Microsoft Research Asia recognizes the following fellows, who represent the best and the brightest PhD candidates in the Asia-Pacific region.
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2012 Fellows
Yang Cao
Beihang University
Supervisor: Jinpeng Huai
Research Interests
Database theory and systems, graph pattern matching, social searching
Long-Term Research Goal
- Graph pattern matching revised for social searching: Graph pattern matching is fundamental to social analysis. Traditional techniques like subgraph isomor-phism and graph simulation either impose too strong a topological constraint on graphs to retrieve meaningful matches and incur high computational complexity (for example, subgraph isomorphism is NP-complete), or are too loose to find correct matches. Moreover, data graphs for modeling social networks are usually evolving over time. Existing models and techniques often fall short of handling this without incurring high computational complexity. To this end, I will propose new graph pattern matching models (queries) as well as techniques (algorithms) for evaluating them. The model should be well-designed such that it could strike a balance between the express power (semantics) and the computational complexity for evaluating them, and should be able to handle the evolvement of data graphs as well.
- Keywords search on graph data: Keywords searching has been a longstanding issue for search engines. Traditionally, keyword searching is often conducted on structured and semistructured data, as well as documents. Existing models and techniques for keywords searching cannot be naively adopted for social search engines. Recent research about keywords searching on graph data mainly focuses on developing techniques for retrieving matched results on graph data. My research for this aspect will instead turn to proposing new keywords searching models specific for graph data, to take the advantages of graph pattern queries to provide more accurate semantics for emerging applications.
- New computational model and complexity classes: In the context of large-scale data, traditional computational classes are too generic for identifying problems that are solvable practically, and moreover, they cannot take properties of data graphs (social networks) into consideration. To this end, I will propose a computational model and a hierarchy of complexity classes, to provide a dichotomy between those queries that are feasible on large-scale data graphs and those that are not, and thus properly classify social searching problems, with regard to the express power and evaluating complexity of the queries.
Menglei Chai
Zhejiang University
Supervisor: Kun Zhou
Research Interests
Computer Graphics, Computer Vision, Image Processing, and so forth
Long-Term Research Goal
My long-term research goal primarily targets portrait image manipulation, especially for recovering semantic information from common images and providing a series of techniques which enables useful applications for average users. As we know, images and videos featuring humans as their subjects are of great interest for both industrial experts and average end users, and have motivated various kinds of research in computer graphics during the past decades. But few of them involve 3-D information to resolve the occlusions and ambiguities issues, making these low-level image processing methods of limited application for specific tasks of portrait manipulation. Our research work will focus on portrait image manipulation, especially for components such as face, hair, body shape, clothing, and so forth. The key thought is to extract 3-D information by utilizing specific prior knowledge achieved with machine-learning techniques, and apply them to drive a broad range of user-friendly applications which were previously challenging.
Jun Kato
The University of Tokyo
Supervisor: Takeo Igarashi
Research Interests
Human-computer interaction and programming language (user interface for programmers)
Long-Term Research Goal
I am interested in the broad area of human-computer interaction, but have been especially focused on interactions between humans and the real world through computers (physical computing and robot applications), their input modalities (natural user interfaces), and their development methods (prototyping toolkits and integrated development environments). In my future vision, everyone can be a weekend carpenter who makes his/her everyday life easy and comfortable with help of information technology—in other words, end-user programming of the real world in the real world. Currently, I am planning to take two steps toward this goal.
- First, I will propose development environments that improve the programmer’s experience (Programmer’s eXperience, PX) on “real-world programming” that involves interactions with the real world, such as robot control and vision-based object recognition.
- Second, I will propose end-user development environments for the same “real-world programming” that make creation of customized applications feasible to end users through specialized user interfaces.
Given my past experience on PX research, I expect that further investigation on the professional user interfaces will serve as the fundamentals for designing new user interfaces for the end users.
Yin-Hsi Kuo
National Taiwan University
Supervisor: Winston H. Hsu
Research Interests
Multimedia content analysis, image retrieval, and mobile visual search
Long-Term Research Goal
Nowadays, the rapid computational power of mobile devices brings the emerging need for mobile visual search. Different from the traditional content-based retrieval system, the mobile devices have the ability to process the query (for example, feature extraction) before the transmission. In recent years, the hash-based method has become promising for approximate nearest neighbor (ANN) search because of its ability to deal with high-dimensional features and large-scale databases in an efficient way. An efficient and effective method on the mobile devices is extremely crucial; hence, the hash-based approach becomes a possible direction in reality. In the future, we attempt to integrate the hash-based approach with contextual information to achieve efficient and scalable mobile visual search.
