December, 2020: In year 2020, Song Lab is pround of
graduating 5 PhD students and publishing 27 peer-reviewed
papers. Thanks to all lab members for their great efforts during
these difficult times. In particular, Song Lab has developed
several epidemiological prediction models and software for the
projection of COVID-19 pandemic. One paper published in Harvard Data
Science Review took seven rounds of revisions before it
was accepted for publication. Another paper "A review of
multi-compartment infectious disease models" published in
International Statistical Review 88(2), 462-513 took
only 11 days of review from submission to acceptance! We
appreciate the great dedication of the statistical community to
the fight against the COVID-19 pandemic.
December, 2020: Congratulations to Mengtong Hu for
recieving the 2020 Outstanding Graduate Student Instuctor Award
at the Dapartment of Biostatistics, University of Michigan.
December, 2020: Kudos to Soumik Purkayastha for recieving
the 2020 Cornell Award for his excellent academic performance at
the Dapartment of Biostatistics, University of Michigan.
August, 2020: Congratulations to Wen Wang for her success
in defending her PhD dissertation "L0 Constraint Optimization,
Homogeneity Fusion, and Mediation Analyses" on August 14, 2020.
Dr. Wang will begin her Postdoc at the Univeristy of Michigan
Kidney Empidemiology and Cost Center.
July, 2020: Advised jointly by Dr. Douglas Schaubel from
the University of Pennsylvania and Dr. Peter Song from
University of Michigan, Lili Wang successfully defended her PhD
dissertation "Flexible Methods for the Analysis of Clustered
Event Data in Observational Studies" on July 28, 2020.
Congratulations!
Dr. Wang will join Google Inc. as a data scientist in August,
2020.
June, 2020: A review paper entilted "A Review of
Multi-Comparment Infectious Disease Models" was recently
accepted by International Statistical Review. This paper made a
new record for the review process; it took only 11 days from
submission to acceptance! This is an amazing record! Thanks to
the journal editors' tremendous effort.
June, 2020: Led by Yiwang Zhou, after seven rounds of
revisions, a paper entilted "A Spatiotemporal Epidemiological
Prediction Model to Inform County-Level COVID-19 Risk in the
United States" was recently accepted by Harvard Data Science
Review. The journal will publish the key correspondences
together with the paper between the author and editor, associate
editor, dataviz editor and five reviewers. There are many
`secrete' communications in the process of the paper revisions
that should be fun to read!
May, 2020: Led by Lili Wang, a COVID-19 research paper
entilted "An epidemiological forecast model and software
assessing interventions on COVID-19 epidemic in China" was
recently accepted by Journal of Data Science as a discussion
paper. We are honored to exchange our opinions on the modeling
of COVID-19 infection dynamics via extensive discussions with
scientists from Johns Hopkins University, University of Chicago,
Duke University, Carnegie Mellon University and Fred Hutchinson
Cancer Research Center.
May, 2020: Congratulations to Lan Luo who successfully
defended her PhD dissertation "Renewable estimation and
incremental inference with streaming health datasets" on May 29,
2020. Dr. Luo will join the Department of Statistics, University
of Iowa as an assistant professor.
May, 2020: Congratulations to Emily Hector for a
successful defense on her PhD dissertation "Distributed
estimation and inference for the analysis of big biomedical
data" on May 13, 2020. Dr. Hector will join the Department of
Statistics, North Carolina State University as an assistant
professor.
May, 2020: Congratulations to Joseph Naiman for
successfully defending his PhD dissertation "Functional
regression and selection with applications in accelerometer data
analysis" on May 12, 2020. Dr. Naiman began to work at the DTE
Energy Financial Group as a data scientist.
March 2020: Updates of the progress on both our
epidemiological forecast model and software. Here is
the link to the technical report available on medRxiv;
here is the link to the R software package eSIR
available in the GitHub with the user's manual;
here is the
link to online prediction of COVID-19 trend in China.
February 2020: Former and current lab members worked
together in the past few weeks and developed a data analysis
toolbox for the analysis of novel coronavirus, COVID-19 in
China. Here are the
first stack of slides
and the second stack
of slides of the lectures on the toolbox; here is the link to the recorded lectures.
