Marina Neseem  

 



  ![](/sites/default/files/person/IMG_5988.JPG)

  

 Marina is a Research Scientist working with the Accelerators and VLSI Research Group. Her research focuses on hardware-software co-design and efficient deep learning. This includes designing efficient model architectures, implementing dynamic pruning and adaptive inference techniques, and creating memory and parameter-efficient training methods.

Marina received her Ph.D. and M.S. in Computer Engineering from Brown University. Her Ph.D. dissertation, entitled "AI at the Edge: Efficient Deep Learning for Resource-Constrained Environments", explored innovative techniques for modeling, training, and deploying deep learning models efficiently.

For more, check out her [personal webpage](https://marinaneseem.me/) and [Google Scholar](https://scholar.google.com/citations?user=f-9tD_UAAAAJ&hl=en).



   Research Area(s)

[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Circuits and VLSI Design](/research-area/circuits)

 

 

  

 Main Field of Interest

[Circuits and VLSI Design](/research-area/circuits)

 

  

 Google Scholar

[https://scholar.google.com/citations?user=f-9tD\_UAAAAJ&hl=en](https://scholar.google.com/citations?user=f-9tD_UAAAAJ&hl=en)