Prasath Murugesan
AI/ML Consultant & Passionate Engineer
Based out of New York City, from India.
Hello👋, I'm Prasath Murugesan. I was born and grew up in India. I have two master's degrees, one in Data Science (integrated master's) from PSG College of Technology, Coimbatore and another in Data Analytics (Big Data Systems) from Pennsylvania State University.
I am a consultant for product teams that seek to build and integrate AI solutions into their roadmap and enable stakeholders - helping to move from prototype to production.
I have gathered experience in solving problems from various domains ranging from FinTech, AdTech, Job market, Media & Social Content, Software Development, Elections and looking to expand further.
My areas of interest - Applied ML, Data Engineering & Analytics, Deep Learning, Natural Language Processing, Computer Vision, LLMs, Generative AI, Graph models & Knowledge graphs, Learning with limited data.
Apart from tech and consulting, my hobbies span Tamil Sangam literature, Ethics in Tech, Open-Source community, Math, Economics, Politics, Cinema and Cricket. I am a big fan of the works of AR Rahman and Mari Selvaraj.
I am currently teaching myself to design cloud solutions and full-stack web development. Trying to be a generalist in a specialized world 😄
உண்பது நாழி, உடுப்பது இரண்டே
பிறவும் எல்லாம் ஓரோக் கும்மே,
செல்வத்துப் பயனே ஈதல்
துய்ப்பேம் எனினே தப்புந பலவே
-நக்கீரனார் (புறநானூறு)
Experience
Jun 2024 - July 2024
Product Intern, MiQ Digital USA Inc.
Worked with AdTech product owners to define Gaming GTM strategy, Automated Programmatic + Social DSP data integration pipelines to support data-backed pitching and continuous channel evaluation. Performed analytics on salesforce reports and presented the insights to the commerce sales strategy team with an interactive and actionable customer segmentation dashboard.
Mar 2024 - Sept 2024
AI/ML Consultant, ANROS Software
Aided the elderly/disability care product design with AI first principle, built prototypes and enable software teams to scale prototypes. Designed workflow for ranking and matching care recipients and caretakers, and Personalized activity suggestions for different care recipients based on their personal needs like Alzheimer’s, Dementia etc. using RAG based pipelines.
Sept 2023 - Apr 2024
Research Assistant, The Pennsylvania State University
Developed a RAG based teaching assistant chatbot in assisting faculties in providing timely and relevant information exp the courses.
Dec 2022 - Jun 2023
Machine Learning Engineer Intern, Growfin.ai
Designed and deployed an ML system for capturing remittance information that helps to expedite the bulk invoice to cash flow process and increase the productivity of collection agents by 60%. Extracted and integrated table contents using computer vision models, transfer learning and fine-tuning QA language models, visual question answering transformer model (DONUT) and prompt engineering for QA models.
May 2021 - Nov 2021
AI Research Intern, AI Institute at University of South Carolina
Conducted research to generalize language agent actions in TextWorld Commonsense upgrading the existing AI’s abilities to be robust to new environments and resources by 7% using Natural Language Processing and Understanding Framework.
Education
2023-2024
The Pennsylvania State University, PA, USA
Master's in Data Analytics, Big Data Systems
I call this chapter பெரிய அடி (Big Step/Big Blow) - polysemous.
Recipient of Chancellor's Scholarship & Warren V. Musser Scholarship.
2018-2023
PSG College of Technology, Coimbatore, India.
Master's in Data Science (M.Sc Integrated)
During this time, I was fortunate to meet mentors who shaped my perspectives on what a fruitful career is.
I am grateful to Dr. R. Nadarajan and Shamshu Dharwez anna for guiding me since then.
2012-2018
Jawahar Higher Secondary School (CBSE), Neyveli, India.
Higher Secondary Education.
I am grateful for the friendships that still continue to this day.
To my parents, Sridhar, Sudarsan & Aswin.
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.