This post originally appeared on MIT Technology Review
The Institute launched the MIT Stephen A. Schwarzman College of Computing (SCC) with three critical objectives: to support the rapid evolution and growth of computer science and AI, to facilitate collaborations between computing and other disciplines, and to address the social and ethical responsibilities of computing. In August 2019, Daniel Huttenlocher, SM ’84, PhD ’88, became the inaugural dean of the college. Having helped to create and lead Cornell Tech, Cornell’s technology, business, law, and design campus in New York City, and Cornell’s Faculty of Computing and Information Science, Huttenlocher had all the qualifications to lead the groundbreaking new college—extensive experience in research, industry, and entrepreneurship, as well as in academic administration.
As of January, Huttenlocher, who also holds the title of Henry Ellis Warren (1894) Professor of Computer Science and AI & Decision-Making, had put the college’s initial organizing structure in place. In addition to appointing key members of its leadership team, he’d also announced which departments, institutes, labs, and centers make up the college. Among these is MIT’s largest academic department, Electrical Engineering and Computer Science (EECS), situated jointly within the School of Engineering and SCC and now reorganized into three overlapping sub-units: electrical engineering, computer science, and artificial intelligence and decision-making. Searches are under way to begin filling 50 new faculty positions. And given that some 40% of MIT undergraduates are now majoring in computer science, an interdepartmental teaching collaborative called Common Ground for Computing Education launched this spring to facilitate multidepartmental computing education, as well as to support students in existing and new undergraduate blended-degree programs such as 6.9 (Computation and Cognition). For more detail on these plans, visit
The MIT Alumni Association sat down with Huttenlocher during his second semester on the job to find out what it’s been like to return to campus as an alum in order to lead one of the greatest structural changes in MIT’s history.
What about your student experience at MIT made you confident that this is an institution where an undertaking like SCC could succeed?
I did my master’s and PhD over in the old Tech Square, in what was then the Artificial Intelligence Lab [a precursor to the Computer Science and Artificial Intelligence Laboratory (CSAIL), now part of SCC]. In the 1980s, MIT’s research in AI and in CS was across the railroad tracks, in its own little universe over there. Fast-forward to today, and it’s very different. When you look at CSAIL, it’s in the heart of the campus, more connected both physically and intellectually to the fabric of the Institute.
For my master’s thesis, I worked on speech recognition, informed by linguistic models. For my PhD, I worked in computer vision and object recognition. I was always interested, and still am, in how machines can perceive the world around them directly.
A big part of the culture of the AI Lab when I was there was to do really bold things. It was a time when the government was funding big, bold experiments in computing, early in the days of AI and computer science. There was a healthy view that we should be pushing the envelope. That’s the way I see the model of MIT.
And building any new academic organization in a university is definitely pushing the envelope. We don’t change very frequently in academia—we change our research, our scholarship, those kinds of things, but we don’t change the structure of institutions. It takes bold thinking to do that. It takes institutions that are willing to think and execute outside the box. And certainly, that was my experience of MIT decades ago.
What excited you about returning to MIT for this role?
When I took the job, I viewed it as this amazing opportunity to help an institution for which I feel a deep affinity develop something new. I believe we’re in a time now that is in many ways similar to the time period in which MIT was founded—when a lot of technology practice had gotten out ahead of our understanding of it. We’re there with computing and AI today, much in the ways we were with what we now know as engineering.
Now, being back, it’s also become much more personal. MIT has changed a lot in 30 years, mainly in ways that are good, and there’s a lot more change we still need to go through. But MIT is peculiar in a way I find really special, and I had forgotten how much it was a part of me until I was back here. People here really roll up their sleeves and flock to get the work done. And there’s a real focus on thinking things through carefully and rigorously and logically. That’s not uniformly true in my experience, let’s just put it that way. Those are aspects about MIT I’d forgotten a little bit about, and I’d missed them.
What new opportunities will SCC create for students? Is the message to them that if you’re passionate about a given discipline, but also want a grounding in computing, we’re going to map a coherent path for you?
Well, it’s still MIT—we’re not going to map every path. One of the great things about this institution is you should map your own path if there’s not a path here for you, and that’s not going away. But we will provide more paths that are thought through, that aren’t just the silos of the disciplines. Part of what the college is doing is putting in structures that support those kinds of cross-cutting missions. Eventually undergraduates—and to some degree graduate students, although graduate studies are still pretty discipline-based and should remain that way—will see the results of that. They’ll see new kinds of classes, new kinds of majors, new kinds of minors. But the dean doesn’t plan those things. I try to build the structures that get the right people working together to make those happen.
Is there a specific way this will affect graduate students?
At the graduate level there are a lot of non-departmental programs—the Operations Research Center, for example, or the Institute for Data, Systems, and Society (IDSS), where there are several master’s and PhD programs—as well as the EECS graduate program in Course 6. Having those all be part of the same college is going to give us a lot more opportunity to coordinate between those programs, to really think about the mix of statistics and machine learning and operations research and computer science, where the boundaries between those disciplines have suddenly blurred. So I think we’re just making it more flexible for grad students.
What questions and concerns have you been hearing from alumni?
Concerns, or mandates? [Laughs.] I hear one a lot: Don’t mess with Course 6.
How do you respond to that?
It’s important not to try to fix what “ain’t broke,” but at the same time to recognize that the world has changed a lot and computing is changing very quickly. Part of what we’ve come up with [with three sub-units in EECS] is something that doesn’t fundamentally disrupt the department structure of Course 6 but allows us to be much more responsive to the changes in the field.
I think the alumni I’ve talked to realize that it’s important for MIT to be doing something to lead in this area. The question is: What’s the something? When people see an institution through a lens from their experience of 20, 30, 50 years ago, sometimes you have to remind them why we have to be going forward today. That’s not just alumni; it’s true of students, faculty, and staff who are here today, too. Everybody comes to an institution like MIT because of its past, in part. Of course they’re here for today’s research and teaching, but they’re also here because it’s MIT, and that’s a century and a half of history.