Young Scholars Academic Journal Summer 2025 Volume - Accepted

A reinforcement learning framework inspired by cognitive science, which systematically translates human metacognitive principles into computational models, overcoming the exploration challenge in sparse reward environments by implementing a two-layer reflective mechanism.

This research bridges the gap between cognitive science and artificial intelligence by developing computational models that mimic human metacognitive processes. The framework addresses the fundamental challenge of exploration in reinforcement learning through innovative reflective mechanisms that enhance learning efficiency in sparse reward scenarios.

Social Justice Awards 2025 Spring Second Prize

Analyzing the urgency of proactive AI regulation and governance frameworks in the era of rapid technological advancement.

This piece explores Elon Musk's calls for AI governance and the critical need for regulatory frameworks that can keep pace with technological development, examining the balance between innovation and safety.

Bronze Award, Harvard International Review Academic Writing Contest (2023)

Exploring technological barriers and security risks faced by elderly populations in the digital era.

This research examines the digital divide affecting elderly populations, analyzing accessibility challenges, security vulnerabilities, and proposing solutions for more inclusive technology design.

Research Paper (2024)

A comprehensive study on leveraging pre-trained models for depression detection through speech analysis using multi-task learning approaches.

This research investigates the application of state-of-the-art pre-trained models for analyzing speech patterns to detect indicators of depression, utilizing the MODMA dataset and exploring multi-task learning frameworks.

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