The "Ideal" Thesis

I attended my collaborator's defense this past Wednesday, which was an incredible piece of work performed over the short course of three years. While the thesis itself, 200-pages, was indicative of the considerable work done by him over the last few years, I think more impressive was still the nature of the work.

Two of the experiments were performed in collaboration when I visited France and another when he visited Pittsburgh. While some of the other experiments were not performed by him at all, but by former students but all the analysis was new and performed by him solely still in relation to his project. Certainly  the experimental design, execution of the experiment, and data collection takes a large amount of time, but analyzing the data afterwards is just as much of a feat afterwards as well.  In particular, for the nature of our data, orientation maps obtained through electron backscatter diffraction, primarily contain grain size, grain orientation, and grain boundary misorientation information immediately. However many other things can still be analyzed on a local level (especially in our in-situ experiments), such as observing grains nucleating from the areas of highest kernal average misorientation, indicative of concentrated dislocations in an area, or the roughness of the migrating grain boundaries.

But in addition to the experiments, he still managed to perform simulation experiments as well by introducing anisotropy (in particular large variations in anisotropy for a twin boundary) in level-set and phase field methods. After achieving this, this was furthermore implemented into a microstructure featuring twins under grain growth, and observe the evolution of the microstructure and compared to observations made from the experiments performed. Anyone who has worked with computational materials science understands these developments are quite time consuming.

So finally one impressive achievement from his work is that he did a balanced amount of simulation and experimental work that complimented one another. It is often more likely to see thesis that are completely focused on experimental work, with a touch of computational work to support the experimental work performed, or vice versa a computational thesis with some experimental work to back up the results of the simulation.

To achieve both in such a short amount of time (3 years), is an absolutely amazing feat.

Conclusions

This is an old thought, but I had a discussion with one of my advisors a while back on what a conclusion entails. While it's true that the goal of the conclusion is to summarize the findings at the end, what makes an effective conclusion from a poor one?

In particular, for something on the scale of an thesis document, where many experiments have been performed and many new findings have been made, what are the key points that the author wants to hit upon? What I learned from my advisor is summarized here.

Conclusions can be divided into three categories:

1. New knowledge
2. New wisdom
3. New technique

Developing, testing, and reporting a new technique should be straight-foward. (Most) papers will not have this conclusion depending on the nature of the work performed, as such I won't discuss it anymore. New knowledge and wisdom on the other hand, is more challenging.

First, what is the difference between the two? New knowledge refers to new information that has been obtained from the findings of the experiment. For example, one variable showing a positive correlation by modifying another variable that was previously not known, is new knowledge. This constitutes a large part of scientific conclusions.

New wisdom refers to a new understanding based on the analysis of the knowledge. Following the example above, the two variables of interest show a positive correlation that is explained by how the system is responding to the input variable to generate the output variable. New wisdom is more challenging and involves the creation of models, theory, or etc. to find that understanding. 

The two are not always distinct from one another either though. New knowledge and new wisdom can be coupled together based on how the experiment is performed. In particular, this will be the case if we have already hypothesized how two variables are related, but no experiment has been performed to confirm this relationship.

Knowing these three types of conclusions have overall allowed me to streamline my writing and presentations, and more effectively bring closure to an experimental finding (despite the fact that there is always more to be done).  

Taken from another source, but a more general sense, knowledge is awareness of the right facts while wisdom is understanding those facts and coupling it with good judgement and common sense. Or alternatively:  

Knowledge is knowing that a tomato is a fruit while wisdom is knowing not to put the tomato in a fruit salad. 

Story Collider

Story collider is an event that allows scientist to share when they fell in love with science. The key is that it must be a story, it must contain a beginning, middle, and an end. It will typically involve a change. It is not a lecture. It is not an oral presentation. It is a story.

