Past featured publications by MIDAS affiliates or MIDAS funded research teams

Publication Title Description
(Mis)information and anxiety: Evidence from a randomized Covid-19 information campaign MIDAS affiliated faculty member, Achyuta Adhvaryu, inquires to what extent information alone explains the effects of phone calls.
Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention MIDAS affiliate faculty members, Walter Dempsey and Inbal Nahum-Shani, investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects.
The Diversity of Three-Dimensional Photonic Crystals MIDAS affiliate faculty member, Sharon Glotzer, investigates the multitude of applications of over 300 previously unreported crystal structures.
Increasing GPS Localization Accuracy With Reinforcement Learning MIDAS affiliated faculty member, Neda Masoud, proposes a reinforcement learning framework to increase GPS localization accuracy.
Genomic heterogeneity affects the response to Daylight Saving Time MIDAS affiliated faculty members investigate how genomic heterogeneity affects the response to Daylight Saving Time.
One Year in the Life of Young Suns: Data-constrained Corona-wind Model of κ1 Ceti
MIDAS affiliated faculty member, Ward Manchester, presents the results of a three-dimensional magnetohydrodynamic (MHD) model which provides a framework to model coronal environments of G–M planet-hosting dwarfs.
A method for characterizing daily physiology from widely used wearables MIDAS affiliated faculty members, Srijan Sen and Daniel Forger, propose a statistical method for analysis of ambulatory wearable-device data that can estimate circadian rhythms.

Iterative single-cell multi-omic integration using online learning


MIDAS affiliated faculty member, Josh Welch, and team describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets.

The Effect of Exposure to Disaster on Cancer Survival

MIDAS affiliated faculty members, Mousumi Banerjee and Matthew A. Davis, publish paper on the effect of exposure to weather and climate-related disasters on cancer survival.

Physician-Training Stress and Accelerated Cellular Aging

MIDAS affiliated faculty, Srijan Sen, contributes to paper discussing mechanisms linking stress and disease.

Association Between Life Purpose and Mortality Among US Adults Older Than 50 Years

MIDAS affiliate member Dr. Bhramar Mukherjee and colleagues studied a cohort of about 7000 US adults and found an association between strong life purposes and decreased mortality.

Investigating harmful and helpful effects of watching season 2 of 13 Reasons Why: Results of a two-wave U.S. panel survey

MIDAS faculty Josh Pasek and colleagues explore how a popular TV series, 13 Reasons Why, can help some viewers but harm others.

Detection of anti-correlation of hot and cold baryons in galaxy clusters

Former U-M data science student Arya Farahi and colleagues show a balance between the amounts of hot gas, stars and other materials.

