Philip J. Guo and Dawson Engler. International Symposium on Software Testing and Analysis (ISSTA), 2011.
Sloppy Python: Using Dynamic Analysis to Automatically Add Error Tolerance to Ad-Hoc Data Processing Scripts. Philip J. Guo. International Workshop on Dynamic Analysis (WODA), 2011.
CDE: Run Any Linux Application On-Demand Without Installation. Philip J. Guo. USENIX Large Installation System Administration Conference (LISA), 2011.
CDE: Using System Call Interposition to Automatically Create Portable Software Packages. Philip J. Guo and Dawson Engler. USENIX Annual Technical Conference, short paper, 2011.
"Not My Bug!" and Other Reasons for Software Bug Report Reassignments. Philip J. Guo, Thomas Zimmermann, Nachiappan Nagappan, Brendan Murphy. ACM Conference on Computer Supported Cooperative Work (CSCW), 2011.

2010

Characterizing and Predicting Which Bugs Get Fixed: An Empirical Study of Microsoft Windows. Philip J. Guo, Thomas Zimmermann, Nachiappan Nagappan, Brendan Murphy. IEEE International Conference on Software Engineering (ICSE), 2010.
Towards Practical Incremental Recomputation for Scientists: An Implementation for the Python Language. Philip J. Guo and Dawson Engler. USENIX Workshop on the Theory and Practice of Provenance (TaPP), 2010.

2009

HAMPI: A Solver for String Constraints. Adam Kiezun, Vijay Ganesh, Philip J. Guo, Pieter Hooimeijer, Michael D. Ernst. International Symposium on Software Testing and Analysis (ISSTA), 2009.
Linux Kernel Developer Responses to Static Analysis Bug Reports. Philip J. Guo and Dawson Engler. USENIX Annual Technical Conference, short paper, 2009.
Automatic Creation of SQL Injection and Cross-site Scripting Attacks. Adam Kiezun, Philip J. Guo, Karthick Jayaraman, Michael D. Ernst. IEEE International Conference on Software Engineering (ICSE), 2009.
Two Studies of Opportunistic Programming: Interleaving Web Foraging, Learning, and Writing Code. Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira Dontcheva, Scott R. Klemmer. ACM Conference on Human Factors in Computing Systems (CHI), 2009.
Opportunistic Programming: Writing Code to Prototype, Ideate, and Discover. Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira Dontcheva, Scott R. Klemmer. IEEE Software, 2009.

2008

Opportunistic Programming: How Rapid Ideation and Prototyping Occur in Practice. Joel Brandt, Philip J. Guo, Joel Lewenstein, Scott R. Klemmer. Workshop on End-User Software Engineering (WEUSE), 2008.

2007

The Daikon system for dynamic detection of likely invariants. Michael D. Ernst, Jeff H. Perkins, Philip J. Guo, Stephen McCamant, Carlos Pacheco, Matthew S. Tschantz, Chen Xiao. Science of Computer Programming, 2007.

2006

Dynamic Inference of Abstract Types. Philip J. Guo, Jeff H. Perkins, Stephen McCamant, Michael D. Ernst. International Symposium on Software Testing and Analysis (ISSTA), 2006.
Automatic Inference and Enforcement of Data Structure Consistency Specifications. Brian Demsky, Michael D. Ernst, Philip J. Guo, Stephen McCamant, Jeff H. Perkins, Martin Rinard. International Symposium on Software Testing and Analysis (ISSTA), 2006.
A Scalable Mixed-Level Approach to Dynamic Analysis of C and C++ Programs. Philip J. Guo. Master of Engineering (M.Eng.) thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2006.

FAQ / Advice

Q: Where can I find this old article/post of yours? The link is broken.

If the link does not appear at the bottom of this page, then it does not exist. Please do not ask about old articles/posts, and do not distribute any unauthorized copies or translations; I have not given anyone permission to redistribute or translate my old copyrighted work.

Q: When are your office hours?

Spring 2021: Fridays 11am-noon Pacific time. If you're a UCSD student, email me for the Zoom link. Happy to chat about anything on your mind!

Q: What's your email address?

I no longer read most cold-emails due to the large amounts of email I get. There are only three exceptions:

I already know you or know someone who knows you (please let me know who referred you).
You are a professional colleague contacting me about work, research, or other related matters.
You are a UCSD student or affiliate. I always prioritize these emails, but see below for details.

If you are a UCSD student or affiliate, feel free to email me about anything on your mind except:

do not email about trying to get into a class (unless we've talked beforehand)
for class-related issues, always use the class discussion forum (you can private-message me on there)
do not email me about grades unless there is a data entry error

For Python Tutor, read this doc and do not email me.

