ScholarWorks@UMassAmherst

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Please see our ScholarWorks guide for more information about submitting your work to ScholarWorks.

Featured Items

Data Do's and Don'ts: A Primer on Indigenous Data Sovereignty

Anderson, Jane; Smith, Vanessa

2026

A Mighty Event: Eight Lessons Learned by Bringing People Together Through Music to Support Our Mission

White, Caroline J.; Fill, Kim D.

On the evening of November 7, 2025, the University Libraries at the University of Massachusetts Amherst held its annual Fall Reception, attended by an audience of 300. “An Evening with Folk New England: A Tribute to Eric von Schmidt” represented both a return to the longstanding tradition of an event held to showcase the Libraries while thanking our donors for all they make possible and an exceptionally successful and memorable version of that event. Celebrating the partnership of Folk New England and the Libraries' Robert S. Cox Special Collections and University Archives Research Center, the event generated so much interest that it was ultimately moved to a larger venue, and the move impacted all aspects of the event. Although the planning team cannot count on replicating the event or its impact in the near future, the team took away many lessons from the planning and pivoting, as well as from the event itself.

2026-03-19

Regional Invasive Species & Climate Change Research to Practice Paper: Rock you like a hurricane: The perfect storm for an invasion

Putnam, Aly B.; Al-Shayeb, Sofia M. A.; Buonaiuto, Daniel M.; Daniel, Wesley; Geiger, Megan Lynn; Lieurance, Deah; Parsons, Elliott W.; Pfingsten, Ian; Williams-Soriano, John Paul; Evans, Annette E.

Hurricanes (also called typhoons or cyclones) and other rare weather occurrences are increasing in intensity and frequency with global climate change. These extreme weather events can drastically alter ecosystems in ways that directly or indirectly facilitate the introduction, establishment, spread, and impacts of invasive species. For example, high winds or flood waters during hurricanes can disperse invasives and displace native species. Impacts could also occur after the main storm event; invasive species can invade during relief efforts or establish more easily in disturbed habitats [1,2]. Various possible mechanisms link hurricanes and invasive species (Figure 1). To identify shared challenges as well as region-specific dynamics and possible impacts, we highlight case studies from the Southeast, Pacific Islands, Caribbean, and Northeast regions of the United States (Table 1). By synthesizing examples across diverse ecological and management contexts, this summary can help practitioners anticipate common mechanisms of spread and consider strategies that could be implemented before and after hurricane events to manage vulnerable species, habitats, and ecosystems.

