Upcoming Events

Oct 13 2026

RuCCS Double Feature Faculty Dialogue: Drs. Adam Jardine & Lesia Semenova : The Power of Simplicity

Information

Tuesday, October 13, 2026

2:00 PM - 3:30 PM

NOTE! Department of Linguistics, Rm 108, 18 Seminary Place, New Brunswick, NJ 08901

Event Type: Talks: RuCCS Colloquia | Semester: Fall 2026

Abstract 1 (Dr. Jardine): The ability to learn language is a fundamental part of being human, but researchers do not yet understand the mechanisms that underlie this ability. However, linguistic generalizations are not arbitrary, and are in fact guided by computational and structural restrictions related to memory and perception. Importantly, such restrictions can be expressed with computationally simple forms of automata. In this talk, I discuss how these computational restrictions can be leveraged to provide a modular, interpretable, and small-data approach to tackle the problem of language acquisition. As a case study, I discuss a solution to the problem of simultaneously learning the mental representations of words and their morphological and phonological patterning. In this way, we can better understand, qualitatively and computationally, the mechanisms which underlie the human capacities for both constructing mental representations of words and learning the morpho-phonological patterns governing them.

Abstract 2 (Dr. Semenova): Machine learning is increasingly deployed in high-stakes domains like healthcare, lending, and criminal justice. In these fields, it is crucial that human decision-makers can actually understand how an algorithm arrives at its conclusions. However, there is a widespread assumption that we must face a trade-off: to get the highest accuracy, we must rely on complex, uninterpretable "black-box" models. Because finding optimal, perfectly tailored simple models is computationally difficult, researchers often don't even undertake the effort to look for them. This talk asks a practical question: do we really have to sacrifice interpretability for accuracy? I will explore the conditions under which simple, interpretable models—like basic rule lists or decision trees—perform just as well as complex black boxes. By looking at the mechanics of how data is generated and the choices analysts make during the learning process, I will show that highly accurate, easy-to-understand models exist in practice far more often than we might expect.