Conference/Journal:

Choi, T., O. Would, A. Salazar-Gomez, X. Liu, and G. Cielniak (2024): Channel Randomisation: Self-Supervised Representation Learning for Reliable Visual Anomaly Detection in Specialty Crops. In: Journal of Computers and Electronics in Agriculture. [Data] [Code]

Choi, T., D. Guevara, Z. Cheng, G. Bandodkar, C. Wang, B. N. Bailey, M. Earles, and X. Liu (2024): DAVIS-Ag: A Synthetic Plant Dataset for Prototyping Domain-Inspired Active Vision in Agricultural Robots. In: Proceedings of the 2024 IEEE International Conference on Automation Science and Engineering (CASE 2024). Bari, Italy. [Data] — To appear

Goyal, S., K. Sasikumar, R. Sheth, A. Seelam, T. Choi, and X. Liu (2024): EnColor: Improving Visual Accessibility with a Deep Encoder-Decoder Image Corrector for Color Vision Deficient Individuals. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24). Vancouver, Canada. [Code]

Bandodkar, G., S. Agarwal, A. K. Sughosh, S. Singh, and T. Choi (2024): “Allot?” is “A Lot!” Towards Developing More Generalized Speech Recognition System for Accessible Communication. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24). Vancouver, Canada.

Choi, T., B. Pyenson, J. Liebig, and T. P. Pavlic (2022): Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms. Journal of Artificial Life and Robotics (AROB) — Extension of the [Best Paper Award] winner at the 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2021). Kyoto, Japan (Virtual). [Data]

Choi, T., O. Would, A. Salazar-Gomez, and G. Cielniak (2022): Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. In: Proceedings of the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022). Philadelphia, USA. [Data] [Code]

Choi, T. and G. Cielniak (2021): Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning. In: Proceedings of the 10th European Conference on Mobile Robots (ECMR 2021). Bonn, Germany (Virtual).

Choi, T., B. Pyenson, J. Liebig, and T. P. Pavlic (2021): Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21). Virtual conference. [Data] [Code]

Choi, T. (2020): Deep Learning Approaches for Inferring Collective Macrostates from Individual Observations in Natural and Artificial Multi-Agent Systems Under Realistic Constraints. In: Ph.D. thesis, Arizona State University.

Choi, T. and T. P. Pavlic (2020): Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems. In: Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration (MFI 2020). Karlsruhe, Germany (Virtual).

Kang, S., T. Choi, and T. P. Pavlic. (2020): How Far Should I Watch? Quantifying the Effect of Various Observational Capabilities on Long-range Situational Awareness in Multi-robot Teams. In: Proceedings of the 1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2020). Washington, DC, USA (Virtual). [Data & Code].

Choi, T., S. Kang, and T. P. Pavlic. (2020): Learning Local Behavioral Sequences to Better Infer Non-local Properties in Real Multi-robot Systems. In: Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA 2020). Paris, France (Virtual). [Data].

Choi, T., T. P. Pavlic, and A. Richa. (2017): Automated Synthesis of Scalable Algorithms for Inferring Non-Local Properties to Assist in Multi-Robot Teaming. In: Proceedings of the 2017 IEEE International Conference on Automation Science and Engineering (CASE 2017). Xi’an, China.

Choi, T. and H. Na. (2016): Stealthy Behavior Simulations based on Cognitive Data. Journal of Korea Game Society (JKGS), 16(2):27–40.

Choi, T. and H. Na. (2015): Making Levels More Challenging with a Cooperative Strategy of Ghosts in Pac-Man. Journal of Korea Game Society (JKGS), 15(5):89–98.

Choi, T. and H. Na. (2015): Stealthy Behavior Simulations based on Cognitive Data. In: Proceedings of the 2015 IEEE International Conference on Machine Learning and Cybernetics (ICMLC 2015). Guangzhou, China.

Preprints:

Choi, T. and X. Liu (2023): Exploiting Unlabeled Data to Improve Detection of Visual Anomalies in Soft Fruits. In: AAAI-2023 Workshop on AI for Agriculture and Food Systems (AIAFS 2023).

Liu, Y., T. Choi., and X. Liu (2023): Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities. In: AAAI-2023 Workshop on AI for Agriculture and Food Systems (AIAFS 2023).

Choi, T. and G. Cielniak (2022): Channel Randomisation with Domain Control for Effective Representation Learning of Visual Anomalies in Strawberries. In: AAAI-2022 Workshop on AI for Agriculture and Food Systems (AIAFS 2022).