Posts Tagged ‘Bin Yu’

The latest issue of ISCA Bulletin published my interview: A conversation with Professor Bin Yu. It is quite long, but informative. Here I picked out some short paragraphs based on my personal bias.

[Before College]

A math book from a cousin gave me my first boost into math when I was in 3rd and 4th grade. I enjoyed taking exponentials and logarithms using a table in the book.  I  believe doing the math problems provided a refuge of certainty and safety for me during a very turmoil time in China.

Another big boost in my interest in mathematics occurred when I was in the Lab School of Normal University in Harbin.  There I had a wonderful and extremely talented sub math teacher, Jianye  Chen (陈建业) in my second year in junior high. [……] Under his strong influence and, in some sense, fulfilling his unrealized dream of going to the math department at Peking University, I chose to do math at Peking University after receiving a very good score on the national college entrance examination in 1980.


The first math analysis discussion class was hard for me since I didn’t know how to do the problems. But you know, I really liked math and we had good professors. We didn’t interact a lot with the professors, because that was not the norm.

In the entrance exam to graduate school in Peking University, I came first in the math subject exams. However, the professor I wanted to work with did not take me after the oral exam. So I switched into Probability and Statistics, although I originally wanted to do Functional Analysis. That was actually a very good move, a forced one, but it has benefited me tremendously.

[Qualify Exam at Berkeley]

Shi: Is it the same format as we took it? 10 questions?

Yu: Yes. If you do three, I think, you pass.


In the summer of 1987, I went back to China and got married to my boyfriend who went to graduate school in China in 1985 in architectural history. He was able to join me a year later in Berkeley and went to Berkeley’s School of Architecture. My American friends were a bit shocked to hear that I married someone that I hadn’t seen for two years. It was a bit risky, but looking back, it was the best decision in my life.

[Suggestion for Young Researchers]

So I would say to junior people who just started their career: take more risks, instead of being more careful. If you work in a very desirable field like Statistics, you could not go too wrong. Ultimately, whether you enjoy your life or not is because whether you are happy, not because you make the system happy. And the system actually becomes happy because you are happy.

[Current Status of Statistics]

I think we are in a golden area for Statistics as an intellectual field. But this field has to be broadly interpreted. Basically a lot of people trained in other fields are also doing this type of work we do.

I think if we rise up to the challenge, we will be the leading data scientists. With our great traditions of critical thinking with us, at the same time, embracing machine learning, database, and computing challenges.

You take some risks, and you cannot really “fail” too much. You have a safe net. You have a Ph.D. in Statistics. How wrong could it go, right?

[Statistics in China]

Shi: By talking with people in China, I do feel industry, especially the high-tech companies, has a huge need for people who can analyze their growing volume of data. Meanwhile, in more scientific area like Biology and Physics, they do have the same need to find people who can work with them in designing and analyzing their experiments and do better science. Is there anything universities in China can do to help foster this type of collaboration?

Yu: I think it is kind of happening already. Peking University is talking about a data science center. You have to have cross discipline centers. Any culture change is going to be a slow process. But when there is a need, especially for economic reasons, things just happen in the end. The statistics majors in China, and here too, have to get on top of computing. At senior level, it is easy to find collaborators because you have ideas and a record. If you are a beginner and you cannot even touch the data, who’s going to hire a statistics undergraduate to give advice to a CS undergraduate? It is a constant struggle that we should keep up with computing training of our students. Eventually I hope we will be just as good as computer science majors. That would be the goal, then we will have both the critical thinking and computing skills. I’m not worried about the mathematical part as much not because it is not important. We have been giving our students that, so it is not the urgent need.  The weaker point is the cross-field critical thinking and computing for statistics students.

[Statistics and Data Science]

Yu: [……] Lots of people think of statistics as counting numbers, but they don’t know all the exciting things we do. That’s a misconception. Either we go all the way out as a community to change it, which is an uphill battle, or we just embrace data science. Just start saying that we do data science. It is psychology. This is a personal opinion, not representing the view of IMS. I’m just wondering and I think it is a discussion worth having because of the popular unfavorable misconception of statistics.

Shi: Yes. I have colleagues who seldom read the Annuals of Statistics. They think the journal mainly concerns about theoretical results and mainly about asymptotic, but they are not.

Yu: It is a dilemma in China. Statistics (统计) is 一级学科. Data science is not one of the 学科 yet. But in certain occasions, we can say that we do data science. We are statisticians and we do data science. At least we should go that far.

[Statistics and Critical Thinking]

Yu: That’s a gradual process. As I feel being the chair is confronting different opinions. As you said, you cannot form critical thinking without people counter you, even just playing the devil’s advocate. If it is all “great”, it is not critical thinking. Critical thinking is not the most natural thing in the Chinese culture because we tend to want to agree with each other, which has strength in lots of situations, but not in Science. It is something I think the western culture has an edge. In the Chinese culture, there are things called “思辨”and “承传”, but it is more about listening to others than questioning.

I’m not disapproving by critiquing, but some students might take that way. So the challenge to me is how to train those students to become critical thinkers. It is almost like they have to establish confidence first somehow.

