Student Peers

Our QBS students come for a wide variety of backgrounds, with a wide variety of research interests and professional goals.

Current PhD Students


Photo of Carly Bobak Carly Bobak, BA, MSc Mentors: Jane Hill, PhD and James O'Malley, PhD Education: University of Guelph - Applied Mathematics and Statistics with a Co-op Option, Certificate in Business, University of Guelph - MSc Applied Mathematics While studying applied mathematics and statistics at the University of Guelph, I quickly realized that mathematical thinking provides important insight in a variety of disciplines, but particularly in the realm of biomedical science. Ever since I've been passionate about data-driven approaches to research in human health, particularly in the realm of infectious disease. When I'm not thinking about science (do we ever really stop thinking about science?) you can usually find me hanging out with my dog, throwing spontaneous dinner parties, or dragging my peers out to various different adventures throughout the New England area.

Photo of Guanqing Chen Guanqing Chen, BS, MA Mentor: James O'Malley, PhD Education: Nanyang Technological University, Singapore - Mathematics and Statistics, University of Rochester - MA Statistics My research interests are causal inference, network analysis and longitudinal data analysis. I like QBS because this integrated interdisciplinary program in biostatistics, bioinformatics and epidemiology will broaden my knowledge base, strengthen my skills in translational sciences and lay a solid foundation for pursuing my career in biomedical and translational research.

Photo of Monica Espinoza Monica Espinoza, BS Mentor: Michael Whitfield Education: University of California Irvine - Biology My research interests include genomics, biostatistics, rare diseases, and the human microbiome- interests that I have combined in my work with Dr. Michael Whitfield. The Whitfield lab studies Scleroderma, a rare autoimmune disease characterized by fibrosis and heightened immune response in various organ systems of the body. In his lab, I am currently exploring the potential associations of the epithelial microbiome with the host immune response presented in patients with Scleroderma (SSc). This is part of an investigative effort to elucidate the potential environmentally-driven etiology of SSc. As a 2016-2017 Burroughs Wellcome Scholar, I was given the opportunity to explore the possibility of a hybrid PhD, one that includes laboratory science and data science training. These opportunities only became available to me through the encouragement of cross-training in the multidisciplinary space that QBS offers. Outside of school, I can be found socializing with friends or exploring the New England area.

Photo of Yasmin Kamal Yasmin Kamal, BS (MD-PhD) Mentors: Christopher Amos, PhD and Robert Frost, PhD Education: Smith College - Biochemistry and Neuroscience My research interests are cancer immuno-genomics. The Amos lab in genomic medicine was a perfect fit for me given my interests in genomics. Dr. Amos and my co-mentor Dr. Robert Frost have been excellent mentors. Working in the Amos lab, I was able to mesh my biochemistry and medical background to design a project focused on the application of immunotherapy in various types of cancer. Specifically, I am examining the interaction of immune cells in the tumor microenvironment with neoplastic cells using sequencing data and determining the impact of immune cells on driving cancer pathophysiology—specifically identifying unique immune signatures indicative of metastasis. In the Amos lab, I am able to collaborate with clinicians, immunologists, surgeons, and bioinformaticians, and am challenged to use a variety of tools to tackle key questions in cancer immunology.

Jai Woo Lee, BS, MS Mentor:Jiang Gui, PhD Education: Carnegie Mellon University - Mathematics and Computer Science, Dartmouth College - MS Computer Science I am originally from South Korea, and I usually enjoy walking or hiking, and/or listening to any kind of music or political podcasts. Before entering graduate programs at Dartmouth College, I mainly focused on graph theory and its application with/to computational methods in Operations Research in the field of Theoretical Computer Science. My current research interests lie in utilizing and developing statistical and computational methods in Bioinformatics, Biostatistics, or Machine Learning to analyze biomedical data related to various fields of Epidemiology. Thanks to enthusiastic faculty and broad scope of interesting research areas in Quantitative Biomedical Sciences (QBS) program, I have worked on statistical and computational methods to study protein-protein interaction, to learn more about the application of Multifactor Dimensionality Reduction (MDR), and to analyze placental metal concentrations data collected by the New Hampshire Birth Cohort Study.

Photo of Jennifer Luyapan Jennifer Luyapan, BS Mentors: Christopher Amos, PhD and Michael Passarelli, PhD Education: University of California Davis - Cell Biology My research background includes genomics, protein biochemistry, oncology and medical device clinical research. I am interested in applying and developing statistical and computational methods to better understand the causes of health and disease conditions.