2026

Recent Submissions

Open Access
School-Based Intervnetion Research: What's New?
(2026-03-13) Smith, Claudia; Cholewa, Blaire; Dimmitt, Catherine
This presentation from the 2026 virtual Evidence-Based School Counseling Conference explores some recent outcome research studies related to school counseling practice. The presenters review each of the studies, describe the intervention's impacts, and identify how school counselors can utilize the interventions/information in their own practice
Open Access
Centered in Joy, Care, Connection and Compassion: Radically Reimagining Schools
(2026-03) Dimmitt, Catherine; Lemberger-Truelove, Matthew; Smith, Claudia
This presentation from the 2026 virtual Evidence-Based School Counseling Conference explores how school counselors can help to shift the dynamics of school environments to best support their students. The presenters describe Advocating Student-within-Environment theory, as well as evidence-based school counseling practice, in order to emphasize how utilizing theory can be a tool for quality practice. This shift is also situated within the ever-changing contexts of school and work. Finally, the presenters describe specific, tangible steps counselors can take at multiple levels of the school environment/context to support student development and implement ASE theory.
Open Access
Caspase-6 Structure and Dynamics by NMR: Uncovering the Complex Motion of a Protease Involved in Neurodegeneration
(2026-02) Kuzio, Nathanael
Caspase-6, a member of the human apoptotic-protease family, caspases, has been implicated in a variety of neurodegenerative diseases. The involvement of caspase-6, particularly in Alzheimer’s Disease, through the cleavage of neuronal structural proteins tau and tubulin, has urged the field to develop inhibitors that can be used as treatments for these dreadful diseases. Unfortunately, conserved substrate preference among all caspases, and the lack of unique mechanisms for selective inhibition have prevented the use of active-site targeting small molecules and led to only a small number of promising inhibitors. Understanding characteristics of the enzyme’s structure and behavior that set it apart from its fellow family members is crucial for the development of much needed selective small molecules probes. In this dissertation, I address this question using nuclear magnetic resonance (NMR) spectroscopy to investigate the idiosyncratic dynamic behavior of caspase-6 in solution. These studies ultimately provide a view into different motions associated with three distinct regions of the protein. We probe the relative rates of motion for the 130’s helix-to-strand interconverting region, the 100’s helix, and the flexible active-site loops of caspase-6. These experiments find that the different regions interconvert at different rates, and that in the case of the 130’s region, 80-90% of the protein in solution resides in the inactive helical state. This new insight is then applied in the context of a new inhibitor discovered in our lab, compound A, in which we find that the inhibitor does not perturb the relative populations of caspase-6, but may instead alter the rate of interconversion between the helical and strand states. Together, this newfound understanding of caspase-6 dynamics can be used for the development of inhibitors that target not just a static protein structure, but the distinct ensemble and dynamic nature of the enzyme. This new detailed view into caspase-6’s behavior in solution also serves as a method of differentiation from other caspases, guiding both effective and selective inhibitor design.
Open Access
STATISTICAL INFERENCE IN THE PARALLEL REGIME FOR ACCELERATING SCIENTIFIC MODELING
(2026-02) Kulkarni, Sourabh
In the scientific method, we attempt to understand natural phenomena by developing hypotheses in the form of models, based on our current understanding. These models are then tested and modified using real-world experimental observations in an iterative procedure, leading to refined models and improved experiments to confirm them. This method is prominently used in all domains of science, ranging from elementary particle physics, chemistry, molecular biology, epidemiology, astrophysics, and cosmology. As our understanding in these domains increases, so does the complexity of the models we develop to simulate the underlying phenomena. In many cases, these model simulations require the use of supercomputers (in some cases, supercomputers are built solely for enabling these). Hence, architectural and algorithmic advancements in the area of scientific simulation models are crucial to further our understanding of the universe. In this work, we focus on developing new architectural and algorithmic frameworks to provide acceleration to scientific applications – specifically, i) a Stochastic Compartmental Model for Epidemiological Analysis of COVID-19, and ii) an Immunology Model for Analysis of Cytokine Response. This work provides several contributions in the domain of simulation-based inference (SBI), a statistical inference technique for fitting model parameters to experimental data. We develop massively parallelized physical model simulations for these two applications (COVID-19 and Cytokine) and leverage this parallelism to perform accelerated Approximate Bayesian Computation (ABC) and ABC with Sequential Monte Carlo (ABC-SMC). This parallelized simulation framework is tested on modern hardware acceleration platforms such as GPUs, IPUs, and TPUs. We perform an in-depth analysis of the effectiveness of these architectures in executing the parallelized physical model simulations. Furthermore, we develop algorithmic advancements on top of Parallelized ABC-SMC, enhancing the utility of massively parallel simulations and advanced hardware acceleration platforms. These enhancements explore novel step-size distribution, new ways to balance exploration and exploitation in the parallel regime, and tuning of the distributions themselves for maximizing the benefits of physical simulations on parallel hardware. We explore additional approaches to increase parallelization by using Machine Learning (ML) techniques such as Neural Networks, Decision Trees, and Gaussian Processes to train models that replace physical simulations. These ML replacements yield an order of magnitude improvement in parallelism and open up new algorithmic strategies. Currently, most scientific model simulations are run on CPUs in HPC clusters. The modern hardware acceleration platforms such as GPUs, TPUs, and IPUs, which have been successful in computer vision and natural language processing, also hold significant potential to accelerate scientific applications through simulation-based inference. This potential is clearly demonstrated by the results obtained across the two diverse applications: i) the COVID-19 application, using a stochastic compartmental epidemiology model, and ii) the Cytokine response application, modeled by a set of 22 ordinary differential equations. Leveraging modern AI hardware for the parallelization of simulations yielded speedups of 30x for the COVID-19 application and 23x for the Cytokine application, compared to CPU implementations. Our parallelism-aware algorithmic advances provided additional speedups of 18x and 4.5x for the COVID-19 and Cytokine applications, respectively, over parallelized state-of-the-art algorithms. Finally, our machine learning-based simulation replacements provided further speedups of 1.2x and 4x for the COVID-19 and Cytokine applications, respectively. The combined results of algorithmic enhancements and acceleration with machine learning yielded 21.6x and 18x improvements, respectively, over the benefits of the parallelization of the standard approaches. Additionally, we also observe that models obtained using the new techniques provide a better fit to observed data, and hence provide better predictions compared to current state of the art.
Open Access
What's New in the Outcome Research: School Counseling-Related Interventions
(2025-03) Dimmitt, Catherine; Cholewa, Blaire
This presentation from the 2025 Evidence-Based School Counseling Conference in Columbus, Ohio, describes a set of outcome research studies related to school counseling practice. While not exclusive to school counseling, all of the studies included examine interventions or practices related to student outcomes that could be utilized by school counselors and educators to improve student wellbeing and development. Suggestions for application are also included.

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