Hongjin Liang
University of Science and Technology of China
Supervisor: Xinyu Feng
Research Interests
Program verification
Long-Term Research Goal
The computer industry has shown explosive growth in the past years. This trend is not likely to slow down. However, the lack of reliable and secure software is becoming a bottleneck for such growth. In particular, programming on multiprocessors is extremely error-prone, but very difficult to debug. Thus, in my long-term research career, I would like to develop new and easy-to-use technologies and tools that could improve the reliability, safety, and security of software on multiprocessors. I am particularly interested in:
- Designing program logics for verifying the correctness (including safety and liveness properties) of concurrent programs
- Developing automatic or semi-automatic tools to support concurrent program verification
- Applying my technologies and tools to verify today’s concurrent libraries (such as java.concurrent.util), runtime support in concurrent systems (such as concurrent garbage collectors), and other concurrent software used in practice
Yongxin Tong
Hong Kong University of Science and Technology
Supervisor: Lei Chen
Research Interests
Database and data mining, machine learning
Long-Term Research Goal
My research interests mainly focus on uncertain data management and mining. With the emergence of many real applications—such as sensor network monitoring, moving object search, protein-protein interaction (PPI) network analysis, and so forth—managing and mining uncertain data has attracted much attention in the database and data mining communities recently. Although current researches have solved some fundamental operations over uncertain data—for example, join, ranking, mining frequent item sets, clustering, and so forth—there are a few works to explore the hidden correlation in uncertain data due to the intricate probabilistic structure and high computational complexity. Therefore, my research aims to address these challenges via incorporating both theoretical and practical viewpoints.
- On the theoretical side, I will first design a novel model to capture intrinsic correlated properties in uncertain data. Additionally, I will propose a series of efficient and effective algorithms in order to adapt complex structural data, such as uncertain data streams and uncertain graphs. This is particularly important in the age of big data as well.
- On the practical side, I hope to extend our theoretical model and algorithms to real application scenarios, in other words, I try to develop a new crowdsourcing platform based on mobile computing and utilize our probabilistic model to handle the uncertainty control in this system, which is one of most urgent problems in the crowdsourcing researches.
In summary, my research goal is to better discover and manage the hidden correlation and rules over massive uncertain data effectively.
Xinggang Wang
Huazhong University of Science and Technology
Supervisor: Wenyu Liu
Research Interests
Computer vision and machine learning
Long-Term Research Goal
My research interests are computer vision and machine learning, especially the problem of object detection. Object detection concerning detecting instances of semantic objects of a certain class in digital images and videos, is a fundamental problem in computer vision, and has wide applications in people’s lives, for example, face recognition, image search, automatic driving, and so forth. Through years of research, the problem of frontal face detection has been well studied, and this technology has been integrated into products. However, detecting most of the other objects in the real world is a very difficult problem due to the great variations of object appearances, such as persons and cars. My previous research mainly focuses on part-based object detection, shape-based object detection, and semi-supervised learning for object detection. It brings together shape feature design, object model design, machine learning, and optimization.
Based on my previous research, my long-term research goal is to build an object detection system in a very weakly supervised way, which can achieve the state-of-the-art performance and be used in some real-life applications. I will put more effort on:
- Studying discriminative object part detector; for example, people can easily recognize a leopard when looking at dapple of leopard. Part detector is robust to occlusion. Ensemble of part detector can reach good object detector.
- Using context information for object detection which combines scene understanding and object recognition.
- Combining modeling based methods with data-driven based methods.
Yizhong Zhang
Zhejiang University
Supervisor: Kun Zhou
Research Interests
Computer graphics, physically based simulation
Long-Term Research Goal
My research interest is in physically based simulation, especially simulation of complex natural phenomena for graphics. Physically based simulation is a powerful tool in both science and engineering. It can help us to predict unknown phenomena that may prevent the success of an experiment or the improvement of a product's design before manufacturing. In graphics, we need simulations to make animations of water, fire, cloth, and many other phenomena that contain high frequency details. We want the simulation to be fast while being reliabile so that it will be easier for artists to control the animation. My research goal is to improve the performance of simulation, including propose reduced physical model, method of efficient and robust handling of geometry, and parallel computing. By using these methods, we can get high quality simulation results more quickly.
