Last time, I see a joke, some kids are making fun of graduate students - lousy dress, disordered hair with the word "I am a graduate student. I am thirty and I make 20 thousand a year". Then their mom says: Don't laugh at them. They are not bad guys. They just make bad decisions of their lives.

Well, is graduate life that bad? With no fun and just boring work? I don't think so. I think most of us choose graduate school because they like to do the research of their majors. And we can have fun in it. Also, graduate school does not mean just work no entertainment. We can make friends here who share interests with us not just academically. So it is our decision to spend graduate school with fun or not.

As for me, I would like to do research than just coding. Exploring new things can make me excited and devoted. Right now I am working on social network analysis. At first, I thought it's just some analysis of models using some math or statistical methods. But after I find out the huge potential applications, I realize the meaningful and attracting parts, and now I really enjoy the work I am doing.

Also, it is best to balance work and rest. The Penn State University has provided us a lot of activities from art to sports, not to mention there are also kinds of clubs and organizations. And there are special activities for graduate and international students. We can have fun here. Also, we can make new friends here who share the interests with us, that's not just have fun, it's also the fortune of life.  

So, do not just listen to others, and do not take graduate school is boring for granted. Just make yourself happy and enjoy the life in graduate school.

It is important to realize that graduate school is not just about technical matters, there are a lot more involved which are equally important for graduate study and career. Among them, in my opinion, character traits and social skills are the most important, like self-motivation, good communication in both oral and writing, persistence and initiative. We need to balance these factors to get the most success in graduate school.

To have a clear goal for graduate school and future career is essential. What is our goal in graduate school, what research directions are we interested in and plan to work on, what academic result do we plan to get during graduate school. And the most important thing is to figure out why do we want the Ph.D. degree. Do we really want to do research or we just want to find a good job with Ph.D. degree? If the answer is the latter one, we should really think about if it worth it, after the long time working on new theories or algorithms with tiredness, loneliness and maybe a lot of failures. But on the other way, research can be interesting to explore those new things, especially when you enjoy the results you have found. And for some technical persons, it is more boring to do coding than research. So if we have the clear goal to gain the Ph.D. degree, and we are determined to do that, we will enjoy our graduate study.  

After we have the clear goal, the rest thing is to work hard and work with skills. Gaining Ph.D. degree is a long journey. During the five years, we may waste our time doing something non-academic for the simple reason of lazy or just because we think we still have plenty of time. And it will be too late years later when we find out that we have fell behind and so little work we have done. Also, we have to work with skills, that means we should follow up the newest trend, get the useful information and tools and so on. 

Another important thing is to enjoy life in graduate school. Don't tire yourself out because you will find graduate school is so boring and gradually you will lose your passion and interest. And don't make graduate school feel like in jail and just think about how to get out. This is the decision we make, and graduate school is also a part of life, try to enjoy it and do out best.

To sum up, in order to flourish in graduate school, we should have a clear goal, work hard and develop with those non-academic skills.

Maybe the social activities I am attending right now are not exactly the clubs or organizations, some of them are probably called exercise or hanging out with friends, but still, through these regular activities, I know more and more friends and I feel relaxed after the intense study and work.

The most frequent activity I have participated is attending the areobics class in white building. I take the class almost every day, except Saturday when the class is not provided.  Usually, I join the Calorie Killer class, while sometimes hip hop class or some other classes I think may be fun and would like to try. After a full day of hard work, I feel tired or sleepy, but when I finish these workout exercises, I really feel myself energetic and full of strength. Some people may relax by listening to music or watching movies, some may relax by drinking beers or attending parties, some may relax by gentle workout like yoga or pilates, but I like intense workout exercise while listening to fast-move music and doing it with many energetic girls. To be honest, the 60-75 minutes workout is not easy, especially at the very first beginning, but I think it's a way to help me exercise my insistence and make me don't give up. Also, beyond my expectation, I get to know some friends who are always attending the same classes frequently and we are having fun together when we workout.

Also, since I have a super fan of movies, I join my Chinese friends every Friday night to go to the theater to watch the newest and popular movies. It is wonderful to appreciate those thought-provoking, touched or magnificent scenes. And I can discuss the content and what I have learned with my friends. It can also help me relax after the intense study and workout.

