Over the years, advances in research have continued to decrease the size of computers from the size of<br/>a room to a small device that could fit in one’s palm. However, if an application does not require extensive<br/>computation power nor accessories such as a screen, the corresponding machine could be microscopic,<br/>only a few nanometers big. Researchers at MIT have successfully created Syncells, which are micro-<br/>scale robots with limited computation power and memory that can communicate locally to achieve<br/>complex collective tasks. In order to control these Syncells for a desired outcome, they must each run a<br/>simple distributed algorithm. As they are only capable of local communication, Syncells cannot receive<br/>commands from a control center, so their algorithms cannot be centralized. In this work, we created a<br/>distributed algorithm that each Syncell can execute so that the system of Syncells is able to find and<br/>converge to a specific target within the environment. The most direct applications of this problem are in<br/>medicine. Such a system could be used as a safer alternative to invasive surgery or could be used to treat<br/>internal bleeding or tumors. We tested and analyzed our algorithm through simulation and visualization<br/>in Python. Overall, our algorithm successfully caused the system of particles to converge on a specific<br/>target present within the environment.

Bad actor reporting has recently grown in popularity as an effective method for social media attacks and harassment, but many mitigation strategies have yet to be investigated. In this study, we created a simulated social media environment of 500,000 users, and let those users create and review a number of posts. We then created four different post-removal algorithms to analyze the simulation, each algorithm building on previous ones, and evaluated them based on their accuracy and effectiveness at removing malicious posts. This thesis work concludes that a trust-reward structure within user report systems is the most effective strategy for removing malicious content while minimizing the removal of genuine content. This thesis also discusses how the structure can be further enhanced to accommodate real-world data and provide a viable solution for reducing bad actor online activity as a whole.

This paper explores the inner workings of algorithms that computers may use to play Chess. First, we discuss the classical Alpha-Beta algorithm and several improvements, including Quiescence Search, Transposition Tables, and more. Next, we examine the state-of-the-art Monte Carlo Tree Search algorithm and relevant optimizations. After that, we consider a recent algorithm that transforms Alpha-Beta into a “Rollout” search, blending it with Monte Carlo Tree Search under the rollout paradigm. We then discuss our C++ Chess Engine, Homura, and explain its implementation of a hybrid algorithm combining Alpha-Beta with MCTS. Finally, we show that Homura can play master-level Chess at a strength currently exceeding that of our backtracking Alpha-Beta.

For my Thesis Project, I worked to operationalize an algorithmic trading application called Trading Dawg. Over the year, I was able to implement several analysis models, including accuracy, performance, volume, and hyperparameter analysis. With these improvements, we are in a strong position to create valuable tools in the algorithmic trading space.