Collaborating Authors


Why Artificial Intelligence is Critical in the Race to SDG Achievement


Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to solve major challenges such as poverty, hunger, inequality, climate change and health. The pandemic has undoubtedly diverted attention from some of these issues in the last couple of years. But even before COVID-19, the UN was warning that progress in meeting the SDGs was not advancing at the speed or scale needed. Greeting them in 2030 will be difficult. The pandemic has demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

New York, Florida shatter single-day records for COVID-19 cases

FOX News

Fox News Flash top headlines are here. Check out what's clicking on New York and Florida have both recorded their highest-ever single-day total of new COVID-19 cases in recent days as new cases surge all over the United States. New York set a record of 85,476 cases on Saturday, breaking the state's record set just days earlier. The surge in cases came amid high numbers of testing.

Artificial Intelligence Expert to Speak at WCSU About COVID Data


Lawrence currently volunteers as the COVID data scientist on Ridgefield's COVID-19 Task Force, providing daily analysis of the latest COVID-19 data to help town officials make science-based policy decisions, and provides periodic analysis of vaccination rates to the Office of the Governor of Connecticut. Lawrence's work has evolved from nuclear science to computer science to machine learning and, most recently, to quantitative finance. He joined IBM Research in Yorktown Heights, New York, in 1987, where he held a number of management positions, most recently as Distinguished Research Staff Member and Senior Manager, Machine Learning & Decision Analytics. From 2016 to 2019, he was president of PCIX, Inc., a New York City venture capital-funded startup that used machine learning to extract quantitative insight on the relationship between private-equity transactions and the performance of public markets. Lawrence received a Bachelor of Science in Chemical Engineering from Stanford University and a doctorate in Nuclear Engineering from the University of Illinois.

Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures Machine Learning

Previous research has shown mixed evidence on the associations between mobility data and COVID-19 case rates, analysis of which is complicated by differences between places on factors influencing both behavior and health outcomes. We aimed to evaluate the county-level impact of shifting the distribution of mobility on the growth in COVID-19 case rates from June 1 - November 14, 2020. We utilized a modified treatment policy (MTP) approach, which considers the impact of shifting an exposure away from its observed value. The MTP approach facilitates studying the effects of continuous exposures while minimizing parametric modeling assumptions. Ten mobility indices were selected to capture several aspects of behavior expected to influence and be influenced by COVID-19 case rates. The outcome was defined as the number of new cases per 100,000 residents two weeks ahead of each mobility measure. Primary analyses used targeted minimum loss-based estimation (TMLE) with a Super Learner ensemble of machine learning algorithms, considering over 20 potential confounders capturing counties' recent case rates as well as social, economic, health, and demographic variables. For comparison, we also implemented unadjusted analyses. For most weeks considered, unadjusted analyses suggested strong associations between mobility indices and subsequent growth in case rates. However, after confounder adjustment, none of the indices showed consistent associations after hypothetical shifts to reduce mobility. While identifiability concerns limit our ability to make causal claims in this analysis, MTPs are a powerful and underutilized tool for studying the effects of continuous exposures.

Barry Blitt's "Learning Curve"

The New Yorker

Only a few months ago, there was a brief window of time when many New Yorkers, among others, watched as the numbers of the vaccinated climbed and dared to hope that the year-long pandemic was finally coming to an end. Vacations were booked, weddings were scheduled, and parents began looking forward to getting their children out of the living room and back to attending school in person. But, as Barry Blitt captures in his new cover, the pandemic has not gone away, and, for students and their parents, the usual anxieties around returning to the classroom have been compounded by an increasing incidence of coronavirus infections in children, many of whom are too young to be vaccinated, and other related uncertainties. We recently spoke to Blitt about back-to-school blues and presenting his work at elementary schools. Were you a good student?

Estimating a new panel MSK dataset for comparative analyses of national absorptive capacity systems, economic growth, and development in low and middle income economies Machine Learning

Within the national innovation system literature, empirical analyses are severely lacking for developing economies. Particularly, the low- and middle-income countries (LMICs) eligible for the World Bank's International Development Association (IDA) support, are rarely part of any empirical discourse on growth, development, and innovation. One major issue hindering panel analyses in LMICs, and thus them being subject to any empirical discussion, is the lack of complete data availability. This work offers a new complete panel dataset with no missing values for LMICs eligible for IDA's support. I use a standard, widely respected multiple imputation technique (specifically, Predictive Mean Matching) developed by Rubin (1987). This technique respects the structure of multivariate continuous panel data at the country level. I employ this technique to create a large dataset consisting of many variables drawn from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can thus be used for comparative analyses of national absorptive capacities and development, transition, and convergence analyses among LMICs.

