Postdoc Profile – Christopher Strickland

Christopher Strickland on a hill with the ocean in the background

Christopher Strickland hiking in New Zealand.

SAMSI postdoctoral fellow, Christopher Strickland was born in Houston, Texas and lived briefly there and in Dallas before he could really remember either place. He grew up in Oxford, Mississippi. His grandfather was Chair of Modern Languages at the University of Mississippi and helped to establish a study abroad program, and his grandmother was originally from France, so many summers his father and grandfather traveled to France. When Christopher went to ” Ole Miss,” in the honors college, he minored in physics before switching degrees and getting a double degree in Mathematics and French.

At first he was following a more pure math route. He went to the University of Florida in Gainesville for his Master’s degree and was studying logic, but after about a year and a half, he realized this was not the area he preferred. After changing his focus to dynamical systems and defending his Master’s thesis, he stayed in Gainesville for a year as he tried to figure out what to do next and taught mathematics at Santa Fe Community College. He knew he would prefer to get into an area that involved applied math instead of pure math. He became interested in mathematical ecology and had heard that Colorado State University had a great program in ecology and the natural sciences, so he applied there to get his mathematics Ph.D.

Christopher considers Patrick Shipman, who was a new faculty member at Colorado State at the time, and Gerhard Dangelmayr, who is the Chair of the department, to be his mentors. They were also his co-advisors. Christopher and Patrick started collaborating on projects right away.

“I was headed toward dynamical systems which is really related to mathematical ecology, so I worked with Patrick and Gerhard for the next six years,” Christopher said, “I still collaborate with both of them, and we are currently applying for a research grant to work with the U.S. Fish and Wildlife Commission.” Christopher has also collaborated with Patrick Shipman and Snehal Shetye at Colorado State on a project modeling the mechanical properties of spinal cords.

Nate Burch told me about SAMSI originally,” said Christopher. “Nate and I were colleagues at Colorado State.” So, when the Ecology program was announced, he applied and was accepted.

Christopher Strickland standing on a rocky edge

Christopher Strickland hiking in Australia.

While he’s been at SAMSI, Christopher has worked on getting various parts of his dissertation re-written into smaller parts so that he can publish each part in various journals. He has three of the four published. The manuscript of the fourth one is completed, and has been submitted as of June 2015.

Christopher has been participating in two working groups this year: The Tipping Point group and the Physical Ecology group. The Physical Ecology group led by Laura Miller, has been particularly interesting for him. “We recently had this really great workshop at SAMSI, which was for the people participating in the working group. We invited Nadia Kristensen from the University of Queensland who brought in all this great data from parasitoid wasp release and spread. That’s been really nice because I mostly do modeling of dynamic systems and the model that she had with this data could be something I could help her improve,” he commented.

“We are also working on a review paper, which is something the working group conceived of sometime around December. The entire working group and even some other people, including some ecologists and my advisor from Colorado State, Patrick, is working on this review,” Christopher said. He believes the review will be completed by the end of this summer.

Much of Christopher’s research focuses on networks, specifically looking at spread and control of contagions on the network. One example would be to look at container shipping networks or airline networks. He is working on a grant that is looking at white nose bat syndrome that involves a network of caves. While bats could spread the disease themselves from cave to cave, there is also the concern that hikers or cavers could get the fungus on their boots and spread the disease when they hike in a different cave. By figuring out how these networks work, it may help ecologists figure out where the disease might spread next, or help them to get a disease under control.

Christopher Strickland makes a kick

Christopher practicing Cuong Nhu.

When Christopher is not at work, he is either playing a game of soccer (he used to be on a math league!) or he is practicing the art of Cuong Nhu, (meaning hard/soft in Vietnamese) a type of martial arts that was brought to the United States in Gainesville, Florida. Christopher is on target to get his black belt, probably in about a year. “A lot of scholarly people actually do this type of martial arts. It has been a good way to network,” quipped Christopher. He also spends time with his girlfriend, Anne Ho, who is a theoretical mathematician. They like to travel a lot, many times to national parks or overseas.

In the fall, Christopher will be teaching at the University of North Carolina at Chapel Hill while he completes his second year as a postdoctoral fellow for SAMSI.

