Hello!

I'm Sukhad Joshi



Data Science | AI

Name: Sukhad Joshi

Location: Syracuse, NY

Skills

SQL 80%
PYTHON, R 85%
Data Visualization 90%
Statistical Analysis 80%
Machine Learning & Deep Learning 85%

About Me

Hello, I'm Sukhad. As a dedicated student with a Master of Science in Applied Data Science from Syracuse University and a Bachelor of Engineering in Electronics Engineering from Mumbai University, I bring a robust academic foundation. My interest lies in the field of Data Science and Artificial Intelligence which is reflected in my projects. I am eager to bring my skills to new challenges and committed to making a meaningful impact.

  • Domain: Data Science and Analytics, AI and Machine Learning
  • Education: Bachelor of Engineering | Master of Science
  • Languages: English, Hindi, Marathi
  • Skills/Tools: SQL(Azure Data Studio, SQLPad, Adminer), Oracle, Cloud, AWS, PySpark, Tableau, Google Analytics, Git, MS Office, R Studio, Visual Studio Code, Keras, Power BI, SAS, JIRA, Hadoop, NLTK, TensorFlow, REST API, MS Excel (Regression, Pivot Tables, Vlookup), Pandas, MATLAB
  • Interests: Traveling, Sports

Resume

This section showcases my experience and education, highlighting my journey through academic achievements and professional milestones.

Experience


Jan 2025 - Current

Research Assistant

Syracuse University
  • Automated LDOS simulations for photonic crystals by developing parameterized Python pipelines, reducing computation setup time by 30%, and enabling precise defect property analysis.
  • Integrated gradient-based optimization tools (Ceviche and NLOPT) to enhance electromagnetic structure designs.
  • Visualized complex photonic behavior using Python libraries, creating insights from over 500 simulations weekly and enabling faster convergence on optimized designs.
Nov 2024 - Current

Data Analyst

iConsult
  • Increased music search accuracy by developing an AI-powered recommendation system that analyzes user preferences and genre trends, resulting in 2x more relevant artist and song recommendations.
  • Processed and structured insights for recommendations by building an automated data pipeline that scales analysis across more than 1000 songs, improving genre classification accuracy.
  • Reduced query execution lag from 5 seconds to under 2 seconds by optimizing structured data analytics.
Jan 2024 - Dec 2024

Teaching Assistant

Syracuse University
  • Communicated with over 50 students in analyzing and visualizing data using R, assisting in projects focused on data manipulation, cleaning, and advanced visualization techniques.
  • Led one-on-one sessions on data management techniques, contributing to a 25% improvement in student assignment accuracy and data analysis proficiency.
  • Guided students through R-based visualization techniques, including time series and geographic data plotting, to build skills in data.
Jun 2024 - Aug 2024

AI Researcher

NY, USA
  • Explored the integration of Large Language Models (LLMs) with RAG frameworks, analyzing their performance with and without prompts to assess improvements in knowledge retrieval accuracy.
  • Conducted data analysis for graph-based retrieval projects, including data cleaning, structuring, and feature selection to improve retrieval accuracy.
Jun 2021 - Aug 2021

SDG Summer Intern

Ramrao Adik Institute of Technology
  • Led a team of 6 in developing a Social Meeting Planner, aimed at secure offline gatherings.
  • Revamped the UI by improving usability and user experience by 40% as measured by feedback scores.
  • Collaborated with the team members to meet project objectives, completing 10 additional tasks within the timeline.
  • Facilitated client interactions and system integration, leading to a 25% reduction in implementation time and addressing 90% of user feedback in later updates.


Education


Aug 2023 - May 2025 (Current)

Master of Science in Applied Data Science

Syracuse University

GPA: 3.7/4.0

Aug 2019 - May 2023

Bachelor of Engineering in Electronics

Mumbai University

GPA: 3.8/4.0

Download Resume

Projects

Here, you will find the details of my projects that have shaped my skills and expertise in Data Science and Artificial Intelligence.

Credibility Detection of Health Web Blogs using Explainable AI

This is a project that employs advanced machine learning and explainable AI techniques to assess the credibility of health-related blogs, ensuring accurate and trustworthy health information for users.

Sentiment Classification of Movie Reviews

This project focuses on sentiment classification of movie reviews using machine learning techniques. It explores various feature engineering approaches, data visualization techniques, and evaluates the performance of multiple classifiers on a movie review dataset.

Predictive Energy Modeling

Analyzed datasets on house info, weather, and energy consumption to predict daily usage. Developed a precise linear regression model, identifying key factors like house size and climate zones. Created a user-friendly Shiny App for interactive exploration of energy predictors.


Parkinson's Disease Detection

Aimed to identify Parkinson's disease using SVM for classification. Analyzed vocal frequency data and other parameters from Kaggle datasets. Implemented the project in Google Colab, performing thorough data preprocessing and feature extraction.

Cryptocurrency Price Prediction

Implemented the Facebook Prophet model to predict upcoming cryptocurrency values. Gathered price data and relevant information from Yahoo Finance as input. Leveraged Prophet’s ability to capture patterns, trends, and seasonality, resulting in accurate predictions of future cryptocurrency price movements.

Reddit Bot Using Python

This Reddit Bot using Python replies to particular comment which is sorted by a keyword given by the user using praw library. It also shows the number of likes and dislikes of any particular posts and is capable of subscribing and upvoting any subreddit.

Impact of weather conditions on motor vehicle collisions in NYC

This project aims to explore how diverse weather conditions affect the frequency and severity of motor vehicle collisions in New York City.

Portfolio Optimization Using Bidirectional CNN LSTM with Attention Mechanism

This project utilized a CNN-LSTM bi-directional model with an attention mechanism to predict stock prices by analyzing historical data. The combination of convolutional layers and LSTM layers with attention allowed the model to identify patterns in the data and learn trends over time.

Contact Me

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