Aaron Kim

whoami

Welcome to my personal website — a space where I share my education, experience, awards, and projects!

I'm passionate about solving real-world problems through algorithms, machine learning, and quantum computing, especially in the context of finance.

Currently pursuing my Master’s in Artificial Intelligence at UPenn, with a B.S. in Computer Science & Applied Statistics from Purdue. I’m also ranked top 0.08% on LeetCode , and love exploring the edge of AI x Finance.

I’m someone who loves traveling, watching orchestra performances, and enjoying a good musical.
Here are some of the amazing places I’ve visited so far: 🇺🇸 🇨🇦 🇦🇺 🇻🇳 🇮🇩 🇸🇬 🇨🇳 🇯🇵 🇭🇰 🇰🇷 🇹🇼 🇹🇭

Out of them all, my favorite city is Chicago — there’s just something magical about its towering skyline, sparkling night views, famous Ferris wheel by the lake, and of course, the incredible food scene (except for the freezing cold!).
If anything here resonates with you, I’d love to connect!

You can reach me at ksh727301@gmail.com or via LinkedIn.

Thanks for stopping by — and enjoy exploring the site!

Me in Chicago!

Me at my graduation ceremony

Me at the aquarium in Korea

Education

University of Pennsylvania (Aug 2025 ~ )

Master of Engineering in Artificial Intelligence (Incoming Student)

Purdue University (Aug 2022 ~ May 2025)

Bachelor of Science in Computer Science
Bachelor of Science in Applied Statistics
Application in Data Science

Experience

AI Intern @ Next Securities Seoul, Korea | June 2025 ~

Contributing to the design of Market Lens, a stock-focused SNS platform, implementing quantitative analysis algorithms to generate customized trading signals based on real-time Nasdaq data, deploying them via FastAPI, and integrating into the platform’s interactive chatbot interface

Leading the development of a QA pipeline using SEAL (Self-Adaptive LLM) to extract insights from corporate disclosures, leveraging Pinecone-based Vector Database to enable explainable, context-aware analysis and support downstream generative AI workflows for content automation

AI & Data Science Intern @ FlavourIQ Sydney, Australia | May ~ Aug 2024

Engineered Graph Neural Networks (GNNs) to link over 70 volatile compounds with 31 sensory traits, accurately identifying key molecular drivers of taste preference

Developed a personalized recommendation system that linked organic compounds in favorite foods with new items, offering tailored suggestions based on chemical profiles

Undegraduate Researcher @ Kihara’s Lab Purdue | Mar ~ Oct 2024

Designed computational methods for Protein Function Prediction (PFP), utilizing deep learning, and achieved top 10% in major scientific assessments like CAPRI and CASP

Undegraduate Teaching Assistant @ Purdue Computer Science Jan 2024 - May 2025

Provided comprehensive support to over 200 students in Data Engineering (CS 176) labs, focusing on practical applications of data engineering and data science

Assisted students in the CS Student Board Help Room for Discrete Mathematics (CS 182) and C Programming (CS 240), with a focus on algorithm design and proofs

Meta Analysis Research Assistant @ Procter & Gamble Remote | Jan ~ May 2024

Developed and implemented a Network Meta-Analysis (NMA) automation application using R and R Shiny, enabling the prediction of prototype effectiveness across various product categories

Automated the comparison and analysis of over 1000 product studies, significantly streamlining product testing workflows and enhancing overall research efficiency

Quantum Computing Research Assistant @ Electrical Engineering Dept Oct 2023 ~ Feb 2024

Engaged in research, focusing on transforming current machine learning models and neural network models (Multilayer Perceptron, etc) to on Quantum Neural Networks models

Data Science Research Assistant @ Nuvve Holding Corp Aug 2022 ~ Dec 2022

Predicted customer driving patterns using Long Short-Term Memory (LSTM) models with an 8-hour prediction window, enabling proactive energy management for Vehicle-to-Grid (V2G) services

Optimized energy price bidding by analyzing 4000+ data points, achieving a 34% improvement in accuracy for more cost-effective energy transactions