Alan Rodriguez, David Baerg, Jessica Womble, Ryan McBride, and Sara Savitz represented USF College of Engineering at the 2018 Florida-Wide Student Engineering Design Invitational held at UCF on April 19th. The students exhibited their BEST project titled “Mixed Reality C-130 Loadmaster Simulation for CAE USA”. The Mixed Reality C-130 Loadmaster simulator, created by a team of USF Computer Science and Engineering students, uses augmented reality, incorporating both the real world and virtual reality into one view, to achieve an immersive training experience for a fraction of the cost. The Loadmaster trainee is responsible for safely loading and deploying cargo from a C-130 cargo bay.
At the IEEE Vast Challenge 2017, held on October 1, 2017 in Phoenix, Arizona, the USF Department of Computer Science and Engineering student team of Sulav Malla, Anwesh Tuladhar, and Ghulam Jilani Quadri received an Honorable Mention. Their submission to the IEEE VAST Challenge was among 56 other entries.
According to the VAST Challenge website, “The Visual Analytics Science and Technology (VAST) Challenge is an annual contest with the goal of advancing the field of visual analytics through competition. The VAST Challenge is designed to help researchers understand how their software would be used in a novel analytic task and determine if their data transformations, visualizations, and interactions would be beneficial for particular analytic tasks. VAST Challenge problems provide researchers with realistic tasks and data sets for evaluating their software, as well as an opportunity to advance the field by solving more complex problems.”
Student Ashley Suh was awarded a $3,000 stipend for her research project, “Using Persistent Homology to Drive Interactive Graph Drawing,” from the Collaborative Research Experience for Undergraduates (CREU). In addition to this, she will receive up to $1,500 for student travel and/or research supplies. The proposal for funding was submitted by Dr. Paul Rosen, who will be Suh’s faculty mentor throughout her research.
Suh and Rosen’s project involves working to develop a new method for drawing and interacting with graphs, such as for a social network. The challenge with many graphs is that their highly interconnected nature causes them to look like a hairball when drawn. Their project uses a technique called “persistent homology” to identify important structures in the data. Those structures are then interactively selected and used to “pull apart” the hairball, enabling clearer analysis of the graph.
The CREU program is sponsored by the Computing Research Association Committee on the Status of Women in Computing Research (CRA-W). Its intention is, according to their website, “to increase the number of women and underrepresented groups enrolled undergraduate studies in the fields of computer science and computer engineering by exposing them to the joy and potential of research.” The criteria for choosing which projects are funded include the stipulation that the project must “enable student empowerment, leadership development, confidence building, and skill building.”
Paul Rosen, in collaboration with Primoz Skraba (Jozef Stefan Institute), Bei Wang (University of Utah), Guoning Chen (University of Houston), Harsh Bhatia (Lawrence Livermore National Laboratory), and Valerio Pascucci (University of Utah), was awarded the best paper award at the IEEE Pacific Visualization Symposium 2016 for their paper titled, “Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion”.
This IEEE sponsored international visualization symposium is held in the Asia-Pacific region, with the objective to foster greater exchange between visualization researchers and practitioners, and to draw more researchers in the Asia-Pacific region to enter this rapidly growing area of research.
More information on the IEEE Pacific Visualization Symposium can be found here.
Paul Rosen and his PhD student from University of Utah, Hoa Nguyen, received two awards at the 2016 International Conference on Information Visualization Theory and Applications.
Paul Rosen and Hoa Nguyen received the Best Paper Award for their publication “Improved Identification of Data Correlations through Correlation Coordinate Plots”. They also received the Best PhD Project Award for “Data Scalable Approach for Identifying Correlation in Large and Multi-dimensional Data”. According to the conference awards page, “Papers receiving these awards were selected from a set of outstanding papers, based on the quantitative and qualitative classifications as well as comments provided by the program committee reviewers, their final classification as full paper and their oral presentation at the conference”.
More information on the awards, as well as previous award winners, can be found here.
Paul Rosen along with Bei Wang (University of Utah), Chris Johnson (University of Utah), Jeff Kern (NRAO), and Betsy Mills (NRAO) received a 1-year ALMA Development Project grant from the National Radio Astronomy Observatory for $185k. The grant is titled “Feature Extraction and Visualization of AMLA Data Cubes through Topological Data Analysis”.
The project is a feasibility study for applying forms of data analysis and visualization never before tested by the ALMA community. Through contour tree-based Topological Data Analysis, we seek to improve upon existing data cube analysis and visualization. This will come in the form of improved accuracy and speed in finding features, which are robust to noise, and a better visual description of features once identified. We will build prototype software, which creates visualizations that help in characterizing and analyzing the spectra of complex spectral line sources within a given data cube.
Paul Rosen along with Bei Wang (University of Utah) received a NSF grant with additional collaborative award for Carlos Scheidegger (University of Arizona) for 4 years totaling $1.03M. The grant is titled “III: Medium: Collaborative Research: Topological Data Analysis for Large Network Visualization.” Rosen’s portion of this grant, to be subcontracted from the University of Utah, is $325K.
This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically.