Real-World Industry Exposure for Bicoastal Students
Working alongside industry sponsors, teams of bicoastal MSIT-IS and MSMITE students at the CMU Silicon Valley campus tackle problems, pilot new ideas and develop solutions in a semester-long capstone project called the practicum.
Practicum projects span a variety of topics in computing, mobile systems and security, and range from fundamental research to software development.
Learn more about past practicum projects:
Contributing to the space journey from Earth’s surface, the team guided NASA’s mission-critical move to cloud infrastructure by investigating two cloud security tools.
"There is no single, magic security tool that will protect from every online threat. This means that education in information security is vital to protect against the threats of tomorrow."
– Hana Habib, 2016 INI Graduate
Imagine being able to point your mobile device at a display screen and fling content from your hand to the display with a flick of the wrist. Students teamed up with Google to explore this "fling-to-display device gesture" experience using Google Chromecast, connecting physical location and gestures to make devices better understand human intentions.
"It was very interesting to deal with something that is still being invented, a very challenging but also curious feeling. In my case, the best part of working with Google was experiencing rapid prototyping and receiving feedback from industry experts."
– Caio Balthazar, 2016 INI Graduate
How It Works
3-5 Students Per Team
Matched according to skill sets and interests.
During CMU's fall semester from August to December.
20 Hours Per Week
Logged by each student team member.
2020 Practicum Projects
Building Browser-Based Data Tools | Sponsor: Carnegie Mellon University
In 2020, the California Consumer Privacy Act (CCPA) was enacted, which enables California residents to opt out of having their data sold or "shared" with other companies online. To make it easier for consumers exercise their rights, this team seeks to design, build and test a browser extension that sends HTTP header signals requesting companies to fulfill CCPA obligations.
Due to COVID-19, telemedicine is quickly replacing in-person clinic visits. However, many conditions still require physical examinations or hands-on intervention, which can be provided through in-person home visits by medical providers. The goal of this project is to efficiently and adaptively schedule providers to these home visits to minimize provider travel time, while ensuring that all patients can be seen.
Security Analysis of Home IoT Systems | Sponsor: Carnegie Mellon University
One of the most significant challenges to securing smart home IoT systems is that users deploy devices from multiple vendors or service providers, and no single entity is able to monitor all of the activity in a home. Instead of relying on individual vendors, this team seeks to develop independent monitoring, analysis, detection, and visualization tools that can view the IoT system holistically, including aspects of wireless, network, device, OS, and application security.
Humanizing Tele-Education with Machine Vision | Sponsor: Carnegie Mellon University
In tele-education, lecturers need to be able to ascertain student engagement. Unfortunately, this can be difficult in a remote setting, as lecturers often have a limited view of their audience. This project will investigate and develop machine vision techniques for alleviating this problem.
Automated Penetration Testing Attack Selection Framework | Sponsor: Ethical Intruder
Manual penetration testing is often a time consuming and complex engagement due to the amount of planning and analysis that is required to successfully select and execute the most appropriate attacks for a specific environment and scenario. This project aims to use machine learning to make that process more efficient and effective through automation.
Detection of Anomalies in Flights Using Explainable Deep Generative Models | Sponsor: NASA
Airlines capture and analyze real-time flight data to log known events, but whether this data may also capture significant unknown safety events is not currently being tracked. The goal of this project is to develop an explainable machine learning model to detect and/or predict anomalies that could lead to adverse events using high-dimensional and heterogenous time series recordings of airspace operation.
Simulation, Modeling, Visualization, Algorithms, and Data Analysis for Improving Airport Surface Operations | Sponsor: NASA
Airport surface operations present a difficult, large-scale logistics problem requiring multi-criteria optimization. This project proposes to investigate this problem, specifically the movement of aircraft at a busy airport, to improve efficiency and avoid serious delays by automating some of the decision-making process, such as ordering pushbacks from gate, runway assignment and wait times.
Internet-of-Things Data Fusion | Sponsor: National Security Agency
As their proliferation increases, Internet-of-Things (IoT) devices continue to generate more data and new types of information. Such devices are increasingly used by personnel in military installations, creating digital dust that presents a growing operational security concern for National Security Agency and Department of Defense organizations. This project team seeks to better understand how different IoT datasets could interact with each other and provide intelligence to adversaries.
Serverless SIEM/Log Management Solution | Sponsor: Procter & Gamble
As a multinational company, P&G has Security Operations Center (SOC) teams that rely on SIEM (Security information and event management) systems to collect and aggregate event logs from across the enterprise to analyze and respond to security incidents. However, SIEM solutions have been expensive and difficult to scale. Therefore, this project team seeks to design and implement a scalable SIEM based on the existing cloud service infrastructure.
Blockchain for Optimized Security and Energy Management (BLOSEM) | Sponsor: SLAC National Accelerator Laboratory
To solve the greatest challenges around renewable penetration and grid resilience, we need data sovereignty, starting with a unified, secure identification framework for all grid assets as well as behind-the-meter smart devices. This team proposes to develop a blockchain-based registry of traditional grid assets and emerging smart devices, and demonstrate the system at scale with industrial control system infrastructure.
DEVINE - Demonstration of Vehicle-Grid Integration in Non-Residential Environments | Sponsor: SLAC National Accelerator Laboratory
While increasing the implementation of electric vehicles (EV) is a major step towards a cleaner transportation sector, it can also create additional demand and grid stress if EV loads are not managed correctly. DEVINE is a tool being designed to help address these issues by utilizing EV charging data. In this project, students will evaluate the existing design of the DEVINE infrastructure and build out data exploration interfaces.
Mobile Peer-to-Peer Blockchain Enabled Sharing (MP2P-BES) | Sponsor: T-Mobile
The goal of this project is to develop a Peer-to-Peer Prototype that provides network access and secure public peer-to-peer sharing of mobile device resources. The major components include a blockchain technology-based engine, smart contract policy enforcer module, peer to peer protocol, hotspot tether slice resource module, private ledger engine, and peer-to-peer subscriber database.
Geospatial Data Visualization for Optimizing Vehicle Routing in Land Gravity Surveys | Sponsor: United States Geological Survey (USGS)
The acceleration due to gravity is not the same everywhere on Earth. Mapping these variations in the Earth’s gravitational field allows scientists to explore for energy and mineral resources and to map faults in earthquake-prone areas. The student team built an interactive data visualization tool that allows scientists to better understand spatial variations in the Earth’s gravitational field and to identify the highest priority areas for collecting new gravity data.