Six problem-solving mindsets for very uncertain times


Great problem solvers are made, not born. That's what we've found after decades of problem solving with leaders across business, nonprofit, and policy sectors. These leaders learn to adopt a particularly open and curious mindset, and adhere to a systematic process for cracking even the most inscrutable problems. And when conditions of uncertainty are at their peak, they're at their brilliant best. Six mutually reinforcing approaches underly their success: (1) being ever-curious about every element of a problem; (2) being imperfectionists, with a high tolerance for ambiguity; (3) having a "dragonfly eye" view of the world, to see through multiple lenses; (4) pursuing occurrent behavior and experimenting relentlessly; (5) tapping into the collective intelligence, acknowledging that the smartest people are not in the room; and (6) practicing "show and tell" because storytelling begets action (exhibit). Here's how they do it. As any parent knows, four-year-olds are unceasing askers.

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning Machine Learning

World Health Organization (WHO) reported that 80% of patients experienced these symptoms mildly. However, older people ( 60 years old) and persons with co-morbid diseases are at a higher risk for severe symptoms and death (Velavan & Meyer, 2020; World Health Organization, 2020). Besides, younger patients with no underlying disease might also experience severe symptoms or even death (Jahromi, Avazpour, et al., 2020; The Washington Post, 2020; Yousefzadegan & Rezaei, 2020). The first positive case of COVID-19 in the United States was reported in the state of Washington on January 20, 2020. By March 17, 2020, Covid-19 has spread across all US states (Centers for Disease Control and Prevention, 2020; Saad B. Omer et al., 2020). Figure 1 shows the aggregated COVID-19 positive case and death count maps for all US states until November 6, 2020. Reports showed that on November 6, 2020, the top states for positive COVID-19 cases are California, Texas, Florida, New York, and Illinois, while the top 5 states for death cases are New York, Texas, California, New Jersey, and Florida.

Responsible Computing During COVID-19 and Beyond

Communications of the ACM

The COVID-19 pandemic has both created and exacerbated a series of cascading and interrelated crises whose impacts continue to reverberate. From the immediate effects on people's health to the pressures on healthcare systems and mass unemployment, millions of people are suffering. For many of us who work in the digital technology industry, our first impulse may be to devise technological solutions to what we perceive as the most urgent problems when faced by crises such as these. Although the desire to put our expertise to good use is laudable, technological solutions that fail to consider broader social, political, and economic contexts can have unintended consequences, undermining their efficacy and even harming the very communities that they are intended to help.10 To ensure our contributions achieve their intended results without causing inadvertent harm, we must think carefully about which projects we work on, how we should go about working on them, and with whom such work should be done.

Blavatnik Family Foundation, New York Academy of Sciences Name 31 Finalists for 2021 Blavatnik National Awards for Young Scientists

CMU School of Computer Science

Showcasing America's most promising young scientists and engineers, the Blavatnik Family Foundation and the New York Academy of Sciences today named 31 finalists for the world's largest unrestricted prize honoring early-career scientists and engineers. Three winners of the Blavatnik National Awards for Young Scientists – in life sciences, chemistry, and physical sciences and engineering – will be announced on July 20, each receiving $250,000 as a Blavatnik National Awards Laureate. The finalists, culled from 298 nominations by 157 United States research institutions across 38 states, have made trailblazing discoveries in wide-ranging fields, from the neuroscience of addiction to the development of gene-editing technologies, from designing next-generation battery storage to understanding the origins of photosynthesis, from making improvements in computer vision to pioneering new frontiers in polymer chemistry. Descriptions of the honorees' research are listed below. "Each day, young scientists tirelessly seek solutions to humanity's greatest challenges," said Len Blavatnik, founder and chairman of Access Industries, and head of the Blavatnik Family Foundation. "The Blavatnik Awards recognize this scientific brilliance and tenacity as we honor these 31 finalists. We congratulate them on their accomplishments and look forward to their continued, future discoveries and success." President and CEO of the New York Academy of Sciences Nicholas B. Dirks said: "Each year, it is a complete joy to see the very'best of the best' of American science represented by the Blavatnik National Awards Finalists."