The Stanford Intelligent Systems Laboratory (SISL) is committed to educating the next generation and encouraging diversity through community outreach activities. SISLers volunteer for a wide variety of activities, spanning from giving lessons at local elementary schools to mentoring high school teachers. Below is a snapshot of a few of the programs SISL helps support.
The AI4ALL program is designed to expose high school students in underrepresented populations to the field of artificial intelligence. It is a three-week, full-time program targeted for rising 10th graders. The program provides a broad exposure to AI through faculty lectures, field trips, and hands-on projects. SISL graduate students presented on control algorithms and how they can be used to provide stability for drones. They provided a hands-on activity where they flew a small hex-copter to demonstrate the algorithms. Prof. Kochenderfer discussed the challenges of translating basic research into technologies that can improve safety and efficiency. Information for applying for next year can be found here.
High School Summer Internship Program
The RISE (Raising Interest in Science and Engineering) summer internship program for high school students is sponsored by Stanford’s Office of Science Outreach. It is an intensive seven week summer program for local Bay Area students. Students spend 30 hours a week on campus, working on research in the lab. The program is designed for low-income students and those who will be the first in their families to attend college. SISL has hosted students who have worked on partially observable Markov decision processes and deep reinforcement learning. Additional details on the program, including the application process can be found here.
Summer Undergraduate Research Fellowship
The Summer Undergraduate Research Fellowship (SURF) is a fully funded, eight-week summer residential program that brings approximately 20 undergraduate students from across the country and provides them with an immersive research experience in the School of Engineering. As part of the school’s recruitment and outreach efforts, the Office of Student Affairs has hosted SURF for the past 16 years. SURF provides students with a stipend, a graduate school preparation program including a GRE study course, excursions around the Bay Area, community-building activities, participation in a research poster symposium, and research mentorship. Additional information can be found here.
Summer Research Program for Teachers
Stanford’s Office of Science Outreach offers eight-week research fellowships for middle and high school teachers in the Bay Area. They work in various labs on campus four days per week and meet once a week as a group for science and engineering lectures by Stanford faculty, lab tours, and seminars on teaching. The purpose of the program is to expose teachers to a broad array of research and provide a hands-on research experience. In SISL, teachers have contributed to research and course development related to optimization, probabilistic models, and building trust in autonomous systems. Information on how to apply can be found here.
Summer Undergraduate Research Program
The Research Experience for Undergraduates (REU) program is designed to give Stanford undergraduates the chance to work with faculty and their research groups for ten weeks during the summer. REU students in SISL have worked on projects ranging from building probabilistic driving models to visualization of dynamic target surveillance problems. Information about applying for this program can be found here.
Army High Performance Computing Summer Institute
(This program is no longer offered.) The Army High Performance Computing Research Center sponsors a summer institute for undergraduates. The summer institute provides training and hands-on experience in the use of computational techniques for science and engineering students. The program is for eight weeks, and participants stay on campus. Undergraduates that have been assigned to SISL have worked on projects ranging from autonomous driving to deep reinforcement learning.