Manisha Mukherjee

Hello! I'm Manisha, a PhD student at S3D in the School of Computer Science at Carnegie Mellon University working with Vincent Hellendoorn.

Research

I develop machine learning techniques to help developers with safe and efficient code reuse. For those interested, here is a model I trained exclusively on StackOverflow data. I am broadly interested in:

• Deep Learning
• Software Engineering
• Natural Language Processing (NLP)
• Security

Before this, I worked in computer networks and computer vision for my master’s thesis, advised by Tom La Porta.

Experience

• Lawrence Livermore National Lab (Summer 2022, 2023)
• Idaho National Lab (Summer 2020)
• Fujitsu Research Labs America (Summer 2021, 2019)
• Cisco Systems Inc (2014-2017)
• Capgemini India Pvt Ltd (2011-2012)

Selected Publications and Patents

• M. Mukherjee and V. J. Hellendoorn, "Stack overflowing with results: The case for domain-specific pre-training over one-size-fits-all models," arXiv preprint arXiv:2306.03268, 2023.
• V. J. Hellendoorn, J. Tsay, M. Mukherjee, and M. Hirzel, "Towards automating code review at scale," in 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’21), 2021.
• M. Mukherjee, M. Bahrami, and W. P. Chen, "Source code retrieval," in US Patent Application 17/085,894, 2020.
• M. Mukherjee, J. Edwards, H. Kwon, and T. F. La Porta, "Quality of information-aware real-time traffic flow analysis and reporting," in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE, 2015, pp. 69–74.
• M. Mukherjee, "Determination of real-time traffic flow parameters in different devices based on qoi requirements," in MS Thesis, 2014.