I submitted something that probably didn't conform to the rules. Although I don't even know if I submitted it correctly anyways as I never received a rejection either. Ultimately I'm posting it here though:

The other morning, delirious from waking up at 4:30 to catch a bus to the Dresden airport after attending a wonderful conference, I sat in the terminal waiting for my plane to start boarding. As I sat there, the sun started to slowly rise beyond the horizon lighting the sky into a golden hue. And like a movie moment, I watched the Boeing (or maybe it was Airbus) 767 illuminate from those golden rays.

In that movie moment, I thought to myself, "wow."

I sat there dumbstruck and amazed by what we, humankind, have created. Commercial jetliners capable of carrying people across countries, continents, and oceans. The years of trial and error by the Wright  Brothers just to develop the first flying machine. The years of research and development to be where we are at now. It's taken physicists and aerospace engineers who study flight and aerodynamics. Mechanical engineers who understanding loading and cabin pressure. And then my favorite, because I am one myself, the materials scientist and engineers who have developed the right turbine alloys, landing gears, and a wide assortment of other materials to send essentially these gigantic pieces of metal into the air.

How far have we come along in technology?

A little later I was in the Frankfurt airport for my layover. Still delirious of course as I hadn't found a source of coffee yet. But I saw a little girl with her mother. The mother stepped onto the moving walkway completely fine, like the rest of us would. But the little girl looked in terror at the moving platform, as if the wrong step would end her life. One second passed, then two, and three as she kept watching the floor move beneath her feet. She was waiting for the perfect moment, a pause maybe, to get on, but it never seem to came. Then she bravely put one foot forward and panicked as she became stretched out by her moving foot to her planted foot. She grabbed on the arm-rail, which of course was also moving. And finally in the last moments of desperation, she lifted off the planted foot onto the moving walkway and everything was alright again.

Newtonian mechanics, general relatively, whatever you want to call it.

Of course when I was a child, I didn't know that was what it was called. I was simply confused, scared, curious as to what was happening. That curiosity drove me to look for answers, sometimes in the classroom and sometimes at home (I was very fortunate to have an encyclopedia set). For me, and probably many of us, it was enthralling to learn how and why things worked they way the did. I believe this is a natural trait given the curiosity we have as children with developing minds. But for me personally, I don't think there was ever a turning point where I said, "Wow, now I love science." For some inexplicable reason at the time, I have always loved it, that somehow there existed answers for the questions I had.

As an adult now, I've become so hardwired to either accept the certain laws, like gravity, or certain technologies, like transistors, have always been around. I've become dull, numb, and fail to appreciate the efforts of scientists, researchers, and engineers before us. And unfortunately once I was finally awake, I failed to notice anymore more spectacular events during my trip departing Germany.

Every once in a while we have our own "Eureka" or "Aha" moments in our own research that continues to satisfy that curiosity. But the fruits of our work have only come about by those before us. This is often far too overlooked on my part, but when I make these realizations, there's something really special about these moments. Science is the progress of a collective, community, process that represents one of the epitomes of humankind. Of course that was something I could not see as a child, but inherently I was doing what I just mentioned.

The two stories I just told you earlier are just one of the many moments that remind me of that. The knowledge I have, the technologies I enjoy, are only because others have taken the same path I am taking now. That is why I love science.

"I don't know"

Like everyone else, I look up to my advisors a lot. They are my role-models as a scientists, mentors to my research career, and advisors to my PhD track. While they are not the biggest names in the field of material science and engineering, they certainly have an untouchable aura around them. (Tony being the father of texture is often our joke, while I assume that Greg came up with the entire idea of grain boundary character distributions). They've produced tens (probably hundreds) of publications, and have given even more talks. Written review papers and chapters as premier leaders of the field they specialize in. And the two further impress me by continuing to do their own research all while advising me and several other students, traveling for conferences, teaching classes, and applying for grants. When I ask a question, I will most certainly always get an answer (usually the response is, "Oh it's already been done," or, "I doubt there will be an significant influence from [x]", and lastly, "Well I think this is the next direction we should go to solve this.") Every time, they are right.

