More specifically, I am interested in truly adaptive/data-driven NLP/AI systems. My research is informed by studying the developmental trajectory of intelligence in young infants who start with little or no knowledge of the world but manage to learn so much so fast, mainly by just adapting to the world around them. In my PhD dissertation, I work on the development of a unified incremental and memory-limited computational model of infant's word learning, integrating syntactic and social cues. More specifically, my work is concerned with computational accounts of early bootstrapping effects during language acquisition in the absence of adult-like structured representations.
Sadeghi, S., Scheutz, M. (2018). Early Syntactic Bootstrapping in an Incremental Memory-Limited Word Learner. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Awarded student scholarship for travel expenses to AAAI-18. pdf
Sadeghi, S., Scheutz, M. (2017). Joint Acquisition of Word Order and Word Referent in a Memory-Limited and Incremental Learner. In Proceedings of the 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom). pdf
Sadeghi, S., Scheutz, M., Krause, E. (2017). An Embodied Incremental Bayesian Model of Cross-Situational Word Learning. In Proceedings of the 2017 Joint IEEE International Conference on Development and learning and Epigenetic Robotics (ICDL-EPIROB). pdf (Demo is available here).
Scheutz, M., Krause, E., and Sadeghi, S. (2014). An Embodied Real-Time Model of Language-Guided Incremental Visual Search. In Proceedings of the 36th Annual Conference of the Cognitive Science Society. pdf
Sadeghi, S., Scheutz, M., Pu, H., Holcomb, P. J., and Midgley, K. J. (2013). A PDP Model for Capturing N400 Effects in Early L2 Learners during Bilingual Word Reading Tasks. In Proceedings of the 35th Annual Conference of the Cognitive Science Society. pdf
Sadeghi, S., and Ramanathan, K. (2011, July). A Hubel Wiesel model of early concept generalization based on local correlation of input features. In Proceedings of the 2011 IEEE International Joint Conference on Neural Networks. (pp. 709-716). pdf
Sadeghi, S., Fusion of Color and Infrared images for pedestrian detection using SVM, B.Sc. Thesis.