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Welcome, my name is Jonathan Adams.
I manage a team of student-researchers in the FSU computer vision lab, located in the WIlliam-Johnston Building. The lab develops or assists with computer vision research projects, such as developing tools and techniques to create synthetic data sets. The synthetic data is then mixed with a much smaller number of authentic images to train computer vision models.
The lab includes some of the best and brightest students who actively conduct computer vision research. Currently, a team of five students work in the lab. In May of 2021 the first graduate of the lab started a company and has been inducted into the NVIDIA inception accelerator program!
The FSU computer vision lab focuses on working with YOLO and OpenCV projects as well as generating datasets that can be used for training AI systems. Our approach focuses on producing explainable algorithms that are capable of being put to work in the field.
The lab started with a research project that examined algorithms for search and rescue. We soon realized that quality images are essential to efficient training, and in many cases, there just are not enough of that kind of imagery in existence to train computer vision models. Our work hopes to provide synthetic datasets for all manner of AI work and research. Shout out to Dean Larry Dennis for supporting the effort to develop the lab.
Learn more at the FSU ML-Computer vision lab (link coming).
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Artificial Intelligence Research (2019-2025 only)
2025
Ravuri, B., Moja, O., Bin Emdad, F., A Mardis, M., Adams, J., & Adams, J. L. (2025). How Complex Is Too Complex?" Evaluating Chatgpt-4o Mini's Response Quality To Authentic Reference Questions. Manuscript submitted for publication, 6 pages. ASSI&T conference, to be held in Washington DC, October, 2025.
Adams, J., Ravuri, B., & Gutowski, W. (2025 submitted). Acceptability of Artificial Intelligence in higher education. International Journal of Higher Education. Manuscript submitted for publication, 12 pages.
Adams, J. L., Ravuri, B., & Gutowski, W. (2025). Acceptability of AI in Higher Education: What's Important? In Association for the Advancement of Computing in Education (AACE) (Ed.), Society for Information Technology and Teacher Education (pp. 6). Association for the Advancement of Computing in Education (AACE), Chesapeake, VA.
Adams, J., (2025). Exploring the Usability of Artificial Intelligence. In Gary H. Marks, & Denise Schmidt-Crawford (Eds.), Society for Information Technology & Teacher Education International Conference (pp. 6). Association for the Advancement of Computing in Education (AACE), Chesapeake, VA.
He, Z., Pang, Y., von Hollen, L., Castano, M., Adams, J., Roberson, K., & Born, P. (2023). Preliminary Understanding the Role of Social Interaction in Adherence to Cognitive Training Software: A Pilot Study. Journal of Cognitive Enhancement. Manuscript submitted for publication, 25 pages.
Submission ID 87200358-3725-4295-9870-83a25e2ae271.
2024
Adams, J. Ravuri, B., Moja, O, Obermaier, L., Roberts, a. (2023). A Method to Engineer Prompts for Image Generation. Unpublished manuscript.
2023
Adams, J., Roberts, A., Obermaier, L., Ravuri, B., & Moja, O. (presented 2023, July). Uses of Artificial Intelligence in Higher Education. Paper presented at EdMedia Innovate Learning, 2023 Vienna, Austria, Association for the Advancement of Computing in Education, Vienna, Austria. (International) Retrieved from https://www.learntechlib.org/p/222633/
Padilla-Rodríguez, B.C., Adams, J. (2023) Acceptance of Online Degrees by Undergraduate Mexican Students: Comparing perceptions a decade later. Submitted to The Association for the Advancement of Computing in Education (AACE), Vienna, Austria.
2022
Sutor J., Adams, J. L. (2022) Exploring Synthetic Visual Data for Training Deep-Learning Based Classifiers.
2021
Adams, J. L.,John Sutor, Ava Dodd and Erin Murph. (2021) Evaluating the Performance of Synthetic Visual Data for Real-Time Object Detection. The 6th International Conference on Communication, Image and Signal Processing (CCISP 2021) which will be held in Chengdu, China during Nov. 19-21, 2021.
Dodd, A., & Adams, J. L. (2021) The Role of Synthetic Data in Aerial Object Detection. Paper presented at International Marine, Aviation, Transport, Logistics and Trade, CMATLT001 2021: XV. In, to be held in Amsterdam, Netherlands. (Top Paper Award, International Conference on Marine, Aviation, Transport, Logistics and Trade (2021).).
Adams, J. L., Erin Murphy, John Sutor, & Ava Dodd. (2021). Assessing the Quality and Production of Synthetic Visual Data. In 9th International Conference on Information and Education Technology (ICIET 2021), Okayama, Japan, March 27-29, 2021 (5 pages). co-sponsored by IEEE, Okayama University (Japan), South China Normal University (China), and International Academy of Computing Technology (Hong Kong).
Adams, J., Sutor, J., (2021). Exploring Synthetic Visual Data for Training Deep-Learning Based Classifiers. The generated synthetic datasets were used to train three classification models. Each model was trained and evaluated to understand limitations across different models. Unpublished manuscript.
2020
Adams, J. L., & Mitchell, A. L. (2020). TESA: A pedagogical approach to engage, study, and activate technology learning in an interdisciplinary setting. In Association for the Advancement of Computing in Education (AACE) (Ed.), Proceedings of EdMedia + Innovate Learning The Netherlands (pp. 778-781). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/217389/.
Adams, J. L., Ava Dodd, John Sutor, & Erin Murphy. (2020). AI and Undergraduate Research: A Dialog in Project-Based Learning. In Gary H. Marks, & Denise Schmidt-Crawford (Eds.), Society for Information Technology & Teacher Education International Conference, Apr 07, 2020 in Online ISBN 978-1-939797-48-3. Association for the Advancement of Computing in Education (AACE), Chesapeake, VA. Retrieved from http://www.learntechlib.org/fromc/56493
Adams, J. L., Ava Dodd, John Sutor, & Erin Murphy. (2020) Perspectives on the Quality and Production of Synthetic Visual Data. Advances in Education and Information TechnologyChiba University.
Startup commercialization efforts
Shark-Finder (2022)
Binaural Beat Box (2022)
OSRAI (Ocean Search and Rescue Artificial Intelligence) (2023)
Grants
2024 Technology Grant $1,082 color printer
2023 Technology Grant $571 audio speakers
Collaborative Collision grant $50,000
2022 Technology Grant $10,149 two Alienware computers with NVIDIA 4090 cards
2021 Technology Grant $3500 Alienware computer 3090 NVIDIA card
2020 Technology Grant $3,500 custom AI computer build, nvidia 2080 card
2019 Technology Grant $417 Tello drones