Data Science newsletter – October 11, 2018

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Getting access to your own medical records might be a lot harder than you think, a new study suggests. Even the top-ranked U.S. hospitals can make records requests arduous, according to the study published in JAMA Network Open.

“This study quantified the everyday experience of many Americans trying to get access to personal health information from a hospital,” said senior author Dr. Harlan Krumholz, director of the Center for Outcomes Research and Evaluation at the Yale New Haven Hospital in Connecticut. “The law is very clear. People have a right to their data. They have a right to digital data without per page charges. Our study revealed that even at the very best places, there was inconvenience, delay and often high cost.”

Deborah Estrin, the Robert V. Tishman ’37 Professor of Computer Science at Cornell Tech and of healthcare policy and research at Weill Cornell Medicine, has been awarded a 2018 MacArthur Foundation fellowship for her innovative work using mobile devices and data to address social challenges.

Estrin, who also serves as an associate dean at Cornell Tech, is one of 25 fellows to receive a no-strings-attached award of $625,000 over five years – widely known as the “genius grant.”

“I was and remain very humbled and grateful,” said Estrin, who thought at first the foundation was calling her to provide a reference for someone else. “I feel a sense of commitment to do good by it, and to live up to it.”

William D. Nordhaus of Yale University and Paul M. Romer of New York University’s Stern School of Business won the 2018 Nobel Prize in Economics for bringing long-term thinking on climate issues and technological innovation into the field of economics.

The two Americans “have designed methods for addressing some of our time’s most basic and pressing questions about how we create long-term sustained and sustainable economic growth,” the Royal Swedish Academy said on Monday.

Johnson C. Smith University has announced a $149,466 award from the National Science Foundation’s Historically Black Colleges and Universities – Undergraduate Program (HBCU-UP) to enhance computer science in the area of data science.

According to the National Science Foundation (NSF), five courses will be developed and one existing course will be enhanced.

Machine learning is enabling investors to tap huge data sets such as social media postings in ways that no mere human could. Yet, despite the enormous potential, its record remains mixed. The Eurekahedge AI Hedge Fund Index, which tracks the returns of 13 hedge funds that use machine learning, has gained only 7 percent a year for the past five years, while the S&P 500 returned 13 percent annually. This year the Eurekahedge benchmark dropped 5 percent through September.

One of the potential pitfalls for machine learning strategies is the extremely low signal-to-noise ratio in financial markets, says Marcos López de Prado, who joined AQR Capital Management as head of machine learning in September and is the author of the 2018 book Advances in Financial Machine Learning. “Machine learning algorithms will always identify a pattern, even if there is none,” he says. In other words, the algorithms can view flukes as patterns and hence are likely to identify false strategies. “It takes a deep knowledge of the markets to apply machine learning successfully to financial series,” López de Prado says.

Carnegie Mellon University alumna Shinjini Kundu is using artificial intelligence to interpret medical images in ways that humans cannot. Her program, 3D Transport-Based Morphometry, could significantly impact diagnoses and treatments for diseases.

“Some statistics say that up to 80 percent of all medical diagnoses are made or confirmed through imaging studies. As imaging machines have only gotten better, I wondered whether there could be subtle information important for disease recognition that was eluding human detection,” Kundu said. “I thought that if we could find hidden patterns, they may indicate emergence of disease in a way we hadn’t been thinking about. That was the motivation behind 3D TBM.”

Kundu, who earned her Ph.D. and M.D. degrees as part of the Medical Scientist Training Program (MSTP) at Carnegie Mellon (Biomedical Engineering) and the University of Pittsburgh, found that 3D TBM can find evidence of disease from medical images that humans can’t see, and that evidence can help predict diseases even before visible or physical symptoms develop.

Northwestern Mutual is trying to extend that innovative mindset throughout Milwaukee. The company has put around $80 million toward venture funds. It teamed up with local universities to launch a data science institute.

CEO John Schlifske has called on city leaders to work together to turn Milwaukee into a technology hub.

The efforts took another step forward with the opening of Cream City Labs, a 17,000-square-foot innovation space at the Northwestern Mutual’s downtown headquarters. Cream City Labs is open to all employees, houses two Milwaukee startups and serves as an event space.

A unique program developed by the National Institute for Mathematical and Biological Synthesis (NIMBioS) to bring quantitative education to graduate students in the life sciences has been awarded funding from the Burroughs Wellcome Fund (BWF), one of only three BWF awards to be made nationally.

“Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science at the University of Tennessee, Knoxville” prioritizes current research topics suggested by biology faculty as a focal point for teaching PhD students in biomedical science.

Carnegie Mellon University announced a new two-year master’s degree program in Automated Science: Biological Experimentation on Tuesday.

The program, set to begin in the fall 2019 semester, will focus on computers that assist scientists in identifying and choosing experiments effective for scientific discovery, said Robert Murphy, head of the computational biology department at CMU and co-director of the new program. Students will learn to design, maintain and operate the systems.

The disruption of automation has touched nearly every industry and is quickly transforming scientific research and academia, Murphy said. The new major aims to combine automated instruments and artificial intelligence to produce scientific discoveries.

A new partnership between an Adelaide university and one of the world’s largest arms manufacturers has been described as “disgraceful”, with the National Tertiary Education Union (NTEU) joining the fight against militarisation of Australian universities.

Univa®, a leading innovator in workload management and optimization solutions for high-performance computing (HPC) and enterprise customers, today announced that it is working with the Information Sciences Institute (ISI), a unit of the University of Southern California’s Viterbi School of Engineering, to help manage their growing infrastructure and accelerate the group’s machine learning research.

The health system needed to broaden the reach of its telephonic interventions and improve uptake of its Epic patient portal. Data-fueled heat maps, homing in on patients’ “ZNA,” are helping improve outcomes.

Way back in 2009, economist Hal Varian of Google dubbed statistician the “next sexy job.” Since then, statistician, data scientist and actuary have topped various “best jobs” lists. Not to mention the enthusiastic press coverage of industry applications: Machine learning! Big data! AI! Deep learning!

But is it good advice? I’m going to voice an unpopular opinion for the sake of starting a conversation. Stats is indeed useful, but not in the way that the popular media – and all those online data science degree programs – seem to suggest.

The 1972 Clean Water Act has driven significant improvements in U.S. water quality, according to the first comprehensive study of water pollution over the past several decades, by researchers at UC Berkeley and Iowa State University.

The team analyzed data from 50 million water quality measurements collected at 240,000 monitoring sites throughout the U.S. between 1962 and 2001. Most of 25 water pollution measures showed improvement, including an increase in dissolved oxygen concentrations and a decrease in fecal coliform bacteria. The share of rivers safe for fishing increased by 12 percent between 1972 and 2001.

Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.

TensorFlow for R has changed that for me. Not only is the R interface that RStudio has developed just beautiful, but now these fun text generation projects provide a step into understanding how these neural networks model work at all, and deal with text in particular. Let’s step through how to take the text of Pride and Prejudice and generate new Jane-Austen-esque text.