Esther Kim, PhD

Innovation in today’s world of multiple methods means identifying ways of leveraging available data, making use of new technologies, and continuing to expand our array of new methodologies, all within the framework of transparency and fit-for-purpose rigor. With the emergence of the global COVID-19 pandemic, the need for innovative approaches to collecting, evaluating and disseminating public health data became critical. Responding to requests from the U.S. Centers for Disease Control and… Show more

Innovation in today’s world of multiple methods means identifying ways of leveraging available data, making use of new technologies, and continuing to expand our array of new methodologies, all within the framework of transparency and fit-for-purpose rigor. With the emergence of the global COVID-19 pandemic, the need for innovative approaches to collecting, evaluating and disseminating public health data became critical. Responding to requests from the U.S. Centers for Disease Control and Prevention and public health researchers globally, The Delphi Group at Carnegie Mellon University and University of Maryland Social Data Science Center created the COVID-19 Trends and Impact Surveys (CTIS), in partnership with Meta (formerly Facebook). Utilizing Facebook as a sampling frame to reach tens of thousands of individuals daily initially in the US and later globally, data from randomly selected users of the platform were used to forecast cases and healthcare needs to allow improved and rapid planning response. Data were subsequently also used to monitor health behaviors, vaccination hesitancy, mental health, and the like. Numerous methodological, legal, and technical challenges were successfully navigated to develop this approach and share such detailed data, providing unprecedented access to partners outside the social media organization. Data are currently collected in over 240 countries, and multiple publications and public reports have already detailed this work. We are proud to honor this initiative with the 2022 Warren J. Mitofsky Innovators Award to this group of researchers and survey practitioners for the novel use of social media and best survey practices at a global scale and enterprise speed to collect timely, critical data through a public-private partnership with transparent methodology, and immediate and broad API access to the public as well as microdata access for researchers worldwide. Show less

Awarded alongside Carnegie Mellon University's Delphi Research Group and their other partners for their COVIDCast Dashboard and the COVID-19 Symptom Survey

For exceptional achievement in the liberal arts & sciences

Our team built hCode, which is an individualized "QR code" for health that uses dimension reductionality to pack medical information into a single picture. We modeled this with multi-dimensional medical records information using a combination of survival analysis, machine learning, functional principal component analysis, and JIVE (joint and individual variance explanation) for encoding longitudinal healthcare variables.

This pictorial code is generated by an interactive web app which… Show more

Our team built hCode, which is an individualized "QR code" for health that uses dimension reductionality to pack medical information into a single picture. We modeled this with multi-dimensional medical records information using a combination of survival analysis, machine learning, functional principal component analysis, and JIVE (joint and individual variance explanation) for encoding longitudinal healthcare variables.

This pictorial code is generated by an interactive web app which collects various health variables (eg. blood pressure) to transform data into pixels which store medical information. These hCodes are easily stored on mobile devices by patients and can be presented to clinics or in emergency settings to understand one's medical history and even for personal reference of actionable health items. Additionally, hCodes integrates information from past codes with new health data to create updated hCodes for storing medical information longitudinally. Show less

Awarded for best exemplifying the award's goal of fostering research and education at the interface of biostatistics and epidemiology.

Awarded for teaching the core epidemiologic methods courses at Johns Hopkins Bloomberg School of Public Health.

Our team won for the best use of big data in our web-based application that predicts traffic accident risk.

With an interdisciplinary team, we created this app by combining movement data from Lyft’s users and drivers with Census data and the national transportation data to develop an algorithm for predicting an individual’s risk of traffic accident given an individual’s demographic profile and location.

Our team won for the best use of the Lyft API in our web-based application that predicts traffic accident risk.

With an interdisciplinary team, we created this app by combining movement data from Lyft’s users and drivers with Census data and the national transportation data to develop an algorithm for predicting an individual’s risk of traffic accident given an individual’s demographic profile and location.

Our study will demonstrate if microRNAs and extracellular vesicles can serve as functional biomarkers to risk-stratify patients and targets for developing therapeutic strategies in diabetes and end-stage kidney disease.

Awarded for investigating the role of microRNAs and extracellular vesicles as functional biomarkers and therapeutic targets for diabetes in kidney disease.

Awarded for academic excellence.

Awarded for academic excellence.