New Lab Members
From DML
Contents |
[edit] A Word About Being a Research Assistant
Working in a research lab may be different from many of the employment opportunities you may have had. For example, if you have been a Teaching Assistant (TA) before, you know that TAs are paid for their presence, that is, you get paid to be in an office or lab, available to students as the need arises. If there are no students, you are typically free to do anything you wish.
Doing research is very different. There is no such thing as "down time" in research. As a Research Assistant (RA) you are not paid for your input (i.e., the hours you spend in the lab) but rather for your output (i.e., the work you perform during the hours you spend in the lab). When one activity is completed, you move straight to the next one, actively seeking to learn and push your research, reading papers, questioning assumptions, discussing with others, formulating new research problems, etc. If there is indeed nothing to do, which would be rather surprising, then you should be off the clock.
Much trust is thus being placed in you as a RA. This is especially true here at BYU since most of our funding (including your salary and mine) comes from the tithing of faithful members of the Church and the generous donations of friends of BYU. In a very real sense, it is the “widow's mite” and as such should be treated with respect and gratitude. Many of those who make it possible for you to be here do not themselves have access to the quality education you are receiving and the many wonderful opportunities we mostly take for granted.
I hope that you will catch that vision and do your very best to make wise and effective use of the time you spend in the lab researching with us.
[edit] Getting Started in the Data Mining Lab
Research is a thing of passion. If the work you do does not keep you up at night as it were, then you ought to find something else to do. Experience suggests that deciding whether something is for you requires a little bit of time and effort. Hence, we have established the following mechanism to recruit RAs to our lab.
Your first three to four weeks in the lab consist of a kind of “orientation” period, during which you familiarize yourself with Data Mining, get a chance to gauge your interest and commitment to the subject, and begin to settle on a specific project you would like to pursue. We will give you all of the help and support you need during that time.
During the orientation period, you are expected to:
- Complete the Task List below.
- Complete a specific research project, usually a small portion of one of the current RAs' work.
- Decide on a broad subject or project you would like to be involved with.
I certainly hope that you will enjoy interacting with our team and will decide to make a long-term commitment out of the experience.
[edit] Task List
This task list will help you complete the necessary administrative work and more importantly bring you up to speed with fundamentals of Data Mining so that you will be effective in the Data Mining Lab.
Administrative Items
- Request a door code from Christophe
- Sign a Non-Disclosure Agreement (NDA)
- Create an account for this Wiki and for the server. Request help from a DML system admin (see Reed or Matt)
- Create a profile page on the Wiki (for example, see Steve's page or Matt's page)
- Start a blog to keep track of your research (you might try, blogspot.com or wordpress.com)
Research Items
- Complete the required reading (below)
- Complete any of the optional reading that interests you
- Begin working on your first project
[edit] Reading
Required
This is a list of over 100 applications of data mining in all areas of business. It will give you a good feel for the breadth of application of data mining.
- Data Mining Algorithm Overview, Two Crows Corporation.
This is a rather technical paper, don't worry if you don't understand all of the details. It will still give you a brief overview of the main classes of algorithms used in data mining.
- Data Mining Methodology: The Virtuous Cycle Revisited, In Mastering Data Mining, Michael J.A. Berry and Gordon S. Linoff, Wiley, 2000, Chapter 3.
This is a rather long chapter, don't get scared. It will give you a nice overview of the data mining process and what is involved in extracting knowledge from data.
This is a recent survey of data mining, its application areas and challenges for the future.
Optional
This will give you links to information about some of our current projects. Keep in mind that this may not be up to date, so ask :-)
