Hello, I'm Sejal ッ Crafting data into insights, innovation, and impact, one algorithm at a time.

Author

I'm a software developer and data science enthusiast who thrives on transforming innovative ideas into tangible solutions. My journey is marked by countless hours dedicated to finding solutions, and my enthusiasm for coding, particularly in Python, machine learning, and engaging in impactful research projects. From deriving insights to crafting AI for social good, I thrive in environments where innovation and purpose intersect. I'm currently on the lookout for new opportunities to contribute, innovate, and grow. Let's connect and explore how we can create impactful stories together.

Analyzing

In a wide range of subject areas, I have analyzed structured and unstructured data to extract actionable business insights. I love to craft stunning and clever visualizations that illustrate surprising results.

Developing

I'm all about taking those smart machine learning models out of their Jupyter Notebook homes and dreaming up ways to make them do real, cool stuff in the real world.

Exploring

Off-screen, you'll catch me binge-watching K-dramas or admiring picturesque skies. Cats are my ultimate mood lifters. Currently watching Lovely Runner. HMU for some good recs :p

Professional Experience

Machine Learning Researcher

May 2023 – Jun 2024

University at Buffalo

Worked on meticulous extracting, cleaning, and validating clinical trial data from various sources such as EHR, Case Report Forms (CRFs), and Clinical Data Management Systems (CDMS) Leveraged AWS Comprehend for natural language processing, which improved term categorization speeds by 50% and accuracy in parent class suggestion by 92% Pioneered an automated definition generation tool that leveraged content from curated PDFs, creating robust definitions for domain-specific terms, enhancing user comprehension

Programmer Analyst

January 2020 - July 2022

Cognizant

Performed exploratory data analysis using Power BI, Jupyter Notebook, and Python (Pandas, NumPy, and Bokeh) to craft 10+ insightful, interactive visualizations, facilitating better decision-making and client understanding Deployed a standalone pipeline through CI/CD to build and integrate test microservices with Docker, AWS, and CDK Managed and optimized database operations for over 8 million records in PostgreSQL and Oracle

Education

Master's Degree

August 2022 - Feb 2024

University at Buffalo

MS in Data Science

Bachelor's Degree

August 2017 - June 2020

Vishwakarma Institute of Information Technology

BE in Computer Engineering

Featured Projects

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AcademiQ AI

Developed an AI-powered teaching assistant that handles real-time academic inquiries, boosting engagement through personalized interactions, utilizing a custom LLM and integrating with Flutter and Django for cross-device compatibility

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Precision Lookalike Modeling in AdTech

An advanced project focused on developing and deploying sophisticated lookalike models using machine learning, clustering, graph-based, and deep learning techniques to enhance audience targeting and expand reach in digital advertising campaigns.

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Satellite Image Dehazing

This project introduces an innovative approach to image dehazing using a deep learning model that leverages convolutional layers, residual connections, and concatenation techniques. Implemented with TensorFlow 2.0, this model demonstrates significant improvements over traditional state-of-the-art methods.

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360° Object Detection & Assistance for Visually Impaired People

The project is centered around developing an AI-driven robotic assistant with 360-degree object detection and voice interaction, offering visually impaired individuals accurate navigation and object detection experience, leveraging 360°cameras and ResNet18 for comprehensive object detection.

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Q-learning Optimization for Grid-based Reinforcement Learning

This project explores the application of Q-learning and SARSA in a controlled grid environment, where an autonomous agent navigates a 4x4 grid to maximize rewards by collecting batteries and avoiding obstacles. Used Python, Gynamsium, Google Colab and Reinforcement Learning Concepts

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AlexNet from Scratch on SVHN Dataset

This project develops an AlexNet model from scratch to classify images using Pytorch, Kaggle Notebook and Python. It explores various optimization techniques and data augmentation methods to enhance model accuracy and generalization, achieving significant improvements in image categorization

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Time-To-Event Analysis in Clinical Trials

This R project conducts a detailed survival analysis of ovarian carcinoma patients, focusing on evaluating the efficacy of two different treatment protocols. Utilizing the Kaplan-Meier estimator and log-rank tests, it compares survival outcomes among patients