Signal Processing for Social Good

Kush R. Varshney, research staff member and manager in the Data Science Group at the IBM Thomas J. Watson Research Center.

Communication, speech processing, seismology and radar are well-known applications of signal processing that contribute to the betterment of humanity. But is there a more direct way that signal and information processing can reduce poverty, hunger, inequality, injustice, ill health and other causes of human suffering? The member states of the United Nations ratified 17 sustainable development goals in 2015, which, if achieved by the targeted year 2030, will end or greatly curtail these problems. Achieving the global goals, however, will require cooperation from all, including the signal processing community. Let me tell you how.

Just a few weeks ago, I visited Barauli, a small village in District Aligarh, India, where I had tea at the well-lit home of a Simpa Networks customer. A social enterprise, Simpa provides pay-as-you-go solar panel systems to households with inadequate access to power from the grid. The company installs a solar panel with lights and a fan – an alternative to unhealthy and unsafe kerosene lamps –  for a very small down payment. Through a simple financing plan, customers can fully repay the cost of the system in monthly installments over two or three years. As a result, clean, reliable energy (and the wellbeing and economic opportunity that comes with it), is now within reach of lower income individuals.

Signal processing is the science behind Simpa’s sustainability and profitability. Two years ago, I led a team on a pro bono project to develop a predictive model of customer repayment behaviors based on signals captured in an application form. Specifically, we wanted to assess an applicant’s risk of repossession from payment delinquency, thus allowing Simpa to make better approval decisions. One of our proposed operating points could have reduced delayed payments by as much as one third, while still accepting seventy percent of the total customer pool.

This project is a prime example of the “data for social good” movement: uplifting humanity by harnessing skills-based volunteering of data scientists (of which I would argue signal processing engineers and scientists are a subset) in combination with the subject matter expertise of non-governmental organizations (NGOs), social enterprises and other similar organizations. At IBM Research, I co-direct a program that pairs student fellows with a team of researchers and a NGO partner to conduct social good projects. Through this program – the first of its kind in a corporate setting –  we have evaluated the effectiveness of programs fighting childhood diarrhea, found causal factors on the innovativeness of countries, and created a recommender system on attributes of large philanthropic projects. We’ve additionally predicted species of primates likely to be reservoirs of Zika virus, built tools to automatically retrieve and classify news articles on humanitarian crises, and developed cognitive technologies to accelerate open scientific discovery such as cures for multiple sclerosis — all using signal processing and related techniques.

The University of Chicago pioneered the concept of the data science for social good summer fellowship, which continues to spread in the academic setting with similar programs now offered at Georgia Tech and the University of Washington. Corporations and research organizations, like Two Sigma and Draper Laboratory, are following suit. Bloomberg hosts an annual conference on the topic. In the non-profit sector, groups like DataKind – which connects NGOs with volunteer teams to conduct projects in their spare time — have their own social good programs. I worked with DataKind on the Simpa project and another project using satellite image analysis of household roofs in Kenya to estimate poverty. DrivenData conducts online competitions to solve social good problems.

Fundamental signal processing theory and methods, when applied to non-traditional application areas, can change the world for the better. We have a unique opportunity to talk with NGOs, understand their most pressing needs, and use signal and information processing tools to create appropriate solutions. This is not a pipedream but a reality.

Kush is a research staff member and manager in the Data Science Group at the IBM Thomas J. Watson Research Center. He also co-directs the IBM Social Good Fellowship program. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology.