Obviously research takes a very important part of PhD life. And probably it is the part that prevent PhD students from enjoying their lives. PhD life is tough, frugal, unexpected obstacles await us anywhere, we are all suffering somehow, but some can enjoy the process at the same time. So what make this difference?

Don't let the panic of having nothing in mind damage you at the time of your proposal. Keep motivating yourself by thinking what you want to do. Sometimes the most difficult part of accomplishing something is just getting yourself to begin, to take that first step. The key is to shift your motivation to a direction that is useful and that allows you to achieve your goals. Don't always wait for external event to force you to change your status. If you don't have a pushing advisor, you'll end up finding yourself drifting around purposelessly for years. So it's better to tell your advisor that what you want to do next rather than always waiting for your advisor telling you what you should.

Research is not research until you have focused it around a solid research question that addresses a problem or issue. But how do you come up with a question that is going to work? Accept the fact that you have to make your advisor interested your research question to make sure he/she will give you funding. You need to Narrow your Topic to one aspect. A big reason why research can fail is that the researcher is trying to conquer the world with one project. You have to choose an aspect that is distinct enough that you can really work with it. And then Identify Controversies or Questions related to your narrowed approach. Avoid questions that are fuzzy, open-ended or infeasible.

Once you have decided on what you are going to do. What's left is Hard working. Don't assumes that research is merely to gather data and synthesize it. Get rid of the disgusting image from your mind that a typical student "research" project involves amassing data, reading and absorbing it, then regurgitating it back onto a fresh piece of paper. Don't imagine that you can finish your PHD by giving a superficial look at a big topic by generalities and surveys while avoiding depth and analysis. Don't expect that your committee will be convinced by a lengthy summary of the past without any fresh ideas.

Every PhD student knows that it's better to make steady progress in research, but many of us choose to wait until the day before the meet with advisor and work overnight to get some stuff done so as to prevent from having nothing to say during the meeting. What's funny is sometimes your advisor is too busy to keep track of your research progress and unconscious of your getting nowhere, which gives you reason to drift around another week. But probably besides your advisor, you also need to report to yourself if you consider yourself as your boss. Just breaking all major tasks into smaller ones. Make a list to do each day and check them off as you complete them.

Someone says don't ask a PhD student how his/her research going, just like don't ask a girl her age or weight. But maybe it's a good strategy to ask yourself such a question now and then "how's your research going?"

Once for a while you may be in such a circle that you end up without doing anything productive and then stay up late which make you even much less productive the next day. If you are in such a circle, jump out right now and reset.

One metaphor for life of phd student is like this: Organize your tasks as if you were juggling them. Juggling several balls requires planning and skill. You must grab and toss each ball before it hits the ground. You can only toss one ball at a time, just as you can only work on one task at a time. The order in which you toss the balls is crucial, much as the order of working on tasks often determines whether or not you meet all your deadlines. Finally, once you start a task (grab a ball) you want to get enough done so you can ignore it for a while (throw it high enough in the air so it won't come down for a while). Otherwise you waste too much time in context switches between tasks.

Don't feel depressed when you find yourself more and more ignorant. Talk about your frustrations with peers or senior graduate students, and don't let academic frustrations take control of your whole life. When frustration mounts, keep in mind that there is life outside your department, and most people have never even heard of what you are studying.

Probably it's not early enough, but it should be not too late to realize how important a well planed schedule is. However, a good plan means no longer to just have the assignment done in time, a PhD student should be able to look further, prepared to encounter difficulties and make important decisions. When you become a PhD student, the emphasis of study shifts from course work to research, since what cares most becomes your achievement in research rather than how high your grade is. But still your time is taken up for other stuff besides research. Course work, social activities, entertainment, etc., all those can cut your time into pieces. You can probably survive if you always just wait for stuff that you should do comes to you. But if you want to spend quality time on most parts of your PhD life, making ambitious feasible schedule is critical.

