October 23rd,
24th and 25th, 2015


Presented by
Faculty & Students in Carnegie Mellon's

School of Computer Science

2015 Our Sponsors:


Our 2013 Sponsors:


Lockheed Martin


Human-Computer Interaction Institute

Institute for Software Research

Language Technologies Institute

Machine Learning Department

MSR-CMU Center for Computational Thinking

Robotics Institute



2015 Research Team Leaders and Projects in Planning

Below you can check out the 2013 team leaders and projects:

Team Leaders and Project Descriptions

Alan Black
Associate Professor
Language Technologies Institute
Carnegie Mellon

Mimic: An Automatic System to Mimic a Human Speech (pdf)
This project will involve the construction of a system that will take examples of a particular person's speech, and build a speech synthesizer that will mimic their speech pattern so that other speech said by the system will sound like that person.

The work will involve research in speech modeling as used in speech synthesis and voice conversion.  The system will take examples of human speech and automatically align it with known synthesized speech and build models to convert the spectrum, intonation and duration of the synthetic speech into the target speaker.  The target speech can be any person speaker, and accent, or even funny characteristic voices.

Henry Cohn
Principal Researcher Microsoft Research
New England
Nadia Heninger
Magerman Term Assistant Professor
Computer and Information Science
University of Pennsylvania

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, though 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 Usersí Privacy-Related Attitudes and Behaviors (pdf)
As has been visible in recent news, making personal information available on Facebook can negatively impact a userís 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 Facebookís settings to protect their information? For this project participants will develop a survey to address some of these questions and explore Facebook usersí and nonusersí privacy preferences. We will gather and analyze actual user data using Amazonís 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 (pdf)
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 (pdf)
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.

Bonnie Holub
CEO of ArcLight, Inc.,
Honeywell Endowed Chair in Global Technology Management,
Graduate Programs in Software,
University of St. Thomas,
St. Paul, MN

Data Analytics and Visualization
Data Analytics and Visualization is the search for meaning in vast repositories of data that swamp scientists, engineers, planners and business people every day. Finding the story in the data and communicating it compellingly requires a skillset that will be required over and over in the future.  The Harvard Business Review calls “Data Scientist” the sexiest job of the 21st century.  The McKinsey Global Institute projects a shortfall of 140,000 to 190,000 data scientists in the United States (and a gap of 1.5 million additional manager and analysts to consume the results) by 2018.

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

Genome Analysis for Personalized Medicine (pdf)
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.

Robert Kraut
Human Computer Interaction Institute
Carnegie Mellon
Carolyn Rosé
Associate Professor Language Technologies Institute
Carnegie Mellon

Communication in Online Support Groups
Substantial evidence indicates that psycho-social interventions, including online social support groups, promote health and help people cope with illness. If online support is effective, then it is the conversations in online support groups that represent the “active ingredient.” In this project, students will become familiar with the content of a breast cancer support group and will use natural language processing and machine learning techniques to understand how the conversations in these groups produce social support.

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

Invent a Mobile Service (pdf)
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
Dave Wilkinson
Graduate Student
Computer Science Department
University of Pittsburgh

Automated Sharing for Communal Progress
How do you share good work and information? Do you have to upload it somewhere and then market it? Well, that works for "celebrities," but not for everybody. Let's be smarter.  Given a specific metric, machines can cooperate to share work (e.g., implementations, data, information) when the work is reportedly "better," according to that metric. The idea is that over time, all machines in our system will substantially improve, based on their collective progress.  Examples include: (a) sharing code/apps, (b) sharing configurations, (c) sharing observations in participatory sensing.  In addition to implementing a system to carry out sharing, we will discuss trust issues, conflict resolution (how to reconcile different opinions), and relevant metrics (how to judge "better").
Jeria Quesenberry
Assistant Teaching Professor
Information Systems, H&SS
Carnegie Mellon
Edward McFowland III
Graduate Student
Information Systems Dept.
Heinz College
Carnegie Mellon

Why Do They Come – Why Do They Stay?: Career Motivations Among Technical Undergraduate Students
Despite the shortage of information technology (IT) professionals in today’s global market, student enrollment in technical undergraduate degrees is on the decline.  In an attempt to address the industry gap between supply and demand, it is important to understand the values and motivations of current undergraduates studying in the IT-domain.  Hence, the purpose of this project is to examine the reasons why undergraduate students select and remain in a technical area of study.  Over the weekend, we will compare existing literature, conduct qualitative interviews with a variety of students, and analyze the results in support of theoretical models.  If time permits, we will develop intervention materials and literature to encourage the recruitment and retention of future IT professionals.

Portia Taylor, Ph.D.
Hardy-Apfel IT Fellow,
Social Security Administration
Washington, DC
Developing for Golden Years:  Technology for the Elderly (pdf)
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. 

Ravi Starzl
Systems Scientist
Language Technology Institute
Carnegie Mellon

The Future of Software Systems in Healthcare and Medicine (pdf)
Participants will discuss and analyze some recent software trends in healthcare and medicine. Participants will brainstorm and iterate through a research challenge related to these fields.  

The workshop will provide opportunities for all participants to work in teams on exploratory research problems. Each team will be led by a researcher from industry or academia who will introduce the research problem and guide the team through the process.

There are several sessions devoted to the research workshops.
The final research session will include presentations/solutions from each team.

For questions about the workshop, see contact page