Lecturer

Dr. John Brand is a Research Scientist in the Department of Epidemiology at the Geisel School of Medicine at Dartmouth College. Trained as a cognitive psychologist, Dr. Brand specializes in attentional mechanisms and cognitive control, with an emphasis on real-time measurement using eye tracking. His research integrates cognitive psychology, public health, and digital media to understand how moment-to-moment fluctuations in attention shape behavior, particularly in digital environments.

Dr. Brand earned his Ph.D. in Experimental and Applied Psychology from Concordia University, where he also taught courses in statistical analysis and perception.

Senior Lecturer

Brittny Calsbeek, PhD, is a Senior Lecturer in the Department of Biomedical Data Science. Dr. Calsbeek received her PhD in quantitative genetics from the University of Vermont, studying the evolution of genetic architecture. Her postdoc at NC State University built on this theoretical work and applied novel dimensionality reduction techniques to large D. Melanogaster genomic datasets. After teaching biostatistics for ten years at Dartmouth College, Dr. Calsbeek spent three years as the Head of Data Science for a tech startup in Silicon Valley before returning to teach data science and statistics in the Geisel School of Medicine.

Professor of the Practice

Keith Drake's consulting work and academic research are focused on brand-generic competition in the pharmaceutical industry. His research has been cited by multiple court rulings in antitrust lawsuits. A coauthored paper on brand-generic reverse payment settlements won the best paper prize from the International Journal of the Economics of Business in 2016. He earned a Ph.D. in health policy from the Dartmouth Institute for Health Policy and Clinical Practice.

Associate Professor, Assistant Dean of the HSE MS programs

Associate Professor of Biomedical Data Science and Pediatrics, and the Assistant Dean of the Health Sciences Master's programs at Geisel. Dr. Emond holds a master's degree in statistics from the University of Massachusetts, Lowell and a doctoral degree in public health (health behavior) from the University of California, San Diego. Dr. Emond is an NIH-funded researcher with a program focused on mitigating risk factors for obesity during childhood. Dr. Emond teaches biostatistics in the residential and online graduate programs.

MS Curriculum Director, Health Sciences Programs

Dr. Espinoza is the curriculum director for the Health Sciences Master's Programs at the Geisel School of Medicine. She completed a doctorate from Dartmouth's Quantitative Biomedical Sciences (QBS) program where she studied the interplay between autoimmunity due to Systemic Sclerosis and affected epithelial tissue microbiomes through the lens of genomics and metagenomics. During her studies at Dartmouth, Monica was named a Burroughs-Wellcomme Scholar, a Burroughs-Wellcome grant recipient, and a recipient of the Immunobiology of Myeloid and Lymphoid Cells T32 grant. She completed a postdoctoral fellowship at San Diego State University where she transferred her knowledge of microbiology toward discovering genetic underpinnings of drug-resistance traits in genomes of M. tuberculosis. She also developed materials for ethics courses on reducing sociocultural biases in biomedical algorithms including AI, which has aided her current course development.

Biomedical Data Science Lecturer
Adjunct Assistant Professor of Engineering
Director of Mental Health Evaluation and Assessment

Jamie Fairclough, PhD, is a Lecturer in the Department of Biomedical Data Science and also serves as Adjunct Assistant Professor of Engineering, as well as Director of Mental Health Evaluation and Assessment at Dartmouth. She earned her PhD in Human Sciences, with specialized training in advanced statistical computing, from Florida State University, and completed postdoctoral fellowship training in behavioral medicine research at Duke University Medical Center. Jamie also holds MPH and MSPharm degrees from the University of Florida, and she completed postgraduate programs in medical statistics, data science, AI/machine learning, and data engineering at Stanford University, the University of Texas at Austin, and the University of Chicago. In addition to her work at Dartmouth, Jamie serves as a data scientist and AI research engineer with the VA Healthcare System and is a member of the postgraduate faculty at the Harvard Macy Institute at Harvard Medical School.

Data Science Consultant - Bioinformatics

Dr. Noelle N. Kosarek is a current Data Science Consultant in Research Computing at Dartmouth. Noelle holds a doctoral degree from Dartmouth College in Quantitative Biomedical Sciences. During her training, Noelle focused on analysis of single cell and spatial transcriptomic data in the area of autoimmunity. She continued to develop her skills as a Bioinformatics Research Scientist at the Center for Quantitative Biology in the Genomic Data Science Core before joining Research Computing. Noelle teachs introductory programming courses in the residential and online graduate programs at Dartmouth.

