Research
New Book on Distributionally Robust Learning
A central problem in machine learning is to learn from data (“big” or “small”) how to predict outcomes of interest.This learning process is vulnerable to data that may have been contaminated with outliers or are too few to accurately represent the entirety of the actual data. A new monograph develops a comprehensive statistical learning framework […]
Five Papers Detail New Computational Models to Predict Severe Illness and Mortality from COVID-19
Data Science and AI Methods Offer Predictive Systems to Aid Healthcare Policy Management and Level-of-Care Requirements Data science and AI methods have evolved to become a powerful tool in developing strong predictive models to help improve our understanding and treatment of COVID-19. “Given ample data, and assuming that the future is not completely random (like […]
New COVID-19 research focuses on Latin America
Informing Policy, Resource Allocation and Workplace Adjustment Policies COVID-19 has taken the world by storm, placing significant pressures on healthcare systems. Particularly in countries with limited testing resources and capacity-constrained health care systems, it is essential to determine who is at most risk for developing COVID-19. Knowing who may or may not need medical attention, and […]
AI in Medicine Advances with BU-MIT Research Team
Researchers from Boston University and the Massachusetts Institute of Technology have pioneered an AI method that learns from existing data how to make specific recommendations (“prescriptions”) to optimize a certain outcome. Their research findings have been published in this month in PLOS ONE. The paper titled “Prescriptive Analytics for Reducing 30-day Hospital Readmissions after General […]
Changing the Ways of Selfish Driving
Salomón Wollenstein-Betech (PhD candidate, SE) develops dynamic, data-driven tools to estimate and optimize congested roadways Boston has the worst traffic in the nation, according to a new study released this week. As city traffic thickens, the quintessential need for a resolution to this problem arises. Boston University PhD candidate (Systems Engineering) Salomón Wollenstein-Betech has created a visual […]
Moths Teach Drones to Fly
Research is first to apply animal data to autonomous vehicle navigation By Liz Sheeley As the moth navigates what it thinks are trees (the light rods), the researchers can capture this movement data and extrapolate how the moth flies around different types of forests. Image courtesy of Professor Thomas Daniel from the University of Washington and […]
PLOS Computational Biology: Learning from Animals: How to Navigate Complex Terrains
PLOS Computational Biology issued a press announcement on a paper that it published today authored by Boston University CISE Director Yannis Paschalidis (Professor ECE, SE, BME), PhD candidate Henghui Zhu (SE), former CISE post-doctoral associate Armin Ataei, former CISE visiting student scholar Hao Liu (Zhejiang University), along with University of Washington collaborators Professor Thomas Daniel (BIO, NEUROSCI) and postdoctoral researcher Yonatan Munk […]
BU-Harvard Team Wins $1.2M NSF Grant to Improve Women’s Reproductive Health using AI and Machine Learning
Photo by 10 FACE on Shutterstock Researchers to advance distributed analytics to enhance fertility in families A multidisciplinary team of researchers from Boston University and Harvard University is working to address women’s reproductive health challenges with the help of a $1.2M, four-year grant funded by the National Science Foundation (NSF) through its Smart and Connected Health […]
BU-led Research Team Wins $7.5M MURI to Create Neuro-Autonomous Robots
Dream Team of Engineers, Computer Scientists, and Neuroscientists from BU, MIT, and Australia to develop neuro-inspired capabilities for Land, Sea, and Air-based Autonomous Robots A Boston University-led research team was selected to receive a $7.5 million Multidisciplinary University Research Initiative (MURI) grant from the U.S. Department of Defense (DoD). With this prestigious grant, the researchers will develop […]
Federated Learning from Electronic Health Records Paper Receives Best Paper Honors
Our paper on “Federated learning of predictive models from federated Electronic Health Records” (with Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, and Wei Shi), International Journal of Medical Informatics, Vol. 112, April, 2018, pages 59- 67, has been selected as a Best Paper and is included in the 2019 Yearbook of the International Medical […]