55
55
NUMBER OF PARTICIPANTS IN CURRENT (2023-24) COHORT
NUMBER OF PARTICIPANTS IN CURRENT (2023-24) COHORT
445
445
NUMBER OF STUDENTS PARTICIPATED TO DATE
NUMBER OF STUDENTS PARTICIPATED TO DATE
130
130
NUMBER OF PROJECTS TO DATE
NUMBER OF PROJECTS TO DATE
What is ERSP?
What is ERSP?
The UCSD Computer Science and Engineering Early Research Scholars Program (CSE-ERSP) is a team-based research apprentice experience for computer science and engineering majors in their second year of the program.
The UCSD Computer Science and Engineering Early Research Scholars Program (CSE-ERSP) is a team-based research apprentice experience for computer science and engineering majors in their second year of the program.
Students work in teams of four, and each team is matched with an active research project in the department. Students learn about research in computer science and then propose and carry out an independent research project over the course of an academic year.
Students work in teams of four, and each team is matched with an active research project in the department. Students learn about research in computer science and then propose and carry out an independent research project over the course of an academic year.
ERSP is a structured and scalable research experience for early undergraduates in computing and engineering
ERSP is a structured and scalable research experience for early undergraduates in computing and engineering
ERSP aims to create a diverse and supportive community within the CSE department by addressing the underrepresentation of minority students.
ERSP aims to create a diverse and supportive community within the CSE department by addressing the underrepresentation of minority students.
Throughout the years, demographics within ERSP includes:
Throughout the years, demographics within ERSP includes:
- 59% Women
- 22% Latinx and Black
Moreover,
Moreover,
- 57% of participants in first 3 cohorts continued with research after ERSP
- 83% of participants expressed interest in graduate school
They are working on mapping abnormal air quality changes, specifically on obstacle detection and modeling air quality. For this purpose, they are testing different sensors to measure accuracy and ensure robustness. They are also working on choosing subgroups of sensors that can accurately predict the values of the other sensors to model air quality efficiently.