Team
We use interdisciplinary approaches, including theory and natural experiments, to understand how computation is embodied in socio-technical healthcare delivery systems. Examples include the origins of complex behavior in healthcare organizations and the augmentation of clinical decision-making through predictive systems. Broadly, we invent new tools for studying non-model organizations with a significant focus at the intersection of medicine and public health – addressing fundamental questions such as how to increase quality and access while minimizing cost and inequality? Finally, we build new data and systems to tackle prevalent challenges in health – 1. Making healthcare delivery safer and accessible to everyone; 2. Investing in healthcare worker and patient safety; 3. Transforming the clinical trials enterprise to make it more efficient and integrated into healthcare delivery systems.
We use interdisciplinary approaches, including theory and natural experiments, to understand how computation is embodied in socio-technical healthcare delivery systems. Examples include the origins of complex behavior in healthcare organizations and the augmentation of clinical decision-making through predictive systems. Broadly, we invent new tools for studying non-model organizations with a significant focus at the intersection of medicine and public health – addressing fundamental questions such as how to increase quality and access while minimizing cost and inequality? Finally, we build new data and systems to tackle prevalent challenges in health – 1. Making healthcare delivery safer and accessible to everyone; 2. Investing in healthcare worker and patient safety; 3. Transforming the clinical trials enterprise to make it more efficient and integrated into healthcare delivery systems.
Diego Martinez
2017-present
dmart101@jhu.edu
Diego did his PhD at South Florida with Jose Zayas-Castro, and his postdoc with Scott Levin at Johns Hopkins. He is Chilean.
2017-present
dmart101@jhu.edu
Diego did his PhD at South Florida with Jose Zayas-Castro, and his postdoc with Scott Levin at Johns Hopkins. He is Chilean.
Ivan Portnoy
2019
iportnoy@uninorte.edu.co
Ivan was a visiting PhD student of mechanical engineering at Uninorte, Colombia. He investigated the epidemic processes by which antibiotic-resistant bacteria spreads over dynamic networks of contacts between humans.
Now a Lecturer in Universidad del Norte
2019
iportnoy@uninorte.edu.co
Ivan was a visiting PhD student of mechanical engineering at Uninorte, Colombia. He investigated the epidemic processes by which antibiotic-resistant bacteria spreads over dynamic networks of contacts between humans.
Now a Lecturer in Universidad del Norte
Gan Shi
2019
gshi10@jhu.edu
Gan obtained his MS in informatics at Johns Hopkins. He built multilevel regression models to measure the association of timely inpatient discharge with length-of-stay and 30-day readmissions.
Now a Data Scientist in Deloitte
2019
gshi10@jhu.edu
Gan obtained his MS in informatics at Johns Hopkins. He built multilevel regression models to measure the association of timely inpatient discharge with length-of-stay and 30-day readmissions.
Now a Data Scientist in Deloitte
Nicolas Martinez
2019
n.martinez07@ufromail.cl
Nicolas obtained his BS in industrial engineering with a minor in computer science at UFRO, Chile. He applied statistical learning methods to investigate drivers of prolonged waiting in Chilean public hospitals.
Now an entrepreneur in Chile
2019
n.martinez07@ufromail.cl
Nicolas obtained his BS in industrial engineering with a minor in computer science at UFRO, Chile. He applied statistical learning methods to investigate drivers of prolonged waiting in Chilean public hospitals.
Now an entrepreneur in Chile
Haoxiang Zhang
2018
hzhan109@jhu.edu
Haoxiang obtained his MSc in applied math from Johns Hopkins. He developed methods to more accurately triage patients in waiting lists of Chilean public hospitals. Also, he contributed to the deployment of machine learning-based models for predicting the onset of acute kidney injury at The Johns Hopkins Hospital.
Now a Data Scientist in Paul Fredrick
2018
hzhan109@jhu.edu
Haoxiang obtained his MSc in applied math from Johns Hopkins. He developed methods to more accurately triage patients in waiting lists of Chilean public hospitals. Also, he contributed to the deployment of machine learning-based models for predicting the onset of acute kidney injury at The Johns Hopkins Hospital.
Now a Data Scientist in Paul Fredrick
Jiarui Cai
2017-2018
jcai11@jhu.edu
Jiarui obtained her MSc in applied math from Johns Hopkins. She developed decision support systems for dynamic fleet management and probabilistic models of infectious disease transmission at The Johns Hopkins Hospital.
Now a Data Scientist in Resurgent Capital Services
2017-2018
jcai11@jhu.edu
Jiarui obtained her MSc in applied math from Johns Hopkins. She developed decision support systems for dynamic fleet management and probabilistic models of infectious disease transmission at The Johns Hopkins Hospital.
Now a Data Scientist in Resurgent Capital Services
Collaborators
Aaron Milstone, Johns Hopkins
Sriram Sankaranarayanan, IIM Ahmedabad
Diana Prieto, Johns Hopkins
Eili Klein, Johns Hopkins
Felipe Feijoo, PUCV
Jeremiah Hinson, Johns Hopkins
Jose Zayas-Castro, South Florida
Jorge Acuna, South Florida
Justin Lessler, Johns Hopkins
Rajib Paul, UNC Charlotte
Scott Levin, Johns Hopkins
Gary Lin, CDDEP
Sumona Datta, Imperial College
Carlton Evans, Imperial College
Kimia Ghobadi, Johns Hopkins
Kathleen Page, Johns Hopkins
Aaron Milstone, Johns Hopkins
Sriram Sankaranarayanan, IIM Ahmedabad
Diana Prieto, Johns Hopkins
Eili Klein, Johns Hopkins
Felipe Feijoo, PUCV
Jeremiah Hinson, Johns Hopkins
Jose Zayas-Castro, South Florida
Jorge Acuna, South Florida
Justin Lessler, Johns Hopkins
Rajib Paul, UNC Charlotte
Scott Levin, Johns Hopkins
Gary Lin, CDDEP
Sumona Datta, Imperial College
Carlton Evans, Imperial College
Kimia Ghobadi, Johns Hopkins
Kathleen Page, Johns Hopkins