Public profile
Research areas
Computer graphics, computational geometry protocols
Average rating
3.9
22 temporary mock ratings
Difficulty
2.7
course-linked average
Courses
5
in seeded sections
Courses taught
COMP 101
Vibe Coding With LLMs
Learn to collaborate with large language models to build Python programs from plain-language prompts. The course covers prompt design, systematic debugging and testing, and the real limits of current models. By the end, students will be able to deliver small-to-medium projects—on the order of a few hundred lines of code—with rigorous human oversight.
Computer ScienceD13 credits
3.96.9hWarren, Joe D.
COMP 390
Computer Science Projects
Theoretical and experimental investigations under staff direction. Repeatable for Credit.
Computer ScienceNone1-3 credits
4.26.2hAliakbarpour, Maryam, Chen, Hanjie, Chen, Ken, Cox, Alan L., Cutler, Scott, Ferreira Flores, Rodrigo, Hang, Kaiyu, Johnson, Dave, Kavraki, Lydia, Kyrillidis, Tasos, Lopes da Silva, Arlei, Myers, Risa, Nakhleh, Luay, Ng, T. S. Eugene, Patel, Tirthak, Sano, Akane, Schreib, Rebecca, Sedlazeck, Fritz, Subramanian, Devika, Treangen, Todd, Unhelkar, Vaibhav, Vardi, Moshe, Veeraraghavan, Ashok, Warren, Joe D., Wong, Stephen
COMP 590
Computer Science Projects
Advanced theoretical and experimental investigations under staff direction. The student must have a full-time internship to receive 4 credits for this course. Repeatable for Credit.
Computer ScienceNone1-4 credits
3.45.7hAliakbarpour, Maryam, Baraniuk, Richard G, Chen, Hanjie, Chia, Nai-Hui, Cox, Alan L., Fallah, Alireza, Goldman, Ron, Hang, Kaiyu, Jermaine, Christopher, Joyner, Mack, Kavraki, Lydia, Kyrillidis, Tasos, Lopes da Silva, Arlei, Mamouras, Konstantinos, Mellor-Crummey, John, Myers, Risa, Nakhleh, Luay, Ng, T. S. Eugene, Ordonez Roman, Vicente, Patel, Ankit, Patel, Tirthak, Rixner, Scott, Shrivastava, Anshumali, Simar, Ray, Treangen, Todd, Unhelkar, Vaibhav, Vardi, Moshe, Wang, Yuke, Warren, Joe D., Wei, Chen, Wong, Stephen, Xing, Jiarong, Yao, Vicky
COMP 665
Data Visualization
Data is being generated by humans and algorithms at an astounding rate. Having the ability to analyze and interpret this data visually is a key technique for coping with this explosion. This class will cover the basic ways that various types of data can be visualized and what properties distinguish useful visualizations from not so useful ones. The class will use Python as both the primary tool for processing the data as well creating visualizations of this data. To enhance the students’ depth of knowledge, the class will also cover some of the geometric algorithms used to create advanced visualizations. In order to enroll in an online section of this course, you are expected to have a working camera and microphone. During class sessions, you must be able to participate using your microphone and you are expected to have your camera on for the duration of the class so that you are visible to the instructor and other students in the class, just as you would be in an in-person class.
Computer ScienceNone3 credits
4.010.4hWarren, Joe D.
COMP 800
Graduate Research
Repeatable for Credit.
Computer ScienceNone1-15 credits
4.28.5hAliakbarpour, Maryam, Braverman, Vladimir, Chen, Hanjie, Chia, Nai-Hui, Cooper, Keith, Cox, Alan L., Fallah, Alireza, Goldman, Ron, Hang, Kaiyu, Hu, Ben, Jermaine, Christopher, Johnson, Dave, Kavraki, Lydia, Kyrillidis, Tasos, Lopes da Silva, Arlei, Mamouras, Konstantinos, Mellor-Crummey, John, Nakhleh, Luay, Ng, T. S. Eugene, Ordonez Roman, Vicente, Patel, Ankit, Patel, Tirthak, Phillips, George, Rixner, Scott, Shrivastava, Anshumali, Subramanian, Devika, Treangen, Todd, Unhelkar, Vaibhav, Vardi, Moshe, Varman, Peter, Wallach, Dan S., Wang, Yuke, Warren, Joe D., Wei, Chen, Xing, Jiarong, Yao, Vicky