Due to the flexibility of research, it's up to the PhD student to control the progress. If loitering until the day before group meeting, I should hurry to pick up what I have left last time, and then work overnight to have some stuff done to keep me from the embarrassment of saying nothing during the group meeting. Or otherwise I can make some achievement every day, probably not as much as I can finish in the last-day situation, but at the group meeting day, I can give a clearly outlined presentation. It's just a matter of self-control, self-management. But these two different choices result in totally different outcomes especially in the long term.

Ramesh Chandra Jain is a scientist and entrepreneur whose decades long career has spanned several universities and startup companies. He is best known for founding three companies--Imageware, Virage, and Praja, and working on some of the early visual information retrieval systems. In addition, he was the founder of IEEE Multimedia. Also Chairman of ACM SIG Multimedia, he was the founding Editor-in-Chief of IEEE Multimedia magazine and at present serves on the editorial boards of several magazines. He has been elected Fellow of ACM, IEEE, IAPR, AAAI, and SPIE. He has published over 250 research papers in scientific journals and conferences.

Dr. Ramesh Jain founded and directed artificial intelligence and visual information systems labs at the University of Michigan, Ann Arbor and the University of California, San Diego. He joined University of California, Irvine as the first Bren Professor in Bren School of Information and Computer Sciences in 2005. Currently, he is a Bren Professor in Information & Computer Sciences, Donald Bren School of Information and Computer Sciences, University of California, Irvine.

He shares his research and technical ideas as well as ideas on selected topics in his blog.

The ACM and IEEE society have many distinguished conferences and publications. The following lists some representative examples.

1. ACM International Conference on Image and Video Retrieval (CIVR)

   Image and Video retrieval have now reached a state where successful techniques and applications start flourishing. The ACM International Conference on Image and Video Retrieval (ACM-CIVR) series of conferences is the place to present and encounter such developments. Originally set up to illuminate the state-of-the-art in image and video retrieval throughout the world, it is now a reference event in the field where researchers and practitioner exchange knowledge and ideas.

2. IEEE TRANSACTIONS ON IMAGE PROCESSING
   

   The IEEE Transactions on Image Processing covers signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, and color reproduction.

3. IEEE Transactions on Pattern Analysis and Machine Intelligence

   The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope. This includes all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Areas of such machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software archictectures are also covered.

The academic communities that interest me are those related to image processing, machine intelligence, information systems, etc. I hope I will be able to contribute to those communities in the future: ACM, IEEE Signal Processing Society and IEEE Computer Society.

1. ACM

   ACM, the world's largest educational and scientific computing society, delivers resources that advance computing as a science and a profession. ACM provides the computing field's premier Digital Library and serves its members and the computing profession with leading-edge publications, conferences, and career resources.

2. IEEE Computer Society

   With nearly 85,000 members, the IEEE Computer Society is the world's leading organization of computing professionals. Founded in 1946, and the largest of the 39 societies of the Institute of Electrical and Electronics Engineers (IEEE), the CS is dedicated to advancing the theory and application of computer and information-processing technology.

   The CS serves the information and career-development needs of today's computing researchers and practitioners with technical journals, magazines, conferences, books, conference publications, and online courses. Known worldwide for its computer-standards activities, the CS promotes an active exchange of ideas and technological innovation among its members.

3. IEEE Signal Processing Society

The IEEE Signal Processing Society is an international organization whose purpose is to: advance and disseminate state-of-the-art scientific information and resources; educate the signal processing community; and provide a venue for people to interact and exchange ideas. They have some publications like IEEE Transaction on Multimedia, IEEE Transaction on Image Processing, and IEEE Multimedia Magazine which publish very good quality papers in areas of multi media types.

Jian Huang is a fourth year PhD student in IST, and his advisor is Prof. C. Lee Giles. He is a member of the Intelligent Information Systems Lab, and so am I. But we are working with different advisors. He has been actively involved in the research and development of the next generation of CiteSeer--CiterSeerX, and now he is in progress of his dissertation on a very challenging and interesting topic of name disambiguation. Simply speaking, name disambiguation is trying to distinguish between persons with the same name. One application of the technology is in search of publication: given a name, there could be more than one researchers of this name among all publications, and the task is to assign these publications to the real researcher.

