Science Inspires Series

  • The Kopchick College of Natural Sciences and Mathematics’ Science Inspires Series (SIS) is offered in collaboration with IUP’s Sigma Xi chapter, an honor society of scientists and engineers that rewards excellence in scientific research and encourages a sense of companionship and cooperation among scientists in all fields.

    SIS presents lectures by prominent researchers on topics interdisciplinary in nature and of interest to faculty and students from a variety of academic fields and to the general public. Every semester, the series schedules three talks by KCNSM researchers and renowned speakers.

    The fall 2020 Science Inspires Series begins with the following virtual program:

    “United and Divided: Resilient, overwhelmed, and antagonistic communities coping with natural and human-induced disasters”

    The capacity of a collective to triumph over shared adversities is based on maintaining and augmenting social cohesion, mutual support, cooperation and a sense of belonging to a valued social group and community. Many disasters initially mobilize affected communities into a heroic and altruistic struggle to fulfill immediate needs, and to shield survivors from an overwhelming sense of loss. However, this heroic stage inevitably ceases and may not be sufficient to conquer the slowly evolving deterioration of social relationships routinely experienced by post-disaster communities. The aim of this lecture is to illustrate the significance of community resilience, defined as the ability to deter insidious erosion of interpersonal connections in the aftermath of crises, as a most fundamental way of deterring lasting negative psychological consequences of collective upheavals. 

    The Science Inspires Series will continue through the fall semester with the following distinguished lecture:

    “Wanted Dead or Alive: On the hunt for microbes below the ocean floor”

    • Thursday, November 12, at 4:30 p.m.
    • Brandi Kiel Reese, Dauphin Island Sea Lab, University of South Alabama
    • Zoom

    The ocean covers over 70 percent of the Earth, and the sediment and rocks beneath the seafloor is home to one of the larger and most diverse biomes on the planet. We still know very little about the microbes—bacteria, archaea, fungi, and viruses—that make their home in this environment. The marine subsurface biome has only recently been appreciated as a metabolically active ecosystem, profoundly affecting global elemental cycles. However, they may not all be alive, and we need to sort out the living microorganisms from the dead and the ones that are dormant. Due to extreme difficulty in sampling this environment, relatively few locations have been studied in depth and over time. Therefore, the diversity, abundance, energy metabolisms, and active fraction of subsurface organisms have traditionally been poorly constrained. My research uses sequencing to comprehensively survey microbial communities in deeply buried marine environments. Unlike other environments, the deep subsurface provides a unique opportunity to study biogeography across four dimensions. These samples are not only isolated by linear space on a global scale, but they are also temporally isolated by, in some cases, tens of millions of years.

    The SIS program wraps-up the fall semester with another distinguished lecture:

    “Real-time Data Fusion to Guide Disease Forecasting Models”

    • Thursday, November 19, at 4:30 p.m.
    • Sara Del Valle, Los Alamos National Laboratory
    • Zoom

    Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, Dr. Del Valle will discuss how her team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast infectious diseases.