Current Fellows

The Neukom Fellows Program launched in 2012. Fellows have two-year appointments with the option for a third year and for their interdisciplinary work which has a computational theme, are co-sponsored and mentored by faculty in at least two departments or programs. Fellows teach one course in each year of their residency. The current Neukom Fellows with their Ph.D. granting institutions and departmental affiliations are given below, along with descriptions from the Fellows of their research plans.

Biology and Psychosocial & Brian Sciences; Mentors- Michael Hoppa, Matt van der Meer, and Geoffrey Luke

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Aman is a biomedical engineer interested in bioelectrical phenomena of the nervous system and how electromagnetic fields can be used to modulate neural activity and treat disorders. In his doctoral work, Aman developed multi-scale computational models of the neural response to noninvasive brain stimulation methods, including a technique called transcranial magnetic stimulation, which provided mechanistic explanations for experimental observations that were previously not well understood. As a Neukom fellow, Aman will use novel genetic and optical tools to characterize the dynamic molecular and electrical properties of axons and synapses during normal brain function, as well as during stimulation with applied electromagnetic fields.

Publications, Conferences, & Courses

German Studies; Mentors – Petra McGillen and Jed Dobson

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Laura's research combines comparative literary studies, computational text analysis, and cultural history to investigate notions of decadence and the co-development of literary and computational cultures in late nineteenth century Europe. Her doctoral research used a blend of literary close reading and computational text analysis methods in order to develop new approaches to analyzing the prevalent yet problematic concept of decadence in German and Swedish literature and culture at the fin de siècle. Her postdoctoral research builds on this work and investigates how developments in ideas about language and literature intersect with developments in new media and computational thinking at the fin de siècle in Germany and Sweden. In a world that is becoming increasingly digitally and computationally mediated, this comparative literary history of computation aims to contribute historical and interdisciplinary perspectives on the practices we use to record and interpret human experiences.

A core aim of Laura's work is to diversify how computation is conceived and deployed by building interdisciplinary connections at the intersections of modern languages and literatures and digital and computational culture. Her "Computational Comparative Literature" course at Dartmouth, for example, seeks to open up alternative pathways to computational literacy and address the uneven access to computational learning opportunities across linguistic and disciplinary domains.

Publications, Conferences, & Courses

Mathematics and Sociology; Mentors - Feng Fu and Kimberly Rogers

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Olivia Chu's research focuses on the dynamics of behavior, in both human and animal populations, and in particular, the effects that heterogeneous population structures have on these dynamics. Her thesis work focused broadly on group-structured populations and the interplay between human behavior, group memberships, and interactions. Olivia began this work by using a combination of computational simulations and mathematical models from evolutionary game theory and opinion dynamics, but has since incorporated data collection and empirical evidence into her models in collaboration with social scientists from psychology and political science. More recently, Olivia has also begun to study the dynamics of altruism in animal populations as well as the spread of infectious diseases in structured populations. 

As a Neukom Fellow, Olivia explores questions relating to: cooperation within and between groups; personality types and their role in social integration; the dynamics of power; the rule of law; puzzling altruistic behavior in animal communities; and how we can take advantage of small-scale, interpersonal interactions to avoid large-scale polarization in an increasingly divided world. By continuing to use a combination of simulations, modeling, and data collection to answer these questions, Olivia aims to gain insight into how we might be able to make our world (or at least our own social networks) more cooperative, kind, and fair.