Liv d'Aliberti

Department: Computer Science
Faculty Adviser: Ben Eysenbach
Year of Study: G1
Undergraduate School: Georgetown University
Undergraduate Major: Mathematics, History

Personal Bio

Hello! Nice to meet you. I am a PhD student within the CS department. My undergraduate work at Georgetown University included a bachelors of science in mathematics and a bachelors of art in history. I also played rugby all four years and worked as a bartender. After completing my bachelors degree, I then worked full-time while getting my masters degree in applied mathematics. I did this because I had a hard time finding a job as a data scientist (which doesn't quite mean the same thing as data science does today) after my bachelors. Towards the end of my masters degree, I left my jobs as a data analyst to take an internship as a research data scientist within the advanced research department of a defense contractor. I had a really positive experience, and I accepted a research data science position in their newly-formed AI/ML Accelerator. I worked there for 5 years, developing new technologies, patenting example systems, writing implementation papers. However, I realized that I wanted to carry out more theoretical research in AI. So, now, I'm here to work on my PhD. I hope that my time at Princeton helps me mature as a researcher.

Fun Fact

My fun fact is that I was a U.S. Expat kid! I grew up living in Thailand, Turkey, and the UK.

Research Pitch

I'm interested in developing efficient, scalable, robust artificial intelligence learning algorithms. Much of this work, right now, is classified as "reinforcement learning", and it is a branch of computer science / artificial intelligence that aims to have agents learn multi-step, sequential decision-making, where the decision made is based upon an understanding of current "state" (i.e. what the agent can observe at this timestep), available "actions" (what the agent can do at that timestep), and (sometimes) the associated reward an agent can achieve for taking an action. I want to investigate the phases of learning, better ways of achieving optimality (i.e. not getting stuck into local minimums during training), limited agent rewarding, interaction between agents, learning behavior alternatives. My research requires mathematics, strong python programming, an interest in human psychology, and a lot of long-running, simulated experiments.

Upcoming Programs That I Am Attending:

Plans for Summer 2025

Interested in participating in Summer 2025 ReMatch+ program.