Data Science newsletter – January 6, 2022

Lots of questions in this subreddit have to do with career advice or discussing the job market, but I want to do something more fun. I’m a senior majoring in Computer Science but have taken a plethora of data science courses offered at the undergrad level at my university (some classes intertwine with master’s level courses) and I wanted to share and see if other students have similar, memorable, or fun experiences in their classes. [153 comments]

I wrote this thread a long time ago but decided not to put it up. Today, because of other things on twitter that you will recognize, I have dusted it off. It is a little raw.

New analysis finds that research by Black, Latinx and Asian scientists is often clustered in certain fields and underrepresented in terms of citation counts. This lack of diversity hurts everyone, the authors say.

Every day, innovators propel the world forward with their problem-solving designs, endless creativity and novel solutions. As we move into 2022, we have our eyes on 16 different innovators from nine projects. These groundbreakers are experts in their fields—which range from social justice to biology to artificial intelligence—and they are drumming up new ways to push the envelope.

The concept is a modernization of the 150-year-old field of epidemiology, similar to how precision medicine has transformed health care, says Muin Khoury, director of the Office of Genomics and Precision Public Health at the US Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia, and one of the idea’s biggest advocates.

The definition of precision public health is sprawling and variable: for most researchers in the field it includes a sweep of data-driven techniques, such as sequencing pathogens to detect outbreaks and turbo-charging data collection to monitor harmful environmental exposures. It also encompasses an ambition to target interventions to specific people who need them.

For Caitlin Allen, a PhD student of public health at Emory University in Atlanta, who organized a meeting on precision public health in October last year, the kernel of the idea is simple. “You’re doing all the things you normally do in public health, but the unique aspect is that we’re using big data and predictive analytics to be more targeted and tailored in these efforts,” she says. The concept promises to save money and lives by targeting interventions to the right people.

Last Fashion Week in Milan, Lior Cole headed to the National Museum of Science and Technology of Milan on her one day off from walking runways. A science buff studying information science at Cornell, she uses her downtime to explore artificial intelligence and how it merges with spirituality and religion. “It works very well with modeling. In between jobs you have downtime, and with computer stuff you can do it whenever you want,” says Cole over Zoom. “I did a photo shoot for a magazine the other day, and I brought my computer, and I was coding.”

Cole, 20, never intended to become a model and instead was busy as a sophomore at Cornell. By chance, she visited New York for the day last June with a friend and was spotted in Washington Square Park by the designer Batsheva Hay, who was in the midst of street casting and photographing her resort 2022 look book. “Lior walked by, and I thought it was too good to be true,” Hay says. “She was so tall and had long curly hair and this sweet naive smile. We took photos of her, and she could not have been more natural.”

arXiv, stewarded by Cornell Tech, is a free resource for scholars around the world in fields including physics, math and computer science, who use the service to share their own cutting-edge research and read work submitted by others.

“These 2 million submissions represent 2 million opportunities for humanity to push forward the frontiers of our understanding,” said Tara Holm, professor of mathematics in the College of Arts and Sciences and arXiv advisory board member. “As we celebrate this achievement, we must also continue the drive to make our disciplines and our research more accessible to researchers and the public around the world.”

Jeffrey Ding, a postdoctoral fellow at Stanford’s Center for International Security and Cooperation and at the Stanford Institute for Human-Centered Artificial Intelligence, argues that China and the United States are both taking the wrong lessons from previous industrial revolutions.

As revolutionary as artificial intelligence may be, he says, both governments are overly preoccupied with being dominant in breakthrough advances. Over the long term, it may be more important to figure out how a wide range of industries can make practical use of them all. In this interview, he explains why.

In 2018, the New York City Council created a task force to study the city’s use of automated decision systems (ADS). The concern: Algorithms, not just in New York but around the country, were increasingly being employed by government agencies to do everything from informing criminal sentencing and detecting unemployment fraud to prioritizing child abuse cases and distributing health benefits. And lawmakers, let alone the people governed by the automated decisions, knew little about how the calculations were being made.

Rare glimpses into how these algorithms were performing were not comforting: In several states, algorithms used to determine how much help residents will receive from home health aides have automatically cut benefits for thousands. Police departments across the country use the PredPol software to predict where future crimes will occur, but the program disproportionately sends police to Black and Hispanic neighborhoods. And in Michigan, an algorithm designed to detect fraudulent unemployment claims famously improperly flagged thousands of applicants, forcing residents who should have received assistance to lose their homes and file for bankruptcy.

Amazon likes to boast that there are “more than 900,000 registered Alexa developers who have built over 130,000 Alexa skills,” but it’s still the case that it’s virtually impossible to actually use more than a small handful of those skills. Hence, it’s not surprising that Priya Anand, after reviewing internal Amazon documents that detail slowing growth in Alexa devices, concluded that Alexa’s biggest problem is “people simply don’t find Alexa that useful.“

This is both true and false. Many of us have discovered great utility in Alexa, albeit in small, discrete functions. As one former Microsoft executive suggests, the secret to Alexa’s future success may well be to double down on creating rabidly loyal fans of these smaller functions, rather than trying to overawe us with 129,995 Alexa skills we’ll never discover or use.

Demand for traditional arts and crafts majors has fallen nationwide, according to the National Association of Schools of Art and Design, while interest in game design and animation have soared; where 30 percent of MECA&D’s students 10 years ago majored in design, now 60 percent do, college data show. So the school has amped up those programs and created spaces like the flexible workroom that can quickly adjust to future changes in interest.

It printed up recruiting materials for prospective applicants that, instead of listing majors, list the jobs graduates go on to get, and changed its name in August, adding “& Design.”

It absorbed the Salt Institute for Documentary Studies, which teaches the hot subjects of podcasting and documentary film, and added a minor in music — the only music minor at a freestanding art school, MECA&D says — and another minor in entrepreneurship.

Pew Research Center has done survey research and demographic analysis for many years. But in 2015, the Center decided to venture into the world of computational social science, creating a Data Labs team to lead that charge. This required us to develop new tools and workflows that were very different from what we used in our traditional survey and demographic research.

Building the Data Labs team came with a huge number of known and unknown challenges. For instance, we had to adopt an entirely new and rapidly evolving discipline of social science research and integrate the values and norms of that field into an organization with a deeply rooted culture in a separate discipline.

But we also had to contend with more practical questions, including a fundamental one: How do we actually do this kind of work?