Data Science newsletter – January 17, 2022

On Thursday, leading public, private, educational and research organizations across the U.S. healthcare and life sciences industries announced the formation of the Artificial Intelligence Industry Innovation Coalition (AI3C). The coalition brings together the Brookings Institution, Cleveland Clinic, Duke Health, Intermountain Healthcare, Microsoft, Novant Health, Plug and Play, Providence, UC San Diego, and University of Virginia with the goal of maximizing technology to provide recommendations, tooling and best practices for AI in healthcare.

“The goal of the newly created AI3C is to establish a pragmatic coalition with public and private organizations to advance health by identifying and addressing significant societal and industry barriers,” said Patty Obermaier, vice president, US Health and Life Sciences, Microsoft. “I am excited about the launch of AI3C and working with its distinguished board as we continue the momentum toward serving the needs of patients and communities through AI innovation.”

One of the country’s top collegiate backstroke swimmers holds on to the edge of the pool at the University of Virginia’s Aquatic & Fitness Center. With a device for measuring acceleration strapped to his waist, the Cavalier swimmer waits while a team of researchers led by math professor Ken Ono makes a final check of the equipment that will record every aspect of his performance in the water.

When the starter shouts “On your mark, go!,” the first-year swimmer kicks off of the wall into a long underwater glide.

Barely containing his excitement, Ono paces the pool deck as the swimmer breaks the surface of the water with a long, efficient stroke that could carry him as far as the 2024 Olympics.

While working as a data science manager at Lyft, second-year graduate student Raman Malik had a revelation: the best way to improve the careers of his junior data scientists is to coach and critique their presentations to company leadership.

Today, Raman Malik is the co-founder and CEO of Rhetoric, a web platform that provides start-up founders with feedback on their pitches. The app uses artificial intelligence-based coaching for the spoken aspects of presentation combined with a remote feedback platform for founders and their mentors.

“In the corporate world, presentation coaches are reserved for executives, not lower level analysts or up-and-coming managers,” Malik said. “We want to build a platform that helps anyone improve their presentation skills.”

Positive feedback is a primary goal at Rhetoric, according to Malik.

University of Texas at Arlington computer scientist Jacob Luber has earned a five-year, $2 million grant from the Cancer Prevention and Research Institute of Texas (CPRIT) to create a database that contains every publicly available cancer dataset from the National Cancer Institute (NCI).

The database will allow researchers and physicians to map where cancer patients have similar traits and improve and expand treatments based on that data. Such datasets are currently too large for physicians to access easily.

Flat materials that can morph into three-dimensional shapes have potential applications in architecture, medicine, robotics, space travel, and much more. But programming these shape changes requires complex and time-consuming computations.

Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a platform that uses machine learning to program the transformation of 2D stretchable surfaces into specific 3D shapes.

“While machine learning methods have been classically employed for image recognition and language processing, they have also recently emerged as powerful tools to solve mechanics problems,” said Katia Bertoldi, the William and Ami Kuan Danoff Professor of Applied Mechanics at SEAS and senior author of the study. “In this work we demonstrate that these tools can be extended to study the mechanics of transformable, inflatable systems.”

Seattle’s Fred Hutchinson Cancer Research Center has recruited a new vice president and chief data officer: biostatistician and data science educator Jeffrey Leek. Leek will start in the newly formed position by July 1, the Hutch announced Tuesday.

Leek will be leaving his current post as a professor of biostatistics and oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of its Data Science Lab.

Harnessing the power of genomics to find risk factors for major diseases or search for relatives relies on the costly and time-consuming ability to analyze huge numbers of genomes. A team co-led by a Johns Hopkins University computer scientist has leveled the playing field by creating a cloud-based platform that grants researchers easy access to one of the world’s largest genomics databases.

Known as AnVIL (Genomic Data Science Analysis, Visualization, and Informatics Lab-space), the new platform gives any researcher with an Internet connection access to thousands of analysis tools, patient records, and more than 300,000 genomes.

Fifty-six percent of registered voters said they support federal data privacy legislation, according to new polling from Morning Consult and Politico — findings that come after civil and human rights advocacy groups presented a 24,000-signature petition to Congress urging action on the issue.

The latest surge of COVID infections has hospitals crowded, short-staffed and, in some cases, rationing care. That means, sometimes, that hospital clinicians have to go through a triage process to prioritize who gets care first, or at all.

For example, a doctor may decide that a patient suffering respiratory failure should be admitted to the intensive-care unit over someone who seems to have minor injuries from a car accident. But that distinction, especially in a crisis, might not be so clear-cut.

