Postdoctoral Researchers

  1. Dr. Samuel F. Way
    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. Sam Way

Doctoral Students

  1. 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. Anna Brodio

  2. 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. Nora Connor

  3. 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. Amir Ghasemian

  4. 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. Andy Kavran

  5. 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. Allie Morgan

Undergraduate Students

  1. 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. McKenzie Weller

Past Group Members

Postdoctoral Fellows

  1. Dr. Andrea E. Berardi (funded by NSF, 2015-2016, then postdoc at University of Bern)

  2. Dr. Daniel B. Larremore (funded by NIH, 2012-2015, then Omidyar Fellow at Santa Fe Institute)

  3. Dr. Leto Peel (funded by DARPA, 2013-2015, then postdoc at Université Catholique de Louvain, in Belgium)
Doctoral Students

  1. Dr. Samuel F. Way (Computer Science, 2014-2017, then postdoctoral fellow at Colorado)

  2. Dr. Abigail Z. Jacobs (Computer Science, 2011-2017, then postdoctoral fellow at UC Berkeley)

  3. Dr. Lauren G. Shoemaker (Ecology and Evolutionary Biology, 2011-2017, then James S. McDonnell Foundation postdoctoral fellow, at Minnesota)

  4. Dr. Sears Merritt (Computer Science, 2011-2013, then data scientist at Mass. Mutual)
Masters Students

  1. Trevor DiMartino (Computer Science, 2016-2017, then freelance web developer)

  2. Kansuke Ikehara (Computer Science, 2015-2016, then developer at LingK)

  3. Christopher Aicher (Applied Math, 2012-2014, then pursuing PhD in Statistics at University of Washington)

  4. Pooneh Mortazavi (Computer Science, 2012-2013, then developer at Microsoft)

  5. Yogesh Virkar (Computer Science, 2011-2012, then pursuing PhD in Computer Science at Colorado)

  6. Chris Schenk (Computer Science, 2010-2011)

  1. Ellen Tucker (Mathematics, 2015-2016, then pursuing PhD in Computer Science at Northwestern University)

  2. Matthias Sainz (Computer Science, 2014-2016, then software developer at FullContact)

  3. Nico Tonozzi (Computer Science, 2014-2015)

  4. Andrew Zizzi (Aerospace Engineering, 2011-2012)

  5. Ken Sheedlo (Computer Science, 2011-2012)