#biostatistics-and-informatics

#biostatistics-and-informatics

In his first email to CDC staff, he wrote that the federal government's "decisions, communications, and processes" broke the public's trust during the pandemic, and that "acknowledging this reality is a necessary step toward renewal." In practice, the CDC has been undergoing a kind of forced renewal for months.

In a single streaming pipeline, you might be processing HL7 FHIR messages with frequent specification updates, claims data following various payer-specific formats, provider directory information with inconsistent taxonomies, and patient demographics with privacy redaction requirements. Our member eligibility stream processes roughly 50,000 records per minute during peak enrollment periods.

Which Algorithm Is This? If you step back, this maps almost perfectly to the Top K Frequent Elements problem.We usually solve it for integers in a list. Here, the "elements" are audience profiles age and body-type combinations. First, define what an audience profile looks like: case class Profile(age: Int, height: Int, weight: Int) What we want is a function like this:

Imagine you're selecting an influencer to work with on your new campaign. You've narrowed it down to two, both in the right area, both creating the right sort of content. One has 24.6 million subscribers, the other 1.4 million. Which do you choose? Now imagine you could find out the first had 8.7 million unique viewers last month, while the second had 9.9 million. Do you want to change your mind?

As the Magerstadt Professor of Cardiovascular Epidemiology, Khan studies the epidemiology of risk for heart failure. Using population-based cohorts and large electronic health record data analyses, she performs mechanistic studies that may enhance risk prediction and identify novel therapeutic agents for the prevention and treatment of cardiovascular disease. Khan and her team have developed a tool to predict risk and prevent cardiovascular disease such as heart failure, stroke, arrhythmia, coronary artery disease and many other conditions.

Most beginner data portfolios look similar. They include: A few cleaned datasets Some charts or dashboards A notebook with code and commentary Again, nothing here is wrong. But hiring teams don't review portfolios to check whether you can follow instructions. They review them to see whether you can think like a data analyst. When projects feel generic, reviewers are left guessing:

Scientists in the laboratory of Rendong Yang, PhD, associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications. Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.

Many of them say that AI's ascendance is already reducing demand for human researchers who can write code or do basic data analysis - tasks often handled by graduate students, postdocs or those without graduate training. Obsolescence of some basic roles in areas such as computer modelling "is not even in the future. It's happening now," says Xuanhe Zhao, a mechanical engineer at the Massachusetts Institute of Technology in Cambridge, because "AI is doing this much better than entry-level scientists".

Public health consultant Dr Ross Keat said supporting people earlier to make small preventative changes would make "a big difference later on". Some 3,500 people in the north of the island within that age bracket are eligible for the checks. The checks will be carried out by two pre-existing nurses that support GP staff and would not replace GP appointments, Keat explained, adding that the cost would be minimal and absorbed by Ramsey Group Practice.

AI plays an important role-but not by fixing fragmented data on its own. The work of organizing, connecting, and interpreting healthcare information still belongs to people and the systems they build. Where AI helps is after that foundation is in place: by bringing the right information forward at the right time, reducing the effort it takes to find what matters, and supporting better decisions in the moment of care.

Kennedy has also called for overhauling the current safety monitoring system for vaccine injury data collection, known as Vaccine Adverse Event Reporting System, or VAERS, claiming that it suppresses information about the true rate of vaccine side effects. He has also proposed changes to the federal Vaccine Injury Compensation Program that could make it easier for people to sue for adverse events that haven't been proven to be associated with vaccines.

We provide thought partnership. When a company is developing a drug, there's a lot of work involved, such as understanding the science, designing a study and generating good data. We come in and explain what the standard of care looks like today for their patient population, and what we think it will look like in five to eight years or whenever they plan to launch their therapy.

The title "data scientist" is quietly disappearing from job postings, internal org charts, and LinkedIn headlines. In its place, roles like "AI engineer," "applied AI engineer," and "machine learning engineer" are becoming the norm. This Data Scientist vs AI Engineer shift raises an important question for practitioners and leaders alike: what actually changes when a data scientist becomes an AI engineer, and what stays the same? More importantly, what skills matter if you want to make this transition intentionally rather than by accident?

A new PhD track is being added to the Walter S. and Lucienne Driskill Graduate Program in Life Sciences ( DGP) for the 2026 application cycle, to enhance student learning and build community around computational biology and bioinformatics at Feinberg. The computational biology and bioinformatics (CBB) track in the graduate program will prepare students through coursework and lectures to use modern computational approaches, including machine learning and artificial intelligence, to extract biological insight from large-scale datasets to address complex biological problems.

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something that can, in some cases, build entire applications from a text prompt. Tools like Anthropic's Claude Code and OpenAI's Codex can now work on software projects for hours at a time, writing code, running tests, and, with human supervision, fixing bugs. OpenAI says it now uses Codex to build Codex itself, and the company recently published technical details about how the tool works under the hood.

My dad was in the emergency room, short of breath, chest tight, upper back aching. He looked pale and confused. An ultrasound showed excess fluid between his lung and chest wall. "We'll drain it," a resident said, as if he were unclogging a sink. For the next five days, thick, red-tinged fluid filled a plastic container beside my dad's hospital bed. Doctors sent his cells for "staining," a way to identify cancer. But no one used that word.