So medical research centers like Johns Hopkins and Stanford are studying how machine learning might help.

Dr. Ron Li is a clinical assistant professor at Stanford Medicine, where he’s medical informatics director for digital health and artificial intelligence clinical integration. The following is an edited transcript of our conversation.

“Precision agriculture is not that precise,” says Soumik Sarkar, an associate professor of mechanical engineering at Iowa State University. Although things like GPS currently provide the most efficient route for planting and harvesting, and farmers use lasers to help level the land, even the most tech-savvy farmers lack the ability to, say, target specific crops with pesticides rather than spraying an entire field.

“Optimizing and reducing the usage of water, chemicals, and other resources, while actually growing the amount of crops to feed a growing population with the land we have, is a challenge that you need to pretty much throw the kitchen sink at to solve,” Sarkar says.

The Artificial Intelligence Institute for Resilient Agriculture at Iowa State University is one of four groups building that proverbial sink with what it calls ultra-precision agriculture.

In the absence of a national data privacy law in the U.S., California has been more active than any other state in efforts to fill the gap on a state level. The state enacted one of the nation’s first data privacy laws, the California Privacy Rights Act (Proposition 24) in 2020, and an additional law will take effect in 2023. A new state agency created by the law, the California Privacy Protection Agency, recently issued an invitation for public comment on the many open questions surrounding the law’s implementation.

Our team of Stanford researchers, graduate students, and undergraduates examined the proposed law and have concluded that data privacy can be a useful tool in regulating AI, but California’s new law must be more narrowly tailored to prevent overreach, focus more on AI model transparency, and ensure people’s rights to delete their personal information are not usurped by the use of AI. Additionally, we suggest that the regulation’s proposed transparency provision requiring companies to explain to consumers the logic underlying their “automated decisionmaking” processes could be more powerful if it instead focused on providing greater transparency about the data used to enable such processes. Finally, we argue that the data embedded in machine-learning models must be explicitly included when considering consumers’ rights to delete, know, and correct their data.

The University of Wyoming’s commitment to raise its performance in computing and technology, both to improve students’ education and better serve the state and nation, has taken a major step forward with the creation of a new School of Computing.

UW’s Board of Trustees voted today (Thursday) to authorize the launch of the new academic unit. Reporting to the provost, it initially will be housed in the College of Engineering and Applied Science to accelerate its development. Eventually, the School of Computing will become a separate unit similar to the School of Energy Resources and Haub School of Environment and Natural Resources, with its own dean and interdisciplinary connections across the university.

Go to any jazz club and watch the musicians. Their performances are dynamic and improvisational; they’re inventing as they go along, having entire conversations through their instruments. Can we give computers the same capabilities?

To answer that question, University of California San Diego Professor Shlomo Dubnov, who has appointments in the departments of Music and Computer Science and Engineering and UC San Diego’s Qualcomm Institute, and Gérard Assayag, a researcher at the Institute for Research and Coordination in Acoustics/Music in Paris, recently received a €2.4 million (around $2.8 million) European Research Council Advanced Grant. Dubnov and Assayag will be working with other international partners on Project REACH: Raising Co-creativity in Cyber-Human Musicianship, which is teaching computers how to improvise, musically.

Ohio has landed its largest economic development project, ever.

Intel is planning to invest $20 billion in Jersey Township, about a half-hour’s drive northeast of Columbus on the western edge of Licking County, a source close to the project told The Dispatch.

Our world is awash in data. The digitization of everyday life means humanity tosses out measurable data in our wake, which might—in the right hands—give us insight into how our economy, our society, even our universe works. Our web browsing histories? Data. Car loan applications? Data. Scientific experiments? More data than scientists know what to do with.

The future belongs to those who can take that data and derive meaning from it—which is why UChicago launched a major in data science this academic year. But how does a field go from being a set of miscellaneous tools for computer programmers to a full-fledged major?

Scientists wielding DNA “vacuums” could sniff out animals living nearby – even hundreds of meters away. The development raises the possibility of tracking biodiversity through DNA floating in the air.

Online January 18, starting at 4 p.m. Eastern. “Sherry Tongshuang Wu, a doctoral candidate in computer science and engineering at the University of Washington, will deliver the inaugural lecture of the Young Achievers Symposium organized by the Center for Socially Responsible Artificial Intelligence.”

I deal with web API’s quite a lot in my daily job. I use org-mode and ob-http to make requests and display their results.

After the Twitter space Q&A 91 @logankilpatrick hosted yesterday on “The future of machine learning and why it looks a lot like Julia,” I thought it would be useful to accumulate some community responses to a few questions about the current state of machine learning in Julia