Recruiting Now: Summer 2019 Research Experience for Undergraduates (REU) Program at Harvey Mudd College

UPDATE: Recruitment for the REU Program has concluded (3/26/19).

Once again this year, Harvey Mudd College will serve as an REU (Research Experience for Undergraduates) Site – offering five different projects for nine students. The REU will last from May 28th to August 2nd, 2019. The following projects (titles and descriptions below) will be running this summer:

Prevalence and Propagation of “Fake News” (professor Susan Martonosi, mathematics)

The prevalence and propagation of “fake news” has garnered international attention following the 2016 U.S. presidential election. The mechanisms by which fake and/or biased news articles are propagated are an active area of research, particularly as social media outlets such as Facebook are increasingly being asked to play an active role in fake news detection and deterrence. This proposed research project will build on last year’s work to further develop data and a probability model for the likelihood that a given user, whose beliefs lie on a continuum, will share a news article characterized by an observable bias and level of truthfulness. Using that probability model, we will develop a framework that determines the optimal distribution of bias and truthfulness of articles produced by a malicious agent to maximize propagation within a population having known belief distribution. This work will provide insights into the optimal characteristics of biased and/or “fake” news, which can then be used within a game theoretic framework to develop defensive strategies. The data science student researchers will assist in validating our models against publicly available social media data.

Invisible Cyclists and Road Network Analysis (professor Paul Steinberg, humanities, social sciences, and the arts; and professor Tanja Srebotnjak, engineering)

Active forms of transportation, such as walking and bicycling, have many documented benefits including improved public health outcomes, reduced pollution and traffic, and enhanced revenues for local businesses. Social justice has emerged as a major theme within active transportation research.  Cycling is not merely a recreational activity, but a vital transportation option for those who lack access to automobiles – particularly the poor, but also undocumented workers, children, and the elderly. Latinos and African Americans report the strongest interest in cycling, yet often lack access to bike lanes and suffer higher accident rates and are rarely represented in policymaking, giving rise to the term “invisible cyclists.” This project entails a transportation needs assessment of underrepresented populations in Claremont to help city officials adopt an equitable and inclusive approach to sustainable transportation planning, including for the extension of the Gold Line commuter rail project.  The student hired through the REU will engage in data collection through in-person surveys as well as statistical and spatial analysis of the results.  Prerequisites include fluent (ideally native) Spanish as well as training in statistical analysis and ideally experience with Geographic Information Systems (GIS).

Analysis of RNA-seq Data (professor Daniel Stoebel, biology; professor Danae Schulz, biology; and professor Jo Hardin, Pomona College mathematics)

Biologists can measure the transcription of all genes in the genome using a technique called RNA-seq. This technique uses modern high-throughput sequencing techniques to sequence all of the RNA isolated from a group of cells. This sequence data is then analyzed to measure levels of expression of each gene. Further analysis is then used to, for example, cluster genes and/or growth conditions, or to determine what genes differ in their levels of expression across conditions. This project focuses on the analysis of time course RNA-seq data. The project is motivated by two experimental RNA-seq data sets. The first is for the parasite Trypanosoma brucei, which causes sleeping sickness in humans and is transmitted from person to person by a tsetse fly. As the parasite cycles between the human bloodstream and insect environments, it changes the expression levels of around 1/3 of the genes in its genome. The second data set is for E. coli, which changes the expression of its genes in response to the transition from exponential growth to starvation. Students will be required to identify appropriate methods for normalizing the data, an essential and experiment-specific first step for all RNA-seq analysis. They will then use current data science approaches to analyze time course data, including clustering methods to identify groups of genes with similar expression patterns and identifying genes and gene networks for problems of both unknown and pre-specified biological structure. It will be important for the student to assess the appropriateness of each bioinformatic tool to the problem at hand. After gaining a thorough understanding of the process and methods, the student(s) will be able to address the biological research questions. Desired skills/background: Course work in statistics, familiarity with R, and an interest in biological problems.

Spatial modeling of the climatic ecology and geographic range of a desert lizard (professor Steve Adolph, biology)

This project will combine mathematical models of population dynamics of the desert lizard Xantusia vigilis with spatial and temporal datasets on precipitation and temperature.  We will use GIS and other spatial statistical methods to define the climatic niche and geographical range of this lizard species. We will then couple this spatial model with local models of population dynamics to predict spatial variation in population dynamics.  Ultimately, we would like to develop a predictive model for how this species will respond in space and time to predicted climate change in California.

Modeling Type I Diabetes (professor Lisette de Pillis, mathematics)

The interested research student will work on the development and computational implementation of a multi-compartment model for describing the onset and progression of Type I diabetes. Part of the work will focus on implementing statistically based nonlinear mixed effects approaches for incorporating diabetes time series data into a deterministic model system. The particular tasks will be: parameter fitting, model testing, simulation (MATLAB), literature review, and documentation of work. Preference will be given to mathematics students who have taken differential equations or modeling and have some prior experience with programming. Knowledge of cell biology, biochemistry or pertinent laboratory experience is highly valued but not required. We expect the student to meet regularly for research meetings and be able to work independently on assigned tasks.

Decisions are made on a rolling basis. For more information, please email the REU administrators Tanja Srebotnjak (tsrebotnjak@g.hmc.edu) or Lisette de Pillis (depillis@g.hmc.edu).