Featured Profiles

Social networks have been around long before Facebook and Instagram, and they take far more diverse forms. Assistant Professor Neha Gondal in our Department of Sociology has studied the gamut. From moneylending in Renaissance Florence to academic hiring to health in public housing, she explores the relationship between networks and culture, and its effect on social inequalities. That’s a lot of ground. To cover it, she frequently employs cutting-edge statistical techniques for modeling social networks, including varieties of exponential random graph models and agent-based modeling to study the diverse contexts. One finding: the cultural basis of social networks helps produce, legitimize, and preserve social inequalities.

Learn more about Neha Gondal’s fascinating work and the data sciences that make it possible.

For our society and economy to rely on machine learning and AI methods, the algorithms underlying these methods must be developed in a way that balances the need for computational efficiency and the desire for statistical optimality while also dealing with the inherent imperfections that come from real-world problems, large and often incomplete or noisy datasets, and complex models. Those are precisely the types of questions that Professor Jonathan Huggins in our Mathematics & Statistics Department considers in his research with a particular emphasis on on methods to enable more effective scientific discovery from high-throughput and multi-modal genomic data.

Learn more about Jonathan’s workon the mathematics that make robust statistical machine learning and trustworthy AI possible.

Dr. Swathi Kiran is the director of Aphasia Research Laboratory at Boston University. The primary goal of the lab is to understand language processing and communication following a brain damage. Research in the lab makes use of Neuroimaging, neurolinguistic, psycholinguistic and neurobehavioral tools in investigating pertinent questions related to Aphasia. Some particular scholarly and practice interest of the lab are bilingual aphasia, aphasia rehabilitation, functional neuroimaging, language recovery, impairments in naming, reading, writing.

Find out what more the lab is involved in!

Interested in the effectiveness of conservation efforts? Check out the impressive range of research questions that Christoph Nolte and his research group are considering: where does conservation happen? Why? And what difference does it make? They answer these questions using large spatial datasets, satellite imagery, and econometrics. Their work is of incredible value to donors, governments, NGOs, and individuals that work to stem the decline of biological diversity on our amazing planet. Current projects pursued by Christoph and his collaborators focus on land acquisitions for conservation, with projects in the U.S., Chile, and Colombia.

If you would like to know more, visit his PLACES Lab website.

A global population of more than 7.5 billion offers the potential for staggering amounts of data we could harness in the interest of better health for all. Spearheading that effort is Assistant Professor Elaine Nsoesie at our School of Public Health. She applies novel data science methodologies to global health problems. In one example, she applied machine learning and statistical modeling to 291,443 Twitter posts about miscarriages to glean important insights into how social media may be used to seek and share health-related information. In another, she used artificial intelligence to analyze over one million reviews of foods on Amazon.com, finding that far more unsafe foods may need to be recalled. Both studies are data-intense, requiring large-scale resources and advanced data sciences, but the potential health benefits are enormous.

Global health is your health. Discover other ways Elaine Nsoesie is working to improve it.

Humans are making machines that “think,” but we barely understand their thought processes. Associate Professor Kate Saenko in our Department of Computer Science is making machine learning more human-like by developing artificial intelligence methods that allow machines to see, talk, and interact with the world in ways humans better understand. Her contributions include state-of-the-art deep-learning (neural network) models for object recognition in images, activity detection in video, automatic video captioning, and vision-based robotic object manipulation. Heady stuff, but ultimately practical for such applications as teaching robots to sort recyclables (one of her recent projects).

Explore more next-generation projects and AI methods at Kate Saenko’s website.

You might say peace of mind is the research focus of Professor Adam Smith in our Department of Computer Science. He works in the areas of data privacy and cryptography, providing techniques to analyze sensitive data sets, while guaranteeing the privacy of individuals whose data they contain. While privacy is his subject, he doesn’t keep his findings to himself. “Differential privacy,” a standard he invented, is the basis for deployed systems in the 2020 US Census and mobile data collections systems at Google, Apple, and Microsoft. Overall, his work has led to novel lines of inquiry within statistical inference, machine learning, algorithmic game theory, and legal scholarship. And, of course, a better night’s sleep for us all.

Visit Adam Smith’s website to find more—very public—information about his work.

Before we can make our complex media system more beneficial to the public, and democratic governance, we have to understand it. That’s what makes the work of Assistant Professor Chris Wells in our Division of Emerging Media Studies important. An innovator in the use of time-series analysis in communications, Wells found that, in 2016, social media—especially retweets of Donald Trump’s Twitter posts—drove coverage by traditional broadcast outlets, contributing to Trump’s meteoric rise. Working closely with colleagues from our Department of Mathematics & Statistics, his team developed a version of spectral clustering that further determined that those retweets were largely the work of small populations, particularly the alt-right.

What else is Chris Wells learning at the intersection of media and data sciences? He invites you to find out.

Are there actually solutions to seemingly intractable social problems such as rural suicide, religious violence, and sexual exploitation of children? Professor Wesley Wildman at our School of Theology is seeking answers. Using computer modeling, he creates populations of cognitively, emotionally, and behaviorally complex AI agents to deepen our understanding of such problems. These replica societies can then provide a platform for virtual experimentation, allowing evaluation of the likely costs, benefits, and unintended consequences of public policy proposals. The simulations are data-hungry and depend on careful theoretical integration across numerous disciplines—just the sort of thing BU is built for.

Wesley Wildman’s research center offers more examples of how he brings together the humanities and data sciences.

As the internet continues to grow, so does interest in the research of Associate Professor Georgios Zervas at BU’s Questrom School of Business. He investigates how online marketplaces, especially shared economies such as Yelp and Airbnb, affect consumer and firm behavior. This places him squarely at the intersection of marketing, data science, and economics, assembling novel data sets that he analyzes through a variety of methods such as causal inference, structural modeling, and machine learning. The insights generated by his work help shape the future of online business and contribute to our broader understanding of the digital economy.

What has data revealed about online marketplaces? Find out at Georgios Zervas’ website.