My research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods. Many of my studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. We are just beginning to understand how to harness and apply medical information. This problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. My current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.
Davis-Kean is the Director of the Population, Neurodevelopment, and Genetics program at the Institute for Social Research. This group examines the complex transactions of brain, biology, and behavior as children and families develop across time. She is interested in both micro (brain and biology) and macro (family and socioeconomic conditions) aspects of development to understand the full developmental story of individuals. Her primary focus in this area is how stress relates to family socioeconomic status and how that translates to parenting beliefs and behaviors that influence the development of children.
Prof. Vershynin’s main area of expertise is high dimensional probability and its applications. He is interested in random geometric structures that appear in various data science problems. The following is a sample of his recent projects: 1. High dimensional inference from nonlinear data Sometimes we are given certain observations of an unknown vector that encodes useful but hidden information, and we want to compute that vector. Examples includes compressed sensing, linear and non-linear regression, as well as binary (yes-no) observations. We are developing methods that can estimate the hidden vector without even knowing the nature of the non-linearity of observations. Areas of application include survey methodologies, signal processing, and various high-dimensional classification problems. 2. Structure mining in networks Complex data sets such as networks often have latent structures, for example clusters or communities. We are interested in developing efficient methods to discover such latent structures. Prof. Vershynin’s methods come from various areas of mathematics and data science, including random matrix theory, geometric functional analysis, convex and discrete geometry, geometric combinatorics, high dimensional statistics, information theory, learning theory, signal processing, theoretical computer science and numerical analysis.
The Language and Information Technologies (LIT) lab, directed by Rada Mihalcea, conducts research in natural language processing, information retrieval, and applied machine learning. The group specifically focuses on projects concerned with text semantics (word/text similarity, large semantic networks), behavior analysis (multilingual opinion analysis, multimodal models for deception detection, emotion recognition, alertness detection, stress/anxiety detection, analysis of counseling speech), big data for cross-cultural analysis (geotagging, understanding cross-cultural differences and worldview), educational applications (pedagogical search engines, automatic short answer grading, conversational technologies for student advising).
Several of the projects in the LIT lab are interdisciplinary, acknowledging the fact that language can be used to deepen our understanding in many different fields, such as psychology, sociology, history, and others. Some of the ongoing projects in the lab are collaborations with psychologists and sociologists, and target a rich modeling of human behavior through language analysis, seeking answers to questions such as “what are the core values of a culture?” and “are there differences in how different groups of people perceive the surrounding world?” The lab is also actively working on multimodal projects to track and understand human behavior, where language analysis is complemented with other channels such as facial expressions, gestures, and physiological signals.
Of interest, Prof. Mihalcea was quoted in a story about sexism and today’s virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana; Refinery29.