University of Maryland, College Park
Master of Science in Applied Machine Learning
2025 - Present
Master of Science in Applied Machine Learning
2025 - Present
Post Graduate Program in Artificial Intelligence and Machine Learning
2024 - 2025
GPA: 4.33/4.33
Bachelor of Technology in Metallurgical and Materials Engineering
2020 - 2024
CPI: 7.85/10
Grade 12: Higher Secondary Certificate (HSC)
2019 - 2020
Percentage: 88.77%
Grade 10 - Central Board of Secondary Education
2017 - 2018
Percentage: 93%
I am Sahil Chaudhari, an AI and Machine Learning enthusiast driven by curiosity and a love for problem-solving. I see myself as someone who thrives at the intersection of data, algorithms, and real-world impact. My journey in AI has been shaped through self-learning, hands-on projects, and industry exposure, where I have explored everything from predictive modeling and deep learning to large language models and deployment pipelines.
This Fall, I will be starting my Master's in Applied Machine Learning at the University of Maryland, College Park — a step that not only reflects my passion for learning but also my goal of building AI solutions that are practical and human-centered. I value clarity, structured thinking, and continuous growth, and I am always looking for ways to connect ideas across disciplines and turn them into meaningful outcomes.
Outside of my academic and professional pursuits, I am an avid guitarist who enjoys watching anime and reading manga. I believe in the power of continuous learning and strive to connect with others who share a passion for technology and creativity. I am committed to making a meaningful impact through both my work and personal interests.
This internship strengthened my ability to translate complex datasets into actionable insights by combining geospatial analysis, automation, and machine learning. Along the way, I built a stronger problem-solving mindset, refined my data storytelling skills, and gained the confidence to apply AI-driven approaches to real-world challenges. These experiences spanned diverse domains, including stock market analysis, sports analytics, social media, and environmental studies.
This internship gave me hands-on exposure to improving business operations through data-driven decision-making. I learned how to leverage dashboards and analytics to optimize inventory management, gaining insights into balancing cost efficiency with waste reduction. By working with customer satisfaction data such as Net Promoter Scores, I strengthened my ability to identify service gaps and translate feedback into actionable strategies for retention. I also deepened my understanding of financial health by monitoring accounts receivable trends, which enhanced my problem-solving skills in managing cash flow challenges. This experience sharpened my analytical thinking, business acumen, and ability to connect operational data with strategic outcomes.
During this internship, I learned how to analyze customer behavior more deeply, from tracking retention patterns to understanding what drives repeat purchases. I strengthened my ability to work with financial metrics like CLTV and transaction value, which taught me how to identify high-value customer segments. Exploring regional and product performance gave me perspective on how data can guide market-specific strategies, while working on customer segmentation and discount analysis helped me appreciate the nuances of consumer decision-making. Overall, the experience sharpened my analytical mindset and gave me confidence in connecting data insights to real business growth.
A selection of my range of projects
This project analyzes octants from the input data. It processes a single file input and visualizes the analysis through a web app.
Octant Batch Processing - Folder Input
An advanced version of Octant Analysis that processes multiple files by uploading a folder location.
(Team Project) Time series forecasting using machine learning and financial indicators like VWAP, EMA, and SMA. The ARIMA model was used for predicting future Bitcoin prices.
Slides View GitHub RepoA detailed breakdown of my Power BI project, which includes the analysis of cricket scorecards, IPL team performance, and bowler profiles. This project features data extracted through web scraping to gather real-time scorecard information, enabling comprehensive visualizations and insights into match performance and statistics.
View GitHub RepoA Python-based project developed as part of a course at IIT Patna, designed to automate the generation of student transcripts. The program processes multiple CSV files containing student information, course details, and grades, and then generates individualized transcripts in PDF format. This project efficiently handles batch processing of large student datasets and ensures accurate document generation.
View GitHub Repo