Zhang-Wei Hong

Research: My aim is to address practical search problems using tools for sequential decision-making such as reinforcement learning (RL) and the multi-arm bandit framework. I broadly interpret search challenges as discovery of nearly optimal solutions within an huge search space. Addressing these challenges is akin to strike a balance between exploration (deciding when to try the unknown) and exploitation (deciding when to leverage what's known). Thus, I believe that the sequential decision-making principles of balancing exploration and exploitation are crucial for devising effective solutions for search challenges. My research is focused on the following key areas:

• Sample Efficient RL: Given that resolving many search problems necessitates costly real-world interactions or the use of high-fidelity simulations, my early works are dedicated to creating RL algorithms that can learn effective policies more efficiently with minimal human intervention and less data. (References: NeurIPS'23, ICML'23, ICML'23, ICLR'23, ICLR'22, and ICLR'22)
• Domain-agnostic Exploration Strategies: At the core of solving search challenges is the capability to experiment with new solutions in an efficient manner, minimizing repetition and accurately estimating outcomes with fewer trials. Existing exploration methods often rely on estimating uncertainty through domain-specific features, necessitating significant human effort and trial and error. This limitation prevents us from applying single exploration strategy to many problems. My goal is to develop domain-agnostic exploration strategies. (References: NeurIPS'23)
• Applications: In addition to algorithms, I'm interested in addressing real-world problems by framing them as search challenges and applying my algorithms to these problems. My recent interests include applications in AI safety (e.g., red teaming), cybersecurity, software testing, and scientific endeavors. (References: ICLR'24)

Bio: Zhang-Wei is a final year Ph.D. student in Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), advised by Prof. Pulkit Agrawal. In 2024, Zhang-Wei was awarded the Qualcomm Fellowship for North America. He completed both his B.S. and M.S. degrees at National Tsing Hua University, working closely with Prof. Chun-Yi Lee and Prof. Min Sun. Previously, Zhang-Wei had the opportunity to collaborate with Prof. Jan Peters at TU Darmstadt in Germany and worked at Preferred Networks.

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