People
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.
Founding Team Members
Ann McNamara
College of Performance, Visualization & Fine Arts Texas A&M UniversityData Visualization, Analytics, Human-Computer Interaction, Perception
ContactDerya Akleman
Department of Statistics Texas A&M UniversityData Analytics, Statistics
James Caverlee
Engineering Academic Coordination Texas A&M UniversityInformation retrieval, data mining, recommendation systems
Cynthia Hipwell
Engineering Academic Coordination Texas A&M University
Surface physics, Sensors, Actuators for haptics and Human Machine Interfaces
Shuiwang Ji
Engineering Academic Coordination Texas A&M UniversityData Visualization & Exploration, Machine/Deep Learning and Data Mining
Jeeeun Kim
Engineering Academic Coordination Texas A&M UniversityDigital Fabrication, HumanComputer Interaction, Design
Vinayak Krishnamurthy
Mechanical Engineering Texas A&M UniversityGeometric Modeling, HumanComputer Interaction, Perception
Courtney Starrett
College of Performance, Visualization & Fine Arts Texas A&M UniversityDigital Fabrication, Data Physicalization, Data Sculpture, Design
Research Interests
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 consumable 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 real world 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 Krishnamurthy, (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.
Affiliate Faculty
If you would like to be affiliated faculty please email [email protected].
Susan Reiser
UNC AshevilleSoftware Development, Tangible Forms
David Retchless
TAMU GalvestonWeather & Climate, risk perception & communication
Jian Tao
College of Performance, Visualization & Fine Arts Texas A&M UniversityData analytics, machine learning, High performance computing