When Erik Palmer started his doctoral degree in mathematics at the University of South Carolina, he did not know what his research focus would be.
Doctoral student Erik Palmer returned to his hometown of Berkeley, California, to perform research on multiphase flows at the Lawrence Berkeley National Laboratory, made possible through the NSF's Mathematical Sciences Graduate Internship Program.
For the first few years, Palmer concentrated on passing his exams and adjusting to life as a doctoral student. When it was time for him to decide on a topic for his thesis, a serious event came crashing into his life—the premature birth of his son.
As Palmer would soon learn, lungs are one of the last organs to develop in babies. His son needed medicine to improve the lubricating qualities of lung mucus to help him breathe. After more than two weeks in intensive care, Palmer’s son came home, and Palmer had identified his thesis topic: developing mathematical models to reproduce human lung mucus behavior via computational fluid dynamics.
As his research progressed, Palmer sought to improve the performance and capabilities of his models, which required more experience and access to high-performance computing tools. Searching for such an opportunity, Palmer turned to the National Science Foundation’s (NSF) Mathematical Sciences Graduate Internship (MSGI) Program. The program provides research opportunities for mathematical sciences doctoral students to participate in internships at national laboratories, industries and other facilities.
Palmer returned to his birthplace of Berkeley, California, to conduct research on multiphase flow at the Lawrence Berkeley National Laboratory. Multiphase flow is the movement of materials with different states or phases. Common in nature, multiphase flow is also used in industries like energy production, pharmaceuticals, and chemical processing.
With the help of mentors Andy Nonaka and Ann Almgren, Palmer investigated the combination of fluid flow simulation with particle movement to create a single model capable of modeling particle effects on fluid behavior.
Palmer focused on studying discrete element particle collision tracking methods in order to determine the most effective method. He analyzed pertinent mathematical equations, created software prototypes, and implemented an effective solution. The proposed method was verified by generating a successful simulation of particles in fluid, which provided a promising step forward to improve modeling methods.
The research was conducted as part of the U.S. Department of Energy’s Exascale Computing Project, established to maximize benefits for the next generation of computing systems. Improved technology and faster computers necessitate enhanced code, and multiphase flow models—ubiquitous in countless applications—are an important part of coding and technology improvements. Advanced multiphase flow models can improve drug dosage and delivery methods.
For Palmer, the experience revealed new career opportunities and brought personal meaning. “The environment of open, friendly, and common exchange of knowledge was truly wonderful. I gained a long list of skills that will help my Ph.D. studies as well as make me a more marketable candidate across a variety of fields and industries in the future,” Palmer said.
Palmer also valued the ability to return to his birthplace with his son. “I grew up in Berkeley and could see the Berkeley lab at the top of the hill from my childhood home. It was really mind blowing to hold up my 2-year-old son and point to the lab and tell him that daddy was working there.”
Palmer returned to the University of South Carolina to complete his doctoral degree in 2018.
Hang Deng, a doctoral student and participant in the NSF’s Mathematical Sciences Graduate Internship Program, spent his summer using statistics and deep learning to research cervical cancer screening management.
Hang Deng also had the opportunity to utilize his MSGI experience toward impactful research in medicine.
Cervical cancer is the fourth most common cancer in women globally, with an estimated 265,700 deaths per year according to the World Health Organization. Cervical cancer deaths in the U.S. have decreased by more than half in the last 40 years with the use of the Papanicolaou (Pap) test, one of the most effective ways to detect pre-cervical cancer. Nearly 85 percent of worldwide cervical cancer cases occur in less developed regions, where access to screenings is limited.
The best way to prevent cervical cancer is through regular screenings. Using Norway as a case study, doctoral student Hang Deng applied his statistical knowledge to better understand the process of cervical cancer screenings and possible ways to improve testing frequency.
Under the mentorship of Ghaleb Abdulla at Lawrence Livermore National Laboratory, Deng helped analyze survey data of Norwegian women’s health information with the goal of developing personalized cervical screening policies.
Typically, women are recommended to have a Pap smear test every 1-3 years between the ages of 20 and 65. However, based on patient history, frequency of testing can be reduced without risk of failure to detect the condition, helping to cut medical screening costs and adding convenience.
To achieve their goal, Deng and the team adopted a deep learning approach using a model called long short-term memory (LSTM) neural networks. LSTM uses a technique that allows learning to occur over many time steps, training the system using a large amount of data.
Deng used a significant amount of women’s health data, as well as knowledge from previous trainings on LSTM neural networks to help train the current model specifically for cervical cancer. By inputting data including women’s previous screenings and test results, LSTM could begin to learn and predict personalized screening recommendations. The methodology can also be applied to similar data in other countries for cervical cancer or other diseases that share the same medical data structure.
“The project has a truly useful application,” Deng remarked. “I was highly motivated when I realized that we may actually make a difference in Norwegian women’s lives and hopefully [others]. The experience was great and unforgettable because it helped me broaden my research horizons. I was able to meet many people with different backgrounds and areas of expertise.”
James Brunner also got the chance to improve our understanding of health and wellness through the MSGI program. When Brunner began on his path of higher education, he knew he was interested in biology and medicine. While pursuing his undergraduate degree, he intended to apply to medical school to become a doctor.
Doctoral student James Brunner merged his interests in mathematics and biology and studied microbiomes during an internship with the NSF’s Mathematical Sciences Graduate Internship Program.
As his studies continued, however, Brunner realized that he was more fascinated by the theoretical problems that biology and medicine presented than their direct implementation. Inspired, he turned to mathematics to satisfy his curiosity and find answers.
Now a doctoral student in applied mathematics at the University of Wisconsin, Madison, he focuses on mathematical biology and bioinformatics.
The MSGI program allowed Brunner to perform interdisciplinary research and merge his passions.
At Los Alamos National Laboratory, Brunner’s internship enabled him to collaborate with biologists to perform microbiome research. A microbiome is a group of microorganisms in a particular environment, including human, animal, and natural or built environments. Microorganisms, which are essential to life on Earth, coexist in a network in each microbiome.
With his mentors Patrick Chain and Karen Davenport, Brunner investigated the use of network inference and analysis as applied to microbiomes. The team sought to determine how networks can be used to improve taxonomic classification from samples containing genetic material recovered directly from microbiomes. Identification of microorganism taxa and classification can be improved by studying microorganisms and their resulting microbial networks. The more microorganisms that can be classified, the more researchers can understand differences between various microbiomes and microbial communities.
Collaborating with biologists and computer scientists, Brunner spent most of his internship researching computational problems that would enable the team to build and subsequently analyze microbial networks. Overall, improved understanding of microbial communities can provide diagnostic tools to enhance detection of pathogens in clinical samples, as well as advance agricultural practices by investigating environmental samples.
Brunner appreciated the opportunity to apply mathematics and learn how it can be used to answer questions in the biosciences. “This program gave me the experience of working directly with biologists, and it taught me not only what sort of questions biologists are asking, but also how they are asking them and how they expect to use mathematical tools to find answers,” Brunner said. “I would certainly recommend the experience to other young mathematicians.”
To find out more about these experiences and further opportunities in STEM, visit the ORISE website. ORISE supports the DOE and other federal agencies’ missions to strengthen the nation’s science education and research initiatives. ORISE is managed for the DOE by Oak Ridge Associated Universities. Applications are now open for the NSF’s 2018 MSGI Program.