Getting Into Graduate Studies at the Best Engineering Schools
In my twelve years working in robotics in the academia, I know there are very few things that excite a professor more than documented experience and a sense of direction in a student. Whenever a talented researcher is identified in the application pool, very few things, not lower GPA or GRE scores, can preclude him/her from receiving an admission. This is because the professor will fight not only for the student’s case, but also for an attractive fellowship package. My goal here is to impress upon the undergraduates to think from this frame of mind.
This doesn’t mean that grades and test scores are not important. They are. There is always a minimum threshold that has to be met. But the same amount of effort one puts into coursework and GRE prep classes should also be put into excelling in undergraduate research programs, for example, by being a co-author on a conference or a journal article.
WHAT CAN I DO?
As a start, collect information about the specific topics you are interested in, about 2 or 3. It is important to start thinking about this around the first semester of your sophomore year (2nd year of university). Ask professors and look through lab websites. The hope is to either have an internship or work at a lab the summer before your junior year (3rd year). This way, you can continue to build upon the experience through the fall and spring of junior year, or in the case where the topic no longer suits you, to pursue the second topic in the list. I have to caution you about switching or pursuing multiple fields: undergraduate is hard; you need to get good grades, have a social life, while figuring out what you want to do. As such, you probably have enough time for at most two additional academic related activities.
It is important to know that the topic selection does not have to be a lifelong decision, only a two-year decision. The dirty little secret is once you are accepted to a graduate program, you can switch topic, and in some cases, field. Furthermore, I have even seen people switch fields between graduate studies and postdoc. The point here is to make an honest judgment of what you can see pursuing up to the first year in graduate studies and evaluate that choice at that point. Of course it would be wise to plan for this by selecting a school that excel in multiple topics and can provide a variety of opportunities.
On the bright side, more so than previous years, there are many opportunities for students to expose themselves to the research culture at an early part of their undergraduate education. For one you can join student-run clubs. Being involved in undergraduate robotic competitions – robot soccer, underwater vehicles, etc – as team member, leader, and mentor, I gained so much knowledge by competing against other universities. More importantly, I developed the right research habits, i.e. to be focused, more persistent, and have a sense of understanding of what it takes to succeed.
In addition, there are also three other options: company internships, working in a research lab, and independent studies. Because there are so many factors that are out of your hands, such as whether the professor is on sabbatical in the summer, my suggestion is to try as many as you can. Always show the utmost enthusiasm because for many that is all you have at this stage, and hopefully some of these opportunities, paid or otherwise, will open up.
As a last resort, depending on your field, you may be able to do independent studies with tangible deliverables. Keep in mind what is the most attractive experience in your field. For example, computer science value experience in large scale projects or being able to use well known code base such as the computer vision library OpenCV. If I hear an undergraduate able to produce a system using OpenCV by him/herself, this shows ability to understand and sift through large amount of information and get things done. I would at least spend a few minutes to hear what else that student knows. Of course, the barrier of entry for computer science is very low. You only need a computer and an idea.
It is important to note, however, from experience, I come to the conclusion that research work that amounts to satisfying results have the highest weight in the application process. The difference here is that professors have a reputation to uphold, while other mentors (supervisors, or even CEO of a company) are less concerned about that. In all cases, maintain relationship with your mentors and tell them your goals early. They can refer you to the people who can provide you with the right future opportunities.
EXCELLING AT YOUR WORKPLACE
This is by far the hardest part in the process: to maintain enthusiasm. I have seen many students with great plans and potential early on, but in the middle of the process things seem to fall apart. First of all, life happens; and when it does, you simply have to take care of it. It is a given that things such as a major surgery or attending an ailing family member will take focus away from your research. However, if you just do as much as you can to continue progress (be creative), you would be glad that you did and you will be a better person because of it.
Another scenario happens more often. The atmosphere in the workplace is not conducive for positive rapid progress. Whether it be less support from a mentor, or lab partners holding you back, you depend on the competence of others to fuel your success. In a big project where all parts have to be finished for the whole system to come together, it is a very difficult situation to be in. As a mentor for student group projects, I have seen many failures where the group members just give up. But I also have seen many occasions where people rise to lead others and salvage the project.
And the last issue that could arise is from the inside: you seem to lose passion for it. First of all you have to clearly identify that this is indeed the case; never mistaken a bad situation with losing passion. It’s true your surrounding can affect you, others don’t work as hard, the project doesn’t seem to take off, but as long as you make the most of it and convey it to your supervisor or whoever will be writing your recommendation letter, it will turn out fine. On the other hand, if you truly want to try other topics, the right thing to do is to inform the people who depend on you right away and find a way to finish your work or at least part of it. This shows integrity.
THE STATEMENT OF PURPOSE AND RECOMMENDATION LETTER
When it’s time to submit your application, you can then reap the benefit of all your hard work. By thinking about how your involvements fit in your graduate studies plan, your experience should readily be translated into a very compelling personal statement. One way to ease the writing process is to write a few paragraphs about each experience right after you finished the internship or the summer research program. This way you have a lot of material to write about and you can focus on putting together a profile of a passionate, full of initiative, creative student, backed with track record. Write about the tangible accomplishments, people you met, and memorable moments that you felt enrich your experience and add to your education.
Focus on what it is that you want to do and why you want to do it. Through this your whole application will be coherent and, in turn, people will understand what you are passionate about and be convinced because of the steps that you have made toward it. In the end, when it’s time to send out your application, accepted or not, you know that you have done your best. Good luck.
Christian Siagian received his Ph.D. in Computer Science in University of Southern California. He is a post-doctoral scholar at University of Southern California and California Institute of Technology. He leads the Beobot 2.0 project which aims to create an autonomous mobile robot that can operate in the unconstrained urban environments, without human supervision and/or alongside policeman, fireman, or coworkers. Dr. Siagian’s research interests includes robotics and computer vision, in particular in the domains of robot localization and navigation, scene classification and recognition, visual attention, gist, object recognition, and human-robot interaction.
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