APPLIED AI | MACHINE LEARNING | COMPUTATIONAL MATHEMATICS | ILLINOIS TECH CHICAGO

Hi, I'm Laasya Priya.

I am a graduate student at Illinois Institute of Technology working across machine learning engineering, applied research, forecasting systems, and practical AI deployment. My work spans large language models, explainable forecasting, evaluation frameworks, and tools designed to make advanced systems more interpretable and dependable.

I am especially interested in the space where technical depth meets real-world usefulness. That means building systems that not only perform well, but also communicate clearly, behave robustly, and support better decisions for the people using them.

Currently seeking full-time roles in AI Engineering or Machine Learning and open to research collaborations.

Interactive Tools

Current Work

AI Infrastructure Impact Dashboard

Interactive geospatial dashboard analyzing the environmental and community impact of AI data centers. Combines environmental indicators, infrastructure datasets, and regional context to explore how large-scale computing infrastructure affects surrounding communities.

ONGOING RESEARCH

Milestones & Experiences

January 2026
- Joined as a Teaching Assistant for CS 331: Data Structures & Algorithms at Illinois Institute of Technology, assisting students with algorithm design, data structures, and core problem-solving concepts.
- Awarded the SoReMo Research Fellowship at Illinois Institute of Technology to conduct research on the environmental, economic, and community-level implications of large-scale AI infrastructure and data center development.
Fall 2025
- Contributed to the QMCPy open-source library, extending the True Measure class to support dependent distributions through improvements to the SciPy wrapper integration.
- Served as a Teaching Assistant for CSP 571: Data Preparation & Analysis at Illinois Institute of Technology, supporting coursework focused on data processing, statistical analysis, and practical data science workflows.
Summer 2025
- Joined Springer Capital as an LLM Analyst Intern, working on evaluation pipelines and experimentation frameworks to analyze and improve the reliability and behavior of large language models in applied environments.

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