Also, some of my Chinese friends and I like to try new recipe of Chinese food every weekend. Each of us cook some new or one's best Chinese food or bake some desert, and then we bring them together and share. It's really a nice moment to enjoy different delicious food and enjoy the talking with friends. Sometimes, I go to some of my American friends' parties or go to bars with them. During the time hang out with them, they teach me some American customs and slangs, and in return, I tell them something about China. Most of the time, they are surprised about the misunderstandings about China. I think that's because sometimes China just shows its amazing things in the ancient history and those new modern changes haven't been emphasized. Maybe we should introduce the both sides of China.

Right now I haven't joined any academic club, I think I will try such clubs where I can share and discuss my ideas and hear other voices in the same area.

Dr. Mark Newman is very famous in social network analysis. He has published and edited a lot of influential books and papers in this area, which can be found here. His research field is statistical physics theory, and his research focus on networks, spin systems and percolation, Monte Carlo methods. I cannot say he is the pioneer of social networks, but he is the pioneer during the recent great development of social networks, especially in the community structure discovery direction.

He got his B.A. in University of Oxford, 1988 and then he received his Ph.D. still in University of Oxford in 1991. Right now he is the Paul Dirac Collegiate Professor of Physics and member of the external faculty of Santa Fe Institute.

Dr. Newman studies the structure and function of networks, including social and biological networks and computer networks like scientific coauthorship networks, citation networks, email networks, friendship networks, epidemiological contact networks, and animal social networks. He employs a combination of empirical methods, analysis, and computer simulation during the research work. And his research of social networks focuses on several directions as follows:

(1)    He studies some fundamental network attributes, such as degree distributions, centrality measures, assortative mixing, vertex similarity.

(2)    His work of social networks is the most famous known for the study of community structure. He proposed Girvan-Newman algorithm in 2002, which is one of the most important and classic algorithm in community detection. His research in this area involves how contact networks form and how structures affect the diffusion of information. Also, he has carried out empirical work on the collaboration networks, and developed computer simulations of the growth and formation of networks.

(3)    Recently, he works on models of information propagation like disease propagation, friendship formation and spread of computer viruses over email networks.

(4)    His other recent projects include navigation in networks and the Internet, mixing patterns within networks, network correlations and phase transitions in network structure.

Last time, I briefly introduced some academic communities related to Social Network Analysis.  In this blog, I would like to describe several academic publication or presentation venues that I hope to have my work in someday.

1. WWW: As I mentioned in the previous article, WWW is the leading conference in the web field which focuses on the future development of the World-Wide Web and its impacts to the world. In the recent years, Social Network has been discussed a lot in this conference.

WWW2009 will be held in Madrid, Spain on April 20-24, 2009. The deadline of paper is November 03, 2008 and the notification date is January 20, 2009. WWW2009 will include the track of Social Networks and Web 2.0.

2. IJCAI: IJCAI is a top conference in the AI area which both surveys the mature area of AI research and/ or practice and introduces novices to major topics of AI. Its impact factor is 1:1.82 (top 4.09%). It is hard to publish in IJCAI not just because that it has a low rate of acceptance, but also it is held every two years. Since Social Network is a branch of AI, it is reasonable to watch this conference closely.

IJCAI2009 will be held in Pasadena, California, on July 11-17, 2009. The deadline of abstract is January 7, 2009, the deadline of paper is January 12, 2009 and the notification date is March 31, 2009.

3. AAAI: AAAI is also a top conference in the AI field as IJCAI. It devotes to advancing the scientific understanding intelligent thought and behavior. Its impact factor is 1:1.87 (top 3.60%).

AAAI 2009 Spring Symposia will be held at Stanford University on March 23-25, 2009. The deadline of paper is October 03, 2008 and the notification date is November 07, 2008. 

4. SIGKDD: SIGKDD is the most important conference in data mining. The primary focus of SIGKDD is to discuss the development in knowledge discovery and data mining, and the adoption of standards in this area. Its impact factor is 1:1.68 (top 6.14%). This conference has a lot of discussions related to social network, for the reason that social network can either use data mining methods and can be applied to it.

SIGKDD-09 will be held in Paris, France on June 28 to July 01, 2009. The deadline of abstract is February 2, 2009, the deadline of paper is February 6, 2009 and the notification date is February 13, 2009.

