Program Format
Students participating in the Summer Research Academies will earn 4 university credits by taking an interdisciplinary research course that teaches fundamental concepts in their chosen track. The program kicks off with the Welcome Event, where students begin building friendships with peers through participation in fun social activities. The Academic Intensive component that follows prepares students for the high-level research experience at UC Santa Barbara.
During the first half of the program, students will form research groups, develop an appropriate research question, and build the framework for their project. They 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 investigation and analysis in order to allow students to present their results in a formal capstone seminar at the conclusion of the program.
The general academic component of the program is as follows:
Kick-off Weekend: Welcome Event | Academic Intensive
Week 1: 4 Lectures | 2 Labs | 1 Discussion | 2 GRIT talks
Week 2: 4 Lectures | 2 Labs | 2 Discussions | 2 GRIT talks
Week 3: 4 Lectures | 3 Discussions | 2 GRIT talks
Week 4: 3 Lectures | 3 Discussions | 1 GRIT talk | Capstone Seminar
SRA is an intensive research program in which students dedicate 25 to 40 hours each week to research. They are expected to utilize the library, participate in all program components, and may occasionally find themselves working into the night. Over the course of the program, students will learn to effectively describe their research findings in a technical research paper, present at a formal capstone seminar, and earn college credits that will become part of their permanent record. Because of the program’s demanding pace, students are not permitted to enroll in any concurrent courses, activities, or programs during SRA.
2026 SRA Research Tracks
Track Descriptions
Track 1: Probabilistic Computing – Advancing Scalable and Energy-Efficient Machine Intelligence
Conventional computing is reaching physical and energy limits, creating demand for hardware-aware algorithms that are smarter and more efficient. Probabilistic computing offers a compelling alternative by using controlled randomness to achieve faster, lower-power computation while maintaining high accuracy. This course explores how deliberate stochasticity, through Markov Chain Monte Carlo methods such as Gibbs sampling and the Metropolis-Hastings algorithm, provides runtime speedups and energy-efficient solutions in optimization and artificial intelligence. Students will work hands-on with foundational approaches like Simulated Annealing and Parallel Tempering, implementing them to solve challenging combinatorial optimization problems in Python. By applying probabilistic algorithms to neural networks, students will train probabilistic models and benchmark their performance against deterministic counterparts. By the end of this course, students will understand the trade-offs between deterministic and stochastic computation, gain practical experience implementing probabilistic algorithms, and appreciate how engineered randomness enables scalable, energy-efficient machine intelligence.
Track 2: Homo Technologicus – Reimagining Human Nature and Technology with Philosophy
We are wise creatures, or, in Latin, Homo sapiens. Yet human history has never existed without the technologies that shape how we think, create, and evolve. Every major intellectual advancement, from early writing systems to algorithmic tools, shows how our wisdom depends on the means that record and transform it, influencing culture, science, religion, and even human nature. We are, in essence, Homo technologicus. This course offers a comprehensive philosophical examination of the human-technology interface, spanning both past and present. By drawing on examples ranging from the invention of the codex to big data analytics, students will revisit enduring questions and learn how key thinkers, from Plato to Michel Foucault, have understood technological invention and its existential, ethical, ecological, and political implications. By the end of this course, students will not only see how humans interact with technologies but also how those selfsame technologies continually transform us.
Track 3: Invisible Cities – Revealing Bias, Absence, and Power in Urban Data Analysis
Cities hold invisible patterns that influence human movement, access to essential services, and the distribution of inequality. Understanding these dynamics and their social effects requires analytical approaches that reveal how urban systems operate beneath the surface. Urban environments are mapped through vast streams of digital information, from navigation apps to social media check-ins; yet, these data remain incomplete and uncertain. In this course, students will explore the science and complexity of working with real-world spatial data. Drawing on examples such as Points of Interest from OpenStreetMap and Google Maps, as well as transit, housing, and safety datasets, students will study how urban data is collected, modeled, and used in decision-making. Through projects that model accessibility to services, classify functional zones, detect urban change, and identify crash and crime hotspots, students will learn how to analyze and visualize imperfect data critically and turn it into meaningful insight about urban space.