Xin Zhao
Peking University
Supervisor: Xiaoming Li
Research Interests
Social media analysis, web text mining, machine learning
Long-Term Research Goal
My general research interests are within the area of online social networks(for example,Twitter and Facebook) analysis. Online social networks—in particular, their rapid growth and development—are continuously attracting users all around the world, which significantly changes the way that people live. Although various text mining methods have been shown effective to deal with traditional document collections, for example, scientific publications, very few of them are tested to achieve very robust and sound performance on real data sets of online social networks due to the fact we still do not fully understand the nature of online social networks, such as their underlying information structure, user behaviors, and connecting patterns.
To overcome these difficulties, the goal of my research is to develop both principled methodologies and innovative applications for automatically analyzing and discovering knowledge from online social networks. Specifically, I will mainly focus on the aspect of content analysis of online social networks in the long term. More challenging than traditional content analysis, we have to first understand the underlying information patterns and uncover the generative process of such information before we can construct effective models. Previous research experiences will be helpful for me to identify and solve real-world problems that are valuable to common users in this direction. During the problem-solving process, I will try to construct formal methods with clear and intuitive motivations, borrow the ideas and techniques from multiple disciplines, and evaluate research results with large-scale real data.
Chong Zu
Tsinghua University
Supervisor: Chi-Chih Yao
Research Interests
Quantum information processing
Long-Term Research Goal
Quantum information and computation is an interdisciplinary subject of computer science and quantum physics. During recent decades, there have been a lot of important theoretical results in this field, including unconditional secure cryptography, true random number generation, exponential speedup algorithm for factoring, and so on. However, realizing quantum information processing and quantum computation is still not an easy task. In my research, I am mainly focusing on two different physical systems, photonic qubit and NV center in diamond, both of which are promising candidates for future quantum information processing and computation.
- Photonic qubit: In my recent research, we are working on a project to theoretically propose and experimentally build a brand-new, reliable, and practical quantum random number generator. By saying reliable, it means that we can always use some methods to certify that the random number we are producing is indeed from quantum power, rather than some classical device or lack knowledge of the device, and the randomness (characterized by min-entropy) can be bounded; while saying practical means that our random number generator can be efficient and easy to be realized. In the future, we plan to generate quantum state with more qubits, with which we can demonstrate interesting quantum information protocols, as well as realize large-scale quantum information processing.
- NV center in diamond: Recently, we have started to build NV center system. We want to use it to realize room-temperature quantum memory, as well as solid state quantum repeater for long-range quantum information. Our long-term goal is to build a hybrid quantum computation and information network with different quantum systems including trapped ion, matter qubit, and photonic qubit, which can be treated as a prototype for a future genuine quantum network.
2011 Fellows
- Dongzhe Ma, Tsinghua University
- Peiran Ren, Tsinghua University
- Xiaohui Bei, Tsinghua University
- Xun Cao, Tsinghua University
- Quan Wang, Peking University
- Xiang Song, Fudan University
- Chenglin Li, Shanghai Jiao Tong University
- Guo Li, Zhejiang University
- Shengjun Huang, Nanjing University
- Jing Yuan, University of Science and Technology of China
- Cuiling Lan, Xidian University
- Guangmin Wang, Xi’an Jiao Tong University
- Yang Cao, University of Science and Technology of Huazhong
- Qiang Hao, Tianjin University
- Zhen Liao, Nankai University
- Lei Cui, Harbin Institute Technology University
- Xiaoshuai Sun, Harbin Institute Technology University
- Bolei Zhou, The Chinese University of Hong Kong
