Summer 2020 interns

Tyler Billingsley (Purdue U.) – industrial internship at Corteva on “Initiating the process of hole formation in two-phase agricultural jets with background review of pollen drift”

The project began open-ended and narrowed to a specific task as we gathered information. I was first tasked to conduct a literature review to learn about techniques used in the pollen and spore transport modelling community. My search uncovered a wealth of research and models which could prove useful for Corteva, but I was particularly drawn to the Hybrid Single-Particle Lagrangian Integrated Trajectory model. This model had already been used in studies involving the spread of soybean rust spores, so I decided to explore how the model could be used for the benefit of the agrochemical industry. Based on the available data, it was decided that my focus should be current year simulations regarding the impact of Tropical Storm Cristobal on the spread of southern rust in June 2020. To check the idea that Cristobal affected the spread, an additional simulation that was identical to the previous one was run, except I used weather data from the previous year (when there was no tropical storm). The difference in the models’ outputs showed a drastic increase in spread to the northwest in 2020, caused by the tropical storm. This may explain why Nebraska is experiencing a southern rust epidemic this year. Corteva is going to work on refining the model, with the goal of translating into a warning system for both farmers and manufacturers of fungicides to determine when a fungicide should be applied, to prevent the impending arrival of southern rust and other diseases.

Dan Huang (Virginia Commonwealth U.) – industrial internship at Corteva on “How transformation affects power”

It was a great experience for me working with my team at Corteva. Our project focused on how transformation affects the power of hypothesis tests in the linear mixed model. Through this project, I learned a lot about simulation, linear mixed models and power analysis. Data with normal error distributions were simulated for two treatments across eight locations, with location effects, and interaction effects of location and treatment, included as random effects. After careful analysis, our results from the calculation of power with 10000 simulated data sets indicate that transformation in this project does not affect the power of hypothesis test based on data sets with normal distribution of error. The team at Corteva will explore further on this topic.

Chi Huynh (U. of Illinois) – scientific internship in Dept. of Electrical and Computer Engineering on “modeling and simulation of comparative epidemiological studies of various team deployment scenarios, to enable better understanding of the implications of gradual opening of enterprises and schools”

The Summer 2020 PI4-IMA project on Covid-19 infections and campus reopening scenarios gave me a glimpse of what I could do as a mathematician outside the academic setting. During this timely project, I used agent-based modeling to simulate the infection dynamics given a campus reopening scenario. I certainly learned a lot about the difficulties and work involved in mathematical modeling. Apart from leveling up my proficiency in computer programming with Python, I also learned the thought process that goes into formulating a computational simulation. All in all, it has sparked my interest in learning more about how mathematical modeling is used in other settings.

Alec Martin (U. of California, Riverside) – industrial internship at Wolfram Research

I was excited when I found that, despite cancellations nationwide due to COVID-19, the team at PI4 was able to set me up with an IMA-funded internship at Wolfram (fully remote, of course). I worked with the Wolfram|Alpha math content team, developing new features for their Step-By-Step Solutions. There was a lot to learn – the Wolfram|Alpha project is immense and I was new to the Wolfram language – but the team was prepared for orienting interns and I was able to develop an entire feature, Step-By-Step Solutions for computing the norm of a vector, during my time with Wolfram. This internship was my first experience with the technology industry and my first experience with a career path outside of academia and I couldn’t be happier with how it went. The teamwork involved, the opportunity for development at whatever level solves the problem, the ability to develop software intended specifically for mathematics – the experience was simultaneously challenging and incredibly rewarding.

Tung Nguyen (U. of Chicago) – industrial internship at Kibo Commerce

My internship at Kibo Commerce has been fun and challenging. I learned tons of new stuff about applied data science in the real world. As well as advancing my technical skills, I learned that the role of a data scientist is not just about building the strongest models but also about helping clients make better business decisions. I really appreciated that the working environment at Kibo is very collaborative: I always got help whenever I ran into a technical problem. Furthermore, my mentor was always generous with his time and expertise.

I am not exaggerating when saying that it is a lifetime experience for me. Unlike my prior beliefs, a career in the industry could be fun, challenging, and meaningful. I am now excited to start a new career in industry.

Jungsoo Park (U. of Illinois) – scientific internship in Dept. of Speech and Hearing Science on “Data analysis of the rest state fMRI traces collected as a part of the Human Connectome project”

The long term goal of the project is to identify the functional connectivity of our regions of interest and their relation to hearing disorders. The particular summer project performed cyclical analysis on 69 regions of interest, using Python packages to determine whether there are possible cycles. We analyzed more than 3500 samples corresponding to subjects in their rest states. Previously, the fMRI data has been preprocessed into leading matrices corresponding to a weighted directed graph. We were able to determine two possible cycles looking at the first and third leading eigenvectors. The first showed higher correlation – roughly more than half the samples had a correlation of 0.5 or higher – while the latter showed a lower level of correlation.

Alyssa Whittemore (U. of Nebraska, Lincoln) – industrial internship at Ocuvera

As a graduate student in Mathematics, I have had quite a bit of experience with math, but little of it has been outside of academia. Because of the Summer Internship Program through the Program for Interdisciplinary and Industrial Internships at Illinois (PI4) and Institute for Mathe matics and its Applications (IMA), I was able to participate in a six-week internship with Ocuvera, an artificial intelligence technology start-up based in Lincoln, Nebraska. Ocuvera has developed a system that helps nurses to prevent patient falls in hospitals. Throughout the internship, I gained experience with coding in Python and C#, using Deep Learning, and sorting through data using MongoDB. I was able to collaborate with mathematicians and software engineers working in industry and help develop a model to detect people sitting in chairs in hospital rooms. This internship allowed me to confirm that my goal is to work in industry or government after I have finished my degree.

Summer 2019 interns

Catherine Chen (Georgia Institute of Technology) – scientific internship in Dept. of Microbiology

Nicholas Connolly (U. of Iowa) – industrial internship at Ameren

Elisa Negrini (Worcester Polytechnic Institute) – industrial internship at AbbVie

Jinhua Xu (U. of Illinois at Chicago) – industrial internship at AbbVie