Projects
Characterizing regularity in semantic shift of individuals
How does linguistic change influence the thought process of individuals? How does individual variation materialize in language use and linguistic style? How are concepts perceived differently across individuals? The answers to these questions remain unclear and are important for understanding the cognitive mechanisms which enable humans to communicate with one another. To try answering these questions, we explore semantic change at the individual-level and how it relates to the population-level. We are also currently expanding this work from the CogSci 2026 paper to a journal submission which will also include emotion shift at the individual-level.
Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms
Every individual has a unique collection of experiences and knowledge, each of which influence their perspectives and opinions. Is it possible to use these attributes and experiences of an individual to predict these opinions and perspectives? This project aims to explore the utility of different types of self-disclosures for modeling annotators of social norms. We used Reddit posts from the subreddit r/AmItheAsshole to explore this question and found that demographic self-disclosures were the most useful, followed by attitude, experience, and finally relationship information.
Power-Efficient, Accelerated, Exponential-Based Activation Functions
Machine Learning offers the promise of great economic, social, and scientific benefits, but this comes at the cost of energy consumption, making it harder to meet the goal of preventing run-away climate change. IT infrastructure is predicted to account for over 20 percent of global power consumption by 2030. In this paper, we look at one of two major costs for neural network computations, namely, activation functions. We present alternative piecewise functions based on the exponential function.