Lorena Anderson

UC Merced is part of several new initiatives aimed at increasing the accessibility and inclusivity of data science studies and opening new opportunities for historically underserved students after graduation.

New grants from the National Science Foundation (NSF), the Department of Energy (DOE) and the California Learning Lab are funding collaborations with a sister campus and several community colleges as well as the Joint Genome Institute (JGI) to accomplish these goals.

About 20 UC Merced students spent the past two weeks working at Lawrence Livermore National Laboratory (LLNL) to see if they can solve a problem that could have a significant impact on cardiology.

The annual Data Science Challenge (DSC), a two-week, full-time internship at LLNL, this year teamed students from Merced and UC Riverside. They attempted to see if machine learning could address a gap in the information provided by the common electrocardiogram (ECG) test.

Adding even a small amount of biochar — a charcoal-like material produced by burning organic matter — to a dairy’s manure-composting process reduces methane emissions by 84%, a recent study by UC Merced researchers shows.

The dairy industry is one of the main sources of methane in California, making up 50% of the state’s methane emissions. Reducing these emissions is a critical part of state and federal efforts to address climate change.

Rising juniors this fall will have an unprecedented opportunity for stem cell research training that could lead directly to careers in stem cell science after graduation.

UC Merced is rapidly gaining a strong reputation for research and scientific computing across many disciplines and a major expansion of its computing infrastructure is about to cement the campus’ status as a research computing hub.

A UC Merced researcher and her lab have unlocked one of the mysteries that could lead to treatments — or even cures — for prion diseases in mammals.

Prion diseases are a family of rare, progressive neurodegenerative disorders that affect both humans — such as with Creutzfeldt-Jakob disease or fatal familial insomnia — and animals, such as mad-cow disease. These disorders are usually rapidly progressive and always fatal, according to the Centers for Disease Control.

A pair of UC Merced researchers are combining computational chemistry and machine learning principles to solve what seems to be an intractable problem at the heart of quantum mechanics: predicting the movement of electrons, also known as electron dynamics.

Math at UC Merced isn’t exactly like math at most other universities.

Sure, there are parabolas and equations, orders of operations and cosines, but at this campus, those lead into topics such as Mad Cow Disease, coral photosynthesis and a crab’s sense of smell.

The majority of people who die by suicide do so with firearms, and there were more firearm suicides in America in 2017 than there were homicides committed by any method. Combined.

Those shocking numbers from the FBI and American Foundation for Suicide Prevention are the impetus for two UC Merced professors from very different disciplines to join forces to try and predict who is most likely to commit suicide using a gun.