Right now I am working on Social Network Analysis, which is related to Web and Information Systems (W3), Artificial Intelligence (AI), Data Mining (DM) and Databases (DB).

For the Web and Information Systems area, the World-Wide Web Conference (WWW) is the leading conference community, which focuses on the future evolution of the Web, the standards and the impacts of the Web to the society. The topics of WWW include Browsers and UI, Data Mining, Mobility, Multimedia, Search, Security and Privacy, Semantic Web, Social Networks and Web 2.0 and so on. Social Network is recently been discussed in WWW and has just developed into workshops - Workshop on Social Web and Knowledge Management (SWKM 2008) and Workshop on Social Web Search and Mining (SWSM 2008). From the workshops, I can learn what research about social network is being conducted and then I can get the research trends, which are very helpful for my research work.

In the Artificial Intelligence field, there are several important and influential conferences, such as International Joint Conference on Artificial Intelligence (IJCAI), American Association for AI National Conference (AAAI), International Conference on Machine Learning (ICML) and Neural Information Processing Systems (NIPS). Generally speaking, the first two conferences discuss different topics of AI while the last two focus on specific topics - machine learning and neural information separately. Since social network is a branch of AI, a lot of influential papers of social network can be found in these conference communities.

Also, social network can either use the data mining and databases methods and can be applied in these two areas. In the data mining area, ACM Knowledge Discovery and Data Mining (SIGKDD) and IEEE International Conference on Data Mining (ICDM) are the top tier conferences. And in the databases field, Very Large Data Bases (VLDB), ACM SIGMOD Conference on Management of Data (SIGMOD) and IEEE International Conference on Data Engineering (ICDE) are the leading conference communities. Also, International Conference on Information and Knowledge Management (CIKM) is also important in database communities. During the conferences of DM and DB listed above, a lot of interesting topics and discussions of social network can be found.

Shizhuo Zhu is the only senior IST student in our lab - the Laboratory for Intelligent Agents, though there are several seniors in the department of Computer Science and Engineering. Shizhuo has been in IST since 2003, and right now he is preparing for his Ph.D. dissertation defense. The topic of his dissertation is "Hypothesis-Driven Story Building: A Framework for Supporting Decision-Making as Partial Information Arrives Over Time", which focuses on the attributes of decision-making especially when information is incomplete and changing.

Shizhuo's research interests mainly focus on artificial intelligence and multi-agent systems. And he has attended several conferences as follows:

1. International Human-Computer Interaction Conference (HCI), Crete, Greece, 2003.

2. North East Student Colloquium on Artificial Intelligence (NESCAI), Ithaca, USA, 2006.

3. Conference on Artificial Intelligence in Medicine (AIME), Amsterdam, Netherlands, 2007.

And he has published 15 papers including the ones he published when he was a master student in China, such as:

1. Shizhuo Zhu, Joanna Abraham, Sharoda A. Paul, Madhu Reddy, John Yen, Mark Pfaff, and Christopher DeFlitch, R-CAST-MED: Applying Intelligent Agents to Support Emergency Medical Decision Making Teams. The 11th Conference on Artificial Intelligence in Medicine (AIME'07), Amsterdam, Netherlands, July 09-11, 2007.

2.  Po-Chun Chen, Xiaocong Fan, Shizhuo Zhu, John Yen: Boosting-Based Learning Agents for Experience Classification. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'06), p. 385-388, 2006.

3. Yanqing Ji, Hao Ying, John Yen, Shizhuo Zhu, Daniel C. Barth-Jones, Richard E. Miller, R. Michael Massanari, A Distributed Adverse Drug Reaction Detection System Using Intelligent Agents with Fuzzy Recognition-Primed Decision Model, International Journal of Intelligent Systems, in press.  

A complete list of his publication can be found at http://my.win.psu.edu/szz104/pulications.htm. 

Academically, he thinks himself focus more on technology though he is an IST student. During his research work, he enjoys the happiness from new discoveries while most time he has to bear the loneliness and depression. Maybe this is what Ph.D. students do, in order to find new research discoveries, we have to pay a lot of time and attention and most time we are destined to be painful and lonely. But who knows, maybe I will find the research is worth it.

Both of Shizhuo's and my research work lie on Artificial Intelligence (AI), but he focus on the direction of intelligent agents like decision-making while mine is social network analysis. Though the two research topics above look very different, we may use a lot of same and similar AI thoughts and methods during our research work. 

