Large-scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub-surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have seen numerous successes over several engineering applications, estimations on quantities of interest (QoI) - such as breakthrough time of particles reaching the edge of the system - suffer from a two distinct types of uncertainty. A run of a DFN simulator requires several parameter values to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameter choices will lead to some amount of uncertainty in the QoI, called epistemic uncertainty. Furthermore, since DFN simulators rely on stochastic processes to place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e. different seeds) of the same random process leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. We make several design choices to handle an observed heteroskedasticity in DFN simulators, where the aleatoric uncertainty changes for different inputs, since the quality makes several standard statistical methods inadmissible. Beyond the specific takeaways on which input variables affect uncertainty the most for DFN simulators, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quartz that gradually dissolves until it reaches a quasi-steady state. At the end of the simulation, we measure the quartz remaining in each fracture within the domain. We observe that a small backbone of fracture exists, where the quartz is fully dissolved which leads to increased flow and transport. However, depending on the DFN topology and the rate of dissolution, we observe a large variability of these changes, which indicates an interplay between the fracture network structure and the impact of geochemical dissolution. In this work, we developed a machine learning framework to extract the important features that support mineralization in the form of dissolution. In addition, we use structural and topological features of the fracture network to predict the remaining quartz volume in quasi-steady state conditions. As a first step to characterizing carbon mineralization, we study dissolution with this framework. We studied a variety of reaction and fracture parameters and their impact on the dissolution of quartz in fracture networks. We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system. For the first time, we use a combination of a finite-volume reservoir model and graph-based approach to study reactive transport in a complex fracture network to determine the key features that control dissolution.

Michael Scott, the protagonist from the US version of The Office, is using an AI recruiter to hire a receptionist. The text-based system asks applicants five questions that delve into how they responded to past work situations, including dealing with difficult colleagues and juggling competing work demands. Potential employees type their answers into a chat-style program that resembles a responsive help desk. The real – and unnerving – power of AI then kicks in, sending a score and traits profile to the employer, and a personality report to the applicant. This demonstration, by Melbourne-based startup Sapia.ai,

Rent the Runway (RTR) is transforming the way we get dressed by pioneering the world's first Closet in the Cloud. Founded in 2009, RTR has disrupted the $2.4 trillion fashion industry by inspiring women with a more joyful, sustainable and financially-savvy way to feel their best every day. As the ultimate destination for circular fashion, the brand now offers infinite points of access to its shared closet via a fully customizable subscription to fashion, one-time rental or ownership. RTR offers designer apparel, accessories and home decor from 700 brand partners and has built in-house proprietary technology and a one-of-a-kind reverse logistics operation. Under CEO and Co-Founder Jennifer Hyman's leadership, RTR has been named to CNBC's "Disruptor 50" five times in ten years, and has been placed on Fast Company's Most Innovative Companies list multiple times, while Hyman herself has been named to the "TIME 100" most influential people in the world and as one of People magazine's "Women Changing the World."

Rent the Runway (RTR) is transforming the way we get dressed by pioneering the world's first Closet in the Cloud. Founded in 2009, RTR has disrupted the $2.4 trillion fashion industry by inspiring women with a more joyful, sustainable and financially-savvy way to feel their best every day. As the ultimate destination for circular fashion, the brand now offers infinite points of access to its shared closet via a fully customizable subscription to fashion, one-time rental or ownership. RTR offers designer apparel, accessories and home decor from 700 brand partners and has built in-house proprietary technology and a one-of-a-kind reverse logistics operation. Under CEO and Co-Founder Jennifer Hyman's leadership, RTR has been named to CNBC's "Disruptor 50" five times in ten years, and has been placed on Fast Company's Most Innovative Companies list multiple times, while Hyman herself has been named to the "TIME 100" most influential people in the world and as one of People magazine's "Women Changing the World."

Since the onset of the pandemic, a growing number of companies have turned to AI to assist with their hiring. The most common systems involve using face-scanning algorithms, games, questions, or other evaluations to help determine which candidates to interview. While activists and scholars warn that these screening tools can perpetuate discrimination, the makers themselves argue that algorithmic hiring helps correct for human biases. Algorithms can be tested and tweaked, whereas human biases are much harder to correct--or so the thinking goes. In a December 2019 paper, researchers at Cornell reviewed the landscape of algorithmic screening companies to analyze their claims and practices.

A few years after the Great Recession, you couldn't scroll through Google Reader without seeing the word "disrupt." TechCrunch named a conference after it, the New York Times named a column after it, investor Marc Andreessen warned that "software disruption" would eat the world; not long after, Peter Thiel, his fellow Facebook board member, called "disrupt" one of his favorite words. The term "disruptive innovation" was coined by Harvard Business School professor Clayton Christensen in the mid-90's to describe a particular business phenomenon, whereby established companies focus on high-priced products for their existing customers, while disruptors develop simpler, cheaper innovations, introduce the products to a new audience, and eventually displace incumbents. PCs disrupted mainframes, discount stores disrupted department stores, cellphones disrupted landlines, you get the idea. In Silicon Valley's telling, however, "disruption" became shorthand for something closer to techno-darwinism.

Roundtable Analytics Inc., an AI/ML-based software company focused on improving the operational and financial performance of Emergency Departments (EDs), expanded its team of data scientists with the recent appointment of Michael Hyman, Ph.D., as vice president. Michael (Mike) Hyman has had a distinguished academic career, graduating with honors from the University of North Carolina with a Bachelor of Science in mathematics and a minor in chemistry. Pursuing his passion for the application of data science, Mike went on to earn both an M. Stat and Ph.D. in statistics from the University of Florida, one of the premier statistics graduate programs in the U.S. During his studies at UF, he was named a National Science Foundation Fellow, receiving an IGERT award for interdisciplinary training, and shared an office with the future founders of Roundtable Analytics Inc. Prior to joining Roundtable, Mike spent over five years working with the U.S. Department of Agriculture, performing statistical and geospatial research and developing the statistical methodology for projects such as the U.S. Census of Agriculture. Previously, during his spare time, Mike organized and completed a charity bike ride across the United States and is delighted to now return to his hometown of Raleigh, North Carolina.

This will be the definitive forum on the shape of the next economy. Be part of the discussion and understand how the technological revolution will shape the future of work and business. "Could a machine do your job?" ask Michael Chui, James Manyika, and Mehdi Miremadi in a recent McKinsey Quarterly article, "Where Machines Could Replace Humans and Where They Can't Yet." "As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won't be replaced by machines? In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail."

If we let machines put us out of work, it will be because of a failure of imagination and the will to make a better future. "Could a machine do your job?" ask Michael Chui, James Manyika, and Mehdi Miremadi in a recent McKinsey Quarterly article, "Where Machines Could Replace Humans and Where They Can't Yet." "As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won't be replaced by machines? In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail."