Why do you want to study your chosen major specifically at Georgia Tech?

Why do you want to study your chosen major specifically at Georgia Tech?

In 2017, I watched Google’s artificially intelligent AlphaZero completely dismantle Stockfish, the most powerful chess engine of its time. This was my first exposure to AI and as a lifelong chess player, I was left in awe. I could not believe the level of creativity a computer program was capable of displaying. AlphaZero made very human moves. It sacrificed pieces, committed to long-term strategies, and played with imagination. To me, the incredible computational power was the least exciting aspect of this chess AI. Rather, I was enthralled by its ability to thoroughly understand the state of the chess match and meaningfully act on its analysis. The College of Computing provides a unique subset of tools that perfectly align with my ambitions in AI and machine learning. My prior experience in AI has been in using neural networks as dataanalysis tools and predictors. However, I have always been intrigued by Professor Isbell’s philosophy of developing interactive AI agents. Intelligent systems without the capacity to deliberately connect with the outside world are worthless. Just as in humans, social viability gives intelligence purpose. In Reinforcement Learning, I will learn to develop agents with the ability to make intelligent decisions. Through working with the legendary pfunk research group and Professor Isbell’s Lab for Interactive Artificial Intelligence, I hope to further efforts in creating systems that possess a human approach to fostering external relationships. Additionally, Machine Learning for Trading allows me to pursue algorithmic trading, a field at the intersection of two of my core interests – finance and computing. I truly believe in the idea that machine learning models should be eternally learning and accumulating previously acquired knowledge. Learning algorithmic trading at Georgia Tech will uniquely allow me to explore this idea of lifelong machine learning in the training of predictive financial models.