After the conclusion of the Summer 2022 Tech Bootcamp, LACC's Economic Development and Workforce Education (EDWE) department hired two of the students from the bootcamp winning team to build EDWE digital versions of their offices in the Metaverse. Below are links to their final product:
Participating in the bootcamp will provide you with an opportunity to acquire 21st Century skills and connect with industry leaders for employment and internship opportunities as seen above.
Tuesday, July 2 (6PM - 8PM PST): Virtual orientation day for students to meet their team coaches
Tuesday, July 9 - August 13 (6PM - 8PM PST): Virtual weekly meetings (see below for format)
Tuesday, August 20 (5:30PM-7PM): In-person Demo Day on Campus
Problem:
As spaceflight becomes more ubiquitous, spacecrafts and satellites will get smaller(ie cubesats) and will be deployed in greater numbers. Planning, scheduling, and managing current and future cubesat missions will become more and more challenging due to finite resources. The process of planning a mission starts with subject matter experts searching for papers and reports supporting the mission goals, which then hopefully leads them to other papers and reports. This is a lengthy process of collecting a large amount of documents and datasets, documenting them, and proving that the plan has been accomplished in similar ways on different missions and will be successful.
Goal:
Utilize the latest advancements in Large Language Models (LLMs) to develop a prototype application that acts as a digital assistant to subject matter experts. This tool should reduce duplicate work by telling subject matter experts if any of these specific objectives have been achieved before and summarize how they were achieved. The application should also allow for documenting these datasets, and a means of showcasing the mission formulation.
Problem:
Vast amounts of data are collected during scientific missions beyond Earth. Typically, analysis of this data is conducted on an isolated, per-target basis, with reference data manually integrated as needed for comparisons. These comparisons occur when scientists recognize familiar elements from previous analyses or their own experiences. Streamlining this process by searching for previously identified relationships in new targets could be transformative, especially for newer scientists without a plethora of background experience, but such searches often involve complex combinations of data from multiple past studies in unprecedented ways.
Goal:
Utilize the latest advancements in Large Language Models (LLMs) to develop a prototype application that bridges the gap between question and meta-analysis of previous studies. This tool should support scientists in two critical phases: the discovery period, when analyzing new targets, and the background research phase, when composing scholarly articles, thereby facilitating the comparison of new findings with historical data.
Technologies to Consider
- LLM API and Prompt Engineering
- OpenAI ChatGPT
- Agile Project Management and Issue Tracking
- Github Projects
- Jira
- Trello
- Code Management and Website/Web App Hosting
- Github
- Github Pages
- Basic Web Development
- React
- Javascript/Typescript
- CSS
- App and User Experience Design
- Figma
Presenter:
Bootcamp Facilitator, Los Angeles City College
Program Director, UCLA Epicenter
Adjunct Instructor, UCLA Master of Quantitative Economics Department
Presenter:
Data Scientist II, Artificial Intelligence, Analytics and Innovative Development Organization
JPL
Presenters:
Principal Consultant
Slalom
Intern @AWS
UCLA
Presenters:
Senior Director
Slalom
Head of Leanring & Empowerment
VCG Markets