Note: More details about Shizhuo Zhu can be found at his webpage: http://my.win.psu.edu/szz104/.

Dr. John Yen has done a lot of research work in the AI area and he has published more than 100 papers. He is currently the Vice President of Publication for IEEE Computational Intelligence Society, the Chair of IEEE FIPA Working Group on Human Agent Communications, a Sponsoring co-Chair of AAMAS 2008, and a member of ACM Senior Member Committee.

Dr. John Yen got his bachelor's degree in Electrical Engineering from Honors National Taiwan University in 1980. Then he came to USA for his graduate study, he got his master's degree in Computer Science from University of Santa Clara in 1982. After that, he came to University of California, Berkely and got his doctor's degree in Computer Science in 1986. There, he met his advisor, Lofti Zadeh, who is the father of fuzzy logic.

After graduation, Dr. John Yen spent three years at USC Information Sciences Institute (ISI) and did research work on logic-based knowledge representation scheme with production rules. Then, he joined Texas A&M University and worked on fuzzy logic, robotics and intelligent systems. In 2001, he attended College of IST, Penn State University.

Dr. John Yen has broad research interests, which mainly focus on agents and network analysis. He is interested in research that transforms information into knowledge, decisions and actions, which can facilitate human teams. Also, he works on extracting knowledge from large-scale social networks and modeling the dynamic growth of them.

My advisor is Dr. John Yen, who is associate dean for research and graduate programs. To be honest, I don't know much about his personal life, because he is really busy and I just came here and don't have a lot of chances to meet him. But I get to know some about him during the barbeque we had several weeks ago.

Dr. John Yen is from Taiwan, so is his wife, Michelle. He has two children, a son and a daughter. His son went to the business school of Penn State for undergraduate study, and now he is in the law School of Harvard for graduate while his daughter is currently in California for undergraduate.

As his hobbies, I have heard several times that he likes to sing and he has a set of wonderful Kara OK equipment in his home. He himself even mentioned that when I first met him.

During the barbeque, when we ask him why he came to Penn State from Texas A&M University, he told us once he went to Europe to participate in a conference, and there he met Dr. Lee Giles. When Dr. Lee Giles gave him a description of the potential future of IST, he made a hard choice to move to Penn State.

What surprised me is that he is really considerate of his students. Once when I was having a class, I suddenly received his call, It turned out that he went to the lab to make sure that we new graduate students were all set. He even arranged our tables by himself! Also, he introduced me to other professors he worked with and other students in the lab. I am so moved by what he does, especially that he is so busy. Moreover, he is always so nice and always smiles, and he never pushes us to do something. He gives us enough research space and allowes us to do research work that we like. I feel so so lucky to be his student! 

The College of Information Sciences and Technology (IST) in Penn State University is built in response to the rapidly growing of information. The goal of IST is to be the best at the conversion of theory into practice. IST's flavor can be seen in various aspects. Job placement of IST students can reach 95% with the average salary of $56,250. Jobs in business analysis, IT security, risk services and consulting are popular among IST students. Also, IST provides inter and multi-discipline research like health informatics, medical informatics, emergency response, enterprise informatics and globalization. IST has diverse kinds of faculty and students. The diversity of faculty can be seen from that faculty come from a lot of different countries and different backgrounds such as psychology, physics and astronomy. The diversity of students is similar.

The structure of IST is different from other colleges since it has no departments. But IST has various centers and labs with different research tracks. For example, IST has center for information assurance, center for the information society, enterprise informatics and integration center and so on, while it also has applied cognitive science laboratory, cyber security laboratory, information science and learning laboratory and intelligence information systems laboratory and so on. Also, IST provides 17 research areas like artificial intelligence and informatics, human computer interaction, community informatics.

This kind of structure, though may cause some problems in the management, can encourage intersection collaboration for faculties and students, facilitate diversity in research and can provide more flexibility for students. For me, my research interest focuses on social network analysis, which also employs the knowledge of artificial intelligence, community informatics, learning and innovation, search and information retrieval. Also, some applications that integrate social network analysis and human computer interaction have shown up. So I think I could be a member of several research tracks and I could benefit a lot from this kind of inter-discipline research.