Parisa
Kordjamshidi
Email: kordjams@msu.edu Phone: (517) 355-8389 Room: 2140 Postal: Engineering Building 428 S. Shaw Lane, East Lansing, MI 48824. Departmental Page, Google Scholar, LinkedIn, C.V. |
If you are interested in doing a PhD or Post-doc in Machine Learning, Natural Language Processing, Information extraction, Combining vision and language, Grounding, and Neuro-Symbolic AI, please send me an Email with your CV. Thank you for reaching out, your Email will not be missed. However, I can only afford responding to the candidates who are selected for zoom interview, I apologize in advance. Also, please see my mentoring plan. About me
Parisa Kordjamshidi is an associate professor of computer science and engineering. She joined Michigan State University on August 2019. Her main
research interests are artificial intelligence, machine learning,
natural language processing, and Neuro-symbolic AI. She is directing the research lab on Heterogeneous Learning and Reasoning (HLR). She has 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. Her notable awards and project grants include NSF CAREER award (2019-2024) to work on combining learning and reasoning for spatial language understanding; Office of Naval Research (ONR) grant under the Science of AI program to perform basic research and develop a declarative learning-based programming framework for integration of domain knowledge into statistical learning (DominKnowS project, 2019-2023); Under the same program she is the leading PI for developing a Neuro-Symbolic framework for compositional learning and understanding multiple modalities of vision and language (2023-2026); She also obtained Amazon faculty research award (2022) for working on Vision and Language Navigation.
Research InterestsArtificial 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. |