Our interdisciplinary team draws on strengths from the Departments of Visualization, Statistics, Computer Science, and Mechanical Engineering. We have also diversified in terms of rank and position. Our goal is to be inclusive, and welcome collaboration to complement the core team, from across the Texas A&M System, and beyond.
|Name||Relevant Research Area, Role||Department/College||Contact|
|Ann McNamara||Data Visualization, Analytics, Human-Computer Interaction, Perception||Visualization/Architecturefirstname.lastname@example.org|
|Derya Akleman||Data Analytics, Statistics||Statistics/Science|
|James Caverlee||Information retrieval, data mining, recommendation systems||Computer Science/Engineering|
|Cynthia Hipwell||Surface physics, Sensors, Actuators for haptics and Human Machine Interfaces||Mechanical Engineering/Engineering|
|Shuiwang Ji||Data Visualization & Exploration, Machine/Deep Learning and Data Mining||Computer Science/Engineering|
|Jeeeun Kim||Digital Fabrication, HumanComputer Interaction, Design||Computer Science/Engineering|
|Vinayak Krishnamurthy||Geometric Modeling, HumanComputer Interaction, Perception||Mechanical Engineering/Engineering|
|Courtney Starrett||Digital Fabrication, Data Physicalization, Data Sculpture, Design||Visualization/Architecture|
The coherence of the initial team, revolves around our common interest in data, how to capture, filter, clean, analyze, predict and present data, in a form that is consumable by the intended audience. That form can be visual or could engage other senses including touch and hearing. In terms of our proposed research, we would like to address the entire Data Visualization and information design pipeline. The symbiotic nature of our individual research areas combines as follows, to contribute to our proposed research themes.
Data Collection, Filtering, and Analysis: Ann McNamara, (Visualization) and Derya Akleman,(Statistics) have experience in Data Analytics and Visualization. They will be the main collaborators for extracting meaningful patterns from existing data sets, analyzing the data, and transforming it to a form consum able in our innovative workflows.
Predictive Models and Machine Learning James Caverlee (Computer Science) targets topics from recommender systems, social media, information retrieval, data mining, and emerging networked in formation systems. Shuiwang Ji (Computer Science) leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory at Texas A&M University and conducts foundational research in machine learning and deep learning and applies machine learning methods to solve challenging realworld problems in biology, chemistry, neuroscience, and medicine. Together they will provide expertise on data mining and integration and developing predictive models to forecast future scenarios.
Digital Prototyping, Human Computer Interaction Ann McNamara, (Visualization) and Vinayak Kr ishnamurthy, (Mechanical Engineering) have research interests surrounding Augmented and Virtual Reality and Intelligent User Interfaces. Their combined expertise will be invaluable when prototyping new interfaces, and iterating on design choices virtually.
Data Materialization, Physicalization Courtney Starrett, (Visualization) is an artist. Originally trained in metalwork, she creates art objects, some of which embed realworld data. Jeeeun Kim, (Computer Science) has research interests in Digital Fabrication, design research, Human-Computer Interaction, and HumanAI Interaction. Starrett and Kim are a formidable team to innovate on the design, and data influence, of new tangible visualizations.
|||Name||Relevant Research Area, Role||Department/College|
|||Susan Reiser||Software Development, Tangible Forms||UNC Asheville|
|||David Retchless||Weather & Climate, risk perception & communication||TAMU Galveston|||
|||Jian Tao||Data analytics, machine learning, High performance computing||Department of Visualization, TAMU|||