Meet Our New PhD Students!
We’ll be featuring mini-profiles of our new PhD students over the next few weeks. We look forward to welcoming them into our community!
William Nickols
Hello! My name is Will Nickols. I’m originally from Dallas, Texas, but I’ve spent the last four years at Harvard College earning a BA in Statistics and Chemical and Physical Biology and a concurrent MA in Statistics. While in college, I worked in the Curtis Huttenhower lab to build biostatistical and computational biology tools for working with metagenomic data.
My first main project was a benchmarking study of popular taxonomic profiling tools in environmental (non- human associated) microbial communities, communities that pose challenges for traditional profilers because of their high diversity and lack of previous characterization. My second main project, which doubled as my senior thesis, involved developing linear modeling software to detect associations between the abundances of microbes in a community and properties of that community (host age, disease, etc.). Because metagenomic data are sparse and compositional, this project focused on simultaneously identifying both prevalence and abundance associations and enabling more robust inference with reference spike-ins or an iterative renormalization procedure. In the summer of 2023, I forayed into aging research at the Buck Institute, where developed 3D image analysis methods and ‘omics pipelines for analyzing mitoribosome data. During the PhD program, I’m interested in working on causal inference methodology, an interest sparked by a fascinating class in causal inference taught by Kosuke Imai last year. Also, I hope to work on applied projects in infectious disease, especially those affecting low- and middle-income countries.
Outside of research, I have served as a TF for two large statistics courses at the College, one focusing on statistical inference and one focusing on linear models and their extensions. In the PhD program, I look forward to being a TF again! Outside of work, I enjoy reading social science, volunteering in Boston, running, and powerlifting (something I’ve carried over from Texas). I’m excited to get to know the students and faculty in the department (and beyond!), and I’m looking forward to using statistics to address difficult but important problems in public health and medicine!
Daniel Paydarfar
Hey! My name is Daniel Paydarfar and I’m super excited to join the biostatistics program this fall. I grew up in Newton, MA, just a few exits outside Cambridge, but my parents moved to Austin, Texas, when I graduated high school. I then attended The University of North Carolina at Chapel Hill, majoring in mathematics, but intent on applying the theoretical knowledge I gained to solve real problems in public health and medicine. At UNC, I became interested in a particular research area of emergency stroke care, spending my sophomore through senior years developing statistical models to optimize emergency transport decisions for suspected stroke patients.
This research experience piqued my interest in biostatistics, leading me to pursue a MS in Statistics at the University of Washington. I found that with every statistics course I took, my passion for the field grew. There’s just an incredible breadth of topics each built on fascinating theory, and endless possibilities for impactful biomedical applications. My statistics coursework and stroke research motivated my methodological interests in causal inference and statistical learning, with applied interests in biomedical problems spanning precision medicine and comparative effectiveness research.
Since graduating from UW in 2023, I have been working at Ascension Healthcare as a data scientist remotely from Seattle. Ascension collects a massive amount of data in every domain imaginable from their network’s hospitals, and in my role I’ve been able to gain experience ingesting, processing, and analyzing “big data” with cloud tools. I also developed a model to predict stroke mortality rates, which is now used to track quality of care at all of Ascension’s facilities nationwide. It quickly became clear during my work at Ascension that real healthcare data can be very messy, having significant missingness and/or high-dimensionality. I hope to focus on these topics in the context of causal inference and statistical learning during my PhD.
I’m thrilled to dive deeper into biostatistics at Harvard through rigorous coursework and the abundance of exciting research opportunities available with faculty & the consortium of hospitals and medical institutions in Boston.
In my free time, I love running (excited to run the Harvard stadium stairs again!), hiking, cooking, and spending time with my partner, Lauren, and our two cats Etta and Fig!
