Alexander Titus, BS, BA, PhD

Mentor: Brock Christensen, PhD

Education: University of Puget Sound - Biology, Biochemistry

I'm interested in data integration methods for 'omic' analyses. There is a growing volume of federally funded, publicly available data that were collected on different technologies. I develop methods to integrate these data sets together for analysis. Specifically, I'm interested in the prospects of combining genomic, transcriptomic, and proteomic data to identify the effect of miRNA-related genetic variation on disease phenotypes. Im also interested in reference-based and reference-free cell mixture deconvolution, using cell type specific methylation markers as additional parameters in modeling

Awards, Abstracts, and Poster Presentations

  • NIH-Dartmouth Big Data to Knowledge (BD2K) Fellow​
  • Titus AJ*, Houseman EA*, Johnson KC, Christensen BC (2016) methyLiftover: cross-platform DNA methylation data integration. Bioinformatics. 32(16):2517-9. *co-first
  • Titus AJ, Faill R, Das AK (In Press) Automatic identification of co-occurring patient events. Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 579-86.
  • Titus AJ, Faill R, Das AK (2016) Automatic identification of co-occurring patient events. Workshop on Methods and Applications in Healthcare Analytics. 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, Seattle, WA, USA. Oral Presentation.
  • Titus AJ, Houseman EA, Johnson KC, Christensen BC (2016) methyLiftover: cross-platform DNA methylation data integration. Computational Life Sciences Workshop @ Bayer. Berlin, Germany. Poster
Ellen Nutter, BS, PhD

Mentors:Tracy Onega, PhD, Giovanni Bosco, PhD and Jennifer Doherty, PhD

Education: University of Great Falls - Mathematics

My area of interest is translational research: participating in all the steps in the process from discovery to delivery is important to me. Here at Dartmouth I work with Jen Doherty's Cancer Epidemiology Laboratory and Tracy Onega's Population Health Laboratory. Working with both Dr. Doherty and Dr. Onega allows me to explore the spectrum of quantitative research topics. We work on a broad spectrum of topics from genetic association studies to geographic analyses pertaining to lung cancer screening usage for cancer prevention, prognosis, and treatment.

Awards, Abstracts, and Poster Presentations

  • Big Data in the Life Sciences Trainee funded by Burroughs-Wellcome Fund Program
  • Recent Publications

Lia Harrington, BS, MS, PhD

Mentors: Saeed Hassanpour, PhD and Matthew Havrda, PhD

Education: Bucknell University - Nueroscience • University of Montana - MS Psychology

My research interest is in leveraging big data and bioinformatics tools to understand diseases, such as cancer. My current project is developing better predictive models of colon cancer risk via information text extraction from electronic medical data. In addition, through the Burroughs-Wellcome Fellowship, I am working with Dr. Havrda to better understand the biological basis of Parkinson's disease.

Craig MacKenzie, BS, MS, PhD

Mentor: Gevorg Grigoryan, PhD

Education: University of New Hampshire Durham - Mathematics • University of Illinois - MS Mathematics

My research involves mining massive amounts existing data on proteins for the purpose of computationally designing novel proteins. More specifically, we have determined a small set of structural motifs capable of describing almost all structural interactions found in proteins (with known structures). We are now using sequence-structure relationships from these motifs to design novel proteins. Beyond this I'm interested in computationally designing proteins (and other molecules) for use in synthetic biology, nanomaterials and therapeutics. I have a bachelor's degree in Mathematics from the University of New Hampshire. The interesting and diverse research conducted by QBS faculty drew me to the program which I joined in 2012. When I joined the program I did not plan on working in the field of protein design, but got hooked after doing rotations in the labs of Chris Bailey-Kellogg and Gevorg Grigoryan my first year.



Mavra Nasir, BS, MS, PhD

Mentor: Jane Hill, PhD

Education: McGill Univsersity - Biology • New York University - MS Bioinformatics

In the Hill lab, my current project is focused on developing breath-based diagnostic for cystic fibrosis patients with polymicrobial lung infections.This involves analysis of volatile organic compounds (VOCs) produced by Pseudomonas aeruginosa and Staphylococcus aureus infection in cystic fibrosis patients using GCXGC -TOFMS. Analysis of volatile organic compounds (VOCs) in the breath that can be used to distinguish between antibiotic-sensitive and antibiotic-resistant strains of S. aureus, particularly MRSA and VRSA using GCXGC -TOFMS Development and application of machine learning methods for fingerprinting VOC profiles.

Jennifer Franks, BS

Mentor: Michael Whitfield, PhD

Education: Purdue University - Genetics, Applied Statistics

My research interests are human genetics, computational immunology, machine learning, and statistical methods for high-dimensional data. My current projects in the Whitfield lab focus on classifying intrinsic molecular subtypes and characterizing the immune repertoire in patients with systemic sclerosis. I really enjoy working with Michael Whitfield because I am able to generate data at the bench and also analyze results using novel computational methods. Through the Big Data in the Life Sciences Training Program funded by Burroughs-Wellcome, I am able to collaborate with Chris Bailey-Kellogg using sequence and structural models to explore cross-reactivity in the adaptive immune system.

Sara N. Lundgren, BA

Mentors: Brock Christensen, PhD and Anne Hoen, PhD

Education: University of Chicago - Comparative Human Development

My thesis work focuses on the developing infant gut microbiome in the New Hampshire Birth Cohort Study, mentored jointly by Dr. Brock Christensen and Dr. Anne Hoen. . I am interested in characterizing the relation maternal factors such as diet and weight and the human milk microbiome and metabolome, and how that variation affects the gut microbiome in early life. My other research interests include epigenetics and usage of machine learning methods for integrating multiple high dimensional datasets.