Meet the Computer Scientist Overseeing Columbia’s $1 Billion Research Portfolio
Jeannette Wing is the first woman, Asian American, and computer science professor to take on one of the most powerful administrative roles at Columbia University: the executive vice president for research. From the third floor of Low Library, in an office once occupied by former Columbia president Seth Low, Wing oversees the university’s research portfolio, which last year had $1 billion in expenditures.
She previously led Columbia’s Data Science Institute, and before that, held executive roles at Microsoft Research, the National Science Foundation, and Carnegie Mellon University. She is credited with popularizing the term “computational thinking” and advocating for the integration of computing across disciplines. As the Avanessians director of the Data Science Institute, Wing was an evangelist for incorporating data science into every department at the university. In her new role, she will continue to highlight the importance of data science and artificial intelligence techniques in basic research. Columbia News recently caught up with Wing to talk about the new job.
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Q. What does the EVPR do?
A. The EVPR oversees Columbia’s entire research portfolio and ensures that the university strives for the highest level of excellence. The office plays a unique role in bringing researchers together from across schools to work collaboratively and to achieve what no single discipline can do alone. The office also upholds the integrity of the research process by making sure that all legal and ethical rules and regulations are followed. Research integrity is essential for the public to have confidence in the results of our research.
Q. How does research fit into Columbia’s mission?
A. Research pushes the boundary between what we know and don’t know. It’s the cornerstone of any great university: to seek, create, and disseminate new knowledge.
Q. Why are you excited to take what looks like one of the most stressful and least appreciated jobs in academia?
A. I’m eager to take on this role now because of what’s happening in Washington D.C. Under the latest version of the Endless Frontiers Act, now part of the U.S. Innovation and Competition Act, Congress is considering an estimated $200 billion in funding for science and technology research. Off the charts! Just a sliver of this money could stretch Columbia in new directions. It could lead to deeper collaborations with companies, government labs, and nonprofits, help us diversify our research labs, and expand our connections with K-12 schools and local communities.
Q. You led the Data Science Institute for four years, but your research focuses on AI. What’s the difference?
A. Data science and AI overlap but aren’t the same thing. The goal of data science is to understand the world through data, which today means digital data. There’s a lifecycle to data science: generating, collecting, processing, storing, and managing data; then analyzing and visualizing the results, and telling a story about your analysis. AI brings novel techniques to the analysis phase, but the ultimate goal of AI is building a machine with true intelligence.
Most of our advanced AI technology today is based on deep-learning algorithms ingesting enormous datasets and producing models for decision-making. The more data, the better the model. Deep-learning models are now as good as, or better than, humans at many narrowly defined tasks. When Deep Mind’s AlphaGo beat the best Go player in the world (who happened to be Chinese) it was China’s Sputnik moment. China stood up and said, ‘By 2030, China will dominate the world in AI.’
Research pushes the boundary between what we know and don’t know. It’s the cornerstone of any great university: to seek, create, and disseminate new knowledge.
Q. How is AI changing the way research is done? What does that mean for Columbia?
A. In traditional computing, people write programs. In machine learning, people feed the computer data, and the computer itself writes the program; it learns the program from data. The term machine learning is germane here. The machine learns the rules on its own. Because the machine, not the human, is writing the program, the program is not easily interpretable to us. In the case of deep learning, the most successful machine-learning technique to date, we don’t really understand the science of how it works or why it’s so successful. It’s an example of applications coming ahead of theory.
These tools are already in our daily lives. AI systems recommend movies and books, respond to our voice commands, and translate web pages from one language to another. AI also adds to our repertoire of scientific methods. In medicine, deep-learning models are processing medical scans faster than humans and catching warning signs that even the experts sometimes miss. And they don’t get tired! In astronomy, they’re analyzing images from telescopes and space probes to make new discoveries about our universe. In climate modeling, they’re helping to reduce the uncertainty around climate change and its impacts.
These tools are accelerating science, and I expect the trend to continue. AI holds great promise for the social sciences, too. At Microsoft, I saw how bringing economists together with machine learning experts helped the company better forecast sales of some products.
Q. What are you most proud of accomplishing at the Data Science Institute?
Creating bridges. Everything I did was about building collaboration across schools and disciplines. The Data Science Institute connected a lot of dots across campuses and beyond Columbia’s gates. When people from different perspectives and areas of expertise come together, sparks fly. Through data science, researchers and educators asked questions they never would have thought to ask, let alone answer.
I also feel good about creating the Trustworthy AI initiative to investigate some of machine learning’s unintended consequences. Our goal is to find out whether the AI systems making decisions about people’s lives can be trusted: Do I really have cancer? Is the moving object in front of my car a ball or a child? Will the bank approve my loan? It turns out that it’s hard to formally define the properties of trustworthiness, let alone prove and guarantee that an AI system has any of them.
A. Columbia Engineering and the Data Science Institute built the IBM Center on Blockchain and Data Transparency under your tenure. And Columbia continues to court corporate funders. Why is industry collaboration so vital?
In certain areas of research, AI especially, industry is ahead. They have the data, which is mostly proprietary consumer data. They also have vast amounts of computing power. Amazon, Microsoft, Google have nearly limitless computing power through their cloud infrastructure. They have GPU clusters academia could never afford. I see enormous potential for collaboration. If faculty could gain access to data and compute, they could validate their algorithms at scale and identify new research directions.
It’s a mutually beneficial relationship. Industry looks to academia for new ideas and talent. Academia looks to industry for real-world problems to solve, and opportunities to scale solutions. It’s an important way to broaden our impact.
Q. You’ve held leadership roles in academia, industry, and the federal government. What skills allowed you to succeed in such different cultures?
A. To be able to listen and learn. To know what you don’t know, and to surround yourself with superb talent.
Q. Why is diversity important?
A. It’s important for advancing research. A more diverse team brings more creativity and perspectives in understanding the problem and designing solutions. That’s the biggest reason for a diverse team.
Q. Advice for women and minorities in tech?
A. Work hard, speak up, and believe in yourself.
Q. What’s one thing readers might be surprised to learn about you?
A. I’m a fourth-degree black belt in karate. I haven’t kept up with it, but I love dance and still take ballet class. It’s good stress relief and it keeps me sane!