BWSI Course Listing

Program Course Overviews

The MIT Beaver Works Summer Institute will be offering the courses listed here.  As we adapt to requirements based on the COVID-19 pandemic, we will try to offer as many of these as possible.  BWSI will off both in-person and virtual courses in 2023.

The BWSI Program consists of online (prerequisite) courses and summer synchronous courses that build upon one or more of these prerequisites.  The online courses are independent study, we need to see your progress and grades when we select students to be part of the summer program, so it is important to show progress by our application deadline on March 31. The courses need to be completely done by June 25 or students' acceptance into the summer program will be at risk. 

We run introductory programs for 9th and 10th grade students in a spring program starting in March.

Information on that Saturday Spring program can be found at this link.

BWSI Steps- more info on this page

  • Online/Prerequisite Course Registration -> Online courses open on 1 Feb
  • Summer Application Opens -> students need to have started and met minimum requirements in Online/Prerequsitie courses
  • Students Notified of acceptance -> students complete remaining modules by June 25
  • Students must complete acceptance form prior to start of July synchronous program

Designing Assistive Technology -  iis now a Challenge and will not offered in BWSI 2023

BWSI Assistive Technology is a four-week course teaches students to product design, rapid prototyping, and product testing skills in the context of building a technology solution for people living with disabilities. Students learn about the experience of living with various types of disabilities, including challenges, adaptations, and assistive technologies that are used by different people. We then tackle real problems faced by people with disabilities or other difficulties with activities of daily living, and learn to work with the end users, stepping through the engineering design process together to come up with personalized, product solutions. The course involves activities such as user interviewing, fabrication, test plan development and execution, and product documentation, and results in products that are given to their end users, as well as the documents required for others to replicate the solution.

Autonomous Cognitive Assistance (CogWorks)

The Autonomous Cognitive Assistant course, CogWorks, uses project-based learning to immerse students in exciting applications of modern machine learning and data science. It was created in 2017 and is a course at the MIT Beaver Works Summer Institute (BWSI). The class consists of three distinct modules, which focus on applications of machine learning in the domains of audio, vision, and language, respectively. These modules bring students in contact with critical foundational concepts in applied mathematics, science, and machine learning via compelling capstone projects that have important, real-world applications. The central ethos to this course is that all of these impressive projects can be completed without our depending on any “mystery boxes” to do so.
Students in Cog*Works will be immersed in a hands-on environment, where all lectures are paired with detailed Python-based exercises that incorporate both applied mathematics and basic algorithms. The capstone projects are completed by teams of students, who will learn to use tools like git and VSCode to work together in a highly collaborative environment.

Autonomous RACECAR Grand Prix

Beaver Works Summer Institute will offerteams of students, each with its own MIT-designed RACECAR (Rapid Autonomous Complex Environment Competing Ackermann steeRing) robot, the opportunity to explore the broad spectrum of research in autonomy, learn to collaborate, and demonstrate fast, autonomous navigation in a Mini Grand Prix to Move... Explore... Learn...Race!

Build a Cubesat

Beaver Works Summer Institute will offer students the opportunity to design, build, and test a prototype CubeSat.  Students will explore all the major subsystems of a satellite and get hands on experience with mechanical, electrical, and software engineering.  The class will use these new skills to demonstrate a real CubeSat science mission in partnership with scientists from Woods Hole Oceanographic Institution.

Cyber Operations

Cyber Operations students will learn how to protect access, users, data, or network assets while denying the same to oppositional forces in the digital domain. Students are introduced to topics in a crawl-walk-run manner through vendor-based labs or team-based projects. Students are placed in high-stress situations and are expected to work together. They will be empowered to lead each other. Cyber Ops is taught through a fusion of both lecturers from MIT Lincoln Laboratory as well as special guest lecturers from industry to contribute partner knowledge or experiences. The course is designed to train students in becoming analysts and operators on-keyboard. Cyber Ops concludes with a digital Field Training Exercise (FTX) or capstone event where students are given minimal guidance and must: plan, research, execute, and present analysis on forensic artifacts or penetration-test. The desired end-state is to foster passion and interest for careers in cyber.

Embedded Security and Hardware Hacking

Computers long ago moved out from under our desks to being in almost every device in our lives. They can be found in safety-critical systems like automobiles, aircraft, and implanted medical devices, and are increasingly present in our lives with the rise of IoT, edge-computing, and wearables. To maintain our safety and privacy as these embedded computing devices have become a standard part of life, we need to ensure that devices are trustworthy and secure against bad actors. The small size and pervasive nature of these devices means that attackers often have access to the physical device they are targeting. As a result, we must now secure against attacks enabled by physical or proximal access in addition to traditional cyber vulnerabilities. This course aims to start the education of the next generation of security engineers - teaching fundamental computer engineering in addition to cryptography, cybersecurity, and system security. Topics covered include microcontrollers and device architecture, low-level programming, protocol design, cryptography, software security, and hardware security. A hands-on approach gets students working with hardware during lab exercises. Students will also work in teams to design and build a secure system in an attack and defense style exercise, targeting other team's designs once theirs is completed.

