Research Summary

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My past research, consisting of my individual research work and several collaborative team projects, reveals two major pathways:

1️⃣ Database ➡️ Deep Learning ➡️ Prediction/Clustering

🟠 Name Disambiguation 🟠 Unsupervised Image-to-image Translation 🟠 Personalized Investment System

2️⃣ Sequential Data ➡️ Reinforcement Learning ➡️ Strategies

🟡 Explore extensions of Qmix (my honours year thesis at UoL)

Besides, I also participated in an academic project where we applied RL for porforlio optimization (related work to be released soon).

Future Work

Alongside continuing my work on the two topics I’ve already explored, I aspire to dive deeper into the overarching framework illustrated in this mind map, trainsitioning from data to solutions. Specifically, my aim is to explore at least one of the following three aspects:

Firstly, I’m keen to investigate the pathway that encompasses sequential data, deep learning and prediction. This involves conducting research on time-series data and generative models, with a focus on their practical applications in domains such as stock prediction, weather forecasting (climate change), and NLP.

Secondly, I plan to delve into the realm of model enhancement. My objective is to explore how reinforcement learning can be harnessed to elevate the peformance of various types of deep learning models. Coversely, I’m interested in the synergy of incorporating deep learning within the reinforcement learning framework to enhance overal efficiency.

Lastly, I intend to explore how each of these pathways within the mind map can be effectively utilized to tackle real-world challenges through data-driven solutions. Furthermore, I aim to investigate potential strategies to bridge the gap between theoretical research and practical applications, seeking specific solutions to facilitate this transition.