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Hello! I’m Suvodeep Majumder


As an accomplished Computer Science  Ph.D. student from North Carolina State University and extensive research experience, I aim to leverage my knowledge and skills in a challenging role in the field of Artificial Intelligence and Machine Learning. My research work includes developing and implementing large-scale machine learning models and algorithms for complex tasks like transfer learning, AI fairness, and large-scale graph mining, among others. My contribution to these domains has been published in top-tier journals and conferences, earning accolades like the ACM SIGSOFT Distinguished Paper Award.

With my recent experience as an Applied Scientist II Intern at Amazon AWS, I have developed novel solutions for problems related to neural machine translation (NMT). I have researched the identification of relevant context for NMT and studied the effect of model architecture on contextual NMT. Additionally, I have developed a system for knowledge distillation for contextual translation and improved the pivot NMT performance by supplying linguistic information as source and target context.

As an enthusiastic learner, I strive to keep myself updated with the latest trends and techniques in the field of machine learning. My goal is to utilize my knowledge and expertise to work with an innovative organization that values creativity, ingenuity, and continuous learning. I aspire to collaborate with a team of researchers and engineers to solve complex problems and create cutting-edge solutions that can make a positive impact on society. My ultimate objective is to become a leading researcher and practitioner in the field of AI and machine learning, continuously pushing the boundaries of what's possible with technology.

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What I am working on

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Context Aware Neural Machine Translation

In this project, we try to investigate how different neural architecture affects contextual neural machine translations and develop new ways to improve the performance of these models.


Socio-Technical graph mining

Working on a system to create code interaction graph and social interaction graph to understand different aspects of Open Source Projects

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Automated Learning

This project is to create an automated tool, that incorporates different types of machine learning models, feature selectors with hyper-parameter optimizer to create models for text dataset and return best possible outcome by evaluating the dataset.

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Active Learning

This research was to create an active learning model, to learn from very small sample of data to begin with, in a domain where it is very expensive to collect the data labels. Then to understand the attributes which are essential for the classification and as new data is collected, the model only asks for labels which it think is interesting or non interesting depending on model setup.


Transfer Learning

Developing a new hierarchical transfer learning method using hierarchical clustering and bellwether method to identify exemplary projects in a community to get generalized actionable conclusions to mitigate conclusion instability.


AI Fairness

Working on identifying and mitigating group bias introduced by machine learning models on protected attributes using multi-goal optimization techniques.

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My Experience

Home: Experience

May, 2022 - August, 2022

Applied Scientist, Amazon AWS

Researched on the identification of relevant context and developed a system to identify context during machine translation.


May,2021 - August, 2021

Applied Scientist, Amazon AWS

Researched on the effect of model size on contextual neural machine translation and developed a system for contextual knowledge transfer between models using knowledge distillation.

May, 2020 - August, 2020

Applied Scientist, Amazon AWS

Developed a process for improving Neural Machine Translation (NMT) performance by disambiguation of word senses and a way of measuring the error propagation in pivot NMT translations.

June 2018 - August 2018

Data Scientist Intern, IBM

Created ML-based insights for dev teams by analyzing GitHub projects as part of the DevOps insight team using. Modeled large-scale data processing system using spark cluster and python to analyze terabytes of software project raw data and create a meaningful usable form to be used in machine learning algorithms.

March  2013 - June 2017

Test Analyst, Infosys Limited

Created Test Scenarios and Automation Scripts for Automation, Manual, Performance testing scenarios for web-based portal, Mobile Application and IVR systems.



August 2019 - Present

North Carolina State university

I am completeing my PhD in Computer Science with research fouce in applicatio of AI in Software Engineering.

August 2017 - May 2019

North Carolina State University

I completed my master degree from North Carolina State University with a specialization in Software Engineering and Machine Learning.

August 2008 - May 2012

West Bengal University of Technology

I Completed my under graduation from WBUT in 2012 with a Major in Computer Science & Engineering.

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"If you want something you've never had, you must be willing to do something you've never done"

Thomas Jefferson

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Let’s Connect

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