Track 4: The Gene Edit – Proposing Targeted DNA Solutions for Single-Gene Disorders
The pioneering use of CRISPR-Cas9 by Dr. Jennifer Doudna and colleagues showed that a single, targeted cut in DNA can reshape the future of genetics. Gene editing is already achieving remarkable impact, as demonstrated by the FDA-approved CRISPR therapy CASGEVY for sickle cell anemia. However, progress toward new therapies remains constrained by limited funding and gaps in public understanding. In this course, students will investigate CRISPR as a foundation for designing “single-cut” genetic strategies that address challenges in medicine, agriculture, and sustainability. They will identify promising genomic loci for targeted interventions and evaluate the feasibility, mechanisms, and potential impact of selected gene-editing targets. They will also learn to communicate complex genetic concepts in accessible language for non-scientists. By the end of the course, students will translate scientific insights into realistic, high-impact genetic solutions through evidence-based CRISPR proposals, supported by rigorous analysis that bridge scientific innovation and societal impact.
Track 5: Photon Forge – Manipulating Light on a Microchip to Engineer Solutions
Light doesn’t just brighten a room—it carries information and measures the world with extraordinary precision. Just as electronic integrated circuits manipulate electrons on the microscale, photonic integrated circuits (PICs) generate, route, modify, and detect light, enabling technologies from LiDAR for autonomous vehicles to high-speed data centers, classical and quantum computing, and space-borne physics experiments. In this course, students will learn to translate desired optical functions into chip-scale photonic circuits, gaining hands-on experience with the full design process, from concept and simulation to chip layout. They will explore how integrated photonic devices mirror and enhance bulk optics, and how these building blocks underpin technologies in communications, sensing, and quantum science. Using professional simulation and measurement tools, they will produce photonic chip layouts in standard foundry formats to address real-world problems. By the end of this course, students will confidently design and simulate integrated photonic solutions for cutting-edge applications.
Track 6: Digital Frontlines – Analyzing the Strategies and Impact of Online Social Movements
TikTok, YouTube, and Twitch are central arenas for political expression and the shaping of social identity, making online activism one of today’s most significant forces. Digital networks enable social movements across the ideological spectrum to gain traction, influencing public debate and heightening tensions. While right-wing groups such as Incels, TradWives, and Red Pill communities amplify misogynistic and white supremacist ideologies through platform-driven virality, anti-authoritarian movements from BLM to the viral frogs of the Portland Protests to the Iranian women’s movement offer alternative visions of democratic ways of being together. This course examines how social media shapes community identities and mobilizes action across these varied movements. Drawing on social movement theory, media studies, and digital ethnography, students will analyze how groups leverage platforms for activism, community building, and political influence. Through scholarly discussions and guided practice, students will collaboratively design, conduct, and present original research focused on online social movements.
Track 7: Molecular Vision – Capturing the Structures of Biological Machines at Near Atomic Resolution
Breakthroughs in imaging technology like X-ray diffraction (XRD) or cryogenic electron microscopy (cryoEM) have revolutionized our ability to probe life at nearly atomic resolution. These advanced tools allow scientists to visualize how motor proteins move along cytoskeletal filaments or how signaling molecules open membrane channels to regulate cellular exchange. This course provides insight into these powerful imaging systems, examining how they function, how researchers design and prepare samples, and how raw data is transformed into high-resolution structural models of important macromolecules. Students will identify key structural features that govern molecular behavior and explore how these features support both robust and diverse biological functions. They will also learn how to apply data-processing methods to generate 3D reconstructions of individual proteins. By the end of the course, students will understand how structural biology reveals the foundations, diversity, and dynamic flexibility of molecules that facilitate life’s essential processes.
Track 8: Modeling Impact – Uncovering the Populations Beneath Policy Decision Making and Outcomes
Policymakers rely on credible empirical evidence to make informed decisions that shape the well-being of communities. However, many traditional approaches to policy analysis assume that interventions have an equal impact on everyone. This oversimplification can obscure important differences across populations and lead to unintended societal consequences. In this course, students will leverage principles of statistical modeling for program evaluation and policy analysis, with applications spanning a range of policy issues. They will learn how to select, estimate, and interpret statistical models to assess the impacts of policies while critically examining assumptions and considering heterogeneous effects across groups. Through hands-on training and applied case studies, students will analyze real-world policy issues, including voting access, healthcare affordability, and equitable allocation of public resources. By the end of the course, students will be able to evaluate model performance, interpret empirical findings, and communicate results that inform evidence-based policymaking.