Medlytics

In Medlytics, short for medical analytics, we teach students to apply machinelearning approaches to real medical problems: predicting hypothyroidism in patients, using physiological signals to classify sleep states, and spotting cancer from mammography images.  Using these problems, we can motivate and demonstrate a wide range of machine learning approaches, including decision trees, support vector machines, and convolutional neural networks, applying them to real-world problems that the students help select.

Quantum Software

The topic of quantum information science has traditionally been gated behind advanced PhD programs, but Beaver Works Summer Institute is collaborating with MITRE to bringing the quantum world straight to the brightest high school students in the nation. Recent advances in quantum computing hardware are beginning to push this critical technology into the realm of solving practical problems, so we’re preparing the next generation of scientists and engineers to take advantage of the opportunities and respond to the threats that the quantum revolution will bring. The course starts with the fundamentals of quantum information, takes students through the concepts underlying quantum computation with hands-on coding exercises, and concludes with the challenge of implementing a quantum algorithm as a software program so it can be tested, analyzed, and run.

Remote Sensing for Disaster Response

Remote Sensing for Disaster Response covers GIS, remote sensing, image processing, network science, and deep learning for emergency management applications. We use Python to process geospatial data, satellite and aerial imagery, and analyze networks to prepare, monitor, and respond to disaster situations. The class features an array of guest lecturers from academia, emergency management agencies, government, first responders, and NGOs to provide real-world perspectives on these problems. The final project is a simulated hurricane scenario that takes place over several days, where the students must predict storm impacts, make evacuation decisions, analyze post-storm aerial imagery, develop routing algorithms to deliver supplies to shelters in need, and deliver a 1-hour press conference documenting their response to the storm. The class teaches programming, GIS, data science, and teamwork skills while immersing the students in the domain of emergency management.

Serious Games Development with Artificial Intelligence

SGAI provides students with an introduction to game design and artificial intelligence by allowing them to create their own modifications to a game. These games are known as “Serious Games” and rather than being designed for entertainment serve the purpose of allowing us to better understand various real world situations such as disease spread, self driving cars and more! Once students are taught the basics of game design, team work, coding and others, they will begin working with their groups to develop a mod for the game we provide them to investigate a research question of their choosing. Artificial intelligence is used to investigate how a computer will handle the moral dilemmas it is put into and see just how well computers are able to compare to humans in the complex environments in the real world that these serious games are meant to reflect.

Autonomous Underwater Vehicles Challenge

This course will introduce students to the challenges faced by real-world ocean engineers in designing, building and programming autonomous underwater autonomous vehicles (AUVs). The culmination of the summer course will be an exciting test of true autonomy – the student AUVs will autonomously navigate a simulated underwater obstacle course, applying real-time decision making based on feedback from onboard sensors. 

Autonomous Air Vehicle Racing

This course provides a holistic introduction to Unmanned Aerial Vehicles, more commonly known as drones. Throughout the course you will be challenged to think deeply about the complexities of software systems needed to operate an autonomous platform. The course begins with an introduction to UAVs. We examine the electrical, software, and aerodynamic characteristics of our platform and discuss the tradeoffs involved in building systems suited for a variety of use cases. You will gain experience in building and debugging code using cutting-edge robotics software and simulation. Building on that knowledge, we’ll then venture into the field of computer vision. We’ll leverage on-board sensors to process visual cues from the world around our drones. We’ll need this to detect and avoid obstacles as well as read QR codes. Underlying all of this is the mathematics of stable flight. We’ll dive into a bit of control theory and write code that will be used to guide the UAV toward goals in a challenging environment. All these topics converge into a single challenge - a choose your own adventure project - where each team will have the opportunity to show off what they’ve learned.

Unmanned Air System–Synthetic Aperture Radar

This course is a chance to get a holistic experience in complex systems engineering for students with introductory / moderate programming experience. Students will build a fully functioning radar imaging system including everything from drones and RF hardware to data processing. To realize this the students will be taught various scientific, engineering, and programming concepts through a series of lectures and guided experiments. Most of the students' time will be spent in groups of 4-5 working as teams of engineers to create and iteratively refine their systems. At the end of this course, students will have built a complex system to be proud of and gained first-hand experience in the world of engineering.