Here’s The Bizarre Reason Self-Driving Cars Might Not Cut Down Traffic After All

Dr. Ming Xu, MIDAS affiliate member, is quoted in the article “Here’s The Bizarre Reason Self-Driving Cars Might Not Cut Down Traffic After All” by David Nield. “We know that self-driving cars are promising to make the roads safer and give us more time to relax, but the energy-saving and pollution-cutting benefits are also important. Now a new study suggests those benefits might not be as big as we thought.”
Passive Suicidal Ideation May Be Underestimated Among Older Americans Work by MIDAS affiliated faculty member, Dr. Rich Gonzalez, and team is mentioned in the article, Passive Suicidal Ideation May Be Underestimated Among Older Americans.
Higher Prostate Cancer Death Risk Among Blacks May Be Tied to Socioeconomics
Work by MIDAS affiliated faculty member, Matthew Schipper, et al. is the subject of the article “Higher Prostate Cancer Death Risk Among Blacks May Be Tied to Socioeconomics”.
Hepatitis C Transmission in Young People who Inject Drugs: Insights Using a Dynamic Model Informed by State Public Health Surveillance
Work by MIDAS affiliated faculty member, Dr. Marisa Eisenberg, et al. “Hepatitis C transmission in young people who inject drugs: Insights using a dynamic model informed by state public health surveillance” was published in Epidemics: The Journal of Infectious Disease Dynamics, June 2019.
Deciphering suitable cancer drug combinations MIDAS affiliate faculty member, Dr. Yuanfang Guan’s, work on deciphering suitable cancer drug combinations was published in Nature Communications.
New Research Is Focusing on Treating Teens’ Suicidal Thoughts With Support of Friends, Family
Work by MIDAS affiliated faculty member, Dr. Brenda Gillespie, and a team from the University of Michigan’s Department of Psychiatry, was cited in the Time article “New Research Is Focusing on Treating Teens’ Suicidal Thoughts With Support of Friends, Family”.
Do no harm: a roadmap for responsible machine learning for health care
MIDAS faculty and Co-director of U-M Precision Health Initiative Jenna Wiens publishes paper outlining responsible machine learning for healthcare.
Surpassing the single-atom catalytic activity limit through paired Pt-O-Pt ensemble built from isolated Pt1 atoms MIDAS affiliated faculty, Bryan R. Goldsmith, contributed to an article in Nature Communications called Surpassing the single-atom catalytic activity limit through paired Pt-O-Pt ensemble built from isolated Pt1 atoms.
Geosurveillance, Location Privacy, and Personalization
MIDAS affiliate faculty member, Syagnik Banerjee’s, work on geosurveillance, location privacy, and personalization.
Detroit River phosphorus loads: Anatomy of a binational watershed
MIDAS affiliated faculty member Branko Kerkez analyzes the phosphorus loads of the Detroit River and its watersheds, which are responsible for 25% of the total phosphorus (TP) load to Lake Erie.
Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households
MIDAS affiliated faculty Ming Xu examines global GHG emissions driven by the U.S. household consumption from 1995 to 2014 in recent publication.
Association of Length of Time Spent in the United States With Opioid Use Among First-Generation Immigrants
MIDAS affiliated faculty Matthew A. Davis discovers that US-born residents more than 5 times likely to use prescription opioids than new immigrants
Political events and mood among young physicians: a prospective cohort study
MIDAS affiliated faculty Brahmajee Nallamothu and Srijan Sen publish paper on the relationship between politics and the mood of young doctors. They have found that the major political events were linked with decline in moods.
Diagnosing bias in data-driven algorithms for healthcare
MIDAS affiliated faculty, Jenna Wiens and Nicholson Price, analyze bias in data-driven algorithms in the field of healthcare.
Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning MIDAS affiliated faculty Jeff Fessler investigates the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models and data-driven methods based on machine learning techniques.
Rates and correlates of risky firearm behaviors among adolescents and young adults treated in an urban emergency department MIDAS affiliated faculty Jason Goldstick investigates firearm behaviors among adolescents and young adults.
A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance MIDAS affiliated faculty David Fouhey and team use machine learning to learn a mapping from EUV narrowband images to spectral irradiance measurements.
Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies MIDAS affiliated faculty Xiang Zhou uses SPARK to analyze spatially resolved transcriptomic datasets.
Choices and trade-offs in inference with infectious disease models MIDAS affiliated faculty, Aaron A. King, conducts inference with mathematical models involving a choice of model, simulation method, inference method and software implementation.
Digitizing and Transforming Mobility Systems: Lessons from the Detroit Region MIDAS Fellow, Arya Farahi, and affiliated faculty, Danai Koutra and Aditi Misra, contributed significantly to the World Economic Forum’s recent publication “Digitizing and Transforming Mobility Systems: Lessons from the Detroit Region”.
