David Byrd has been a Research Scientist in the Interactive Media Technology Center since 2014, specializing in time-series machine learning, data analysis and visualization, database architecture, software and app development, and video game development. David's application areas of particular focus include quantitative finance, business intelligence, and activity recognition. While part of IMTC, David has also worked on a broad range of projects including AR for STEM education, jaw gesture recognition for wearables, a mobile gait study app, an observation studies app, and cognitive training games for disabled persons.
David is also a PhD student in the School of Interactive Computing and a member of his advisor Dr. Tucker Balch’s Quantitative Software Research Group, where he has investigated mutual fund portfolio inference, intraday equity market forecasting, and stock market simulation. David also instructs classes for the College of Computing: CS 3600 Intro AI (Spring 2017, Summer 2017, Summer 2018 planned) and CS 7646 Machine Learning for Trading (Summer 2016, Summer 2017).
Prior to working at Ga Tech, David spent fifteen years as a pioneering software developer and data analyst in the Atlanta and Dallas start-up communities, where he gained extensive entrepreneurial experience in high-tech innovation.
- Time series machine learning
- Intelligent agents, multi-agent systems
- Data science
- Quantitative finance
- Business intelligence
- Activity recognition
Additional Technical Skills
- App development
- Web development
- Software development
- Database architecture/optimization
Recent Publications & Presentations
- Bedri, A., Sahni, H., Thukral, P., Starner, T., Byrd, D., Presti, P., Reyes, G., Ghosvanloo, M., and Guo, Z., "Toward silent-speech control of consumer wearables," IEEE Computer, vol. 48, no. 10, pp. 54-62, 2015. Byrd contributed substantial data analysis, developed the real-time detection algorithm, and assisted in writing the paper.
- Bedri, A., Byrd, D., Presti, P., Sahni, H., Gue, Z., and Starner, T., "Stick it in your ear: building an in-ear jaw movement sensor," Adjunct Proceedings of the 2015 ACM International Join Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 1333-1338, 2015. Byrd contributed substantial data analysis and developed the real- time detection algorithm.
- Thompson, B., Levy, L., Lambeth, A., Byrd, D., Alcaidinho, J., Radu, I., and Gandy, M., “Participatory design of STEM education AR experiences for heterogeneous student groups: Exploring dimensions of tangibility, simulation, and interaction,” IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2016 Adjunct, ISBN 978-1-5090-3740-7, pp. 53-58, 2016. Byrd designed and developed the laser-optics experience used in the study.
- Balch, T. and Byrd, D., “Deep Q-Learning for Trading,” QuantCon 2017, video archived. Byrd authored and delivered a substantial portion of this talk, describing the research and implementation process, results obtained, and lessons learned.