Photo of Daniel Mattox Daniel Mattox, BS Mentor: Christopher Bailey-Kellogg, PhD Education: University of North Carolina at Chapel Hill - Quantitative Biology An NIH-Dartmouth Big Data to Knowledge(BD2K) Big Data Trainee, my research is focused around integrating computational and experimental approaches to protein design. More specifically, I am interested designing and applying novel computational tools to aid and supplement experiments. In the Bailey-Kellogg Lab, I am working to better understand glycan-protein interactions and possible roles for lectins in bioengineering. Outside of research, I enjoy exploring the beautiful New England countryside through any number of outdoor sports.

Photo of Meghan Muse Meghan Muse, BS Mentors: Brock Christensen, PhD and Diane Gilbert-Diamond, ScD Education: Dartmouth College - Biology I am interested in studying maternal and child health through the lens of bioinformatics and epidemiology. I chose the QBS program at Dartmouth because it provides the unique opportunity to be cross trained in these fields as well as the field of biostatistics. Outside of the lab, I enjoy running, hiking, and taking advantage of all the other outside activities that New Hampshire has to offer.

Photo of Xingyu Zheng Xingyu Zheng, BS Mentors: Christopher Amos, PhD and Robert Frost, PhD Education: Fudan University - Biology My research interests are bioinformatics and biostatistics, especially cancer genomics and statistical genomics. I chose QBS because it's an interdisciplinary program which is so exciting and I enjoy analyzing big data. I'm working in the Amos lab. Dr. Amos and Dr. Robert Frost co-mentor me and give much help and advice to me. Outside of the lab, I like walking around and really love the trees all around here which bring surprises in all seasons.


Photo of Alexander Titus Alexander Titus, BS, BA 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

Photo of Jason Wells Jason Wells, BS Mentor: Todd Miller, PhD Education: University of California, Davis - Genetics I have a background in genetics and genomics research. Previous projects that I have worked on mainly focused on muscular dystrophy from a genomics perspective. My current interests include using genomic and bioinformatic approaches to study various aspects of cancer.

Photo of Lia Harrington Lia Harrington, BS, MS 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.

Photo of Katherine Antosca Katherine Antosca Mentor: Todd MacKenzie, PhD and George O'Toole, PhD Education: University of New Hampshire, Durham - Mathematics My current research interests center around the function of the microbiome. I am currently evaluating the role of breastfeeding and probiotics in the development of the gut microbiome of children with Cystic Fibrosis. We hope to determine the specific mechanisms associated with better health outcomes of Cystic Fibrosis patients. I am also creating a program to provide insight into the metabolic function of unclassified microbes within the microbiome.


Photo of Jennifer Franks 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.

Photo of Sara N. Lundgren Sara N. Lundgren, BA Mentors: Brock Christensen, PhD and Anne Hoen, PhD Education: University of Chicago - Comparative Human Development I came to Dartmouth in 2014 to begin interdisciplinary training in epidemiology, bioinformatics, and biostatistics after studying Comparative Human Development at UChicago. As a student in both the Christensen Lab and the Hoen Lab, I will gain experience working on epigenomic and human microbiome projects including the assessment of exposures such as aspects of diet and lifestyle. A particular research interest is to characterize the variation in breast milk and its association with both maternal and infant health using the New Hampshire Birth Cohort Study. I enjoy working with Dr. Christensen and Dr. Hoen because they encourage students to form their own research interests and goals, so I am able to focus on the questions I find the most intriguing!

Photo of Ellen Nutter Ellen Nutter, BS 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

Photo of Christiaan Rees Christiaan Rees, BS (MD-PhD) Mentor: Jane Hill, PhD Education: University of Massachusetts, Dartmouth - Biology Prior to beginning my dual degree program, I graduated from the University of Massachusetts Dartmouth with a B.S. in biology, minoring in biochemistry and history. My current research focuses on the utility of small volatile molecules to differentiate patterns of antibiotic resistance in Klebsiella pneumoniae, with a particular interest in carbapenem-resistant K. pneumoniae (CRKP). The ultimate goal of this project would be to develop a rapid, highly accurate, non-invasive diagnostic to guide administration of antibiotic therapy. Working with Dr. Hill has been an outstanding experience because it affords me the opportunity to do cross-disciplinary work, ranging from basic microbiology to analytical chemistry to computational biology.
Awards, Abstracts, and Poster Presentations
  • Dartmouth Graduate Studies Graduate Alumni Research Award
  • Cystic Fibrosis Foundation Student Traineeship
  • NIH BD2K T32 training grant
  • Recent Publications

Photo of Mavra Nasir Mavra Nasir, BS, MS 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.


