Kevin Zeng Hu #46

Portrait by Alan Savenor

Portrait by Alan Savenor

Graduate Student MIT Media Lab Macro Connections Group


This story appeared first on BetaBoston November, 20,2014





by Heidi Legg

Last March, MIT Media Lab Grad student Kevin Hu and his colleague Amy Yu saw their Pantheon project land on the front cover of the New York Times Magazine with the headline "Who's More Famous than Jesus?" Not bad for two grad students who are part of a generation using today’s seemingly unlimited technological innovations to change the world.

Focused on human data interfaces, Hu and his colleagues at the MIT Media Lab are part of the Macro Connections Group led by Cesar A. Hildago where their sole mantra is “transform data into knowledge.” What I discovered is that all those maps we explore in capsules on Buzzfeed, The Atlantic, and The New York Times explaining everything from the conflict in the Middle East to which states have more adultery, are only going to be more prevalent if Kevin is able to bring DIVE to the general consumer. He wants to democratize data visualization so that anyone with data can map an image that explains things, removing the middlemen who interpret data for us.

But he and his classmates also want to know more about human emotions with another project called Quantify. They want a computer that, or should we say “who,” can feel out the squishy stuff. Can emotions be data? When Hu and fellow lab student, Travis Rich (also his roommate - is there any other way when you are 20-something?) invented Gif Gif, the theory of Quantify took shape. I sat down with Kevin to learn how a kid with his gifts and opportunities plans to change the way we live.

What happened with Pantheon after the New York Times Magazine cover?

It was really Amy Yu’s project and she had worked on it for a year before I joined. When the cover hit, we got a lot of attention. We had a couple hundred thousand page views. We also ran into a lot of controversy because people, and the New York Times, focused on the rankings rather than our goal of cultural production over time. We were less interested if a celebrity were number five or six but rather the aggregate: how many physicists have changed over time, what is our country's cultural composition? Instead we had angry emails from Canadians asking why Avril Lavigne was above Frank Gehry.

And Johnny Depp ahead of John Lennon?

Exactly. People were confused about the rankings and about how we resolved locations. We map by modern country boundaries, so once we reached Greece, we classified a lot of the people who modern day people think of as Greek as Turkish, because empires have changed. Instead of understanding our goals, readers reacted with, 'oh, how could you do this to us?'

The real point of the project was to see cultural production and how it changes over time. We think of cultural production, in the broadest sense, as information that's transmitted by non-genetic means, like what we're doing right now. Anything that's not encoded in our DNA: the shoes we wear, the coffee we drink, and the language we speak. We consider that all to be culture and we proxy it by people. 

How are the results of Pantheon being applied?

The site itself is still being pretty heavily trafficked and used by people doing historical, cultural, and anthropological research. We accomplished the main goal of getting Pantheon itself and the idea of cultural production into the economic discussion. How do famous people live and die and how do their migration patterns change over time? Where is there a brain drain?

When the printing press came out, not only did writers have a chance to disseminate their work, but so did scientists and mathematicians. Some argue that the printing press was the real agent of change for the scientific revolution. We can argue the same thing for broadcast television being the catalyst for the success of athletes, because who are the biggest beneficiaries of TV? Probably soccer players, right?

Amy is now researching how communication technology affects cultural production.

What's on your desk today?

DIVE. It's trying to make data visualization accessible. It's trying to democratize the use of data visualization, like the charts you see in the New York Times. One of the real powers of Pantheon was that anyone could look at this tree map or scatter plot or diagram and understand the story being told. The trouble is it takes a long time to build this tool. The New York Times has great interactive visualizations but they have a whole team dedicated to it.

DIVE is a way that people can automatically build visualizations, allowing a journalist to easily imbed a data-driven graphic, or an educator or researcher to easily build a visual tool.

How would you define the challenge of data visualization?

The fundamental problem is that we're trying to translate between three worlds: the world of information: bits; the world of knowledge: neurons and cells; and the world of visualization: pixels. Until we all are cyborgs and can plug in this data and automatically get what we need, we will need pixels.

