Jiwon Lee, Colin Meyer, and Liz Murnane

The program seeks to fund both the development of novel computational techniques as well as the application of computational methods to research across the campus and professional schools.

Jiwon Lee

Hyperglycosylation of ImmunogensviaIn SilicoEngineering (HyperImmunISE)

Glycosylation is one of the most important post-translational modifications of proteins. Enveloped viruses such as HIV-1 and SARS-CoV-2 often exploit the machinery of their host to heavily glycosylate their surface proteins. These glycans can effectively mask various antigenic epitopes on viral proteins to avoid recognition by antibodies, allowing the virus to evade the immune system. We aim to leverage this process to develop an algorithm which introduces non-native glycans on immunogens to block areas on the protein surface and focus immune responses to a particular epitope, with the goal of designing vaccines focusing immune responses to epitopes associated with broad protection. Completion of this proposed study will facilitate in silico protein design and enable the use of these designing tools outside of our research group, as the computational design tool has versatility to engineer many different proteins and will be accessible publicly as a webserver.

Colin Meyer

Storage of Water in Snow During Climate Change: Preferential Flow through Snow

The Arctic is warming faster than anywhere else on earth. In 2019, the Summit region of the Greenland Ice Sheet experienced surface melting, which has only happened a handful of times in the last thousand years, and the frequency is increasing: the last time that the surface melted at Summit was in 2012. At the same time that new areas of Antarctica and Greenland are melting now because of climate change, alpine snowpacks are experiencing more weather variability, increasing the frequency of deadly avalanches and putting snow supply for water resources at risk. These downstream effects present a need for predictions of how snow will evolve on the surface of ice sheets and in alpine systems. Numerical models for these processes are being developed but are limited to one vertical dimensional and based on empirical parametrizations. In this CompX project, we will develop a two-dimensional snow evolution model to understand the heterogeneous infiltration of water into snowpacks. Focusing on the role of lateral water flow, we will numerically analyze the structure of flow pathways, location of refreezing, and net water fluxes. We will compare our simulation results to existing one dimensional simulations and field observations from the surface of the Greenland ice sheet.

Liz Murnane

Building Children's AI Literacy through Play-based Educational Technologies

Artificially intelligent (AI) technologies continue to integrate into daily life, including for children born into today's digital era. As AI advances, it is imperative to foster children's "AI literacy" to enhance their skills and savviness surrounding emerging technology. Research finds such introductions to computational concepts early in childhood help increase later interest in computer science, including among students from traditionally underrepresented groups. Early exposure can also build professional AI competencies that are likely to be in high demand and exceed workforce supply. Furthermore, demonstrating the limitations of AI can help prepare young people to guard against age-of-AI hazards such as data breaches, surveillance capitalism, biased algorithms, and dis/misinformation.

This CompX project will undertake the iterative design and proof-of-concept evaluation of a play-based educational platform that builds children's intuition and understanding of AI. Specifically, we will use the interactive scaffold of mini-games, crafted through human-centered design work with kids and families to develop effective, engaging, and inclusive learning activities. We have begun creating prototypes for a suite of games that will be playable on-demand and can be adaptively recommended. To evaluate our designs, we will conduct lab studies and short-term pilot deployments to measure children's performance on recognition and recall tests, assess self-efficacy and other psychological reactions, and gather metrics of user experience and system acceptance. Findings will inform longitudinal studies to monitor effects over time. Moving forward, we also aim to extend these ideas to additional aspects of computing/STEM education, other interaction paradigms, and broader learner populations.