- Anna Broido (PhD Applied Mathematics and IQ Biology)
My research applies techniques from math, statistics, and computer science to problems in data science. I have a background in math and spent the summer interning at FullContact using machine learning to classify types of nodes in a graph of address book contacts. I am currently studying properties of network structure using the Index of Complex Networks database in order to test the universality of the scale free hypothesis.
- Nora Connor
(PhD Computer Science and IQ Biology, and a NSF Graduate Research Fellow)
My research focuses on interpreting correlative data using tools from network science and machine learning. I have applied these methods to the soil microbiome to infer ecological relationships between microbial taxa. I am also studying interactions in social and political data. As a data science consultant for The Messina Group in Washington DC, I incorporated machine learning methods in individual-level demographic models in the United Kingdom. My ongoing research involves using cluster analysis to analyze public opinion polls.
- Amir Ghasemian
(PhD Computer Science)
My research involves inference and learning in graphical models. My focus is mostly extracting the structure of networks through generative models. I am interested in looking at inference problems from different angles like Bayesian approaches in statistics and machine learning, free energy in statistical physics and information theoretic perspective from electrical engineering and physics. Previously I worked on detectability limits and optimal algorithms for community detection in dynamic networks. Now I am working on analyzing various model selection approaches in networks. My research interests lie in machine learning, information theory, statistical inference, data mining, and signal processing.
- 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.
- Andy Kavran (PhD Chemistry and Biochemistry and IQ Biology, co-advised with Prof. Natalie Ahn)
I am interested in using network models to describe the behavior of biological systems, typically at the cellular and molecular scales. My research pairs community detection algorithms with high-throughput data in order to understand the differences in community structure between systems. Currently, I am applying this approach to protein expression data in order to understand the molecular relationships among several different types of cancer. Also, I am using protein phosphorylation data to investigate how melanoma cancer cells respond differently to intermittent and continuous drug treatment.
- Allison Morgan
(PhD Computer Science)
I'm interested in the emerging field of computational social science, particularly using data mining and machine learning to examine issues of social inequality. Drawing from my background in physics, I hope to build upon and develop efficient analytical and numerical methods to analyze these complex social problems. My current research will analyze the evolution of faculty hiring networks by developing a web crawler to collect faculty directories over time.
- 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.
- Sam F. Way (PhD Computer Science and IQ Biology)
My current research focuses on developing novel techniques for analyzing microbial communities and genomes. Coming from an engineering background, my approaches often borrow ideas from text mining and signal processing to devise fast and efficient algorithms for classifying, searching, and interpreting biological data. Currently, I'm collaborating with Rob Knight and Ken Krauter's labs, developing a system to measure substance abuse using the oral microbiome.
- Trevor DiMartino (MS Computer Science)
My curiosities are currently focused on mechanisms behind macroevolution. Using a random-walk model, and corroborating results with compiled data from the paleo record, I am looking to answer questions about evolutionary irreversibilities. Ultimately, I would like to find how such irreversibilities have contributed to the diversity and complexity of extant species.
- McKenzie Weller (BS Computer Science)
I'm a current sophomore undergraduate at CU, pursuing a Bachelor of Science in Computer Science and a minor in Technology, Arts, and Media. My interests within CSCI are mainly human-centered focused and in regards to human interaction and response. I enjoy front end work, including design, but am also interested in data analysis (such as in social sciences) and machine learning built upon it. I am still taking time to discover where I'd like to specialize, and generally enjoy learning and practicing new trades.
Past Group MembersPostdoctoral Fellows
- Dr. Andrea E. Berardi (funded by NSF, 2015-2016, then postdoc at University of Bern)
- Dr. Daniel B. Larremore (funded by NIH, 2012-2015, then Omidyar Fellow at Santa Fe Institute)
- Dr. Leto Peel (funded by DARPA, 2013-2015, then postdoc at Université Catholique de Louvain, in Belgium)
- Dr. Sears Merritt
(Computer Science, 2011-2013, then data scientist at Mass. Mutual)
- Kansuke Ikehara (Computer Science, 2015-2016, then developer at LingK)
- Christopher Aicher (Applied Math, 2012-2014, then pursuing PhD in Statistics at University of Washington)
- Pooneh Mortazavi (Computer Science, 2012-2013, then developer at Microsoft)
- Yogesh Virkar (Computer Science, 2011-2012, then pursuing PhD in Computer Science at Colorado)
- Chris Schenk (Computer Science, 2010-2011)
- Ellen Tucker (Mathematics, 2015-2016, then pursuing PhD in Computer Science at Northwestern University)
- Matthias Sainz (Computer Science, 2014-2016, then software developer at FullContact)
- Nico Tonozzi (Computer Science, 2014-2015)
- Andrew Zizzi (Aerospace Engineering, 2011-2012)
- Ken Sheedlo (Computer Science, 2011-2012)