Current Projects

Spectral Anomaly Detection in EELS SI via 3D Convolutional Variational Autoencoders

Was awarded High Distinction (3rd place overall, 1st among science posters) at UIC Honors College Fall 2024 Research Symposium!

Arxiv / Poster

Advisors: Prof. Robert F. Klie, Prof. James P. Buban

  • Developed a novel 3D Convolutional Variational Autoencoder for unsupervised anomaly detection in high-dimensional spectral imaging data, implementing a custom cross-entropy loss based function for EELS data. Shortened the time required to perform analysis on large scale samples from weeks to seconds.

LASSI: A Self-Correcting Pipeline for Code Generation

GitHub

Advisors: Prof. Mike Papka, Prof. Lan Zhiling

  • Enhancing LASSI, a self-correcting code generation pipeline, by refactoring the system to incorporate handling of power measurements and runtime data collection. Developing cross-platform compatibility between Nvidia and Intel architectures while transforming the system from Jupyter notebook to a production-ready standalone application. Supporting EVL community by providing technical expertise for Intel GPU Cluster implementations and experiments.

Knowledge Graph Generation for Scientific Literature

Advisors: Prof. Mark Grechanik

  • Developing an automated knowledge graph system for scientific literature using Large Language Models, enabling researchers to efficiently discover and understand paper relationships beyond traditional citation-based or simple triplet based approaches. Implementing LLM-driven graph generation to provide more contextual paper recommendations and relation explanations, improving upon existing tools like Connected Papers and Inciteful.xyz.

Past Projects

Automated P&ID Diagram analysis (2024)

Company: Ashling Partners

  • Developed an online learning Deep Learning pipeline to automatically analyze P&ID diagrams to extract dimensions, elements, and relationships between elements from scratch.
  • Optimized the system to process diagrams in under 12 seconds using Azure On-Demand Compute, dynamically scaling the system with Kubernetes
  • Impact: 87% accuracy at sub-dollar cost per 1000 diagrams, shortening weeks long diagram vetting process to 12 seconds

NSF REU: Nanoscale Robo-Spider fabrication and locomotion (2023)

Advisors: Prof. Igor Paprotny

  • Designed and fabricated nanoscale robotic spiders using two-photon polymerization (2PP) lithography, utilizing Solidworks for design and NanoScribe PPGT2 for fabrication. Investigated potential locomotion mechanisms using pneumatics and laser actuation, though achieving controlled movement remained challenging.

Early Research Scholars Program (ERSP): Automating Comment Generation for Personalized Augmenting Tool for Homework in Science Education (PATHWiSE) platform using GPT-3 (2022-2023)

Abstract / Poster / Final Report / Project Website

Advisors: Prof. Joseph Michaelis

  • Integrated GPT-3 into the PATHWiSE platform to automate comment generation for student assignments. Evaluated GPT-3’s effectiveness in creating customized homework experiences and implemented the OpenAI API into the existing platform. First research experience through the Early Research Scholars Program (ERSP).