[Data Collection and Quality in China]

Shi: I found it amazing to see on the Internet that comments about any data or any article written by Bureau of Statistics of China are usually like people don’t trust any of them. It seems don’t matter what the report is about. When it says something is good, they don’t trust it; when it says something is bad, they don’t trust it.

Yu: Yeah, that’s a big problem you bring up that is data quality. It is not unrelated to plagiarism in doing research at every level. For statistics, if we cannot trust the data, we are done. Maybe theoretical statistics will develop further first before data analysis or data science. But companies care a lot more about good quality of data. They cannot fake their data as much because it is related with their revenue. That’s why I say industry would play a huge role in pushing the development of statistics or data science, whatever it is called, in China.

Again, the full interview can be found here: A conversation with Professor Bin Yu

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After graduation six years ago, I was back in Berkeley for the first time. Geez, it does take this long.

On the way from downtown BART station to Evans Hall, I saw the first familiar face, Deborah Nolan, besides the play field outside the main library. It just feels oddly right that she was the first person I met on campus, although it is random enough. Deb was the department chair when I graduated and she awarded me the degree at commencement. I still remember that she bought lots of bagels for the students during the final exam of a class I was TA for her. It turned out that the number of babels a student  ate negatively related with his/her exam performance. Deb has not changed a bit, but I look quite different now. In her words, “you look more, more,  …, mature.” Aha, aging reflects 🙂

When I arrived at Berkeley in summer 2000, the project of painting the outside walls of Evans Hall was just about to finish. Eleven years later, the inside wall is still in its concrete looking, like rigorous math expressions. Funny enough, the construction is still going on outside of the building, which reminds me the joke “UC (University of California) stands for Under Construction”.

Due to random nature of trip scheduling, the person I want to talk to most, Bin (my advisor),  is out of town during my visit.  Also randomly, her research group meeting is on the same day so I got a chance to meet her current group instead. Really nice to meet and chat with them and see many interesting things happening around. Students come and go, groups and buildings stay.

To top all these random events, I got a chance to talk with Peter Bickel for an hour. I saw his door open so I just drop by to just say “hi”. He starred at me and asked in some confusion, “what time did we schedule to meet?” Now it is my turn to be confused. I told him I have not contacted him ahead and just wanted to come and say Hi. He waved his hands and said, “Oh, that is fine. Let me see, I have a meeting finishing at 3:30 and let us chat then”. As a result, we sit down for more than an hour to discuss some thoughts in network data analysis.

I feel I am so luck to work in a field that I have met wonderful role models and mentors I can look up to. I happily tell myself, “At the end of the day, if I can work and treat others as nicely as they did, I would go to sleep with smiles on my face.” Life is random, but most parts work out nicely when we are eager to explore and try enough.

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Got a heads up about a recent paper involving the usage of Statistical modeling to design a process for decoding dynamic natural visual experiences from human visual cortex. The work has been in the news coverage under many fancy titles, e.g. Scientists use brain imaging to reveal the movies in our mind, …

Very impressive stuff! The work is done by the Gallant Lab at UC Berkeley in collaborations with Statisticians. It is also interesting to see words like “maximum posterior” showing up in the demo.

Here is a simple outline of the experiment given at the project website:

The goal of the experiment was to design a process for decoding dynamic natural visual experiences from human visual cortex. More specifically, we sought to use brain activity measurements to reconstruct natural movies seen by an observer. First, we used functional magnetic resonance imaging (fMRI) to measure brain activity in visual cortex as a person looked at several hours of movies. We then used these data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set used to build the model). Next, we used fMRI to measure brain activity elicited by a second set of movies that were completely distinct from the first set. Finally, we used the computational models to process the elicited brain activity, in order to reconstruct the movies in the second set of movies. This is the first demonstration that dynamic natural visual experiences can be recovered from very slow brain activity recorded by fMRI.

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It is only fair to start this blog with what I leaned most from, my advisor Bin Yu. On her research group page, there is an old chinese story about a lucky farmer, an unlucky rabbit, and a stump:


-《韩非子 – 五蠹》

“There was a farmer of Sung who tilled the land, and in his field was a stump. One day a rabbit, racing across the field, bumped into the stump, broke its neck, and died. Thereupon the farmer laid aside his plow and took up watch beside the stump, hoping that he would get another rabbit in the same way. But he got no more rabbits, and instead became the laughing stock of Sung.”

– Hanfeizi Book 49 (233 BC)

To be honest, It took me a while to start paying attention to the story on that page. Of course, I’m very interested in new stuff she is working on and publishing about. As a result, I usually go to other research pages without bothering with the most obvious (lessons learned here). Thinking about it now, the story itself takes me more time to taste than any of her papers.

I finally came up with my statistical thought of the story: When an interesting observation is made based on one dataset, the first question is if it is “for real”.  In most situation, statistical analysis is applied to answer that question. Believing the observation without going through this second step may lead to a similar situation as the farmer put himself in. Now the story seems very statistical and insightful, at least to me.

An (once) lucky farmer, a (forever) unlucky rabbit, and the (never-moved) stump

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