Henry Cohn
Principal Researcher,
Microsoft Research
New England

Attacks and Defenses
How evil can you be?  In this project, we'll learn to think like attackers and explore ways in which systems can be compromised, through several case studies based on real incidents.  This adversarial mindset is not emphasized in most classes, but it is essential to the field of computer security.

Lorrie Cranor Associate Professor Institute for Software Research Carnegie Mellon

Manya Sleeper Graduate Student Institute for Software Research Carnegie Mellon

Understanding Facebook Users’ Privacy-Related Attitudes and Behaviors
As has been visible in recent news, making personal information available on Facebook can negatively impact a user’s offline life. In a recently-publicized example, a teenage girl accidently posted an invitation to her birthday party publicly and received over 21,000 responses. Facebook information being used in unexpected ways has also led to a variety of other negative consequences, such as job loss and loss of health insurance. Despite these incidents, many people still choose to post photos, personal information, and messages online using Facebook. Understanding why users post information online, despite the potential dangers, raises a variety of interesting questions: Do Facebook users believe that they are appropriately using Facebook privacy settings to protect the information they post online? Do Facebook users have a lower privacy threshold than those who choose not to use Facebook? Do Facebook users adopt privacy strategies, successful or not, outside of Facebook’s settings to protect their information? For this project participants will develop a survey to address some of these questions and explore Facebook users’ and nonusers’ privacy preferences. We will gather and analyze actual user data using Amazon’s Mechanical Turk marketplace, allowing participants to draw conclusions around these important issues.  

Roger Dannenberg Professor of Computer Science, Art, and Music Computer Science Department Carnegie Mellon

Anders Oland Graduate Student Computer Science Department Carnegie Mellon

Automatic Generation of Drum Tracks
What makes a good beat? How do drummers rock? Or swing? We can synthesize "perfect" drumming according to standard drumming patterns, but the results are disappointing. In this project, we will analyze real drumming data and try to discover what makes real drumming so much more interesting and musical than naive computer-generated drumming. Our goal is to make a computer drummer that competes with a human. We will begin by considering representations and models of drumming. Using statistics and machine learning techniques, we will fit these models to real drumming data and test how well these models explain the data. Using these models, we can synthesize new drumming patterns. We will discuss evaluation techniques including human evaluation of synthesized drumming.

Anca Dragan
Ph.D. Student
Robotics Institute
Carnegie Mellon

Convey Intentionality using a Robot's Head and Arm Motion
This project is about designing motion that conveys a robot's internal state (e.g. intended goal, task difficulty, etc.). We will brainstorm to identify the parameters we want vary in the motion, produce motions for the HERB robot (http://personalrobotics.ri.cmu.edu/), and test out our hypotheses in an online user study. Some knowledge of Python and familiarity with the Linux operating system would be useful for this project.

Seyoung Kim
Assistant Professor
Lane Center for Computational Biology
School of Computer Science
Carnegie Mellon

Genome Analysis for Personalized Medicine
Since the first draft of a human genome sequence became available about a decade ago, the cost of genome sequencing has decreased dramatically. It is expected that personal genome sequencing will become a routine part of medical examinations for patients in clinics for prognostic and diagnostic purposes. Personal genome information will also play an increasing role in lifestyle choices, as people take into account their own genetic tendencies. In this project, we will apply machine learning techniques to genome data of diabetic patient cohorts to explore the genetic causes of diabetes.

James Morris
Professor and Former Dean of SCS
Human Computer Interaction Institute
Carnegie Mellon

Invent a Mobile Service
Cell phones are today's computing platform, especially in the developing world. Our team will develop an idea for a new mobile service. Some examples:

• Ridesharing: Connect and motivate drivers and riders with real-time support
• Favor Net (invented by the previous OurCS team): Facilitate requesting and performing favors on a college campus.
• Family Memory Book: Billions of photos are being taken of children every day. How will their parents, the children grown, and their descendants enjoy them?
• Product Finder: Snap a picture of an item you like, from TV or real life, and  be told where it came from.

The team will decide on a service and develop usage scenarios.

Daniel Mosse
Professor and Chair of Computer Science Department
University of Pittsburgh

Project TBA

Aarti Singh Assistant Professor Machine Learning Department Carnegie Mellon

Discovering Network Topology
The Internet has become a ubiquitous and indispensable tool in our lives. To maintain seamless network services, it is important that the administrator maintains an accurate knowledge of the current network topology or structure. However, sending extra packets to learn the network structure places additional burden on network routers. In this project, we will use machine learning techniques to learn a network topology using only passive information collected from packets received at computers at the periphery of a network.  

Portia Taylor, Ph.D.
Hardy-Apfel IT Fellow,
Social Security Administration
Washington, DC
 
Developing for Golden Years:  Technology for the Elderly
A worldwide “graying” of the population will lead to increased financial burden on both personal/patient and public resources.  In America alone, 20% of the population will be over the age of 65 by the year 2030.  People are living longer lives despite the physical and mental changes that occur during aging.   This project will focus on technology for the elderly that can assist in assuring a high quality of life during the later years.  We will survey current technologies, explore barriers to aging and look at the financial costs of healthcare that is associated with aging.  Students will take what they learn and propose an application to address a problem concerning elders.