It is my second time submitting a VIS paper. This year I visited NYU and coauthored the paper with Prof. Bertini. It's really exciting to work with Enrico and we are satisfied with what we have done so far. Hope there would be other opportunities to collaborate with him. Though it's not my first time working on the VIS deadline, this is still a fresh experience and I learned a lot from it.
Finally, I am a Ph.D. "candidate" now.
This is the first thought came to my mind after I got passed the PQE.
I have to throw myself into bed for two days.
That is my second immediate thought. Honestly speaking, my PQE is far from a good one. My time management is like a disaster. Luckily, the oral presentation is finished as expected.
This year, VIS was held in Phoenix, Arizona. As the name implies, Phoenix is a hot and dry city located in the Sonoran Desert.
This is my second year attending VIS. Unlike last year, I am able to present a conference paper on VAST this year. This is really a great experience for me. More importantly, it's really nice to have the opportunity to learn what others are doing in this community.
This is a paper list that I summarized for my PQE.
I have been reading papers and articles and searching for ideas of my Ph.D. Qualification Exam (PQE) for a few days. Since I am interested in working on the interdisciplinary field of Visualization and Machine Learning, the idea of "explainable AI" (XAI) seems promising to me. After discussing with my professor, I decided to fixed the survey topic to "Visualization for Explainable Machine Learning". This blog summarizes my understanding on the motivation, scope and application of XAI.
This paper is written by Amershi, a researcher in MSA, who is kind of a leading researcher in the crossing area of ML + HCI.
I am working closely on RNNs these days, trying to reveal the ``black box'' and see what RNN learned to use its the hidden states and gates.
After intensively trying to do experiments, I suddenly realize that maybe first analyze them mathematically would give some clues for better visualization
There are already many good articles introducing RNNs and its variants (LSTMs, GRU) on the internet right now. So this is just a post for myself to summarize things up on RNNs.
So first, what is Recurrent Neural Network (RNN)?
In short, RNN is a type of neural network that deal with sequence data. Classical neural networks, e.g. Multi-layer Perceptron (MLP) or Convolutional Neural Network (CNN) takes a fixed sized input an produce a fixed size output. Although for CNNs you can resize images of different size into a standard size so that the model can work with variable size input, but for the CNN part it still only accept fixed sized input.
I am thinking of starting a blog for a long time. And this kind of unable-to-finish-my-goals feeling is actually driving me ill.
And here, this blog serves as a FLAG noting I am going to start blogging "seriously". Don't mock me for my strange English, I am trying to make it serious here!
As for the stimulus that drives myself really starting this comes from Yuehan. Her suggestion of writing things down is actually working -- it kinds of saving my presentation today: Writing down things really helps me make sure I really understand what I want to presents. And it helps me clear my thoughts. People who writes logically and clearly must have the clear thoughts in mind, right?
I think I will keeps this habit from now on, for clearing my thoughts, recording my ideas, and exercising my English (Though I am not saying all of them will be in English :D).
Tsinghua Jump is a doodle jump like game developed by our group Miaow. The game has similar rules as in Doodle Jump, but with many Tsinghua-flavored elements and interesting game items, which increased the game’s creational values. The game mainly has 2 game modes – Single Mode and Multi-Player Mode, a score-ranking system, and a game-item shop system.
The game is developed and maintained via Github. Link for the repository is here. I am the group leader as well as the main contributor. It is still being developed and maintained by myself.
Ray Tracing is a toy project for an undergraduate course: Advanced Computer Graphics in 2015 Fall at Tsinghua University. Ray Tracing is an implementation of classic ray tracing algorithm for photorealistic rendering. It is written in C++11, developed using vs2013 , with dependencies of openmp and opencv. You can view the repo here
In the 2013 winter term, I was in charge of the poster design of the student festival of our department. The student festival, "砼年不同Young", and the publicity of the festival have received a lot of attention on campus.
Inspired by famous movies, including V for Revenge, Iron Man, and etc., I designed a list of six animated posters, which have received a lot of views on the Internet. The prototype of these designs is 砼仔 (concrete boy, pronounced [təŋ tsai]).
The six posters are originally posted on RenRen. The posters received a total of 500,000 views with more than 50,000 views for each poster.
Guokr.com also invited us to re-post our posters on their site and posted them on weibo, where the posters received more than 6,000 re-post and 1,000 likes.
The followings are the animating posters that I designed.