About Me

I am a PhD student at Tufts University studying Computer Science and Cognitive Science.  Before coming to Tufts, I recieved a M.Eng in Electrical and Computer Engineering from National University of Singapore and a B.Sc. in Computer Engineering from Iran University of Science and Technology. Broadly speaking, my research interests lie at the intersection of AI, machine learning and cognitive science.
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.

Publications

Sadeghi, S., Oosterveld, B., Krause, E., Scheutz, M. (submitted). Continuous Learning of Word-Object Mappings in Robots from Human Instructions and Human-Human Dialogues.

Sadeghi, S., Scheutz, M. (2018). Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018).pdf data

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 travel scholarship. 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

Patents

Ramanathan, K and Sadeghi, S., Data structure and a method for using the data structure. US20130018832.

TALKs

Monroe, B.M., Laine, T., Gupta, S., Sadeghi, S. and Farber, I. (January 2012). A Test of Predictive Validity of Connectionist Models of Person Judgments. Talk presented at Social Dynamics and Computational Modeling Preconference, Society of Personality and Social Psychology Annual Conference, San Diego, USA.

Unpublished Manuscripts

Sadeghi S., A Hubel Wiesel Model of Early Concept Generalization based on Local Correlation of Input Features. M.Eng. Thesis.

Sadeghi, S., Fusion of Color and Infrared images for pedestrian detection using SVM, B.Sc. Thesis.