Research Data Science Specialist (Research Data Services)

Jeremy Mikecz is a Research Data Science Specialist with the Research Data Services (RDS) department at Dartmouth. He is an experienced data scientist, digital humanist, historian, and teacher specializing in the application of data visualization and digital mapping to tell new stories and uncover hidden patterns. He received his PhD. in Latin American History at UC-Davis, his M.A. in Medieval Studies at Central European University, and his B.A. in History and Secondary Education at Washington University in St. Louis. Before joining RDS, Jeremy was a postdoctoral fellow in the digital humanities at USC (2017-19) and a postdoctoral fellow with the Neukom Institute here at Dartmouth (2019-22). His first book, At the Edge of the Map: The Andean World during the Spanish Invasion applies digital cartography and data visualization to recover Indigenous histories of European invasions (forthcoming). With RDS, he assists students, postdocs, staff, and faculty in performing computational research and analysis with quantitative, qualitative, textual, and spatial data.

Assistant Professor

Li Song is an Assistant Professor at the Department of Biomedical Data Science at Geisel School of Medicine at Dartmouth. He obtained his Ph.D degree in Computer Science from Johns Hopkins University in 2018, and a second master degree in Applied Mathematics and Statistics there. He then was trained as postdoc at Dana-Farber Cancer Institute and started his own lab at Dartmouth in 2022. His work focuses on algorithm development to better analyze next-generation sequencing data, especially for immunology and microbiology.

Senior Lecturer and Research Software Engineer II

Simon is a Research Software Engineer for High Performance Computing and Artificial Intelligence in Research Computing. He joined Dartmouth in 2022 from Technische Universität (TU) Dresden in Germany. Simon holds a doctoral degree in electrical and computer engineering from TU Dresden, and a master’s degree in electrical engineering and information technology with a minor in medical engineering from RWTH Aachen University. In his role as a Research Software Engineer, he supports computational research projects, especially those with a heavy sprinkling of Artificial Intelligence (AI) and Machine Learning. Simon also teaches on AI- and Data Science-related topics in various formats, and seeks to share his expertise with the whole Dartmouth community.

Research & Grant Program Director, IMPACT Lab & Lecturer, Department of Epidemiology

Research & Grant Program Director & Research Scientist. Dr. Sullivan is an interdisciplinary health services researcher, cross-trained in epidemiology, biostatistics, and health informatics. She holds a Master of Public Health in Epidemiology from Tulane University and received her doctoral training in Quantitative Biomedical Sciences from Dartmouth College. Her research focuses on developing or improving methodologies to support predictive analytics in clinical and population health.

Lecturer

Ramesh Yapalparvi, PhD is a Lecture in the Department of Biomedical data science. Apart from this tole, I currently serve as Senior Director of Enterprise Data Science at Mass General Brigham (MGB), where I lead data-driven initiatives that support strategic healthcare operations across our network. My work focuses on deploying AI/ML solutions to optimize operational efficiency, resource utilization, and clinical decision-making at scale.

Prior to MGB, I led data science teams at Optum within the Payment Integrity space. There, I designed and deployed machine learning models to identify fraud, waste, abuse, and error across both pre- and post-payment workflows. My work spanned multiple domains, including Clinical and Non-Clinical Analytics, Coordination of Benefits (COB), Revenue Cycle Management (RCM), Claims Cost Management (CCM), and Behavioral Health, covering commercial, Medicare, and Medicaid lines of business.

Earlier, at Dartmouth-Hitchcock Medical Center, I built predictive analytics platforms for remote patient monitoring and collaborated with orthopedic surgery teams to improve patient-reported outcomes through data science. These tools supported proactive care models and population health strategies.

I hold a Ph.D. in Applied Mathematics from the University of Manchester, with a research background in fluid mechanics, optimization, and computational modeling. Over the years, I’ve developed analytics and modeling solutions across sectors—including software development at ANSYS, insurance analytics at Milliman and John Hancock, and engineering innovation through European Union research collaborations and fellowships with MIT and INRIA.

In addition to my technical work, I’ve supported academic program development, including co-establishing a Master’s in Health Data Science at Dartmouth College, and I’ve advised early-stage startups in healthcare AI and predictive modeling.