Track 9: Moral Medicine – Exploring Ethics From Historical Injustice to the Frontiers of Medicine
Bioethical questions lie at the center of some of the most consequential decisions in science and medicine, revealing how societies weigh justice, responsibility, and human dignity. These debates span infamous historical cases and today’s rapidly evolving technologies, highlighting the field’s enduring impact. This course explores the ethical challenges that arise at the intersection of scientific progress and social consequence. Students will study cases such as the Tuskegee Syphilis Study, the use of HeLa cells, and Nazi medical experimentation, along with contemporary debates on genetic engineering, organ transplantation, and reproductive technologies. Through real-world examples, students will analyze themes such as consent, exploitation, scientific responsibility, and shifting ethical boundaries. They will learn to evaluate diverse perspectives, navigate moral ambiguity, and connect past injustices to current innovations. By the end of the course, students will gain a nuanced understanding of the complex bioethical questions that shape modern scientific research and practice.
Track 10: Rethinking AI – Diving into Deep Histories of Intelligence and Machine Learning
Thirteenth-century theology, Enlightenment theories of perception, Victorian ecological crises, and postwar studies on vision in frogs and cats all prefigure the development of modern machine learning. Studying these institutional and intellectual precursors of artificial intelligence helps us to contextualize the design and deployment of today's technical systems. By revealing the philosophical debates, cultural collisions, and material realities underlying the history of computing, this course reframes the contemporary “revolution” in artificial intelligence as the confluence of developments that reach far back into the past of both the human and natural sciences. Students will explore the various forces that have shaped deep learning and consider artificial intelligence in terms that extend beyond the pervasive discourse of novelty and disruption. Through this interdisciplinary, historically grounded investigation of machine learning, students will develop the tools to analyze the technology of the present with close attention to its past.
Track 11: Expression Intelligence – Leveraging Computational Methodologies to Decode Human Facial Display
Faces are rarely still. Across cultures and contexts, the arching of eyebrows, tightening of lips, and subtle muscular shifts reveal emotion, intent, and thought that shape our social world. With dozens of muscles capable of producing thousands of expressions, the human face carries significant communication power that often defies simple interpretation. This course explores the face as a multisignal, multimessage system, drawing on behavioral science, cultural studies, and computational methods. Students will investigate how facial movement informs perception, interaction, and social outcomes in domains such as attachment, courtship, dominance, and professional life. They will learn to apply automated tools to capture and analyze fine-grained expression data. Building on centuries of artistic and scientific attempts to decode the face, this course leverages technology to advance systematic inquiry into this enduring puzzle. By the end, students will understand how faces move, what they convey, and how to study them with rigor.
Track 12: Robotics Revealed – Understanding the Dynamics, Behavior, and Control of Robotic Systems
Robots navigate the world with remarkable speed, precision, and intelligence, yet every motion is driven by a deep framework of mathematics, physics, and strategic decision making. As robotics transforms fields from manufacturing to healthcare, understanding these foundations is essential for designing systems that can adapt to dynamic conditions. This course explores how engineers model robotic systems, predict their behavior, and design feedback strategies to ensure reliable operation amid uncertainty. Students will learn to describe position and orientation, analyze how forces, energy, and constraints shape mechanical behavior, and examine how feedback enables robots to balance, track trajectories, and resist disturbances. They will study core ideas in optimal and intelligent control to understand how robots make decisions when navigating complex or unpredictable environments. By the end of this course, students will see how engineering principles transform equations into action and power technologies from autonomous vehicles to surgical and assistive systems.
Competitions and Further Research
After completing SRA, students may wish to share their research experience in a variety of contexts, including but not limited to competitions, college applications, and other academic activities. In order to reference the research conducted during the program, students must receive proper permission from the involved parties — their Instructor and the Director of Academic Programs. Failure to receive proper permission is subject to legal action. In some instances, it is possible for our students to continue their research remotely throughout the school year, though this must only be done with the guidance and consent of the Instructor and the Director of Academic Programs.
For more information or to download the research consent form, please visit SRA Alumni Resources.