Data visualization is entirely concerned with how we represent these bits in terms of pixels on a 2D screen. How do we turn the invisible to the visible?

What companies are most interested in DIVE?

I think it’s a mix of finance and consulting firms who deal with a lot of data and are trying to make use of it. Executives know that they have data but they can't really understand it.

For me, the real motivation for DIVE was my sister. She's a neurobiologist and when she was writing her thesis I thought, 'wait a second - you're an expert in biology. You should not have to deal with this. The current tools don't respect people's time or intelligence. When my sister makes a data visualization, the tools should only require her knowledge of biology, nothing else.

What drew you to explore macro-connections?

I was studying physics but I was kind of frustrated with the current research scope of physics. It seems to me that people are concerned with either what's very big (cosmology and astrophysics), or very small (high energy physics). But I was interested in learning how to understand everyday phenomena.

I was interested in looking at people who are applying physics to social problems and to things that we do not understand such as organizational structures, social dynamics, and the spread of epidemics.

What do you see that we don't? How do you apply physics to social structures and problems?

I think it's mostly a cultural thing. For the longest time social sciences could only tell us how we can think about a problem, not how we can actually solve it.  But now we actually have the data to solve the problem. That’s very frightening but it's very powerful and it's a very new phenomenon.

Ten years ago, we didn’t have the data to create things like Pantheon. Now we have Facebook and OK Cupid that have great data logs. For the first time, we have actual data about self-identity. We now have how we view ourselves and how we view others. Physics is all about modeling these phenomena.

So these 'squishy' social things start to seem more linear?

Yes, exactly. Exactly.

How will DIVE change our lives? And can you already see it taking place?

I can. Imagine if journalists could use data visualization in their articles. Imagine if consultants could use it. The pipe dream is that in the future we have a completely data-literate society where when we talk about policies or about disaster relief, we have real-time, high-resolution, clean data sets and anybody has the ability to think about social issues rigorously.

For many issues, we see them through another person's interpretation. A great deal of science reporting, for instance, is very second and third hand. Very few people actually read the research paper. Data visualization allows everyone to understand issues. DIVE is trying to close that gap such that when we acquire information about the world, we can get it first hand and we can mine it ourselves.

You sent me a test called Place Pulse before this interview. Why? 

Travis and I had this vision that we want to give computers the capability to reason about objects the way that we do. When computers think of gifs, they think of bits. When we think of gifs or videos, we think of their content. We may think of this video as being very emotionally compelling or this picture being very angry. That's how we may think of an image but that's not how a computer does.

Are you're trying to give the computer emotions?

Yes, to give it the capability to think of media emotionally. It can reason very well, better than humans for anything that's very computational and linear. But when we try to attach emotional intelligence to computers, we are not yet there. A computer cannot yet measure that this atrium is very clean, but a human can. We need a human in the loop.

In the test you vote on whether you perceive a place safer than another place and then after a million votes, we can say for sure that New York City is one of the safest cities. We need a human in there first and once we have enough of our input, then we can generalize with it.

And then you don't need a human anymore?

We don't need a human anymore.

…Until the city changes?


Which is happening all the time.

It is true. It's like trying to build the first aircraft. Maybe we’re not trying to make it fast or maybe it can't fly for a long time, but it flies.

We’ve built this comparison tool off the Quantify platform. You can imagine a whole list of comparable media that we can better measure if only we had the tools. How useful would it be if you could search Netflix this way? Or compare articles of clothing and know which one looks better on you or which one is more acceptable? Or compare experiences and know which one is more painful?

Quantify allows this. If we compare two videos and we have millions of people giving us votes, slowly we can say that one is the most intellectually stimulating and we have a real profile of this entire movie where we can say it is 100 in terms of its stimulation, but zero in terms of character development.

How do you convince people to give you that data?

We made it fun. Travis and I also built Gif Gif last March and it inspired Quantify. We have two million votes already. People like viewing gifs and contributing to knowledge but furthermore, we can give you a sense of what you like. What is your emotional profile? How did you vote in comparison to others? That makes it more interesting to share.

