Parisa Kordjamshidi
Phone: (504) 247-1543
Fax: (504) 865-5786
Office: 305B Stanley Thomas Hall
Postal: 6823 St. Charles Avenue, Tulane University, New Orleans, LA 70118
Official Website, Google Scholar, LinkedIn, C.V.
Parisa Kordjamshidi

About me

I am an assistant professor of computer science at Tulane University and hold a joint appointment as a research scientist at IHMC from July 2016. My main research interests are artificial intelligence, machine learning, natural language processing, information extraction and declarative learning based programming. I have worked on the extraction of formal semantics and structured representations from natural language, with a specific focus on spatial semantics representation and structured output learning models. My current research is dedicated to declarative learning based programming (DeLBP). The goal of this programming paradigm is to facilitate programming for building systems that require a number of learning and reasoning components that interact with each other and receive data from heterogeneous resources. Such a language would help machine learning researchers as well as experts in various domains who are not experts in machine learning, to design complex intelligent systems and evaluate them. I am working on developing such a language and applying it on various application domains.
I received my PhD from KU Leuven University in 2013. I was a post-doc in UIUC in cognitive computation group and was working for KnowEng, Big Data To Knowledge Project  granted by NIH, before joining Tulane and IHMC. I am a member of Editorial board of Journal of Artificial Intelligence Research (JAIR) and have organized several international workshops and served as program committee of conferences such as IJCAI, AAAI, ACL-IJCNLP, COLING and ECAI.

Research Interests

Artificial Intelligence, Machine Learning, Natural Language Processing, Extraction of Spatial Semantics from Natural Language Text, Information Extraction from Biomedical Text, Structured Output Prediction Models, Probabilistic Graphical Models, Statistical Relational Learning, Learning Based Programming, Probabilistic Programming.