Exploring the dispersal patterns of insects at the SAMSI Ecology Transition workshop

Erin Schliep

Erin Schliep attended the SAMSI Ecology Transition workshop and wrote this blog post.

The following was written by Erin M. Schliep, PhD, Postdoctoral Fellow, Department of Statistical Science at Duke University

During the first week of May, the Ecology Transition Workshop was held at SAMSI, once again bringing together statisticians, mathematicians, and ecologists from around the country. This workshop marked the conclusion of the the Statistical/Mathematical Ecology Program that began in August, 2014.

At the beginning of the program, interdisciplinary working groups were established, each focusing on a different research area within mathematical and statistical ecology. The working groups held both in-person and virtual group research meetings throughout the year. At the Transition Workshop, members of the different working groups presented on current research projects that stemmed from the year-long program as well as exciting directions for on-going collaborations. Some of the main themes from the workshop included networks, infectious disease, dispersal patterns of insects, joint species modeling, and data fusion of multiple data sources.

woman checking her cell phone

Checking messages during a break.

As a statistician focusing on ecological and environmental applications, I thoroughly enjoyed the meeting that combined technical statistical methodology and detailed information on the ecological processes of interest. The diverse scientific backgrounds of participants in the program led to interesting discussions, including the behavior of ant colonies, the distance at which scientists aboard ships can accurately identify birds, and the polygamous mating patterns of the North American barn swallow.

Speaking on behalf of the multivariate models working group focusing predominantly on statistical methodology, the workshop was a great opportunity for us to present our current research and to learn of new and exciting ecological datasets to apply our new methods. Our current aim is developing computationally feasible joint models for species distributions that allow for multiple types of data, such as continuous, count, or composition data. Even though the program is completed, our group continues to meet and has established research goals for the coming months.  I know that many of the other working groups within the Statistical/Mathematical Ecology Program also have on-going research and I am excited to learn of their future progress.


My Impressions of the SAMSI Multivariate Modeling in Ecology Workshop

By Andrew Johnson, postbacc at North Carolina State University

group listening to a talk

SAMSI held the Multivariate Models in Ecology Workshop March 2-4.

Folks came in from across the country earlier this week to contribute to SAMSI’s Multivariate Modeling in Ecology workshop. Living in Raleigh, I shared in the excitement but enjoyed a much shorter commute than most. During my drive to SAMSI on the morning of the first day, I must say that I had a few reservations. I was new to the working group and unsure about my ability to contribute to the group’s progress. I was immediately relieved to find that my fears were misplaced. The atmosphere throughout the workshop was warm and industrious, welcoming of all questions and suggestions. Two of my favorite features were the flexibility of the schedule and the fluidity of the subgroups, both being obvious products of our group’s openness to input. There was a predetermined schedule for each day that was quickly adjusted by the group’s suggestions. This was also the case for group sizes. When appropriate, the group would quickly split into smaller, project-based subgroups. These self-guided adaptations made it possible for everyone involved to get the most out of their experience.

Three women discussing a poster

Poster session at the workshop.

The workshop was designed to address a few of the issues that the Multivariate Modeling working group has grappled with in the past. The group has been interested in understanding how the spatial positions of multiple species can be interrelated and co-vary with environmental influences, as well as the best way to model those relationships. Needless to say this poses a few inherent challenges. One of the primary questions that the subgroups focused on was that of dimension reduction for datasets describing the interactions among multiple species and environmental variables. Complementing the efforts on dimensional reduction, the subgroups also worked on developing improved methods for interpreting their computational results. In addition to the questions above, our group discussed strategies for assembling separate datasets into one coherent “picture.” This required consideration of each dataset’s reliability and corresponded to the computational weight placed on each. Fortunately, my favorite question of “where to eat” was answered with ease each day, as the workshop was fully catered with excellent food!

Andrew Johnson

Andrew Johnson at the poster session and reception.

I gained a great deal from this workshop, and had a blast doing it! I am thrilled to have been a part of it.

Impressions of the ECOL Opening Workshop

The following is from Eric Eager,Assistant Professor, Mathematical Biology, University of Wisconsin – La Crosse

Room full of people with laptops

Participants listen to speakers at the Ecology opening workshop at SAMSI.