See QBS Graduates


Craig MacKenzie, BS, MS 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.

Photo of Qian Yang Qian Yang, BE, MEM Mentor: Tor Tosteson, PhD Education: University of Michigan‐Shanghai Jiao Tong University (UM‐SJTU) Joint Institute, Shanghai Jiao Tong University - Electrical and Computer Engineering, Thayer School of Engineering, Dartmouth College- MEM Engineering Management Now a fourth year QBS PhD candidate, I am a member of the Biomedical Statistical Science Laboratory (BSSL) directed by Dr. Tor Tosteson. My current research focuses on joint modeling of longitudinal medical cost and survival. My research interests also involve decision analytic modeling and comparative effectiveness research. I enjoy the emphasis of novel statistical methodology and their application to real healthcare data and challenges which is the hallmark of our lab.

Photo of James Rudd James Rudd, BS, MS Mentor: Jennifer Doherty, PhD Education: North Carolina Central University - Music, North Carolina Central University - MS Computer Science I recieved my MS in Computer Science from North Carolina Central University and joined the Quantitative Biomedical Sciences program in order to better integrate my computation background with biostatistical and epidemiological frameworks. My research interests involve the use of bioinformatics to solve epidemiological problems. As a member of the Doherty lab, I am working to model molecular subtypes of ovarian cancer and have been awarded an F31 fellowship from the National Cancer Institute.
Current MS Students

2017 Inaugural Class

Photo of person Nu Student Mentor: Currently in First Year Rotations Education: University of Here, University of Now This is some personal introductory bio text.
QBS PhD Graduates


Photo of Robert Frost Robert Frost, MS, PhD Education: BS and MS Stanford University - Mechanical Engineering QBS Mentor: Jason Moore, PhD

Thesis title: Statistical methods for gene set annotation optimization, unsupervised gene set testing and independent gene set filtering.

Current position: Assistant Professor of Biomedical Data Science, Division of Biostatistics, Geisel School of Medicine at Dartmouth

Research interests: Biomedical informatics, biostatistics, applied mathematics

For publications, see my ResearchGate or Google Scholar profiles.

Matthew Davis, MPH, PhD Education: BA Colby College - Chemistry, DC New York Chiropractic College, MPH, The Dartmouth Institute at Dartmouth College QBS Mentor: Margaret Karagas

Thesis Title: Prenatal and childhood exposure to low-level arsenic in the US population

Current Position: Assistant Professor Department of Systems, Populations and Leadership at University of Michigan

Dr. Davis is a quantitatively-oriented health services researcher who has additional training in health informatics, epidemiology, and statistics. He is motivated by questions related to healthcare delivery and he uses rigorous methods to study important policy-relevant issues.

Dr. Davis's research has received national media attention. Articles about his research have appeared in outlets such as Forbes, The Huffington Post, USA Today, Time, The Atlantic, and Consumer Reports. His research has also been featured on television and radio broadcasts including NPR's Marketplace, the Today Show, and other national news outlets.


Anala Gossai, HBSc, MPH, PhD Education: University of Toronto, Trinity College - Immunology, Yale - MPH Epidemiology of Microbial Diseases QBS Mentor: Margaret Karagas, PhD

Thesis title: Epidemiologic characterization of human polyomaviruses in adults from the United States, and their relation to cutaneous squamous cell carcinoma

Working with Dr. Margaret R. Karagas, I investigate the seroepidemiology of human polyomaviruses, and their relation to skin cancer, in a large population-based case-control study. I hope to extend my research into the New Hampshire Birth Cohort, where I'd like to explore the association between infant health and measures of polyomavirus immunity. Having been given the opportunity to address novel hypotheses at the level acceptable for peer-reviewed publications, develop collaborations with other labs worldwide, and attend multiple conferences yearly with additional support from graduate school travel awards, I have greatly enjoyed my time with my research group.