- Lin Ma, The Chinese University of Hong Kong
- Nobuyuki Umetani, The University of Tokyo
- Yefeng Liu, Waseda University
- Hyunson Seo, Yonsei University
- Yohan Chon, Yonsei University
- Jaesik Park, Korea Advanced Institute of Science and Technology
- Sangwon Seo, Korea Advanced Institute of Science and Technology
- Kazi Rubaiat Habib, National University of Singapore
- Gang Yu, Nanyang Technological University
- Nikolay Gravin, Nanyang Technological University
- Shuo-Hung Chen, National Tsing Hua University
2010 Fellows
- Haris Javaid, The University of New South Wales
- Yue Deng, Tsinghua University
- Chongyang Ma, Tsinghua University
- Xiong Li, Shanghai Jiao Tong University
- Bo Geng, Peking University
- Shiliang Zhang, Institute of Computing Technology, The Chinese Academy of Science
- Xiulian Peng, University of Science and Technology of China
- Xiao Zhang, Tsinghua University
- Xinying Song, Harbin Institute Technology University
- Jinbao Wang, Harbin Institute Technology University
- Wei Wu, Peking University
- Linghe Kong, Shanghai Jiao Tong University
- Liang Wang, Nanjing University
- Ping Chen, Nanjing University
- Weiwei Wu, University of Science and Technology of China
- Xiaojun Qian, The Chinese University of Hong Kong
- Dongxiao Yu, The University of Hong Kong
- Lu Wang, The Hong Kong University of Science & Technology
- Seokhwan Kim, University of Tsukuba
- Adiyan Mujibiya, The University of Tokyo
- Seungjin Lee, Korea Advanced Institute of Science and Technology
- Tae-Joon Kim, Korea Advanced Institute of Science and Technology
- Gae-won You, Pohang University of Science and Technology
- Sungjin Lee, Seoul National University
- Shenghua Gao, Nanyang Technological University
- Yi-Ling Hsieh, National Cheng Kung University
- Cheng-Te Li, National Taiwan University
- Tsung-Te Lai, National Taiwan University
2009 Fellows
- Novi Quadrianto, The Australian National University
- Ashnil Kumar, The University of Sydney
- William Voorsluys, The University of Melbourne
- Shenghua Liu, Tsinghua University
- Hao Wen, Tsinghua University
- Zhiwei Xiong, University of Science and Technology of China
- Dong Liu, Harbin Institute of Technology
- Bo Yu, Harbin Institute of Technology
- Litian Tao, Beihang University
- Yufeng Li, Nanjing University
- Wei Wang, Nanjing University
- Huanhuan Cao, University of Science and Technology of China
- Xiaoyin Wang, Peking University
- Jun Lang, Beijing Institute of Technology
- Derek Hao Hu, The Hong Kong University of Science & Technology
- Kaiming He, The Chinese University of Hong Kong
- Tasuku Oonishi. Tokyo Institute of Technology
- Yoshida Yuichi, Kyoto University
- Jun Hatori, The University of Tokyo
- Jongwuk Lee, Pohang University of Science and Technology
- Jung-Tae Lee, Korea University
- Bingjun Zhang, National University of Singapore
- Lixin Duan, Nanyang Technological University
- Yu-Chen Sun, National Chiao Tung University
- Kai-yin Chen, National Taiwan University
2008 Fellows
- Feng Zhang, Beijing Institute of Technology
- Xiangyi Meng, Beijing Institute of Technology
- Tong Wu, Beijing University of Post and Telecommunication
- Li Xu, Chinese University of Hong Kong
- Jun Lang, Harbin Institute of Technology
- Qingqing Zhang, Institute of Acoustics, Chinese Academy of Sciences
- Yanyan Lan, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
- Xiaoqin Zhang, Institute of Automation, Chinese Academy of Sciences
- Xiubo Geng, Institute of Computing Technology, Chinese Academy of Sciences
- Souneil Park, Korea Advanced Institute of Science and Technology
- Ki-woong Park, Korea Advanced Institute of Science and Technology
- Dafan Dong, Nankai University
- Yi Huang, Nanyang Technological University
- Yu-Lin Wang, National Cheng Kung University
- Yi-Hsuan Yang, National Taiwan University
- Yantao Zheng, National University of Singapore
- Lijiang Chen, Peking University
- Sunghyun Cho, Pohang University of Science and Technology
- YongDeok Kim, Pohang University of Science and Technology
- Dikan Xing, Shanghai Jiao Tong University
- Jingjing Fu, The Hong Kong University of Science and Technology
- Jun Hong, The University of Hong Kong
- Florian Mueller, The University of Melbourne
- Tomoaki Higo, The University of Tokyo
- Pinyan Lu, Tsinghua University
- Jialin Zhang, Tsinghua University
- Qiming Hou, Tsinghua University
- Kun Xu, Tsinghua University
- SYifei Don, University of New South Wales
- Hao Xu, University of Science and Technology of China
- Yuan Liu, University of Science and Technology of China
- Myung - Suk Song, Yonsei University
