Major: Economics and Computer Science
I’m Daniel Kim, a senior at Rensselaer Polytechnic Institute, dual majoring in Computer Science and Economics. My academic journey has centered on leveraging AI to transform financial data analysis. As a Research Assistant at RPI, I fine-tuned FinLoRA models to classify SEC XBRL filings with over 85% accuracy, applying QLoRA methods to optimize memory and training efficiency. I architected a retrieval-augmented FinGPT Search Agent using FAISS and Django that improved financial document retrieval accuracy, enabling real-time ingestion of 10-K, 8-K, and 10-Q filings. By automating XBRL data pipelines with Arelle and dynamic logging mechanisms, I streamlined structured data extraction for large-scale machine-learning computations. Professionally, I aim to advance responsible AI solutions for regulatory compliance and financial decision-making. Beyond research, I enjoy contributing to open-source FinTech projects and mentoring peers in machine-learning best practices.
Minor/Pathway: Capstone in Artificial Intelligence
Favorite Class: My favorite class so far has been Operating Systems. In that course, I learned more about how CPUs manage processes and resources—implementing schedulers (FCFS, SJF, Round-Robin, etc.) in C++ gave me a concrete appreciation for concurrency, context switching, and memory management. Wrestling with threads, synchronization primitives, and race conditions not only strengthened my programming skills but also taught me to think carefully about performance trade-offs and reliability—lessons I carry into every software project.
Other thoughts related to Academics: Beyond coursework, it is important to build strong connections with faculty and peers alike. I regularly attend professors’ office hours—not only to deepen my understanding of class topics, but also to explore new research directions and become like friends.
Goals for after Undergrad is complete: I plan to do a co-term in Business Analytics, and I am working on my own business while also looking for a full time job.
Internships/Work Experiences: Research Assistant Summer 2024 - Columbia University/ Rensselaer Polytechnic Institute | AI Learning and Innovation Intern 2025 - T-Mobile (Bellvue, WA)
Clubs/Organizations: KSA- Korean Student Association (member), Badminton (member), Fencing (member)
Undergraduate Research Program: FinGPT Search Agent: Developed a retrieval-augmented pipeline that ingests and indexes SEC filings (10-K, 8-K, 10-Q) using Arelle for XBRL parsing and FAISS for vector similarity search. This agent enables real-time querying of structured financial data directly from regulatory documents.
FinLoRA-XBRL: Fine-tuned Llama3-8B (FinLoRA) on XBRL-tagged SEC filings, applying QLoRA techniques to reduce GPU memory requirements. Achieved 85.5% classification accuracy across core filing types and optimized training workflows for large-scale regulatory datasets.
I designed and executed experiments comparing memory-efficient fine-tuning against full-precision baselines, built dynamic data pipelines, and conducted iterative benchmarking to refine model performance. Presentations at conferences honed my ability to translate technical findings into actionable recommendations.
Faculty Supervisor: Dr. XiaoYang Liu (“yanglet”), Department of Computer Science, Rensselaer Polytechnic Institute.
Arch Away Semester: Undergraduate Research Assistant - Worked on FinGPT (full-stack)
Advice for students preparing for their Arch experience: Take it as a time to think of an idea!
Seniors, advice for undergrad students: Focus and be consistent.
What you enjoy the most outside of school: I enjoy exercising and sports!
Advice for incoming or prospective students: Always network with your professors.