SAMSI Graduate Student Profile: Wen “Jenny” Shi

Wen “Jenny” Shi is a graduate student at the University of North Carolina at Chapel Hill (UNC) in the Statistics and Research Operations Department. She is a graduate fellow at SAMSI this year.

Wen Shi hiking with her father

Wen “Jenny” Shi with her father.

Jenny was born and raised in Wuhan, China, the capital of the province where the Three Gorges Dam, one of the largest hydroelectric power stations in the world is located. Jenny’s parents separated and her mother moved to the United States when she was 9-years-old. At first, Jenny’s father was reluctant to have her follow her mother, but he agreed that Jenny would have better scholastic opportunities in the United States and finally let her apply for immigration in 2000. It took about four years to get her green card because it was held up after the events of 9-11. While she was waiting to come to the United States, she started studying finance in China, and thought that she was going to work in the field of business.

Right before her 20th birthday, Jenny moved to the United States and entered Peralta Colleges in Northern California. Her major took a turn after meeting an incredible mathematics teacher, Salvador Garcia, who noticed her talent in mathematics and encouraged her to consider mathematics as a major. Instead of making her take the final exam, Salvador gave Jenny a book on the history of mathematics to read and let her keep the book as a gift. “I realized how fascinating the world of mathematics is and that studying math can make me truly happy,” said Jenny.

Jenny Shi and her mom and brother

Wen “Jenny” Shi at her graduation with her mother, Lily and her brother, Lewis.

After two years in Peralta Colleges, she transferred to the University of California at Berkeley (Cal) as a math major with focus on pure mathematics. Jenny treasured the opportunity of studying at Cal very much. In fact, she tried to maximize her education there by taking as many classes as she could, at both undergraduate and graduate levels. She had her mind set on pursuing graduate study in mathematical sciences. A Ph.D. program in mathematics was her top choice. However, after realizing the extreme competitive nature of pure mathematics and a wealth of career options with a graduate degree in statistics, Jenny decided to apply for the statistics Ph.D. program at UNC in 2009.

Jenny Shi and Mac

Jenny with her dog, Max.

In the following year, Jenny moved across country and became a Tar Heel. Her dearest companion, Mac, her German Shepherd/Golden Retriever mix, also made the move with her. Mac and Jenny love running in the neighborhood in Chapel Hill/Carrboro, hiking in the woods around the Triangle region, and swimming in Jordan Lake in summer time.

At UNC, Jenny works with Professor Jan Hannig in the Department of Statistics & Operations Research and Professor Corbin Jones in the Department of Biology. Her research interest focuses on fiducial inference and statistical applications to biology and genomics. She is participating in the Dependence in Evolutionary Models working group at SAMSI and working on stochastic modeling of gene expressions with phylogenetic dependency. “I am learning a lot from the biologists, statisticians, and mathematicians in the group. There are many intelligent minds bringing interesting questions and great ideas to the table,” noted Jenny, “I am really glad that Chris, the postdoctoral fellow at SAMSI who is a bioinformatician, is leading the group. He has been very organized. He makes sure that throughout every meeting that people get something good out of it.”

Jenny has asked many questions and learned a lot from the other people in her working group. She is going to present her project later this spring.

Jenny has also been attending the graduate school classes at SAMSI. “I think these classes are challenging to teach because the instructor has to teach a diverse class. There are graduate students, postdocs, junior faculty members, and people from industry; there are biologists, mathematicians, statisticians, and MD. Ph.D. students.” She also noted that the instructors are making great effort to ensure that the lecture materials are useful and graspable to the majority of the class.

Jenny is applying for graduation this summer. She is leaning towards staying in academia but is open to a teaching and research role, or going into industry.

Jenny loves to cook and bake when she is not at school or hiking with Mac.

Finding the Tipping Point

The following was written by Karna Gowda, a graduate student at Northwestern University in the Department of Engineering Sciences and Applied Mathematics, and SAMSI visiting fellow for the Ecology Program this year.

Group shot of Tipping Point working group

Pictured (L-R): Christopher Strickland, SAMSI postdoc, Jake Norton, NCSU graduate student, Karna Gowda (on the screen), graduate student at Northwestern University and Lou Gross, University of Tennessee-Knoxville. Not Pictured but frequent participators are: Philip Dixon, Iowa State and Michael Just, NC State

Imagine balancing a pencil on your finger. If you tap one end a little bit, it will return to balance. Tap it more forcefully, and it will fall. This simple example illustrates one notion of a “tipping point”: the tipping point here is the smallest force you can apply that causes the pencil to fall.

Tipping points are thresholds. When crossed, significant change follows. A classic example occurs in lake ecosystems, where small changes in water nutrient composition can result in sudden qualitative changes in the water’s clarity due to growth of microorganisms. A change like this can disrupt the balance of the ecosystem. Sunlight levels drop more rapidly as the water’s clarity drops, which reduces growth of food resources for many aquatic species, including fish.

There are numerous other examples of potential tipping points in ecosystems, many of which may result in a loss of biodiversity and land degradation. This has led researchers to study how data and mathematical models can be used to predict tipping points, so that catastrophe can be averted. As part of the SAMSI Program on Mathematical and Statistical Ecology (2014-2015), we in the “Tipping Points with Forestry Applications” working group broadly focus on this topic. How can data from forest inventories, bee population surveys, and long-term grassland ecosystem studies be used to understand and potentially predict tipping points?

A current focus of our group is on the time series analysis methodology developed by Sugihara et al. (Science 2012). The methodology is based on the idea of attractor reconstruction, wherein partial information about a highly complex (nonlinear and potentially chaotic) dynamical system is used to construct a representative picture of broad system behavior. This picture is used to assess whether different elements are causally related (i.e. coupled or driven) within this dynamical system. Sugihara et al. use time series of sardine and anchovy populations, along with sea surface temperature data, to infer that the populations are driven by temperature and are not clearly affected by one another.

So far, the working group has explored how this methodology can be used to understand the relationships between populations and their drivers when ecosystem data are sparse. Attractor reconstruction techniques require ample well-resolved data, and often these data simply do not exist. A proposed work-around entails using multiple time series collected from different points in space in lieu of only a single time series. Though the data at each point in space may be short, putting together the data over space may provide the attractor reconstruction technique with enough information to draw a clear conclusion.

Understanding the relationships between ecosystem populations and their drivers is a crucial aspect of tipping point prediction, and is an inherently interdisciplinary task. The SAMSI Program on Mathematical and Statistical Ecology has brought together researchers from a number of backgrounds to tackle problems such as this. Collaboration between statisticians, ecologists and mathematicians within the Tipping Points working group so far has been illuminating and fruitful, and we look forward to sharing our insights as they develop!