SERA Research Tracks

Program Format

Students participating in the Science & Engineering Research Academy will earn 4 university credits by taking an interdisciplinary research course that teaches fundamental concepts in the particular track they choose, leading to more specific topics current in the field. Students will learn how to compose a formal research report and key communication skills to effectively present findings.

During the first half of the program, students will participate in specially designed hands-on labs that demonstrate concepts and reinforce principles learned in lecture. In the second half, the focus will shift from labs to group discussions in order to allow students to develop an appropriate research question, investigate findings, and present the results in a formal capstone seminar on the last day of the program.

The academic component of the program is as follows:

Week 1: 4 Lectures | 3 Labs | 2 GRIT talks

Week 2: 4 Lectures | 3 Labs | 2 GRIT talks

Week 3: 4 Lectures | 3 Discussions | 2 GRIT talks

Week 4: 3 Lectures | 3 Discussions | 2 GRIT talks | Capstone Seminar

2017 Research Tracks

Track 1: Living in Color – An Introduction to the Interactions of Light and Matter
Disciplines: Chemistry, Physics, Cognitive Sciences

Have you ever wondered why there’s no brown in the rainbow? Or how lifelike photographs can be printed using only four inks? Are the colors you see really the same as the ones your friend sees? In this course, we’ll answer these questions and more as we explore the rich science behind color – bringing together concepts from physics, chemistry, biology and cognitive science. First, students will learn how to form colors using both light and pigments, and why they don’t mix in quite the same way. In the laboratory, they’ll explore how chemical structure determines what energies of light are absorbed or emitted, and work together to build a library of color-changing pigments. We’ll also cover how modern technology harnesses these interactions of light and matter – e.g., how solar panels on your roof collect electricity, and how computer screens reverse the process to create light. Finally, the course will tackle how we, as humans, perceive and interpret color. For their final project, students will investigate and share an aspect of color science with the group – like how glowsticks and mood rings work, how chameleons can be so colorful and how dogs and birds see the world differently. After the course, students will better understand where the colors around them come from and why they change, both physically and perceptually.

Track 2: Data-Driven Research – Creative Problem Solving with Python
Disciplines: Data Science, Machine Learning, Network Science, Data Mining

Can election poll results be predicted from Twitter?  What qualities does a persuasive forum post have?  Does gender diversity and genre affect a film’s IMDB ratings? Can you detect where an earthquake was and it’s intensity using only tweets?  How does sentiment differ for different religious texts?  These are the types of research questions which can be investigated in “Creative Problem Solving for Research with Python,” a project driven course focusing on using machine learning and data analytics as a tool to gain meaningful information from large datasets. This course requires no prior experience with computer science, giving new and experienced programmers the skills to use Python for diverse applications and answer research questions with a broad social impact. The course will expose students to current research in data science and interdisciplinary research laboratories at UCSB.  Students will learn how to extract data from databases, documents, and webpages, process text and images, and analyze the data with machine learning algorithms. This course will cover regular expressions, databases, image processing, supervised and unsupervised machine learning, and neural networks. Students in groups will define a research question and collect the data needed, analyze their dataset and present their findings.  They can analyze data from existing datasets like Facebook, Twitter, Tumblr, YouTube and Reddit accessible through UCSB’s Interdisciplinary Research Collaboratory, as well as public datasets or datasets students collect themselves.