Archives: Portfolios

In gamma-ray astronomy, the ability to combine data from multiple observatories that sample different regions of the gamma-ray spectrum is crucial to understanding processes–such as particle acceleration and diffusion–that take place in and around astrophysical phenomena. Ground-based gamma-ray observatories therefore provide an important complement to NASA’s Fermi Gamma-Ray Space Telescope (FGST).

In particle astrophysics, extended maximum likelihood methods, which can be used to derive the fractional contributions as well as spatial and spectral parameters of multiple data components with different astrophysical origins, also provide a natural framework for combining data from multiple observatories. Such techniques are already the standard approach for analyzing data from the FGST. Our research seeks to address the problems that arise when applying the technique of binned extended maximum likelihoods to data procured for ground-based gamma-ray astronomy. These problems stem from the fact that gamma-ray astronomy data spaces are largely dominated not by gamma-rays, but by cosmic rays. The large uncertainties associated with Monte Carlo models of cosmic ray background in conjunction with the low uncertainties of gamma-ray models result in a data space comprising regions of high uncertainty and other regions of low uncertainty. A data space of this form introduces serious mathematical issues for present maximum likelihood methods. Our objective is to run a series of tests that emulate this problematic scenario in order to better understand the statistical considerations that must be made for maximum likelihood analysis of gamma-ray data.

X-ray science can shed light on key physics, probing how black holes behave, how they influence galactic evolution, and how massive stars shape their environments through powerful winds. Observations at these wavelengths require sending technology above the Earth’s atmosphere, where the desired signals are not absorbed. This makes CubeSats and SmallSats important tools for conducting X-ray science, providing low cost access to space for focused experiments. However, these missions require efficient, moderately sized X-ray detectors at low costs in order to perform their target science. My group is examining the performance of commercial CMOS sensors for use in soft X-ray observations and their potential as low-cost alternatives to CCD sensors.

We have shown that the CMOS performance is on par with CCDs currently in use in several major spacecraft, and their readout rates and operable temperatures are more favorable. My research focuses on measuring the quantum efficiency (QE) of the CMOS sensor, which will inform us upon the sensitivity of the instrument. I will develop a test bed in a specialized vacuum chamber equipped with an X-ray source that will allow us to make preliminary measurements at different X-ray wavelengths to determine the QE. Additionally, we plan to eventually further these measurements during a test campaign at the Advanced Light Source, an X-ray synchrotron facility.

While space flight is a crucial and exciting part of NASA’s mission, it can inflict a significant blow to one’s physiology. The intense vibration of an aircraft rattles the brain, resulting in changes on the cellular, molecular, and even cognitive level. Studies have demonstrated that helicopter pilots enduring chronic, low-level vibrations are more vulnerable to central nervous system (CNS) degeneration. Furthermore, mild traumatic brain injuries (mTBIs) can mimic neurodegenerative conditions such as Alzheimer’s and Parkinson’s Disease in their molecular and cognitive manifestation. Therefore, it is critical to the safety of astronauts to better understand the CNS effects of the vibrations endured during space flight.

Our lab employs a mouse model of mTBI on cognition and neurodegeneration. We test mice that have had mTBIs in different cognitive and behavioral dimensions, including an operant conditioning paradigm that assays working memory, attention, and decision-making. We then examine biomarkers of neurodegeneration in the prefrontal cortex, striatum, and hippocampus. A previous study conducted in our lab has highlighted a potential neuroprotective role of female sex hormones following mTBIs. We hope to expand this research to specifically investigate the repetitive vibrational injuries endured during spaceflight to inform safety procedures and possible neuroprotective interventions and treatments for traumatic brain injuries.

As Urban Air Mobility (UAM) emerges as a transformative transportation solution, ensuring equitable access and community acceptance becomes paramount for successful integration into urban environments. Current UAM development often overlooks community needs and may exacerbate transportation inequities if not carefully designed. This project addresses the critical gap between advanced autonomous air transportation technology and inclusive urban planning by developing machine learning frameworks that prioritize both safety and social equity in UAM system design.

Our research employs a multi-faceted approach combining deep reinforcement learning policies with hybrid LSTM-Graph Neural Network architectures to enable safe, efficient multi-agent UAV collision avoidance in complex urban airspaces. Using real-world data from Austin, Texas, we are creating robust simulation environments that model realistic UAM operations while developing equitable algorithms for airspace management. The project includes optimization of vertiport placement through Monte Carlo tree search methods and the creation of VertiCAP, an innovative planning tool that integrates community input into UAM infrastructure design decisions.

The research will produce scalable algorithms capable of managing high-density UAM traffic while ensuring equitable access across diverse urban communities. By incorporating community-driven design principles, the project aims to prevent the digital divide from extending into aerial transportation, ensuring that UAM benefits all urban residents rather than creating new forms of transportation inequality. The VertiCAP tool will provide municipalities with data-driven insights for inclusive vertiport placement and operational planning.

This project directly supports NASA ARMD’s Strategic Thrusts by advancing autonomous collision avoidance systems (Thrust 6), enabling safe vertical lift vehicle integration (Thrust 4), developing scalable airspace management for global operations (Thrust 1), and creating real-time safety assurance capabilities (Thrust 5). Our work provides NASA with foundational technologies for next-generation aviation infrastructure while pioneering community-centered approaches essential for sustainable and equitable aviation transformation in urban environments.