Last week I took part in SAMSI’s 2014-2015 Program on Mathematical and Statistical Ecology (ECOL) Opening Workshop. The workshop combined a collection of polished talks by established researchers on the interface of mathematics, statistics and ecology with the stimulating environment elicited by numerous energetic participants with an eclectic set of academic backgrounds, career goals and scientific expertise. This was my second stay at SAMSI. Like my first, this workshop significantly altered my view of mathematics’ place in the study of ecological systems and the environment. It challenged me to explore areas of mathematics and statistics for which I had previously been uncomfortable and ecological applications I have always been interested in but didn’t have the time to study.

As a graduate student in applied mathematics I studied a class of models called integral projection models (IPMs). These models allow one to study the evolution of a population in time by specifically structuring the population by one or more continuous traits (like size for an animal, or soil depth for a plant’s seed bank). Much of my time was spent proving theorems about various versions of these models, which was very enjoyable for me at the time. However, after attending Jim Clark’s talk Dynamic Multivariate Analysis for Biodiversity and Climate Change and the session on multivariate inference and prediction in general, I became convicted about the way scale was incorporated into my IPMs and how this would influence my ability to predict population dynamics using these models. Many times we glean population and environmental parameters for our models from estimates in the literature, without adjusting for (or taking into account) the scale assumptions inherent in our model – at least statistical novices like me do. How do we make sure we are using parameters that are assuming the correct scale? How do we even attempt to make numerical predictions from these models?

One way to explicitly incorporate scale and perform model validation when studying a population is to parameterize and simulate an individual-based model (IBM) and see if the emergent properties of the IBM resemble those of the analogous IPM (the work of Rees, Childs and Ellner 2014 hints at such a comparison). IBMs were the topic of many of the talks at the workshop, and are flexible enough to tackle problems in network ecology, hydrology and the ecology of animal movement, among other areas. IBMs are not a class of models I studied as an undergrad or graduate student, and it’s my guess that many applied mathematicians and mathematical biologists are also in this boat, given the discrete nature of these models. I suspected for some time now that I needed to take the time and learn a thing or two about IBMs, and this workshop confirmed those suspicions. This week I picked up a copy of Railsback and Grimm (2011) and started the process of learning NetLogo.

The workshop also exposed me to many exciting subdisciplines of ecology and environmental biology that I have always been aware of, but never had the time or maturity to fully appreciate. One of those areas is mathematical epidemiology. I have always admired Carlos Castillo-Chavez for his work as a mathematical/computational biologist as well as his ability to give those in historically underrepresented groups a chance to succeed in mathematics. He was even kind enough to give a talk at our inaugural Midwest Mathematical Biology Conference at UW-La Crosse in May. His talk, Introduction to the Mathematics of Infectious Disease Dynamics opened a great series of talks in the ecology of infectious diseases session, which concluded with Jennifer Hoeting’s Parameter Inference and Model Selection in Deterministic and Stochastic Dynamical Models: Modeling a Wildlife Epidemic. In this talk, Jennifer discussed a something that has long interested me – the study of inverse problems for systems of stochastic differential equations (SDEs). This area combines analysis, probability theory, stochastic processes, dynamical systems, numerical analysis, computer science and statistics. Seeing a wonderful opportunity to explore a wide array of interesting problems, I decided to join Jennifer’s working group, Statistical and Mathematical Methods for the Ecology of Infectious Diseases. While the set of possible research questions for this group is quite large, I am extremely interested in analyzing various data sets for Dengue fever, West Nile and Malaria to determine how the error structures seen in real data sets relate to those predicted by SDE models, as some have (rightly, in my opinion) questioned the ability of SDE models to effectively model the dynamics of infectious diseases.

Eric Eager talking to someone at the reception and poster session

Eric Eager talking at the reception and poster session.

The opening workshop of SAMSI’s 2014 Program on Mathematical and Statistical Ecology had a transformative effect on me as a mathematical ecologist. I am very grateful for the diverse perspectives of all of the speakers and attendants, only a few of which I mentioned in this blog, as I am much more aware of the sheer size of the field of ecology, and the challenges that face us as we try to tackle ecological problems using quantitative approaches. These workshops have a way of simultaneously exhausting and rejuvenating me academically, and I am looking forward to continuing this work now that I am back in La Crosse.