Medical supply chains are fragile in the best of times and COVID-19 will test their strength
Affiliated faculty member, Mark Daskin, and Emily Tucker, Ph.D. candidate in Industrial and Operations Engineering, discuss how COVID-19 will test the strength of the medical supply chain.
Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients
MIDAS affiliated faculty and colleagues validate the Epic Deterioration Index (EDI), a predictive model implemented in over 100 U.S. hospitals that has been recently promoted for use in COVID-19 patients.
Mapping global carbon footprint in China MIDAS affiliate faculty, Ming Xu, and team map CO2 emissions in China driven by global consumption in 2012 at a high spatial resolution (10 km × 10 km) using a detailed, firm-level emission inventory.
Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome MIDAS affiliated faculty, Veerabhadran Baladandayuthapan, investigates personalized network modeling of the pan-cancer patient and cell line interactome.
Saving social media data: Understanding data management practices among social media researchers and their implications for archives MIDAS affiliated faculty members, Libby Hemphill and Margaret L. Hedstrom, explore conceptual, technical, and ethical challenges for data archives based on the similarities and differences between SMD and other types of research data, focusing on the social sciences.
Pilot of an Asynchronous Web-Based Video Curriculum to Improve Firearm Safety Counseling by Pediatric Residents MIDAS affiliated faculty Jason Goldstick and team pilot a self-paced online curriculum surrounding firearm injury prevention counseling.
Availability of Statistical Code From Studies Using Medicare Data in General Medical Journals MIDAS affiliated faculty, Tom Valley and Brahmajee Mallamothu, investigate the availability of statistical code from studies using Medicare data.
Recommendations to enhance rigor and reproducibility in biomedical research MIDAS affiliated faculty, Jun Li and Lana Garmire, provide recommendations to improve reproducibility, transparency, and rigor in computational biology.
Mental Health of Young Physicians in China During the Novel Coronavirus Disease 2019 Outbreak MIDAS affiliated faculty, Margit Burmeister and Srijan Sen, assess anxiety, depression, mood, and other established factors associated with mental health problems in a cohort of young physicians in China before and during the COVID-19 outbreak.
What Drives U.S. Congressional Members’ Policy Attention on Twitter?
MIDAS affiliated faculty, Libby Hemphill, and team leverage politicians’ social media data to study political attention using a supervised machine‐learning classifier that detects policy areas in individual tweets.
Association Between Blood Pressure and Later-Life Cognition Among Black and White Individuals MIDAS faculty, Andrzej Galecki, and team discover that black individuals’ higher cumulative blood pressure levels may explain racial disparities in cognitive decline.
Changes to Visitation Policies and Communication Practices in Michigan Intensive Care Units During the COVID-19 Pandemic
MIDAS affiliated faculty, Tom Valley, and team investigates changes to visitation policies and strategies used to communicate with family members due to COVID-19.
HEAT – Human Embodied Autonomous Thermostat MIDAS affiliated faculty, Eunshin Byon, and team proposes a new paradigm named Human Embodied Autonomous Thermostat (HEAT) that considers human occupants as an embodiment of smart and connected thermostats.
The shape of educational inequality MIDAS affiliated faculty, Ceren Budak and Paul Resnick, present a conceptual framework for the cumulative effect of all factors in relation to educational success.
The Impact of Vaccination Efforts on the Spatiotemporal Patterns of the Hepatitis A Outbreak in Michigan, 2016–2018
MIDAS affiliated faculty members, Jon Zelner, Marisa Eisenberg and Joseph Eisenberg, analyzed surveillance and vaccination data from Michigan for Hepatitis A.
A sequential multiple assignment randomized trial (SMART) protocol for empirically developing an adaptive preventive intervention for college student drinking reduction MIDAS affiliated faculty members, Inbal Nahum-Shani and Daniel Almirall, investigate the development of an adaptive preventive intervention (API) to reduce high-risk drinking among first-year college students.
Designing Inclusive Learning Environments MIDAS affiliated faculty member, Christopher Brooks, and team outline design opportunities in the scaled learning space for creating more inclusive environments.
Beyond the eye-catchers: A large-scale study of social movement organizations’ involvement in online protests MIDAS affiliated faculty member, Ceren Budak, uses Twitter data pertaining to BlackLivesMatter and Women’s movements and employ crowdsourcing and nested supervised learning methods to identify more than 50K SMOs.
Burst Case Scenario: Why Shorter May Not Be Any Better When It Comes to Corticosteroids
