Prasath Murugesan
I'm a Researcher and Data Science student.
I'm looking for Data Science roles from December 2022.
About Me
My name is Prasath M. I’m currently pursuing final year of 5-year Integrated M.Sc Data Science offered by the Department of Applied Mathematics and Computational Sciences at PSG College of Technology, Coimbatore. Recently I founded and lead the Computational Data Science Club at PSG College of Technology. I’m currently exploring knowledge graphs and am inclined to build context-driven AI systems which has wide range of applications in the industry.
Languages
Python, R
MySQL, PostgreSQL
C, C++
Java
Libraries proficient
Numpy, Pandas
Scikit, Tensorflow
NLTK, Spacy
NetworkX, Stellargraph
Matplotlib, Seaborn
BeautifulSoup, Streamlit(UI)
Areas of Interest
Machine Learning
Graph Models & knowledge Graphs
NLP
Data Analytics & Mining
Graph Mining
Explainable AI
Education
2018-2023
PSG College of Technology, Coimbatore, India.
Master's in Data Science (M.Sc Integrated).
CGPA till VIII Semester - 8.69 CGPA.
2012-2018
Jawahar Higher Secondary School (CBSE), Neyveli, India.
Computer Science & Mathematics.
Graduated Grade XII with 93.2%.
Graduated Grade X with 10 CGPA.
Research Experience & Internship
August 2020 - September 2022
Collective Classification Models for Data Without Explicit Linkage.
Prasath Murugesan, Shamshu Dharwez
In this paper, we consider the task of text categorization as a graph classification problem without explicit linkage. We demonstrate that even on low volumes of labelled data, the graphical inference models built using the proposed methodology outperform the traditional models and also a few other models of the collective classification family.
May 2021 - November 2021
Research & Development Intern, AI Institute of University of South Carolina.
During the internship, I was managing the team and developed on the problem of “Generalization in TextWorld Commonsense” where we leveraged ConceptNet Knowledge graph and KagNet to improve generalization and explainability in RL agents using the TextWorld Commonsense Sandbox environment.
My Projects
News Recommendation System at Scale
A news recommendation system built using MIcrosoft News Dataset (MIND) containing 750K users and 17.5M click events. It uses Collaborative Filtering, KNN and Graph Projections which helps to recommend similar news articles based on user preferences and enable rank-ordered recommendation queries personalized to each user.
Music Meta Brain
A contextual information retrieval system which leverages knowledge graph built in an unsupervised setting which helps to extract relevant information for music search queries. It is helpful in extracting relevant and similar documents to the queries generated.
Sentiment Detection Web app
It is an end-to-end interactive web app used to predict and visualize probability distribution of emotions using Multinomial Logistic Regression Pipeline.
Balancing the imbalanced
Resolved the imbalance in credit card fraud detection dataset which is highly imbalanced containing 0.01% of fraud transactions. Built a Random Forest model to construct a comparison flow chart for 8 resampling techniques. It helps to reduce the number of False Positives and leads to better precision and recall measures.
Time Series Analysis on beverage drink consumption
Analyzed the trend in beer consumption of Australians during 2000 - 2018 using R. Trailed to find patterns and modeled exponential smoothing and trend methods. The beer sales were increasing annually with a seasonal decrease in mid-quarters every year.