I’m a neurologist and an amateur astronomer, interested in combining my profession with my hobby by being involved in the field of space neurology. I am interested in furthering humanity’s understanding about space neurology and how to mitigate known neurological adverse effects in astronauts.

Currently our understanding about space neurology is very limited, and only very few neurologists are knowledgeable about what we know in this field.

I am currently involved in staying updated on the latest research about space neurology, and educating neurologists and astronomers about this field so that more neurologists are aware and prepared for future increase in space travel and the potential they will be involved in this.

Neurological symptoms and complications are very common among astronauts, by improving our understanding of space neurology we are able to better help astronauts with these symptoms which can greatly enhance the experience of space travel.

Measure kinetics for high temperature surface reactions responsible for etching, sublimation, and passivation of materials like carbon, ultra-high temperature ceramics (e.g. HfC).

Arc or plasma torches, or oven/furnace methods. Typically measure phenomenological rates for mass loss or oxide layer thickening. Oven/furnace methods limited in upper temperature acheivable.

We trap single particles in the gas phase, laser heat, measure temperature by emission spectroscopy, and determine reaction rates by measuring particle mass loss. Single particle work is complemented by surface science and microscopy measurements.

There is interest in developing and modeling materials for non-ablative thermal protective layers, and our work provides rates for microscopic reactions involved in etching, sublimation, oxide decomposition/desorption, and passivation as a function of temperature.

We undertake experiments to study the interactions of fluids and surfaces in relative motion. Our goal is to enable quieter and energy-efficient vehicles in aerospace and naval applications.

Most technologies in practice today are based on laboratory based investigations which are usually scaled studies under simplified conditions. To study them at full scale is very expensive and is not amenable for repetitive and controlled investigations.

Our approach uses atmospheric flows over unique natural surfaces to mimic the vehicle flows, that translate to full scale and realistic scenarios.

If successful, the datasets obtained will be used to validate cutting edge computational models. This in turn will enable batch testing of several candidates for noise reduction and energy efficiency. This will further enable practical technologies that will translate to enhanced public health, climate preservation, military superiority, and tax dollar savings.

Ensure the health and well-being of all aerospace workers.

We can direct the setup and integration of a thorough employee clinic within the University of Utah healthcare system to provide timely and exceptional care.

Work collaboratively with the University of Utah.

To protect all aerospace workers throughout their career.

I am an engineer and neuroscientist in the field of Radiology with 25 years of experience using imaging to study neurodegenerative disease and CNS disorders and developing creative methods to investigate brain function. My research goal is to make a significant impact on therapeutic and diagnostic options for neurological disorders. I pioneered research at the University of Washington that applied low dose microtubule stabilizers via intranasal administration to treat Alzheimer’s disease and brain injury. At the University of Utah, I partnered with world-experts in therapeutic drug delivery to advance this idea with a novel polymer-drug compound.

Treatments for neurodegenerative diseases and neuronal injuries are few and have limited effectiveness.

Our therapeutic approach stabilizes microtubules with targeted delivery to the CNS. Microtubule function not only underlies the neuronal cytoskeleton and is critical for essential processes such as axonal transport, but also other structural and functional aspects of brain function such as astrocyte blood-brain-barrier integrity and microglia motility and proliferation.

If we succeed in slowing or reversing neurodegeneration it will significantly impact CNS health for millions, perhaps billions of people worldwide.

My lab focuses on building and designing materials for extreme aerospace/space environments, with applications from rocket engines to extra-terrestrial habitats.

Low throughput techniques that limit materials design.

Using high-throughput techniques to accelerate materials design for aerospace and space applications.

NASA and many space/aerospace companies. Facilitate reusability of rocket engines to launch payloads as well as help with deep space missions.

Understand thermal transport properties of materials at ultrahigh temperatures.
Find new materials that can withstand ultrahigh temperatures with extreme (either high or low) thermal conductivities.

Current theoretical methods are good for low temperatures, and will fail in predicting thermal transport behavior at ultrahigh temperatures.

We develop new atomic simulations and explore the deep physics of thermal transport at ultrahigh temperatures. From that, we design new materials with desired properties.

Thermal management of hypersonic vehicles, thermal barrier coatings of gas turbines.

We want to be able to detect, diagnose, and locate electrical faults in aircraft wiring and interconnection systems. We can do this today (but the system would be best if integrated into the system at the design stages. We would like to expand this to use with batteries and electrical storage systems. In addition, we want to be able to predict (prognose) electrical problems in advance of them becoming catastrophic failures.

