Over the past decade, most of my discovery research has focused on a new framework, inductive process modeling, that combines background knowledge in the form of generic processes with time-series data to construct explanatory models stated as sets of differential equations. The basic approach carries out exhaustive search through a space of model structures followed by gradient descent through the parameter space for each candidate structure. Later work extended the framework to use constraints among processes to guide search through the structure space and even to induce constraints to discriminate between successful and unsuccessful structures.

The mission of the Center for Discovery Science and Health Informatics is to research computational methods to improve healthcare cost, quality, safety and effectiveness. Specifically, it conducts basic and applied research on developing computational theories, analytic methods, and software applications that support decision making and discovery of knowledge from healthcare data. This includes data mining, artificial intelligence and other knowledge discovery methods and tools tailored towards the meaningful use of health data, health services research, evidenced based practice, and decision support for a variety of health system stakeholders and end-users (clinicians, managers, researchers, policy makers, and consumers) from all sectors of the health system.

Science is an activity of human societies. It is our belief that computer-based scientific discovery must support strong integration into existing the social environment of human scientific communities. The discovered knowledge must add to and build on existing science. We believe that the ability to incorporate background knowledge and re-use learned knowledge together with the comprehensibility of the hypotheses, have marked out ILP as a particularly effective approach for scientific knowledge discovery.

Important dual aspects of this research program are to contribute to both basic computer science (creativity as an ill-understood phenomenon) and scientific applications. The collaborative application of current tools, as well as explorations of new tasks, are welcome.

Can a planet be alive? Lynn Margulis, a giant of late 20th-century biology, who had an incandescent intellect that veered toward the unorthodox, thought so. She and chemist James Lovelock together theorized that life must be a planet-altering phenomenon and the distinction between the "living" and "nonliving" parts of Earth is not as clear-cut as we think. Many members of the scientific community derided their theory, called the Gaia hypothesis, as pseudoscience, and questioned their scientific integrity. But now Margulis and Lovelock may have their revenge. Recent scientific discoveries are giving us reason to take this hypothesis more seriously. At its core is an insight about the relationship between planets and life that has changed our understanding of both, and is shaping how we look for life on other worlds.

Genetic engineering is tricky business. Its potential for good, for bad, and for unintended consequences is almost unlimited. How do you realize the good while avoiding the bad? In 2012 a research team led by Jennifer Doudna and Emmanuelle Charpentier published a landmark paper that gave scientists a gene-editing tool known as CRISPR-Cas9 that makes it much easier to turn genetic engineering's potential into reality. On December 29, 2016, a team led by Benjamin Rauch and Joseph Bondy-Denomy at UC San Francisco published a paper in the journal Cell that may well turn out to equally groundbreaking.

This article was written by Sunil Ray. Sunil is a Business Analytics and Intelligence professional with deep experience. I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data. But, one instance sticks out in all these transitions.

Consider samples from two different data sources $\{\mathbf{x_s^i}\} \sim P_{\rm source}$ and $\{\mathbf{x_t^i}\} \sim P_{\rm target}$. We only observe their transformed versions $h(\mathbf{x_s^i})$ and $g(\mathbf{x_t^i})$, for some known function class $h(\cdot)$ and $g(\cdot)$. Our goal is to perform a statistical test checking if $P_{\rm source}$ = $P_{\rm target}$ while removing the distortions induced by the transformations. This problem is closely related to concepts underlying numerous domain adaptation algorithms, and in our case, is motivated by the need to combine clinical and imaging based biomarkers from multiple sites and/or batches, where this problem is fairly common and an impediment in the conduct of analyses with much larger sample sizes. We develop a framework that addresses this problem using ideas from hypothesis testing on the transformed measurements, where in the distortions need to be estimated {\it in tandem} with the testing. We derive a simple algorithm and study its convergence and consistency properties in detail, and we also provide lower-bound strategies based on recent work in continuous optimization. On a dataset of individuals at risk for neurological disease, our results are competitive with alternative procedures that are twice as expensive and in some cases operationally infeasible to implement.

Axiom, a legal staffing-turned-technology company, recently announced a five-year deal with Johnson & Johnson (J & J) to provide multi-shore contract management services to the pharmaceutical giant. Axiom will support J&J's global procurement contracting function, helping to standardize its vast trove of procurement agreements across a dozen contract types and 10 languages. This is not Axiom's lone big dollar, long-term contract with a major corporation. A couple years ago, it inked an eye-popping $73 million deal with Credit Suisse to process the bank's "master trading agreements." Axiom's metamorphosis from staffing to technology is emblematic of the maturing face and changing focus of legal service providers.

Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn't happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side's perspectives and paradigms. The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4].