Dr. Amanda Kowalski

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Professor Kowalski’s recent research analyzes experiments and clinical trials with the goal of designing policies to target insurance expansions and medical treatments to individuals who stand to benefit from them the most. Her research has also explored the impact of previous Medicaid expansions, the Affordable Care Act, the Massachusetts health reform of 2006, and employer-sponsored health insurance plans. She has also used cutting-edge techniques to estimate the value of medical spending on at-risk newborns.

 

 

Yuehao Bai

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My research interests lie in design and analysis of randomized controlled trials (RCTs), partial identification, identification and inference with multi-valued treatments and instruments, and quantile regression. In one recent paper I study the optimal stratified randomization procedure in RCTs, and found a certain kind of matched-pair design is optimal. In another paper (coauthored with Joe Romano and Azeem Shaikh), we provide asymptotically exact inference procedure for matched-pair designs. In another paper we study inference with moment inequalities whose dimension grows exponentially fast with the sample size. I also have a paper in which we study the sharp identified sets for various treatment effects with multi-valued instruments and multi-values treatments.

Amiyatosh Purnanandam

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My primary research is focused on measurement and monitoring of risks in banks, both at the individual bank level and at the level of financial system as a whole. In a recent paper, we have developed a high-dimension statistical approach to measure connectivity across different players in the financial sector. We implement our model using stock return data for US banks, insurance companies and hedge funds. Some of my early research has developed analytical tools to measure banks’ default risk using option pricing models and other tools of financial economics. These projects have often a significant empirical component that uses large financial datasets and econometric tools. Of late, I have been working on several projects related to the issue of equity and inclusion in financial markets. These papers use large datasets from financial markets to understand differences in the quantity and quality of financial services received by minority borrowers. A common theme across these projects is the issue of causal inference using state-of-the art tools from econometrics. Finally, some of ongoing research projects are related to FinTech with a focus on credit scoring and online lending.

Thomas Valley

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Dr. Valley’s research focuses on understanding and improving decision-making in the intensive care unit (ICU). His primary line of research seeks to identify the patients most likely to benefit from intensive care, allowing clinicians to safely triage patients between the ICU and the general ward. Ultimately, he hopes to identify ICU-based therapies that can be transferred to the general ward to improve hospital efficiency and reduce healthcare costs. Dr. Valley’s research interests also include enhancing diagnosis in critical illness, improving the ICU experience for family members of ICU patients, and reducing barriers to cost-effective pulmonary and critical care.

Gregory S. Miller

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Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.

Jim Omartian

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My research explores the interplay between corporate decisions and employee actions. I currently use anonymized mobile device data to observe individual behaviors, and employ both unsupervised and supervised machine learning techniques.

Andrew J. Admon, MD, MPH, MSc

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I am a pulmonary and critical care physician who is passionate about improving critical care delivery by applying advanced methods for causal inference to observational data. My prior work has leveraged real-world data clinical and administrative data to study the epidemiology of critical illness, the organization of critical care, and health care financing.

My current work leverages real-world clinical data to understand whether and how care team fragmentation (transitions of physicians and other providers while a patient is still hospitalized) influences clinical outcomes like survival and recovery. Answering these questions correctly requires methods that are attentive to the complex causal structure underlying the relationship, depicted here. It features time-varying exposures (A), confounders (L), and mediators (M), all of which can influence clinical outcomes (Y). Arrows in the figure identify directional (i.e., causal) relationships between variables.

Nancy Fleischer

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Dr. Fleischer’s research focuses on how the broader socioeconomic and policy environments impact health disparities and the health of vulnerable populations, in the U.S. and around the world. Through this research, her group employs various analytic techniques to examine data at multiple levels (country-level, state-level, and neighborhood-level), emphasizing the role of structural influences on individual health. Her group applies advanced epidemiologic, statistical, and econometric methods to this research, including survey methodology, longitudinal data analysis, hierarchical modeling, causal inference, systems science, and difference-in-difference analysis. Dr. Fleischer leads two NCI-funded projects focused on the impact of tobacco control policies on health equity in the U.S.

Gary L. Freed

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I conduct a broad range of research on health policy and health economics focused on children. I will be launching a program on child health equity in the fall of 2020.

Aditi Misra

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Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.

This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.