My email address is on the first page of my recent publications.

Q: What classes do you teach, and should I sign up for them?

Please talk to the department academic advisors about all issues related to class registration, prerequisites, degree planning, etc. They are the experts on these sorts of questions, not me. Also, my syllabuses often change, so I cannot make them available before each term starts.

Here are the classes I currently teach:

COGS 127: Data-Driven UX/Product Design (Fall)

work on an individual or team project to redesign/extend a popular web or mobile app
use design tools such as Figma, no coding required
in the end, create a design case study that you can use to apply for various HCI/UX/design jobs

COGS 121: Human Computer Interaction Portfolio Design Studio (Spring)

take old projects that you've already done for other classes, internships, or extracurricular activities and learn how to present them to employers
create a web portfolio, write case studies, make pitch slide decks, and practice talking about your work (no coding required)
in the end, create and enhance your web portfolio that you can use to apply for various HCI/UX/design jobs

COGS 124: HCI Technical Systems Research (Fall)

learn how to read HCI research papers (like the ones in my own publications list)
in-class discussions about research papers, everyone participates!
in the end, understand how academic research works and how it relates to the tech industry

COGS 231: Design Seminar on Human-Centered Programming Tools (Spring)

graduate-level class, no undergrads can enroll unless I already know you from prior classes or research
learn how to read HCI research papers (like the ones in my own publications list)
in-class discussions about research papers, everyone participates!

COGS 197, 198, 199:

These are not 'real' classes; they are for students who already work with me to earn credits. You cannot sign up unless I already know you and have given you permission.

Q: As a student or newly-hired employee, how can I stand out amongst the hundreds of peers in my field who are all around me?

This is a very common and important question! Two main thoughts come to mind:

Think outward. Don't think about what you can do to impress people, but rather about what you can do that is directly valuable to those around you. For instance, write a Google Doc to help students embarking on a similar journey as you; be a mentor to others; make yourself useful to people who need help. If you do this consistently with the intention of helping others rather than trying to stand out, then you will naturally gain the respect of those around you.
Spend at least 25% more time on tasks than your peers normally do. For instance, if your classmate spent 4 hours on an assignment, then put in those same 4 hours but add one more hour polishing it up (5 hours is 25% more than 4). If you do this consistently as a habit, you'll stand out because your work will usually be better than your peers'. (The real goal is to do everything 25% better, but spending 25% more time is something you can act on right away.)

Q: How can I get a job in a research lab on campus?

This is the most common question I get from students, especially undergrads. Here are some suggestions:

Take upper-division courses, project-based courses, or even graduate courses, do well in them, then ask the professor at the end of the term whether there are available opportunities.
Find any professor's office hours that you're interested in, even if they're outside of your department. Then go visit their office hours and ask for opportunities. Even if they don't have any job openings, they might be able to give you valuable advice. (Email them to ask for their office hours times if they're not posted publicly.)
Find professors' lab webpages and look for postdocs (postdoctoral researchers), research scientists, research staff, lab technicians, graduate students (Ph.D. or masters students) there; then email them to ask for opportunities. They will be easier to reach than professors.
Find any graduate student's office hours if they're TA-ing for a class, and go attend to ask them about research. They will usually be happy to talk about things other than the class they're TA-ing for.
You can also directly email professors, but your chances of getting a response are lower since they get a lot more emails. Don't be discouraged if they don't respond; it's not personal! Keep trying hard to find new opportunities.
Look for newly-hired professors (often called assistant professors) since they may be looking to grow their new lab so are more likely to respond to your email.
Again, be willing to look outside your department for opportunities.
If any professors are giving special talks or guest lectures on campus, attend and go up to them afterward to ask for relevant opportunities.
If you have friends or TAs who currently work in a research lab, try to get them to introduce you to the relevant graduate students, postdocs, research scientists, or professors. A good referral can be very effective.

Q: How can I get an internship or job related to HCI/UX/Design?

Check out HCI/UX/Design Jobs for New College Grads.

Q: How can I best ask for a recommendation letter?

Read Asking for Recommendation Letters.

Q: How can I improve my Ph.D. program applications?

Read A Five-Minute Guide to Ph.D. Program Applications.

Q: Got any advice for new Ph.D. students?

Q: Got any tips for giving talks and live software demos?

Q: Got any advice for aspiring faculty?

Q: Got any advice for new assistant professors?

Last updated: Jan 23, 1993 ⚰️🐮