- Dr. Dan Larremore
I am interested in the development of principled mathematical methods to understand dynamic processes on complex networks. Working with Caroline O. Buckee (HSPH) and Aaron Clauset, my primary research focus is genetic recombination in the parasite that causes a majority of malaria cases, Plasmodium falciparum. In this work we map sequences to networks in which recombination patterns manifest as large-scale network structures. With this in mind, I also work on statistical inference and generative network models. Previously, I studied the dynamics of excitable networks modeling neuronal avalanches in the mammalian cortex.
- Dr. Leto Peel
My research is focussed on developing scalable and robust machine learning methods for network datasets by utilising generative models and Bayesian inference. I am also interested in collecting and exploring novel datasets of social interactions and relationships in both virtual and physical environments. Within these settings I wish to model temporal features of a network such as frequency and durations of interactions and attributes of network nodes that can change over time (e.g. location, opinions etc.). Currently I'm working on developing methods for detecting anomalies and changes in the structure of dynamic networks.
- Anna Broido (PhD Applied Mathematics and IQ Biology, co-advised with Prof. Lladser)
My interests span disciplines: broadly, I am interested in using mathematical and statistical techniques to answer questions in biology, particularly in areas that relate to public health. I am currently using topic models to answer questions about the effects of genetics on the human oral microbiome.
- Nora Connor
(PhD Computer Science and IQ Biology, and a NSF Graduate Research Fellow)
I'm interested in using adaptive, stochastic computational models to describe biological phenomena. In collaboration with Rob Knight's lab at CU, I am currently building a model of genome size evolution to describe the distribution of microbial genome sizes (which range from about 200 to 120,000 genes), with the goal of understanding the macroevolutionary role of horizontal gene transfer. I'm also working on a stochastic population growth model incorporating genetic inheritance and mutations to inform in vitro genetic sampling of Tribolium beetle populations in collaboration with Brett Melbourne's lab at CU, with the goal of better understanding population distributions across variable landscapes.
- Amir Ghasemian
My current research involves inference and learning in graphical models. I am working on inference in dynamic networks using belief propagation and its relation to variational approximation methods. I am interested in looking to inference problems from different perspectives - from free energy to information theoretic view. My research interests lie in signal processing, machine learning, Information theory, information geometry and statistical inference.
- Abigail Z. Jacobs (PhD Computer Science, and a NSF Graduate Research Fellow)
I am interested in methods for inference in dynamic, complex social and biological systems, with a particular focus on generative models for networks, non-stationary time series, and temporal networks. Drawing from statistics, machine learning, and physics, I seek to develop practicable tools and algorithms at the interface of theory and data. Currently, I am focusing on latent space models for inference and prediction in dynamic social systems. I am working in collaboration with Cris Moore on inference with generative models for networks, as well as with Jennifer Dunne on the role of parasites in the topology of food webs.
- Lauren Shoemaker (PhD Ecology and Evolutionary Biology and IQ Biology, co-advised with Prof. Melbourne, and a NSF Graduate Research Fellow)
My research focuses on applying mathematical and computational biology to address current macroevolutionary and ecological questions. Broadly speaking, I study how communities coexist through time and space, and the morphological patterns that emerge within communities or clades. I am currently examining patterns of body size distribution in the horse family and the mechanisms driving body size distribution through evolutionary time. I am also working on identifying the mechanisms for coexistence and community persistence in metacommunities, as well as patterns in metacommunity assembly across an environmental gradient.
- Christopher Aicher (BS/MS Applied Math, 2012-2014, then pursuing PhD in Statistics at Washington)
- Sears Merritt
(PhD Computer Science, 2011-2013, then data scientist at Mass. Mutual)
- Pooneh Mortazavi (MS Computer Science, 2012-2013, then developer at Microsoft)
- Yogesh Virkar (MS Computer Science, 2011-2012, then pursuing PhD in Computer Science at Colorado)
- Andrew Zizzi (BS Aerospace Engineering, 2011-2012)
- Ken Sheedlo (BS Computer Science, 2011-2012)
- Chris Schenk (MS Computer Science, 2010-2011)