Current Research Projects and Interests
Exploring the Factors Affecting Pulmonary Toxicity and Other Occupational Risks Associated with Nanomaterials
Given the unique properties of nanomaterials (engineered particles smaller than 100 nanometers) in which they can act both unlike bulk solids and also unlike chemical solutions, it is possible that new risks to human health or the environment may arise from a nano-version of a material we already know. Since we do not have the same wealth of experience with these materials as we do with other potential chemical pollutants, there is not yet agreement on which specific measurements or attributes of the nanomaterials will be necessary to predict risk.
This work has focused on a meta-analysis of in vivo pulmonary exposure experiments with machine learning models known as Regression Trees (RT) and Random Forests (RF) that can effectively extract information from the sparse data set. The intent is to gain as much understanding from the accumulated data to date, creating mathematical models that can inform lower risk nanomaterial design, regulation policy, and occupational safety assessments.
▪ Journal article (Risk Analysis, 2013), author′s pdf, “How physical dimensions and impurities affect the pulmonary toxicity of carbon nanotubes”
- Published data set and model validation document (via nanoHUB.org)
▪ Conference paper (Proc. of Intl. Mech. Engr. Congress and Expo., 2013), author′s pdf, “Visualizing nano-characteristic interactions and pulmonary toxicity risks to inform the design of safer nanoparticles”
- Published machine learning models (via nanoHUB.org)
Continuing research in this area focuses on geometric-chemical interactions between nanoparticle properties and toxicity; the development of models for taking a variety of characteristics into account when setting occupational exposure limits for nanomaterials; and developing an understanding of the correspondence between the results of low-cost, high-throughput in vitro nanoparticle toxicity assays and high-cost low-throughput in vivo toxicity experiments.
The Use of Machine Learning Models and Surrogate Risk Data to Inform the Engineering Design Process
The engineering landscape is one of constant technological evolution and innovation. Meanwhile, the most serious risks like fatal workplace accidents, major oil spills, or critical system failures (e.g., wide area electricity blackouts) can be relatively or very rare. Traditional statistical analyses are not very useful in assessing rare events against constantly changing technology. This situation can leave design engineers without the quantitative guidance required to fully evaluate the tradeoffs between cost and the likelihood of serious failures.
Using automated learning algorithms coupled with the wealth of surrogate data compiled from test results, non-critical failures, and the performance of similar systems or feedback from distributed sensor networks may provide the knowledge necessary to improve engineering designs to reduce the likelihood and severity of possible risks resulting from hardware or software failure modes.
The Safety and Reliability of Complex Engineered Systems
Engineered systems were once relatively costly, self-contained mechanical or electrical units with well defined single functions, and a very limited set of physical inputs overseen by an individual worker. Predicting the maintenance requirements and safety hazards of such a system is relatively straightforward. Today, inexpensive devices are combined into complex networks, which are often repurposed after assembly and installation to serve new functions not envisioned during their design.
Predicting how component failures will impact the performance of such complex networked systems, or how emergent system functionality may fail without any specific sub-component failures is a task critical to understanding the risks posed by these technologies and their proposed uses, and a task that necessitates novel simulation techniques to fully explore possible scenarios that even the designers may not have imagined.
Design of a Methanol Microreformer for Portable Fuel Cells
As fluids pass through smaller and smaller channels Newtonian behavior and common assumptions like the no-slip condition begin to break down, since molecules start to act more like beads running through a pipe rather than a continuum of infinitely differentiable units. My work involved the computationally intensive optimization of a channel geometry operating in the transition range between microchannel flow dynamics and Newtonian flow dynamics. The result was the design of a small scale device to produce hydrogen gas from methanol sufficient to power a 1 Watt fuel cell.
▪ Journal article (Heat Transfer Engineering, 2009), author′s pdf, “Spiral methanol to hydrogen microreformer for fuel cell applications”
▪ US patent (No. 8,034,134; Oct 2011)
▪ MS Thesis (Rice University)
The Health and Safety Criticality of Microgravity Countermeasures Equipment
While in a microgravity environment, the human body adapts in ways that can be detrimental to health and safety upon return to Earth. These effects can be slowed and sometimes halted by the use of countermeasures, mostly specialized exercise equipment. However, NASA initially classified these countermeasures as non-critical equipment given their seemingly simple designs, ease of repair, and lack of immediate failure consequences. Experience in the early stages of the International Space Station (ISS) revealed that the non-critical designation adversely affected design for reliability, and that logistics supply issues often prevented timely resupply of spare parts contributing to a significant lack of system availability.
▪ Conference paper (Proc. of Intl. Mech. Engr. Congress and Expo., 2004), author′s pdf, “Mitigating crew health degradation during long-term exposure to microgravity through countermeasure system implementation”
Identifying Poor Reliability Parts from Logistics Warehouse Orders
Accurately tracking the usage rate of a widely dispersed fleet of equipment after the end of the warranty period can be difficult or impossible, yet understanding that usage rate is critical to parsing out reliability issues from high rates of otherwise normal consumption. As a Research Associate with the RAND Corporation, I investigated the potential to identify poor reliability parts from changes in trends of parts orders recorded by a centralized logistics warehouse database. These kinds of statistical tools can aid manufacturers in more quickly identifying and correcting emerging reliability or longevity problems regardless of the dependability of customer surveys or other performance tracking methodologies.
Other Research Interests
Agricultural Systems and Nutrition
By many measures agriculture is the most dominant impact of human civilization on the global environment. Yet, this system oversupplies easily digestible calories and undersupplies vegetables in the U.S., while undersupplying adequate nutrition in the developing world. With world population now over 7 billion and heading upwards of 9 billion by 2050 combined with the uncertainty of climate change effects, the management of agriculture will be critically important to future human health and security. A systems optimization approach to this problem may provide useful criteria with which to benchmark our progress in achieving stability in our food supply.
Effects of Engineering System Design on Public Health and Safety
The existence of building codes, workplace regulations, and the legislative mandates of safety features for automobiles and aircraft reveal the potential for benefit or harm engineering design can have on the health and safety of the general public. While regulatory agencies and liability law keep most organizations focused on meeting the letter of existing standards, this paradigm limits the amount of improvement engineers may be able to achieve. Research to increase engineering understanding of public health impacts of design, and increase public health researchers understanding of engineering design capabilities could accelerate improvements in public well being.
Originally from the Houston, Texas area, I earned a B.S. in Mechanical Engineering from Texas A&M University. Following two years in the U.S. Peace Corps as a Math and Physics teacher in Guinea, West Africa, I returned to Houston as a Safety and Reliability Engineer at NASA′s Johnson Space Center. There I analyzed the severity and likelihood of the risks of engineered systems contributing to improvements in the safety and reliability of the International Space Station crew health care equipment. I later continued these efforts with greater quantitative emphasis at Northrop Grumman Electronic Systems as a reliability engineer predicting failure rates of new equipment.
During my time in Houston, I earned a M.S. in Mechanical Engineering from Rice University conducting research into micro-scale fluid and heat transfer, and designing a microchannel methanol-steam reformer. In 2013, I completed a PhD at Carnegie Mellon University in Engineering and Public Policy developing methods to enable early quantitative risk assessment of the inhaled toxicity of engineered nanomaterials. Now at Penn State University as an Assistant Professor of Industrial Health and Safety, I am developing a research program focusing on the occupational health, system safety, and reliability risks of new technologies, and improving assessment of risks within the engineering design process.