55
55
NUMBER OF PARTICIPANTS IN CURRENT (2023-24) COHORT
NUMBER OF PARTICIPANTS IN CURRENT (2023-24) COHORT
Embedded Files
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 and create opportunities for all students to participate in research.
ERSP aims to create a diverse and supportive community within the CSE department and create opportunities for all students to participate in research.
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
Using different machine learning techniques to distinguish obese from lean individuals. The key feature they are using is gut microbiota, which has been shown to be an important factor in determining the success of bariatric surgery.
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.
Working on text-to-speech conversion for one of Prof. Kahng’s classes. Their work involves synthesizing speech based on the text given, and the sound files of the speaker.
Page updated
Report abuse