Lightning Talks
Dynamic development of a sensory system in the zebrafish
Victoria Prince
Professor of Organismal Biology and Anatomy, Professor of Neuroscience Institute, Committee on Neurobiology
Abstract
Aquatic vertebrates possess a mechanosensory “sixth sense” called the lateral line system, which allows them to detect changes in water flow and pressure. The lateral line comprises a superficial network of distributed sensory organs, the neuromasts, arranged over the head and trunk and innervated by lateral line nerves. We have used confocal and light sheet imaging of accessible developing zebrafish embryos to study development of the cranial component of the lateral line. Our data intensive imaging has revealed new details of how this sensory system in built, including that it forms in close proximity to another vertebrate-specific cell type, the cranial neural crest. Moreover, we have established that removal of neural crest cells leads to major disruptions in the overlying cranial lateral line. Together, our results establish that two vertebrate-specific tissue types must function in concert to build a complex sensory system.
Bio
Prince’s research program at the University of Chicago focuses on vertebrate axial regionalization during development, primarily using the zebrafish as a model. Her group takes a variety of molecular, cellular, genetic, and comparative approaches to these studies, and they have made important contributions to a variety of research areas, including the understanding of Hox gene regulation of hindbrain patterning, evolution of duplicated genes and genomes in the vertebrates, and patterning of endoderm-derived tissues. Active areas of research in Prince’s lab include ongoing studies of neural crest and anterior lateral line development.
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Dorian Abbot
Professor of Geophysical Sciences
Abstract
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather/climate models. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test this, we train independent versions of the AI model FourCastNet on the 1979-2015 ERA5 dataset with all data, or with Category 3-5 tropical cyclones (TCs) removed, either globally or only over the North Atlantic or Western Pacific basin. We then test these versions of FourCastNet on 2018-2023 Category 5 TCs (gray swans). All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms. The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins. This is encouraging and surprising because regional information is implicitly encoded in inputs. No version satisfies gradient-wind balance, implying that enforcing such physical constraints may not improve generalizability to gray swans. Given that current state-of-the-art AI weather/climate models have similar learning strategies, we expect our findings to apply to other models and extreme events. Our work demonstrates that novel learning strategies are needed for AI weather/climate models to provide early warning or estimated statistics for the rarest, most impactful weather extremes.
Bio
Abbot has an undergraduate degree in physics (2004, Harvard) and a PhD in applied math (2008, Harvard). He came to the University of Chicago as a Chamberlin Fellow in 2009 and stayed on as a faculty member in 2011. Abbot uses mathematical and computational models to understand and explain fundamental problems in Earth and Planetary Sciences.
AI Adoption in a Non-Monetized Space: Archaeological Remote Sensing
Mehrnoush Soroush
Assistant Professor, Ancient Near Eastern Studies; Director, Center for Ancient Middle Eastern Landscapes (CAMEL) Lab
Abstract
AI adoption across industries is uneven, driven primarily by monetization potential. Academic and research sectors have adopted AI primarily in fields for which low technical barrier tools, such as Large Language Models (LLMs), are readily available. AI revolution has not reached Archaeology given the complexity of tasks and the limited commercial incentives inherent in these tasks. Archaeological remote sensing (RS), a crucial method for modern archaeological research, has remained slow and costly_limited by manual feature detection processes_ hindering scalability, reproducibility, and data sharing. Growing large-scale datasets exacerbate these challenges, overwhelming researchers with data management complexities. A3RD is an initiative led by UChicago archaeologists, supported by RCC, to develop custom AI workflows to overcome the RS obstacles. We will review project objectives, major challenges, and summarize key lessons from the project’s first year.
Bio
Soroush is a landscape archaeologist who examines the intersection between urban and water history in the Ancient Near East. She got her Ph.D. from New York University (ISAW) and her MA in Architecture from the University of Tehran, Iran. Soroush’s research explores the extent to which the resilience of ancient cities was tied to their ability to adapt to environmental changes and socio-political developments through adopting new hydraulic strategies and technologies. She is particularly interested in examining the water history of the Parthian, Sasanian, and Islamic periods, the periods that often fall on the margins or outside the scope of traditional Near Eastern archaeological research. Her interdisciplinary approach draws on all available data, including archaeological fieldwork, textual and archival research, Geographic Information Systems (GIS) and remote sensing, and computational methods. Previously, Soroush (re)-examined the history of canal irrigation in the Sasanian and early Islamic periods in southwestern Iran. As the Assistant Director of the Harvard-led Erbil Plain Archaeological Survey (EPAS), she is now investigating the water history in the Kurdistan Region of Iraq, in particular the timing and cause(s) of the shift of historically dry-farmed plain to one irrigated by subterranean qanat systems (locally known as karez) that dominated the landscape in the later medieval periods.
Experience and Experimentation: Studying Water and Soil Management at the Ancient City of Petra, Jordan
Sarah Newman
Assistant Professor of Anthropology and Social Sciences in the College; Director of Undergraduate Studies
Abstract
For the past six years, the University of Chicago/Brown University Petra Terraces Archaeological Project has combined archaeological survey and excavation with aerial photogrammetry and terrestrial lidar to document and visualize hydrological and agricultural features in the northern hinterlands of the ancient Nabataean city of Petra, in Jordan. In collaboration with UChicago’s Research Computing Center, we used those archaeological and remote sensing data with continuous simulation, deterministic models to assess the impacts of those features on local soil erosion and water management. Our work reveals that small-scale landscape modifications, including some likely made by individual farmers, had large-scale impacts that made life at Petra possible, especially when individual elements worked in tandem to create an interconnected infrastructural system.
Bio
Newman is an anthropological archaeologist. Her research combines archaeological, historical, and art historical methods and evidence to explore anthropological and environmental issues, including histories of waste and reuse, long-term landscape transformations, and human-animal relationships. Newman primarily conducts research in Latin America, with a particular focus on Mesoamerica and the ancient Maya, but she is also involved in comparative projects to study landscapes, infrastructure, and environment in other parts of the world.
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