MIDAS affiliated faculty member, Akbar K. Waljee, explains why shorter may not be any better when it comes to corticosteroids.
Rapid and quantitative detection of SARS-CoV-2 specific IgG for convalescent serum evaluation MIDAS affiliated faculty member, Xudong Fan, and team present a portable microfluidic ELISA technology for rapid (15 min), quantitative, and sensitive detection of anti-SARS-CoV-2 S1 IgG in human serum.
Digital Paywall Design: Implications for Content Demand and Subscriptions MIDAS affiliated faculty member, Paramveer Dhillon, along with Sinan Aral from MIT, conducted an in-depth study of the current paywall design at a top U.S. newspaper.
Innovation Policy, Structural Inequality, and COVID-19
MIDAS affiliated faculty member, Shobita Parthasarathy, argues that innovation policies tend to benefit the privileged.
Continuous quality improvement in statistical code: avoiding errors and improving transparency MIDAS affiliated faculty members, Thomas Valley, Akbar Waljee, and Brahmajee Nallamothu, demonstrate how better practices with statistical coding sharing at the time of publication may improve the quality of research.
Probabilistic Characterization of Wind Diurnal Variability for Wind Resource Assessment
MIDAS affiliated faculty member, Eunshin Byon, develops a new probabilistic modeling approach for quantifying variation in the wind diurnal pattern for assessing wind resource at unmonitored locations.
Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys
MIDAS affiliated faculty member, Bryan Goldsmith, and team develop a theory-guided machine-learning model that can quantify and explain the link between the geometric structure of an adsorption site and the chemisorption strength.
Continuous quality improvement in statistical code: avoiding errors and improving transparency MIDAS affiliated faculty members, Thomas Valley, Akbar Waljee, and Brahmajee Nallamothu, demonstrate how better practices with statistical coding sharing at the time of publication may improve the quality of research.
Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US MIDAS affiliated faculty member, Andrew J. Admon, and team assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19.
Long-term healthcare provider availability following large-scale hurricanes: A difference-in-differences study MIDAS affiliated faculty member, Matthew Davis, and team examine the availability of healthcare providers following large-scale hurricanes.
Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System MIDAS affiliated faculty members, Thomas Valley, Karandeep Singh, Brahmajee Nallamothu, and Bhramar Mukherjee, systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.
Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations MIDAS affiliated faculty member, Bhramar Mukherjee, evaluates the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India.
Seeing Blue in Black and White: Race and Perceptions of Officer-Involved Shootings MIDAS affiliated faculty member, Josh Pasek, investigates race and reactions to a novel officer-involved shooting.
Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment MIDAS affiliated faculty member, Henry Liu, and team demonstrate the effectiveness of the proposed environment in a highway-driving simulation.
Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks MIDAS affiliated faculty member, Jon Zelner, and team suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings are driven primarily by variation in household, community, and workplace exposure rather than case-fatality rates.
Seeing Blue in Black and White: Race and Perceptions of Officer-Involved Shootings MIDAS affiliated faculty member, Josh Pasek, investigates race and reactions to a novel officer-involved shooting.
The economic complexity of US metropolitan areas MIDAS affiliated faculty member, Robert Manduca, highlights the need for caution when interpreting the relationship between complexity and socioeconomic outcomes.
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
MIDAS affiliated faculty members, Srijan Sen and Ambuj Tewari, review federated learning and assess whether it can be useful in the mHealth field.
Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate — Making a case for standardized reporting to improve communication MIDAS affiliated faculty member, Santiago Schnell, identifies key metadata for the COVID-19 fatality rate after a thorough analysis of mathematical models, serology-informed studies and determinants of causes of death for the COVID-19 pandemic.
Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning MIDAS affiliated faculty members, Ji Zhu and Ming Xu, demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.
AI-powered effective lens position prediction improves the accuracy of existing lens formulas MIDAS affiliated faculty members, Joshua Stein and Nambi Nallasamy assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas.
Population-based estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) prevalence and characteristics MIDAS affiliated faculty, Nancy Fleischer, examine the long term effects of COVID-19.