Joymallya Chakraborty

I am currently a Ph.D. student in the RAISE Lab (Real-world Artifical Intelligence for Software Engineering) at North Carolina State University , under the supervision of Dr. Tim Menzies . My interest includes machine learning,data mining and optimization.

Before coming to NC State, I was a full-stack software developer at TCG Digital . I obtained my bachelors degree in Computer Science from Jadavpur University .

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My research interests include using data mining and artificial intelligence methods to solve real world problems in software engineering field. Such as exploring new techniques in search-based optimization to improve the performance of current SE predicting tasks (like effort estimation, text mining, etc.). I believe these software engineering tasks are not always need to be hard (but it may not be easy to find the easy ways to do it), and enjoy finding path to make them better and better.


Measuring the Effects of Gender Bias on GitHub

Nasif Imtiaz, Justin Middleton , Joymallya Chakraborty , Neill Robson, Gina Bai, Emerson Murphy-Hill
ICSE 2019

Diversity, including gender diversity, is valued by many software development organizations, yet the field remains dominated by men. One reason for this lack of diversity is gender bias. In this paper, we study the effects of that bias by using an existing framework derived from the gender studies literature. We adapt the four main effects proposed in the framework by posing hypotheses about how they might manifest on GitHub, then evaluate those hypotheses quantitatively. While our results show that effects are largely invisible on the GitHub platform itself, there are still signals of women concentrating their work in fewer places and being more restrained in communication than men.


Algorithms for generating all possible spanning trees of a simple undirected connected graph: an extensive review

Maumita Chakraborty, Sumon Chowdhury, Joymallya Chakraborty , Ranjan Mehera, Rajat Kumar Pal
Complex & Intelligent Systems (Springer),2018

Generation of all possible spanning trees of a graph is a major area of research in graph theory as the number of spanning trees of a graph increases exponentially with graph size. Several algorithms of varying efficiency have been developed since early 1960s by researchers around the globe. This article is an exhaustive literature survey on these algorithms, assuming the input to be a simple undirected connected graph of finite order, and contains detailed analysis and comparisons in both theoretical and experimental behavior of these algorithms.

Industrial Experience

Software Engineer Research Intern

May 2018 - August 2018 (Bellevue,Seattle)

I explored optimization opportunities of .NET Core Garbage Collection and implemented PoC (Proof of Concept) prototypes. The prototypes were then verified against different workloads.


Software Developer

July 2015 - June 2017 (Salt Lake,Kolkata)

I was core developer for two different projects. I designed and developed a B2B Travel Search Engine. I was responsible for implementing middleware services and integrating those with front end. For the second project, I designed & implemented an intelligence software to retrieve, analyze, transform and report data for business intelligence. It allows users to create different dashboards using its own customizable visualization. It also features advanced analytics concept like data modelling, forecasting, determining product affinity.

TA Experience

CSC230 - Fall 2017 (C and Software Tools)

CSC326 - Spring 2018 (Software Engineering)

CSC520 - Fall 2018 (Artificial Intelligence)

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