Outstanding Graduate Research in Computational Science
1st. Prize: Christopher Callahan (Geography) Advisor: Mankin
The economic impacts of climate change are potentially severe but poorly understood. In my dissertation project, I combined climate simulations and large economic datasets to understand the costs of global warming. I showed that heat waves and El Niño, two major climate hazards, reduce economic growth in tropical countries. I then showed that the emissions of countries and fossil fuel firms in the global North have, as a result, cost these low-income, low-emitting regions billions of dollars. These findings demonstrate how computational science can support societal responses to the climate crisis and highlight the economic threat of global warming for vulnerable people.
1st Prize: Ali Massa (Molecular & Systems Biology) Advisor: Leach
Up to 8% of our DNA consists of ancient retroviral sequences known as endogenous retroviruses (ERVs). Accumulating evidence suggests that expression of ERVs in cancer – creating a state of 'viral mimicry' – may enhance anticancer immunity. However, computational mapping of ERVs has historically been challenging, limiting our understanding of their clinical and immunological impact. We utilized a recently developed bioinformatic pipeline Telescope (Bendall et al. 2019) to quantify the expression of 14,968 ERVs at specific genomic locations across 8 different cancer types using patient sequencing data. We identified both tissue-specific and commonly expressed ERVs across cancers, with a subset being significantly prognostic. Our findings suggest that expression of favorably prognostic ERVs correlates more strongly with an innate immune response, although the strength of this association is tissue specific. Using an in-silico RNA structure and stability pipeline ScanFold (Andrews et al. 2018), we found that prognostic ERVs were in fact more likely to form double-stranded RNA, providing further support for viral mimicry. This work adds to a new and growing framework for ERVs in cancer immunity.
2nd Prize: Paxton Fitzpatrick (Psychological and Brain Science) Advisor: Manning
We developed a computational framework for tracking and characterizing conceptual knowledge and learning in real-world classroom-like settings. We used text embedding models to extract the conceptual content presented in each moment of an online course lecture. We then applied these models to simple multiple-choice quizzes to identify the specific moments in which the knowledge needed to correctly answer each question was conveyed. This allowed us to estimate how well students understood each individual moment of a lecture, predict their ability to correctly answer specific quiz questions, and recommend specific concepts to review in order to fill in their knowledge gaps and more successfully learn from future lectures.
2nd Prize: Luyang Zhao (Computer Science) Advisor: Balkom
In nature, many creatures are capable of working collectively by joining together to form large-scale assemblies to achieve a unified goal. In robotic construction, robotic manipulators often build structures from rigid load-bearing blocks. On the other hand, soft robots can adapt their shapes to suit tasks. My research focuses on designing intelligent soft modular lattice-based blocks that bridge the gap between rigid blocks and soft robotics. These blocks exhibit versatile capabilities, transitioning between deformable states for locomotion and manipulation, while also having the ability to rigidly lock into specific shapes to support heavy loads. Their autonomous self-assembly into complex structures without external assistance unlocks opportunities for adaptive and self-reconfigurable robotic systems. To our knowledge, this is the first example of active deformable blocks that can reconfigure into different load-bearing structures on-demand .
Luyang Zhoa, Y. Wu, Y. Wen, W. Zhan, X. Huang, J. Booth, A. Mehta, K. Bekris, R. Kramer-Bottiglio, and D. Balkcom. StarBlocks: Soft Actuated Self-connecting Blocks for Building Deformable Lattice Structures. IEEE Robotics and Automation Letters, May. 2023 (accepted) Video: https://youtu.be/xno0FBs3ZdQ
3rd Prize: Subiga Nepal (Computer Science) Advisor: Campbell
The university years are transformative, posing unique opportunities and challenges. Understanding students' well-being during this critical period is vital, especially given the unprecedented impact of the COVID-19 pandemic. In our study, we extensively analyze the experiences of undergraduate cohorts over four years, employing a novel combination of mobile sensing data, surveys, and qualitative interviews. We investigate patterns in students' daily activities, mental health, and resilience, from pre-pandemic years through the pandemic and once the pandemic subsides. This comprehensive longitudinal approach provides invaluable insights into the resilience and adaptability of students under extraordinary circumstances, which could guide effective strategies for supporting student mental well-being. This research represents one of the most extensive mobile sensing studies to date, promising significant implications for developing effective interventions and support systems in higher education.
3rd Prize: Natasha Kelkar (Microbiology and Immunology) Advisor: Ackerman
Insights into mechanisms of protection afforded by vaccine efficacy field trials can be complicated by both low rates of exposure and protection. However, these barriers do not preclude the discovery of correlates of reduced risk (CoR) of infection. Given the significant investment in large scale human vaccine efficacy trials, novel approaches for analyzing efficacy trials to optimally support discovery of correlates of protection (CoP) are critically needed. We investigated the value of applying Positive/Unlabeled (P/U) learning to classify study subjects using model immunogenicity data based on predicted protection status in order to support new insights into mechanisms of vaccine-mediated protection from infection. Our work demonstrated that P/U learning methods can reliably infer protection status, supporting discovery of simulated CoP that are not observed in conventional comparisons of infection status cases and controls.
Kelkar, N.S., Morrison, K.S. and Ackerman, M.E., 2023. Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning. Human Vaccines & Immunotherapeutics, p.2204020.
Honorable Mention: Martin Ying (Physics and Astronomy) Advisor: Chaboyer
The absolute age of the oldest stars in our galaxy is of fundamental interest for a wide range of applications but is difficult to measure in practice. Messier 92 is one of the oldest and most metal-poor globular clusters, and its absolute age provides a lower limit to the age of the universe which is independent of cosmological models. We conduct Monte Carlo simulation to generate 20, 000 sets of isochrones using Dartmouth Stellar Evolution Programs, and fit to the Messier 92 data from HST ACS globular cluster treasury program using an innovative 2D isochrone fitting method. We found the absolute age of M92 to be 13.80 ± 0.75 billion years.
Honorable Mention: Mijin Kwon (Psychological and Brain Science) Advisor: Wager
Predictive models of neuroimaging data ("brain signatures") have provided new insights into the neural basis of affective processes. However, a systematic evaluation of these models' performances is essential to establish their boundary conditions and potential use cases. This study utilizes a comprehensive, multi-study fMRI dataset from a collaborative data-sharing consortium to assess the predictive performance of 21 existing task-based fMRI data across various affective domains such as pain, appetitive states, aversive states, and cognitive control. The results revealed variability in the sensitivity and specificity of these signatures, suggesting specific limitations to be addressed. The project is now focused on developing a new generation of brain signatures with enhanced specificity, sensitivity, and generalizability.