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Internship 2: One Possible Niche for High-School Students

Internship 2: One Possible Niche for High-School Students

By James H. Choi
http://column.SabioAcademy.com
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To Sabio Parents,

Research, by definition, deals with cutting-edge knowledge. Looked at another way, being involved in research implies a mastery of existing knowledge. How can high-school students participate in research when this tall order is placed in front of them? It’s impossible for even a high-school student considered a “genius” by his peers.

So, how in the world can your high-school student be involved in — and actually contribute to — any scientific research as an intern? Simple: By finding a niche. Niche-finding is the same way small start-up companies thrive among industry giants.

Scientific research requires people doing many types of work. Examples include data analysis, statistical distribution fitting, and visualization. Anyone can use Excel to plot simple graphs. But if the data becomes large, multi-dimensional, or multi-modal (combining data from different measurements), then the data analysis and visualization need professional expertise.

Some researchers and graduate students, like those in engineering, physics or mathematics, are experts in data analysis. But others, such as those in biological or medical sciences, are often less well-versed. As medical technology improved, for instance, biological and medical fields started producing more detailed data (e.g. MRI) or went digital (e.g. microscopes), thus creating a flood of data in biological and medical research by order of magnitude. This is where your high-school student can come in.

Analysis and visualization of biological and medical data is one niche a high-school student can learn and then perform at professional level. This is not exactly as simple as it sounds. For anyone to competently analyze and visualize scientific data, he or she has to know not only programming, but also signal processing, image processing, statistics, etc. These are several semesters’ worth of college-level courses.

How can a high-school student master all those topics?

It is not possible. Writing FFT (Fast Fourier Transform) code, for example, requires knowledge of calculus in the continuous domain, and then difference equations in the discreet domain — both of which are well beyond most high-school students. The same goes for the filter design in image processing.
https://i1.wp.com/dl.dropbox.com/u/6378458/Column/Info/English/SpecialEvents.gifThen how can a high school student master, let alone contribute to research?

By using tools that already provide algorithms and routines as commands. While it is important to know how to write FFT, students can learn it in college. For now, understanding how to apply FFT by calling commands, developed by professionals, is sufficient. Of course, students must know what FFT is for and where to use it. Smart high-school students can learn that level very well, speaking from my experience.

The same goes for 3D visualization, animation, pattern recognition, and data filtering. As long as a goal is clear and the data provided, a smart high-school student can summon relevant tools, commands, and algorithms to accomplish the goal. For example, a student could take a 3D MRI image of a patient’s head and overlap it with 3D PET image of the same patient’s head without understanding the z-plane, or how 3D graphics are rendered, how MRI/PET images are acquired. And the student doesn’t have to know what the results mean. It is for the research director to decide.

Many biological and medical laboratories are in need of data-visualization wizards. Although most instrument makers provide software for their own data, research always goes beyond in scope (such as a need to overlap with other manufacturers’ image data) or capability (such as a need to analyze a change over past six months instead of a single image). Thus, the labs need someone who can take, analyze, and visualize any data the way the researchers specify. As biological and medical instruments become evermore digital, and as ever more disparate data are associated together, the ability to read data and make associations becomes extremely valuable, even essential to research.

Since this analysis and visualization will play a vital role in the research, the wizard who serves up the graphics as specified has a chance to be recognized as a contributing member of the research, and thus the chance to appear as one of the authors.

Needless to say, this deep involvement at the heart of research and constant interaction with the researchers would make it impossible for the student to learn nothing or to take no personal interest in the research. In fact, it is common for students to formulate their own hypotheses about how the data should behave and make their own predictions. Such hypotheses might become the topic of the student’s own science-competition research. With data on hand, and already equipped with an intimate understanding of the topic, the student has only to analyze and visualize the data the way he or she hypothesizes to have a unique research topic.

This is how Intel and Siemens Science Competition winners are born. They do incredible levels of research—often at Master’s or Ph.D. level—because they didn’t have to start from ground zero.

What I described above is not the only niche-skill a high-school student can learn to contribute to scientific research as an intern. But I discuss this because this area is my field of expertise, and I’ve had great results preparing high-school students.

No matter what field or specialization students seek, they should know what skills are in demand and “masterable.” They must prepare only for ones at which they can excel. They cannot do this alone. A mentor in the field should guide the students’ preparation for six months to a year. But this extra work is what prepares a high-school student for obtaining and contributing to a research internship.

Sincerely,

James H. Choi

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