Today, this could be done with spread spectrum time domain reflectometry (SSTDR) IF it can be integrated with the aircraft (generally from the design stage). It can also be used after market, but has more limitations that way.

SSTDR can test on electrical systems while they are live / energized and fully functional without requiring turning the system off and disconnecting the loads.

This could greatly improve aircraft (and other types of) electrical maintenance.

My research group develops models for the mechanical behavior of materials using experimental methods, microstructure characterization, and machine learning.

Material models are currently developed with a heavy reliance on mechanical testing, which limits our ability to introduce new materials and manufacturing methods into the market quickly and efficiently.

Machine learning, guided by the principles of mechanics and physics, has already been demonstrated to develop more accurate models while reducing the heavy reliance on mechanical testing.

If successful, next-generation materials and structures can be certified faster, accelerating the introduction of, for example, new vehicles for urban air mobility (travel).

Observe, aggregate, and disseminate weather observations and simulate atmospheric flows relevant for unmanned aerial flights.

Operational atmospheric prediction models have insufficient detail for planning drone flight operations.

Simulations of boundary layer flows at 5 meter resolution using GPU computing systems have been shown to have potential for providing the detail required for predicting winds affecting drone operations.

Obtaining atmospheric parameters from drones coupled with improved modeling will improve the safety and reliability of drone operations.

In my lab, we quantify material behavior under extreme conditions using a variety of novel tools and imaging techniques. The materials investigated have applications in impact mitigation, vehicle weighting, solider protection, and nuclear energy production.

The information that can be extracted during high strain-rate experiments is limited due to the short timescales involved. Timescales range from 100 nanoseconds to milliseconds, making it difficult to obtain precise full-field measurements. Furthermore, most high strain-rate experiments are conducted under ambient conditions, which do not reflect the application temperatures to which the obtained data is applied.

We focus on maximizing the output of each experiment conducted through the incorporation of as many diagnostic tools as possible. Typically we integrate precise specimen heating, real-time temperature measurement (>1M samples/second), and full-field imaging. Using these tools, we push forward our understanding of material behavior to allow for greater modeling fidelity.

The work done in our lab broadly benefits society by identifying candidate materials that provide improved fuel efficiency, reduce greenhouse gas emissions, secure energy futures through advancing next-generation nuclear reactor technology, and ensure the safety of soldiers and military vehicles.

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My work focuses on understanding and predicting the cognitive and motor deficits in aging and neurodegeneration. I use neuropsychological testing and motor behavioral assessments to map behavioral profiles. Using magnetic resonance imaging I evaluate the relationship between functional and structural brain aspects with these behavioral profiles. In previous work, I assessed how cognitive, motor, and brain health are affected by spaceflight and if brain metrics can predict post-landing task performance.

Most work today focuses on a limited number of behavioral assessments that are being evaluated using mass univariate analyses. This overlooks correlational structures between behavioral assessments and may be less powerful in detecting brain-behavioral associations.

I extract motor and cognitive profiles using supervised machine learning tools. This takes advantage of the breadth of motor and cognitive functioning and can identify those individuals who are most affected by the effects of microgravity on the central nervous system due to spaceflight.

Identifying those individuals whose cognitive and motor performance is most affected by spaceflight could be selected for training to mitigate such effects.

Research focuses on control systems, robotics, uncrewed aerial vehicles, machine learning, mobile robots, and estimation theory.

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Our group leverages advanced control system design, probabilistic techniques, and estimation and machine learning algorithms for autonomy.

Applications for search and rescue, environmental monitoring, etc.

Advance medical imaging for people in space. Help develop methods to protect humans from cosmic radiation.

Cross-sectional imaging devices, essential for medical care, are challenging to implement and operate in space and on other planets due to their size, complexity, and durability requirements. Humans will be exposed to higher levels of radiation during space travel and potentially on other planets.

Industry collaboration and brainstorming to develop new imaging devices that can be operated in spaceships and on other planets. Help develop small molecule drugs that make cells more resistant to radiation. Also, help devise radiation protection methods in space.

Help support health and healthcare for people in space.

Develop simulation and modeling strategies to improve chemically reacting flow predictions.

Current models are often insufficiently general, inaccurate, and expensive.

My group leverages physical insights combined with state-of-the-art computing techniques to achieve better models.

Better models will lead to faster, cheaper design of advanced propulsion systems and hypersonic vehicles.

Alloy design and non-traditional manufacturing catering to the needs where extreme conditions exist, including high temperature , highly corrosive environments, and highly complex systems like human body.

