Hello... I'mAditya Thewarkar
Hello
... I'mComputer Science & Industrial Engineering Student
Building innovative solutions at the intersection of software engineering and business systems. Currently studying at the University of Illinois Urbana-Champaign.
About Me
Hi, I'm Aditya Thewarkar, I am currently a student at the University of Illinois Urbana-Champaign - Grainger College of Engineering. I am very passionate about implementing technology to create solutions to the many issues that we, humans, face on a daily basis. Using my foundational knowledge in programming its different subsets, I hope to be able to create more of an impact on my community and those within it.
Education
University of Illinois Urbana-Champaign (UIUC)
Grainger College of Engineering
B.S. Industrial Engineering (Minor in Computer Science + Statistics)
Expected Graduation: May 2026 | GPA: 3.90/4.0
Relevant Coursework:
Data Structures & Algorithms
Discrete Structures
Linear Algebra
Statistics & Probability
Data Analytics
Business in Engineering
Introduction to Electronics
Introduction to Computing
Multivariable Calculus
Introduction to Data Science
Skills & Interests
Java
Python
JavaScript
TypeScript
HTML/CSS
SQL
C++
C
Experience
My professional journey in software engineering and development.
Software Engineering Intern
Diaspark
May 2024 – August 2024
- •Boosted product sales by 15% by implementing and modifying Luxare's ERP to optimize style and inventory management
- •Managed and led the creation of Luxare's image processing system with the use of Pillow, edge detection, and Fourier transform, making an optimized and automated backdrop for a multitude of products, allowing the seamless implementation of new products
- •Integrated MES and ERP systems to enhance end-to-end production visibility, enabling real-time tracking, streamlined workflows, and data-driven decision-making in order to achieve the simplification of numerous processes for Luxare's wide variety of clients
Software Engineer
Outlier AI
September 2024 – Present
- •Developed 150+ code prompts to be utilized with Python and JavaScript in order to model and train LLMs for multiple companies
- •Reviewed numerous amounts of AI-created prompts and instructions to enhance usability in a variety of implementations
- •Coordinated with numerous other developers to consult companies on the most effective prompts to use configure for their business
Software Engineer
Project Code
February 2024 - Present
- •Spearheaded efforts to develop methods for scoring the similarity between any Wikipedia articles using sentiment analysis and text-based machine learning algorithms, demonstrating proficiency and ability of implementation in Python and JavaScript (React)
- •Collaborated with front-end developers to create a user-friendly product visualizing our model of grouping together Wikipedia articles of similar sentiments with the ability to simplify students' daily researching and web-searching
- •Engaged in cross-functional teamwork within Project Code, collaborating with individuals from diverse backgrounds and skill sets to leverage collective expertise and drive project success in order to create a functional product for all persons of academia
Projects
A selection of my technical projects showcasing my skills and interests.
MoodMusic
- •Created a responsive music recommendation engine using Next.js, React, and Tailwind CSS, featuring a custom-built UI with 10+ reusable components and a black/red theme optimized for user engagement and accessibility.
- •Implemented OpenAI API integration (GPT-3.5 Turbo) that analyzes natural language mood descriptions with 95% accuracy, enabling users to discover music through conversational AI that extracts mood parameters and genre preferences from free-text input.
- •Developed a scalable music database system managing 80+ tracks across 15 genres with dual-parameter mood classification (energy/happiness), complete with dynamic album cover generation and intelligent artist-based search capabilities that improved recommendation relevance by 40%.
Next.js
React
Tailwind CSS
OpenAI API
TypeScript
Node.js
News Sentiment Trading Strategy Backtester
- •Engineered a responsive financial analytics platform that processes 15,000+ news articles daily with 72% sentiment classification accuracy, implementing a TypeScript/Next.js architecture with custom hooks that reduces rendering time by 22% on mobile devices while maintaining 28ms refresh rates across all interfaces.
- •Developed a high-performance backtesting engine that simulates 1,260 trading days in 0.15 seconds, processing 6,300+ price-sentiment data points while incorporating an event-driven notification system that provides real-time feedback on strategy performance and parameter optimization effectiveness.
- •Created an intuitive dashboard featuring dynamically rendered visualizations that demonstrate a 38% improvement in Sharpe ratio and 29% reduction in maximum drawdown compared to traditional technical strategies, enabling users to optimize trading parameters through an adaptive UI that automatically adjusts to desktop and mobile viewports
TypeScript
React
Next.js
Recharts
NLP
Get In Touch
Have a question or want to work together? Feel free to reach out!