Jian is very productive in research. He has 5 publications in 2008, and more in previous years. He has presented his work in many conferences and earned several awards for his papers. He was a research intern in Yahoo! Research during the summer of 2007 and 2008, working on information extraction.

Jian's research is focused on learning and mining knowledge efficiently from massive data, including data mining, social network analysis, supervised learning methods, information retrieval, natural language processing, etc. Compared with Jian, my research subject is images rather than text. The primary goal of my research is to extract high level information from image data. But we share interests in areas like data mining and information retrieval.

The creation of IST started with a visionary challenge from Penn State President Graham Spanier: How could the University address the enormous workforce gap in information technology and help government, industry, and society face the daunting technological and human challenges of tomorrow? This initial goal endues IST with the mission of bridging the gap between traditional research paradigms and the emerging problems, and looking for synergy of multidiscipline. The ITP triangle--Information, Technology and People--is the fundamental idea, underlying both research and education of IST. The college highlights the interdisciplinary nature and provides encouraging environment of interdisciplinary development for faculties and students to form a creative community.

Different from other colleges, IST has no departments. That means there is no collaboration boundary between departments. IST faculties are experts in different research areas, like HCI, Artificial Intelligence, Information Retrieval, Security and Privacy, Social Network, Psychology, Information Policy, etc., but they collaborate with each other in projects across disciplines such as Health Informatics, Medical Informatics, Emergency Response, Enterprise Informatics, Globalization, etc. so that real world problems are studied from multiple perspectives, including technical, cognitive and social aspects. Besides, new concepts and theories are developed in these kinds of cross-disciplinary collaboration. IST as a whole is structured more like a web, emphasizing connection as well as diversity.

For myself, I'm more on the technical side. My research is mainly about image processing, image content understanding, and data mining which is also related to cognition, information visualization, art, etc. Human visual perception is a magical process, and scientists from different disciplines devote great efforts to understand this process. As engineers, we make use of knowledge of other fields and develop practical systems to assist appreciation of images.

Frankly speaking, it is after I came to IST when I first heard about the term "I-school", and my understanding of this term at the beginning is places where researchers from different areas work together, and where education is of a multi-disciplinary nature. I-schools have various origins, such as information science, library science, computer science, HCI, economics, management, policy, sociology, etc., but all have the same focus: information. The cross-disciplinary, inter-disciplinary nature is probably the most distinguishing feature of I-schools compared to other traditional disciplines.

As being a computer science student for six years, I found myself quite interested in research areas where different aspects of technology and people are combined. It is not easy to say which side is more important, but lots of people begin to believe that studies based on a more comprehensive view is probable to achieve valuable and even unexpected results.

It is obvious that today's technology is highly relied on the understanding of people's role in different domains of the society. Traditional disciplines of information like library science, computer science, etc. are increasingly intersected with disciplines on the social side. The recognition that study of these areas separately cannot provide solutions to the complicated problems existing in today's social environment encourages more effort devoted to this newly emerging area. Instead of focusing on the technical aspects of information generation, storage, distribution, retrieval, etc., I-school also concerns the people side of these activities. Though information flow is extremely variable, understanding of certain patterns in different domains can facilitate information usage in those areas, which in turn can have great impact on people and technology in that society.

Imagine ISchools: information, initiate, intellect, impact, innovate, introspect, idealize, insight, imagine?

Education

Research Interests

• Semantic sensitive image retrieval
• High-level image analysis and understanding
• Image annotation
• Shape analysis

Publication

1. Lei Yao, Jian Liu, Jiangqin Wu, "An Approach to the Compression of Residual Data with GPCA in Video Coding," Proceedings of the Pacific-Rim Conference on Multimedia, pp 252-261, China, 2006.
2. Jian Liu, Yueting Zhuang, Lei Yao, Fei Wu, "A Novel Scalable Texture Video Coding Scheme with GPCA," Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp I-993 - I-996, Honolulu, 2007.
3. Jian Liu, Fei Wu, Lei Yao and Yueting Zhuang, "A Prediction Error Compression Method with Tensor-PCA in Video Coding," Proceedings of the International Workshop on Multimedia Content Analysis and Mining, pp 493-500, China, Springer Berlin, 2007.