Photo of David Qian David Qian, BS (MD-PhD) Education: Dartmouth College - Biophysical Chemistry, Economics QBS Mentor: Christopher Amos, PhD

Thesis title: Characterization of cancer risk and response to immunotherapy through pathway-based genomic analyses

I am now doing clinical rotations in the final 2 years of the MD/PhD program following graduate study in QBS. I look forward to using my training in cancer genomics and big data as a physician-scientist who tailors precision therapy in one of the oncology specialties: medical oncology, surgical oncology, or radiation oncology. Current rotations will help guide that decision.
  • **Qian DC**, Busam JA, Xiao X, Eeles RA, Schumacher FR, Phelan CM, O'Mara TA, Amos CI (2016) "seXY: A tool for sex inference from genotype arrays". Bioinformatics [accepted]
  • **Qian DC**, Xiao X, Byun J, Suriawinata AA, Her SC, Amos CI, Barth RJ Jr (2016) "PI3K/Akt/mTOR signaling and plasma membrane proteins are implicated in responsiveness to adjuvant dendritic cell vaccination for metastatic colorectal cancer". Clin Cancer Res [epub ahead of print]
  • **Qian DC** , Han Y, Byun J, Shin HR, Hung RJ, McLaughlin JR, Landi MT, Seminara D, Amos CI (2016) "A novel pathway-based approach improves lung cancer risk prediction using germline genetic variations". Cancer Epidemiol Biomarkers Prev 25, 1208-1215.
  • Halloran JW, Zhu D,**Qian DC**, Byun J, Gorlova OY, Amos CI, Gorlov IP (2015) "Prediction of the gene expression in normal lung tissue by the gene expression in blood". BMC Med Genomics 8, 77:1‒6
  • Qian DC, Byun J, Han Y, Greene CS, Field JK, Hung RJ, Brhane Y, McLaughlin JR, Fehringer G, Landi MT, Rosenberger A, Bickebller H, Malhotra J, Risch A, Heinrich J, Hunter DJ, Henderson BE, Haiman CA, Schumacher FR, Eeles RA, Easton DF, Seminara D, Amos CI (2015) "Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions". Hum Mol Genet 24, 7406‒7420.
  • Cordell HJ, Han Y, Mells GF, Li Y, Hirschfield GM, Greene CS, Xie G, Juran BD, Zhu D, Qian DC, Floyd JA, Morley KI, Prati D, Lleo A, Cusi D; Canadian-US PBC Consortium; Italian PBC Genetics Study Group; UK-PBC Consortium, Gershwin ME, Anderson CA, et al. (2015) "International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways". Nat Commun 6, 8019:1‒11.
  • Onyango EO, Kelly AR, Qian DC, Gribble GW (2015) "Novel Synthesis of 1-bromo-8-methylnaphthalene and 1-bromo-5-methylnaphthalene". J Org Chem 80, 5970‒5972.

Britney Graham, BS, QBS MS Education: New England College - Mathematics QBS Mentor: Scott Williams, PhD I chose the QBS program because I was intrigued by the multidisciplinary aspect of the research. This gave me the ability to apply my knowledge of mathematics in a practical setting. I am working on applying network theory to novel genetic associations. I like working with Scott Williams because he encourages me to think independently, yet offers my guidance when I need it. I have come a long way in understanding since I joined his lab.


Photo of Jeffrey Thompson Jeffrey Thompson, BS Education: University of Southern Maine - Computer Science QBS Mentor: Carmen Marsit, PhD

Thesis title: Genomic and clinical data integration for improved cancer prognostic and predictive models

I'm originally from western Maine and prior to 2010 spent almost 15 years in the health publishing industry. Eventually I went back to school at the University of Southern Maine and during my undergrad, I joined the Congdon lab, where I developed a machine learning approach to inferring cis-regulatory modules of transcription factor binding sites. This research led me to become interested in all pre- and post-transcriptional regulation of genes and to notice that epigenetic data are underutilized by many projects that seek to unravel disease etiology. I joined the QBS program at Dartmouth because of its vision of a new type of researcher that can help connect specialists in different areas: epidemiology, biostatistics, and bioinformatics. Dr. Marsit has built a lab with people of diverse backgrounds to do research that cuts across all of these fields. As part of the Marsit Lab, I am developing new tools that enable integrative analyses of multiple, high-dimensional datasets using machine learning to reveal the connections between environmental exposure, epigenetic control, gene expression, and disease.