Shekoofeh Mokhtari

School of Computing and Information Sciences

Lecture Information:
  • November 16, 2018
  • 9:00 AM
  • ECS 241

Speaker Bio

Shekoofeh Mokhtari is a Ph.D. candidate in the School of Computing and Information Sciences at Florida International University, co-advised by Dr. Tao Li and Dr. Ning Xie. She received her B.Sc. in Computer Engineering from the University of Isfahan, Iran in 2010. Shekoofeh’s research interests lie in the intersection of natural language processing and deep learning. Shekoofeh’s research work has been published in top conferences such as AAAI, ACM SIGKDD, NACCL, and IRI. She was also awarded the FIU Dissertation Year Fellowship in Fall 2018. In summer 2016, 2017 and 2018, she had three great internships at IPSoft and Microsoft as a research intern to solve the real problems in the products.


As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks.

In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures, and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals, and has attracted tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing.

This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing applications such as sequence tagging, sentiment analysis, semantic similarity and etc.