I am a researcher specializing in Deep Learning (DL) in Finance and Natural Language Processing (NLP) Models. My interests span various aspects of financial NLP (related to my M.S. thesis), and I keep an open mind toward different applications. On the NLP side, I focus on understanding how models learn and process natural language, particularly exploring why Large Language Models (LLMs) perform so well. Additionally, I have a growing interest in small LLMs (sLLMs) and the diverse empirical techniques used to apply LLMs across various tasks.

I initially entered this research field with an interest in financial market analysis and prediction, which I developed during my economics studies. Expecting AI to serve as a powerful tool in this domain, I took my first steps into the field. However, as I delved deeper, my fascination with AI itself grew, eventually leading me to shift my focus entirely toward AI research. Interestingly, the mathematics I studied in preparation for financial engineering unexpectedly bridged the gap between finance and AI, making the transition feel even more natural.

With this foundation, I approach research with an open and inquisitive mindset, staying actively engaged in ongoing discussions and projects in the lab while continuously seeking new ideas and perspectives to explore.

I have always valued the principle of “cold head, warm heart” and strive to embody it in my daily work. I make it a priority to approach research with dedication and clarity while fostering a warm and collaborative environment. Ensuring smooth coordination among team members and minimizing disruptions in collective workflows is a key value I uphold in my research approach.