Alloy design has been developing by trial-and-error, which is time and labor consuming. With this, tailored non-traditional manufacturing has not caught up yet, with a huge gap to enable the application-oriented uses of alloys under extreme conditions.

We follow new materials design approach of integrated computational materials engineering approach, which makes alloy design straightforward. With this, we bridge processing/manufacturing-structure-performance knowledge to enable critical alloy applications under extreme-condition service.

Create truly useful new alloys and expand their applications in aerospace, defense, navy, and bio devices. More importantly, an one-stop alloy design, manufacturing optimization, and performance enhancement solution can be provided.

At the VPR level, we are developing an Aerospace Hub at the U by creating and strengthening relationships between local Aerospace industries, NASA, the DoD, government agencies, and other academic institutions.
Individually, we are validating fiber optic strain sensors embedded in aerospace composite materials.

Today, the development of relevant, multi-disciplinary research that meets national aerospace objectives is ad-hoc, narrowly focused, and less productive.
Conventional aerospace strain and temperature sensing rely on limited numbers of obtrusive, surface-mounted sensors and heavy bundles of electrical wiring, which are inefficient for aerospace applications.

The Hub’s approach is to invest in fewer but more strategic aerospace research areas that directly support university, state, and national objectives.
On the research level, the approach is to develop embedment methods, characterize performance, and validate measurements of optical fiber sensors embedded within composite materials.

A successful Aerospace Hub at the U will provide researchers with opportunities to work on larger, more game-changing problems that align with the goals of the university, state, and national leadership.
Lightweight, composite structures that can accurately infer external structural shape, characterize internal strain state, and measure temperature field response, in real-time can improve the capability of aerospace vehicle structural safety and performance.

In our lab, we try to predict when, where, why, and how structural materials fail, with an emphasis on fatigue and fracture.

Most predictions of material failure fall short in terms of representing the salient microstructural features that play a role in mechanical behavior, or, if they do succeed in representing the salient features, are too computationally expensive to solve meaningful problems in real applications.

We implement and leverage state-of-the-art computational frameworks to represent the salient microstructural features that play governing roles in material deformation and failure, while pushing the limits of computational efficiency. Our physics-based models include crystal plasticity implementations with novel approaches for capturing grain-size effects, representing three-dimensional fracture surfaces, and performing high-throughput microstructural simulations. Our approach to data science leverages our domain expertise to account for the nuances of feature engineering, data sampling, cross validation, and requisite context needed to achieve an envisioned application of AI.

The physics-based and AI models that we develop will enable advanced structural prognosis as well as materials design for a broad range of applications.

My research program in aerospace focuses on two main objectives:

(1) Developing Sustainable Energy Sources for Aerospace Applications: The primary focus is creating lightweight, renewable solar energy systems that can function effectively under harsh environmental conditions, such as extreme temperatures, varying pressures, and high radiation levels. These systems are designed to be resilient and efficient in aerospace applications.

(2) Characterizing Active Defects in Semiconductor Materials and Devices: Another key objective is to analyze and understand the evolution of defects within semiconductor materials and devices, especially under different environmental conditions. This research aims to enhance our knowledge of how these defects influence material performance, which is crucial for improving the reliability and efficiency of semiconductor devices.

(1) Sustainable Energy: Photovoltaic (PV) devices are mainly produced from bulk single crystalline Si materials. Current technologies need to fully leverage the potential of alternative materials, such as microstructured semiconductors or thin films, which could offer superior radiation resistance and cost benefits.

(2) Defect Characterization: Current defect analysis is often limited due to the measurement scale that is not well-aligned with the dynamics of carriers within the materials. Moreover, these methods tend to overlook critical local characteristics, frequently the primary limiting factors in high-performance devices.

(1) Enhanced Device Performance: Our approach leverages the unique properties of microstructured semiconductors, allowing light absorption and conversion processes to decouple. This can lead to significant improvements in device performance under radiation. By focusing on these advanced materials, we aim to develop more resilient and high-performing energy devices suitable for aerospace applications.

(2) Advanced Measurement Platform for Semiconductor Defects: We are developing a novel measurement platform that provides a deeper understanding of material properties under various environmental conditions. This platform is designed to capture the critical characteristics of semiconductor materials at the micro and nanoscale, offering valuable insights that can guide the development of next-generation semiconductor devices.

(1) Energy Devices: Successful development of these technologies could result in cost-effective, high-performance energy devices that are better suited for aerospace applications. Our reliable energy source can open new pathways for novel integrated systems and space exploration.

(2) Semiconductor Device Improvement: The new measurement platform would identify and mitigate defects in semiconductor materials, leading to the creation of more reliable and durable devices.