Scientific Visualization (CMSC 23710/33710).
Introduction to what has traditionally been called "scientific visualization"
with an emphasis on the underlying mathematics.
[Winter 2021] [Winter 2020] [Winter 2019] [Winter 2018] [Winter 2017] [Winter 2015] [Winter 2013] [Autumn 2010] [Spring 2009]
Data Visualization (CMSC 23900).
[Spring 2020] [Spring 2019] [Spring 2018] [Spring 2017] [Spring 2016]
Laser Cutting by Program.
Intruduction to the mathematical, computational, and materials science
elements of creating things from 2D sheets via lasercutting, with coding in Julia.
Computing Imaging-Based Science (CMSC 34900-01).
A seminar designed to connect computational scientists with local scientists
who use imaging as part of their research.
[Autumn 2013] [Autumn 2011]
History and Principles of Data Visualization (CMSC 34900-01).
A seminar about data visualization organized around
foundational papers and books.
Introduction to Computer Systems (CMSC 15400).
How UChicago students learn about what's actually happening inside a CPU
when it executes their programs.
[Spring 2014] [Spring 2013] [Spring 2012] [Spring 2010]
Introduction to Computer Graphics (CMSC 23700).
While John Reppy was working
at NSF, I covered his graphics class, and updated the material to use OpenGL 3.
UChicago is on quarters; each academic year consists of "Autumn" of calendar year N (Sept-Dec), "Winter" of year N+1 (Jan-March), and "Spring" of year N+1 (April-June).
For prospective students
I advise UChicago CS PhD students, UChicago students in the joint BS/MS program, and ambitious UChicago undergraduates who want to gain research experience. Collaborating with students at other institutions can be productive if I know the advisor there. I do not accept non-UChicago students for internships.
For prospective BS/MS students: Hopefully we've already met in a class or in the hallway; in any case I'd love to talk before you apply to discuss your research interests and plans. Email me to set up a time.
For prospective PhD students: If you want to work with me as a PhD student at University of Chicago, then go ahead and apply. Apply before the deadline. Give your letter writers plenty of time to write strong recommendations. Read the department's info for prospective students, including the FAQs. Students are admitted to the department as a whole, as decided by the faculty as a whole. Acceptance is more likely if some faculty member advocates for you.
If you want me to advocate for you, include my name in the section of the application about why are you are applying. If we haven't already met, introduce yourself by email to me. To distinguish yourself from applicants who spam all faculty, and to provide a better starting point for further communication, include your statement of purpose when you email me.
Statements of Purpose: I read these to learn why you want to get a PhD, and to see if your research interests connect to mine. I keep the following four points in mind when I read statements of purpose:
- Is there evidence of thinking/working in an independent and self-determined way, versus working only on whatever has been handed to you? If you don't have research experience, see #2.
- Can you give a coherent explanation of why some research area is personally interesting and important to you, or more generally, can you give a thoughtful and self-aware answer to "Why do I want to do research?"
- Do you have a sense of scholarship context: why is your particular area of interest important to the world? How does it complement or relate to other areas of published research? This is particularly important for the applied research that I pursue.
- Is there evidence that you can finish what you start? This is also hard to judge without any research experience, but doing well in project-based class assignments is a good sign.
Restating the facts of your resume (CV) does not answer these questions, since that doesn't answer why you want to do research. Also, instead of "I'm really motivated" or "I'm a hard worker", describe some concrete byproduct of your hard work, or what kind of things you work tend to hardest on, and why.