Data Science- Research Assistant

SAGAR SRINIVAS PRABHU

812-***-**** ***************@*****.*** linkedin.com/in/sagarprabhu98/ New York. NY

(Open to work immediately)

EDUCATION

Indiana University (Luddy School of Informatics, Computing and Engineering) Aug 2022 – April 2024 Master of Science in Computer Science GPA: 4.00/4.00 Coursework: Elements of Artifical Intelligence, ML, Data Analytics, Cloud Computing, Applied Algorithms, Software Engineering Thadomal Shahani Engineering College, Mumbai University Aug 2016 - Aug 2020 Bachelor of Engineering in Information Technology GPA: 8.69 / 10.00 WORK EXPERIENCE

Research Assistant - Data Mining, Indiana University July 2023 – Present

Extracted article and image journals metadata in a modular coding strategy using Python and Selenium scripts for web scraping.

Optimized code quality using Python - Pyinstrument profiler, eliminating 420 missing values rows leading to normalized model.

Designed interactive visualization platform using HTML/CSS, D3.js based on author collaborations and publications trends.

Web Analytics Intern, Our National Conversation (ONC), Los Angeles, CA June 2023- August 2023

Conceptualized interactive text and video chatrooms which enhanced user engagement and existing site viewership by 58%.

Reduced Python Django’s API response time by 3x using Distributed Caching with enhancing cross-platform compatibility.

Optimized SQL Server DB, crafting structured queries for enhancing backend performance, earning Best Intern Award. Software Engineer, LTIMindtree, Mumbai, India – Client: Cadent TV, New York, NY Aug 2020 - July 2022

Functioned in Python Analytics project in extracting insights and behavioral patterns from Mosaic platform’s 14000 consumer reviews utilizing LLM, Named Entity Recognition, Power BI vsiualization which improved platform viewership by 52%.

Implemented the Unified Login Portal for Aperture Networked Advertising platform with Front-end in React & Windows based and Base-64encoded token-based REST API authentications utilizing Springboot-LaunchDarkly Saas platform.

Utilized Apache Kafka’s event-driven architecture to Kubernetes, enabling live monitoring and notification on API malfunction.

Migrated Java- Springboot-Hibernate Framework to Kubernetes containers using NuGet modification with CI/CD automation.

Tested SQL query performance using Orion Profiler, front-end app using Jest Framework and Swagger for API testing. Machine Learning Engineer Intern, Billeasy, Mumbai, India Jan 2020 – May 2020

Collaborated with cross-functional teams utilizing financial data pre-processing, Python- EDA on ML modeled data with advising sales trends on Power BI tool to stakeholders and leading to a improvized customer feedback process by 8x.

Analyzed ML Grid Search computations and predictive PyTorch and Tensorboard visuals which increased efficiency by 62%. PROJECTS

NLP Analytics Framework – Helios-Web Open Source Platform, Cnets, Indiana University [Ongoing]

Orchestrating modular Python-Flask server to process JSON sentences data and output into efficient Sentiment Analysis scores.

Processing and optimizing the python model through Fine-tuning, LLM - BERT Transformer and Topic Summarization.

Integrating the server with web front-end framework with designing the Visuals component tasked on showcasing sentiments.

Loan Prediction Analysis System (ML)

Predicted client’s loan repayment operating 7 ML Algorithms and Tensorboard visuals on the Docker imaged dataset.

Conceptualized grid search, modeling OHE pipelines with 0.84 cross-validated F-1 score employing 79% MSE precision.

Implemented Boosting Algos, Softmax and ReLu Neural Networks with 0.92 test accuracy for MLP and 0.776 kaggle score. Face Emotion Recognition (Hadoop-Spark)

Employed CNN and Haar Cascade’s 75% accurate model with parameter tuning, preprocessing to detect intricate facial features.

Extracted dlib Face Landmarks and HOG features culminating in a 70% mean average precision on test data.

Operated modeled One Hot Encoding and Hyperparameter tuning while orchestating Hadoop-Spark pipeline to query HDFS. Sentiment Analysis of E-Commerce site product reviews (NLP)

Architected MERN website integrating diverse real-world data, featuring a personalized purchase recommendation component.

Exploited NLTK resources like vader_lexicon,stopwords, averaged_perceptron_tagger over substantial SQL data chunks.

Proposed best purchases analyzing the user’s emotions in product reviews, enhanced with products visual insights feature for admin. SKILLS

Languages and Technologies: Python, C#, Java, JavaScript, AWS, Linux, Windows Machine Learning/ Web Tech: Sklearn,Flask, ASP.NET, VB.NET, PyTorch, NLP, LLM,Tensorflow, ReactJS, NodeJS, HTML5, CSS3 Tools/ Databases: SQL, PostgreSQL, MySQL, MongoDB, Power BI, Google Analytics, Docker, GIT, CI/CD, Profilers Certifications: Microsoft Technology Associate, IBM Data Science Professional, AWS Certified, Python Institute(PCAP/PCEP) ACHIEVEMENTS

Grant Thornton’s GT-IDEA: Leveraging Generative AI technology into sales, marketing business case competition, United States

Awarded First place, proposing ‘3D Wishlist- WiseBuys’ AI app to maximize client’s ROI for Black Friday electronic sales. Deloitte’s TechnoUtsav: TechE intercollege coding competition, India

Secured Third place out of 40 finalists, implementing efficient model on Machine Learning – US Housing case study.

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