Rohit Nagotkar's Portfolio

About Me

A passionate Full Stack Developer and Data Science Enthusiast pursuing a Master's in Computer Science with a specialization in Data Science at Seattle University. With expertise in Machine Learning, Data Science, and Web Development, I thrive on using technology to solve real-world problems. My goal is to create impactful solutions that drive innovation and positive change.

To know more:

Seattle University

Seattle, WA

M.S. in Computer Science, Specialization in Data Science
GPA: 3.9/4.00
09/2023 — Present (Expected 06/2025)

Pune Institute of Computer Technology

Pune, India

B.E. in Electronics and Telecommunication
CGPA: 9.40/10
07/2017 — 06/2021

Web Developer Assistant

Seattle University, 06/2024 - Present

Building, testing, and maintaining the Seattle University Website and other similar websites related to the university to ensure seamless functionality and user experience.
Troubleshooting technical issues related to university websites and web applications, ensuring timely and effective resolution.

Graduate Teaching Assistant

Seattle University, 01/2024 - 03/2024

Supported students enrolled in the CPSC 5071: Data Management for Data Science course, offering explanations of complex concepts, and assisting with problem-solving to enhance comprehension.
Assisted in the grading process for assignments, projects, and exams, ensuring fair and constructive feedback to aid students’ learning and improvement.
Facilitated open communication channels through Teams, creating an accessible environment for students to seek guidance, ask questions, and receive timely assistance.

SUM Corps Maths Tutor

Seattle University, 09/2023 - 03/2024

Provided one-on-one and group tutoring for high school mathematics for students at Seattle World School, improving student performance.
Explained complex topics such as calculus, algebra, and geometry in a clear and approachable manner, enhancing student confidence and problem-solving abilities.

Lead Software Engineer, Fullstack

FinSoftAi Solutions Private Limited, 07/2021 - 08/2023

Spearheaded SST-Research, an adaptable component-based tool made using ReactJS, NodeJs and ElasticSearch which combines sentiment, search and technical analysis to provide diverse visualization and real-time insights for institutional investors while also aiding demos for potential clients.
Automated data processing for visualizations using REST-based serverless APIs developed in Python and AWS Lambdas.
Revamped the mobile-friendly alternative product for consumers to get regular and timely market sentiment alerts.

Software Engineer

FinSoftAi Solutions Private Limited, 03/2020 - 06/2021

Engineered the Querybuilder, a flexible querying tool to develop complex logically grouped queries and to provide precise results from the ElasticSearch database.
Offered a concise overview of sentiment and price trends for specific stocks through the initial prototype SST-Beat.

Junior Machine Learning Engineer

Omdena, 03/2020 - 05/2020

Collaborated with 34 AI experts, data scientists, and the World Food Program globally to predict food and non-food items in cyclone-struck areas.
Extracted insights from a proprietary dataset based on public data using seaborn and matplotlib by performing data cleaning and EDA to gain important features to use for modelling.

2025

Streamlining Domestic Violence Protection Order (DVPO) Drafting with LLM Powered Automation

Jan 2025 - Mar 2025

Collaborated with Family Law Center to develop an LLM-powered writing assistant aimed at supporting survivors of domestic violence in drafting emotionally sensitive and legally sound protection order petitions. The tool generates personalized feedback questions to help survivors provide clearer, more detailed narratives, significantly reducing the time law students spend on follow-up interviews.

2024

DocuMentor: Tailored Learning from PDFs

Nov 2024 - Jan 2025

Intelligent learning tool designed to help students better understand and retain concepts from PDF documents. Built with Generative AI (GenAI), React, and Flask, the tool enhances learning by generating explanations, FAQs, and up to 50 unique, customized questions for every uploaded document.

Netflix Data Analysis

May 2024 - June 2024

Created a dashboard which gives geographical and annual distribution of TV shows and movies. This dashboard is an effective way to play around and extract insights which will help in global marketing

Mamoo

January 2024 - March 2024

Assisted to develop Mamoo, an event organizer app that helps users plan events smoothly by connecting them with vendors and offering tools for easier management. It simplifies event planning for both users and vendors alike.

Running SugerScape on TaskVine

January 2024 - March 2024

The project aims to improve Sugarscape simulation efficiency using TaskVine on HTCondor, allowing concurrent execution of multiple simulations to study societal dynamics. Scaling on distributed platforms like OSG seeks to accelerate analysis, optimize resource utilization, and enhance data management.

Spotify Hits Analysis

January 2024 - March 2024

Collaborated with a team to create a strong process for cleaning and analyzing Spotify song data. Utilized statistical methods and machine learning to predict song popularity with 80% accuracy, showing the importance of selecting the right features and fine-tuning models.

2023

Heart Attack Classification

September 2023 - December 2023

Conducted exploratory data analysis to identify significant health and demographic factors, which improved the audience's understanding. Developed predictive models like Logistic Regression, K-Nearest Neighbors, and Support Vector Classifier, achieving 94% precision and an recall of 0.93 for heart disease risk prediction using KNN.

SSi - FAANG

March 2023 - August 2023

Spearheaded SSi-FAANG, a tool to get daily social sentiment details for FAANG Companies to get insights and make trading decisions. Developed the website and performed regular updates and bug fixes.

2021

Quantum RL for Zero-Sum Games

August 2020 - May 2021

Developed a Quantum Deep Reinforcement Learning model by integrating Quantum feed-forward layers into a Deep Reinforcement Learning setup. Trained using Deep Q-learning, it demonstrated superior performance compared to classical Deep Q-Network models in zero-sum games such as Tic-Tac-Toe.

2020

Understanding the impact of Nature-based solutions on climate change

October 2020 - November 2020

Engaged in extracting actionable insights and building recommendation systems and Q&A platforms for NbS initiatives like AFR100, Cities4Forests, and Initiative20x20. Developed data scraping pipelines, including PDF scraping, and implemented a Q&A system utilizing REST APIs for accessing and analyzing information efficiently.

Customer Segmentation Analysis on Retail data

May 2020 - June 2020

Using elbow diagrams, we identified five distinct customer groups, finding the optimal number for segmentation. Through data analysis and the implementation of the K-means clustering algorithm, we provided actionable conclusions to improve customer focus on particular groups.

Predicting the Logistics required Post Cyclone Disasters

March 2020 - May 2020

Created a model that would help the World Food Program provide a count of food and non-food items to disaster-stricken areas specifically for cyclones. Created an in-house dataset cumulated from a combination of multiple datasets and used various regression models and an artificial neural network model to predict the people affected and created a report using mathematical models.

2019

Real Time Emotion Recognition

July 2019 - September 2019

Implemented a custom Convolutional Neural Net model which is trained on the fer2013 challenge dataset, incorporating data augmentation techniques for enhanced model robustness and performance. Developed a model capable of analyzing real-time webcam feeds to accurately identify users' emotions, with the ability to detect up to seven different emotional states.

Power Backup System

540602, 08/2020

Designed a power source that can be worn on the wrist to charge mobiles or other wearables.
It uses battery modules for lightweight and longer charging time. Currently supports normal charging speeds but future work includes support to charge upto 40W.