Who uses Gif Gif now?

People from all over the world use it. I'd say that the demographics are probably mostly teenagers because, really, who's voting on gifs at 2:00 pm on Tuesday?

There is also a display in the lab called Mirror Mirror linked to Gif Gif. It’s a mirror with a webcam that uses facial recognition to measure emotions and it gives you back a gif. People love it and we didn't expect people to love it, but it turns out that five-year-old children touring the lab and sixty-year-old executives are all in front of it trying to say hi or trying to be angry.

What public opinion would you like to change?

I’d like to change the public’s opinion about experimentation. Human experimentation is an incredibly loaded term, for many very good reasons, and when Facebook said that they were experimenting with people's news feeds, there was outrage. I think it's kind of absurd. This is how software companies make tools. They test on their users and provide a service for free, and in exchange they use your data set. Clearly, if they give it to the wrong people, there's potential for evil and abuse. I would like to see people be open and accepting of the fact that by contributing a little bit of anonymous information, they can help scientists better understand bigger issues like information flow, social network formation. I think that that should definitely change.

Why is the Media Lab so illustrious?

I think I was definitely drawn here as an undergrad because it was cool. What I really look forward to every morning is the conversations I have with the people here in the lab. Gif Gif and Pantheon and DIVE - all those ideas really merged organically and there's no real source: they all kind of came from the network and from conversations. A lot of people imagine people at the lab as people off in the air dreaming about what the next big thing will be, but really it's just regular people having conversations and they happen to be asking, ‘what could be impactful?’ We're aiming towards more paradigm shifts than incremental research.

Science is a hit industry. Everyone wants to do the next big research paper but it turns out that most of the work is a little bit incremental. Most papers don't get many citations. Whereas at the lab, we ask does this thing have impact? Is this going to be a game changer? Most of the time, the answer is 'no,' by definition, but it's nice to be in a place where that is one of the first questions.

At what point do you abort a project at the Media Lab?

When it doesn't turn out to be interesting.

Who decides that?

It depends. Advisors. Students. A lot of projects are self-led by the students. Gif Gif was completely separate from our advisors until we said, 'hey, we did this. Check it out.'

How do you keep going when things get tough on a project?

I'm taking this class at the lab called Tools For Wellbeing, as there's a big initiative here about wellbeing, especially since MIT isn't doing so well in that category. Pattie Maes actually teaches it sometimes. Last week's subject was reframing. How do I reframe the situation? My answer to your question would definitely be to reframe it. Let's say I'm trying to make this product but a feature isn't working out. Well, one, can we design around that? Two, can we make do without it? 

You dropped out of high school to go to Simon's Rock School. How was that for you?

Simon's Rock was probably the most formative two years of my life. Everyone at that age is very impressionable.

It's considered an 'early college'. When I transferred to MIT from there, they accepted most of my Simon's Rock credits.

It's all gifted kids? 

That's how they frame it.

How would you frame it?

I would say it's all people who really like to change environments and who are up for more intense environments. It's three hundred kids in the Berkshires in the middle of nowhere in a pretty high-stress academic environment. It was very formative for me and I would do it again, but I don't know if I'd enjoy it that much.

Where do you get your news?

My media diet is one third Twitter and Facebook, one third very specific news sites that I like such as The New Yorker, New York Times, Huffington Post - the classic ones - The Economist - that sort of stuff - and then one third Reddit.

What event are you looking forward to?

I'm looking forward to the MIT Media Lab's Spring Members’ Meeting, which is sometime in April. During this meeting, lab sponsors (companies) come by for three days for research demos and updates. I'd love to get member's feedback on DIVE, FOLD, and QUANTIFY when they're further along, since outsiders are always candid with their comments and needs. 

Secret source?

It’s a lame answer but McDonalds. I'm a huge McDonalds fan.

What do you order?

Fries and McFlurry. I grew up with McDonalds and Lunchables. I try to eat healthier now but that's definitely my go to. I go there at least twice a week.

That stuff's poison!

It's true but it's too good.