2013-2014 LIFT Award Recipients

The following proposals were selected to receive full funding for FY 2013-2014.

Peer Instruction on the Fly: Developing an Innovative System that Encourages Conceptual Understanding, Persistence, and Connection in Online Environments

As one of the most popular research-based pedagogies, Peer Instruction improves conceptual understanding, problem solving abilities, persistence in courses and majors, and social connection among students. To date, the benefits of such research-based pedagogies have been realized primarily in synchronous classes. Students in large-scale online and/or asynchronous courses have yet to experience the fully benefits of pedagogical innovation--this LIFT grant will help address the increasing need for research-based pedagogy in online teaching.

Participants: Brenda L. Berkelaar, Communication Studies; Julie Schell, Center for Teaching and Learning

The Innovation Station: A 3D Printing Vending Machine for UT Austin Students

The goal of the proposed project is to create the Innovation Station, a 3D Printing Vending Machine that allows students at The University of Texas at Austin to fabricate almost any part they can imagine, automatically, in front of their very eyes. Students will create 3D models of their parts using standard CAD (computer-aided design) software, upload the virtual models into the Innovation Station's online portal, and then watch as the 3D printer builds their parts behind a plexiglass screen and then drops them into an open retrieval bay, much like a soda is dispensed from a traditional vending machine.

Participants: Dr. Carolyn Conner Seepersad, Mechanical Engineering

Mapping the UT BIOME

The proposed UT BIOME project addresses this need by: (1) engaging the UT community in the collection and analysis of environmental samples from across the UT Austin campus (essentially using UT Austin as a “living” laboratory) and (2) developing an interactive mapping platform that users within and outside the university can access to download biome results and associated environmental data by simply clicking on the interactive map. While the initial platform will be developed for the microbiome and associated environmental data collected from across campus, we envision that the platform will provide a foundation that can be used in the future by the broader community for any variety of environmental, energy, infrastructure or even health related coursework or research.

Participants: Kerry A. Kinney, Paola Passalacqua, Harish Sangireddy, Juan Pedro Maestre, Mary Jo Kirisits, Lynn Katz, Rich Corsi, Civil, Arch.& Environmental Engineering; Jay Banner, Environmental Science Institute; Christine Hawkes, Integrative Biology; Sharon Horner, School of Nursing

Robots for Everyone!

Specifically, Building Wide Intelligence (BWI) will comprise a system of autonomous robots that will be available for research, education, and general interaction by the entire UT community. At project completion, the robots will roam the halls of the CS building, ready to be called into service by any and all visitors to the building. The robots will guide newcomers to their destinations, perform deliveries for building inhabitants, and provide tours for visitors. Most importantly, the robots will inspire new, unforeseen applications by the students who will be encouraged to contribute their ideas and programs to the development of a continually evolving platform.

Participants: Peter Stone, Kristen Grauman, Computer Science Department, Artificial Intelligence Lab

A Technology for Addressing the “Error Rate Problem” Associated with Next-Generation DNA Sequencing, with Application to Cancer and Immunology Research Being Conducted Across the UT Austin Campus

The project team is an interdisciplinary group that has been developing “circle sequencing,” a technology that dramatically increases the precision with which DNA can be sequenced. We have already successfully reduced the error rate from 1% to 0.001%. Our method is general enough that it can fit into a broad range of existing next-generation sequencing workflows, enabling it to have an immediate and broad impact.

Participants: Sara Sawyer, Molecular Genetics and Microbiology, College of Natural Sciences; William Press, Computer Science; Mark Deinert, Mechanical Engineering, Cockrell School of Engineering; Jeff Hussmann, Computational Science, Engineering, and Mathematics Graduate Program; Dianne Lou, Computational Science, Engineering, and Mathematics Graduate Program; Ross McBee, College of Natural Science, Undergraduate Program

Appsoma – an innovative “cloud-based” approach to promote on-line coding, sharing and learning, jumpstart research activities, and to perform research computations with reproducible results.

A novel proof-of-concept for a programming and research ecosystem that promotes collaboration, reproducibility, scalability, and education. The service provides web-based and cloud based tools that allow programmers or researchers to develop in their own programming language of choice and experiment using on-demand, configurable virtual compute servers in the UT Austin private cloud instead of their own hardware.

Participants: Zack Booth Simpson, Center for Systems and Synthetic Biology (CSSB); Scott Hunicke Smith, Genome Sequence and Analysis Facility (GSAF); Ken Dermast, (Appsoma); Jamie J. Cannone, Center for Computational Biology and Bioinformatics (CCBB), College of Natural Sciences; Huapei Chen, Chris Thorpe, Information Technology Services (ITS)