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欢迎来到我的主页。

我现在是台湾国立清华大学 Large-scale System Architecture (LSA)实验室的一员。
我现在在做erasure codes和cloud storage system方面的研究
我的导师是周志远教授。

在此之前,我是:
»   法国巴黎第十一大学(南巴黎大学)LRI (Laboratoire de Recherche en Informatique)实验室的Proval组的研究实习生。
我所做的题目是“数据库约束自动化验证”
我的导师是Véronique BenzakenÉvelyne Contejean教授。

»   浙江大学创新软件研发中心(Eagle实验室)VIPA组/浙江大学-微软联合实验室 的成员。
我所做的题目是“图象场景音频识别”
我的导师是宋明黎教授。

这里是我的简历(html | pdf)。

这里是我的博客

学术

研究领域

  • 云计算
  • 存储系统
  • 抹除码
  • 约束检查
  • 机器学习

研究经历

I joined Microsoft Visual Perception Laboratory of Zhejiang University in 2010 when I was a junior student. Under the direction of Prof. Mingli Song, I started reading papers in the wide area of Speech-driven facial animation, Speech emotion recognition, AED (Audio event detection), Music emotion recognition, Sound localization, Unstructured audio scene recognition and also Image inpainting and Image completion. Later, we combined the research work of image scene classification and auditory scene recognition, and develop an approach to recognize the scene sounds of images. That is, given an image, to find the environmental sounds that are fit to the scene of the image. Probabilistic Latent Semantic Analysis (pLSA) and Matching Pursuit (MP) algorithms are applied to extract the features of training images and sounds respectively. Then machine learning approach is used to recognize the corresponding environmental sounds for the specified image. In the training stage, For each image, pLSA is used to obtain its topic distribution P(z|d) while MP algorithm is used to get the first 10 Gabor atom reconstructed signal for each environmental sound. We joint topic distribution P(z|d) of image with the reconstructed signal of the corresponding audio to obtain the vectors of mixed feature of the training pair of images and sounds. And then we calculate the cluster indices of each vector of mixed features and the centroid locations of the clusters. In the testing stage, For an input image, we mix its topic distribution P(z|d) with the reconstructed signal of different audio to get different vectors of mixed features. Then we compare this set of testing mixed features with the centroid vector of training clusters and obtain the category of the test mixed feature vector which is most similar to the centroid vector of a certain training cluster. The target audio are the sounds in the same category.

During my stay at Proval Group as a research intern, I was working on my bachelor's thesis "automated constraint verification for databases" under the guidance of Véronique Benzaken and Évelyne Contejean. Our motivation of this thesis is from the observation that currently no real DBMS (database management system) have fully support the management of integrity constraints. Instead, they use triggers as an alternative. However, the behavior of triggers is complex and the semantics of them are hard to understand. We present a strategy to automatically verify the integrity constraints of databases. Our method is based on the weakest precondition and predicate transformer approaches. First we reduce database integrity constraints in SQL into SQL assertions, and then transfer assertions into FOL (first-order logic) formula. Based on the logical formalization of both SQL assertions and data modification operations, we implement integrity constraints checking for databases with the help of the program verification platform Why3. For the input SQL statements, our program translate them into WhyML program, later Why3 is called to compute the weakest preconditions and generate the verification conditions for the back-end provers (such as Alt-Ergo, CVC3, etc.). Finally the provers will check whether the databases after executing the data modification operations satisfy the constraints. All the process is fully automatic. My bachelor's thesis now is available in Chinese and the English slides are also available. The source code is opened under the GPLv3. (View assertion-verification on GitHub)

Now I am working on issues related to cloud storage system, and erasure codes. Recently, I implemented a GPGPU approach to accelerate Reed-Solomon coding. Source code and some documents are available. (View GPU-RSCode on GitHub)

技术

技能树

  • 常规技:输出平稳,但装备大众。
    C, C++, Java(做软件开发的大众语言)
    Verilog HDL(搞VLSI的大众语言)
    Matlab/Octave(玩科学运算的大众语言)
  • 半吊子技能:
    Ocaml(在法国以外的地区是小众语言,偶接触的第一种函数式编程语言XD)
  • 附加技能:在历史战绩里还只是被用来打打小怪练练级而已。
    Python(写testcase、socket程序,粗略玩过Pygame
    Markdown, HTML Markup.
  • 高阶技:
    • GPGPU: CUDA, OpenCL
    • 并行运算:Hadoop, MPI
    • 图形:OpenGL
    • 数据库:会SQL(本科就是靠这个毕业的敢不会么),用过PostgreSQLMysql等DBMS
    • 网页开发:对后端较熟,写过JSP/Servlet
  • 计划要学习的技能树新分支: Ruby, LISP, Haskel, 等等。

利器

工欲善其事,必先利其器

《论语·卫灵公》
以下这些是我用过的“利器”:
  • 版本控制:git偶最喜欢啦, 偶之前也用过svn甚至老掉牙cvs
  • 编辑器/集成开发工具:VIM忠实粉一枚。IDE嘛,以前用过Eclipse, Visual StudioXilinx ISE
  • 偶现在很少用IDE啦,一般是用GNU Make生成可执行文件。不过偶更喜欢用的是autoconfautomake
  • GNU/Linux下的折腾党(目前是Archlinux粉),会bash脚本,会sed(VIMer升级必备呵)和awk
  • 文本编辑相关:偶用LaTeX写文档,用Graphviz DOT来生成图片~

项目经历

参见:

兴趣

音乐


偶素“古粉”,全称叫“古典音乐粉”。目前状态是:随性涉猎,再重口味的现代音乐偶也不排斥;不甚精通,某些「大俗」作品偶也可能不识。
这里有一篇我很早前写的“乐评”:Innovation and Conservation -- Classical Music of the UK,大致是讲我那时候对不列颠音乐的理解(受限于文笔拙劣,见识有限,建议专业人士慎入XD)。
我自己也是个小提琴的爱好者和演奏者学习者。曾走过很长的弯路,后幸获明师指点,在技巧和演绎方面都有了突飞猛进的进步。不过如今都是自己胡乱摸索,技巧上几已停滞不前,寄望于深入理解和更好表现所能演奏的音乐,然此又绝非一载之功,唯有自娱自乐耳。

运动

我最喜欢的运动是羽毛球和乒乓球,我也喜欢看网球和篮球比赛——不过不会打~


电子邮箱
yszheda AT gmail DOT com

电话
0988473989/(886)988473989

通信地址
30013 台灣新竹市光復路2段101號國立清華大學資電館836

工作地址

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