Research Programs-Silicon Valley Campus - Carnegie Mellon University

Topics of Interest and Research

The Software Systems and Management faculty, in conjunction with colleagues in ECE, Silicon Valley companies and NASA's Ames Research Center, is engaged in a broad range of leading edge research projects across software systems and management. The Silicon Valley climate of innovation and entrepreneurship has a strong influence on our research, resulting in an emphasis on projects that can be applied to real world problems rather than purely focusing on foundational research.  Silicon Valley faculty have been highly successful in obtaining research grants, publishing research results in leading conferences and archival journals, and in bringing their research into the classroom.  Our location puts us in close proximity to many of the world's most innovative companies, and we have been able to draw on their top technical leaders to present their work in our weekly colloquia. In addition to direct software engineering and software management research, several other  software intensive research projects engage faculty, MS and PhD students in building novel applications and systems involving sensors, networks, mobile and vehicular systems, using speech and machine learning technologies.

faculty and students

The Mobility Research Center was created to address the rapid growth of “smart” mobile devices as a primary means of access to the Internet, as well as a medium of communication. Research projects include methods and tools for mobile application development, and their growing interaction with social networks and cloud computing. Many of the research projects in the center use, study or enhance mobile applications and systems. Sensor-rich, mobile devices are a potentially rich source of context, enabling the creation of applications that adapt to the user’s need and situation. The Disaster Management Initiative was a direct outgrowth of the center’s activities. 

The security research group at Carnegie Mellon Silicon Valley is part of Carnegie Mellon CyLab, one of the largest university-based cyber-security research and education centers in the U.S. Faculty and students work on several problems in many areas of security with a focus on the following core areas: Foundations of Security and Privacy, Web Security, Wireless Network and System Security 

Faculty: Anupam Datta, Collin Jackson, Patrick Tague

Innovation and Entrepreneurship

The Center is particularly focused on understanding and fostering innovation and entrepreneurship in the context of rapid-paced delivery of highly-valued software and services, indicative of the Silicon Valley culture. Students prepare for leadership roles in business environments as diverse as start-ups and Fortune 100 enterprises.

Innovation in Software Engineering Education
We research and apply modern education techniques to maximize transfer between the academic learning environment and the student’s professional practice. From the working professionals’ point of view, the most valuable graduate education aligns with and anticipates their evolving professional work. Toward that end, we employ a learning approach that functions as an enhanced, guided and practical version of the life-long practices that graduates will employ on the job. We have adopted a pedagogy that is based heavily on team-oriented projects, simulations, just-in-time coaching and tutorials, and industrial practicums, delivering our courses as Story-Centered Curricula.

Software Verification
We perform research in software verification. We investigate the use of abstraction and symbolic execution in the context of the open source Java PathFinder verification tool set, with applications to test input generation and error detection. Our main interest is in developing, extending and maintaining Symbolic PathFinder, a symbolic execution tool for Java bytecode. We are also working on using learning techniques for automating assume-guarantee compositional verification. Currently we are working on compositional techniques for probabilistic systems. Other research interests include parallelization of verification tasks and modeling and analysis for multiple statechart formalisms.

Open Source Software
We conduct research on evaluation, adoption, and use of open source software. Projects include analyzing business models for companies using free and open source software, quantitative analysis of open source projects, and . As thought-leaders in this discipline, we actively participate in the international open-source community.

Teamwork and the Craft of Software Development
We examine effective practices to create high performing teams and we study the craft of software development and how to improve individual development practices while programmers are creating code. We are also involved with the evolution of agile development processes, widely used in Silicon Valley, especially among small entrepreneurial companies.

Papers: Towards Teaching The Craft of Software Development
Faculty: Todd Sedano

Software Metrics
Explorations of standardized approaches to defining, collecting and interpreting measurements appropriate for effective management of different levels and phases of the software lifecycle and software business.

Faculty: Patricia CollinsSheryl Root

Software Component Reuse and Product Line Methods
Studying methods and technologies to improve the construction and use of components and frameworks, such as crowdsourcing, domain engineering, aspects and generators.

Book: Software Reuse: Architecture, Process, and Organization for Business Success
Faculty: Patricia CollinsMartin Griss

Statistical Methods
The Intelligent Systems Laboratory (ISL) develops state-of-the-art statistical learning and reasoning algorithms to build intelligent systems for real world problems. The group is developing intelligent systems for electrical power system management, speech translation, user behavior and mobile context information detection and modeling (e.g., indoor positioning and gesture detection). The research is centered on statistical learning, which draws on methods from statistics, machine learning, and data mining, is motivated by the massive amounts of data available nowadays. Many of the implementations draw on high-performance, GPU-based systems.

Wireless Networks and Sensors
The group is designing, building and programming systems of mobile sensors, such as SensorFly, and enhancing sensors associated with mobile phones.

High Performance Computing