Taehyun Cho

I received my Ph.D. in Electrical and Computer Engineering from the Cognitive Machine Learning Laboratory at Seoul National University, advised by Jungwoo Lee, and I am currently a Postdoctoral Researcher. My research focuses on reinforcement learning, human-aligned sequential decision-making, and uncertainty-aware learning. I received my B.S. in Mathematics from Korea University.

Personal Interest

Strategic Games:
As a former top-9 Teamfight Tactics (TFT) player during a competitive season, I treat strategic games as experimental laboratories for studying decision-making under uncertainty. Competitive multi-agent environments like TFT require continuous adaptation, opponent modeling, and risk-sensitive planning. By analyzing my own high-level gameplay, I study how strategies are formed and adjusted in response to stochastic dynamics and strategic interaction. I am particularly interested in formalizing how these strategies emerge in competitive settings and translating these principles into reinforcement learning agents.

Behavioral Economics and Cognitive Science:
Behavioral economics and cognitive science play a central role in how I think about human decision-making. Many reinforcement learning formulations focus primarily on maximizing expected return, abstracting away inference-time constraints and bounded rationality. I am interested in understanding how human biases, risk perception, and cognitive limitations shape actual decisions. This perspective motivates my research on modeling human-aligned decision processes that go beyond expectation maximization and instead account for uncertainty, regret, and subjective evaluation of outcomes.

Philosophy:
Since 2020, I have participated in regular philosophical discussions through "Sunday Salon." Our dialogues, initially inspired by French "Baccalauréat" topics, have expanded to address AI and its societal implications. These discussions have shaped my thinking on human uniqueness, responsibility, and subjective fulfillment. I am focused on understanding which aspects of human reasoning (such as regret, narrative self-reflection, and emotional responses) remain difficult to formalize, and how they might inform the development of human-aligned AI systems.

Research Interest

My academic research focuses on sequential decision-making under uncertainty, particularly in the context of human feedback. I have extensively studied distributional reinforcement learning (distRL), reinforcement learning from human feedback (RLHF), and regret analysis, aiming to bridge theory and practice.

Drawing inspiration from how humans make decisions, I aim to develop mathematical models and optimize for human-in-the-loop systems, uncovering both theoretical insights and practical algorithms for robust decision-making. Currently, I’m interested in reasoning LLM agents and regret-based decision theory.

I’m actively looking for research scientist or postdoctoral opportunities in theoretical foundations of reinforcement learning or reasoning LLM research.