Greg, as an expert, giving an keynote talk.

So very rarely, when the words, "I don't know," come out, I suddenly become very confused and lost. Have I somehow broken research? Asked something that I shouldn't have? What do you mean you don't know the answer when you're the expert in the field?

Then the silence begins.

Arms crossed. Eyes closed. Fingers tapping on the desk.

And the silence continues.


"Well, if we really want to figure this out, we should probably try [y]." Pause, "Yeah, let's definitely try that first. I'm not entirely sure, but if it gives something, it should help explain [z]."

They've fallen from the pedestal I've placed them upon.

They're human, like the rest of us.

When this occurs, I realized how many years it's taken for them to become the experts of the field. How many experiments they must've performed. And how many hypotheses must've failed before getting to where they are.

It makes me feel infinitely better that I don't have the answers to everything, that I don't know everything, and that I won't do everything right. It takes time, and even then, like them, I still might not know.

I did my very best to find the most "normal" looking picture of my advisor to emphasis this point

Playing with Dream.3D

Forewarning - This post is mainly a plug for Dream.3D

The acronym Dream.3D stands for Digital Representation Environment for Analyzing Microstructures in 3D. I bring it up because last week I attended a workshop dedicated to analyzing 3D data, which has become an integral part to advancing material science.

The 2nd 3D Material Science Conference also occurred two weeks ago, which my advisor gave a talk for me. The first 3DMS conference was held in 2012 (when I first arrived at CMU). The fact that there is an individual conference dedicated to this area of material science emphasizes the interest, growth, and advancement of the field.

Anyways, Dream.3D is a (free) software for reconstructing microstructures from EBSD scans or other techniques, analyzing crystallographic and morphological statistics, as well as surface meshing. Another powerful tool integrated with Dream.3D is the construction of synthetic microstructure, although this is something I hardly use.

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The software can give a lot of information. The challenge is always how to interpret the information though. I've been playing with my own 3D datasets of high purity nickel undergoing grain growth. In particular, I've previously been quantifying twins in a very a qualitative manner, describing them as edge twins, plates, (or at times even hamburgers!). I would like to implement a more quantitative metric though, which so far, the results of Dream.3D have provided a B/A and C/A ratio.

Obviously, as most twins exist as plate-like structures, the B/A aspect ratio (mis-labeled on the graph) will be rather uniform, but the C/A ratio will be more skewed, confirmed by the histogram populations.

Another part I've recently played with is trying to look at recrystallization interfaces. Although only a single grain is shown (which has been poorly reconstructed), it is obvious that there is a large degree of curvature and misorientation across an individual grain boundary.

Whether any of this holds significant information, I have yet to tell. As always, all tools are only as useful as the user can make them to be. The advantage of Dream.3D is processing of the volume of data, which in my previous experience, can be large, cumbersome, and slow to deal with at times. 

Give it a try and see if it can bring anything new to your work: Dream.3D

NSF Funding and Budget Allocation

Looking at the blue points, the NSF budget generally been increasing over the years with a few slight stagnations here and there. On the other hand, when adjusted for inflation rates according to 2014 is shown by the red points (this is generally how I think, which is apparently wrong). The proper adjustment should be adjusted to the rate of 1998, which is depicted by the green points.

In which more obvious behaviors are presented, in particular where the stagnations generally correlate to declines in the US economy. From 1998 to 2004 we see a steady increase in allocated funds, where starting after 2004 there starts an oscillation behavior in funding regardless of how you interpret inflation.

To be honest, while this image is rather stark on the state of funding, it was surprisingly less bad than I thought. Of course, I'm only looking at one scientific funding department, and I'm sure the NIH has a much more depressing outlook. We've been talking about government sequestrations, how funding has been cut back, and so forth, but in general, the overall trend is increase, albeit far slower than I would like to see.

I'm sure if we look at how funds are allocated as a percentage of government spending, it would produce a very different image however. That I have not looked into yet...

So why do I bring this up? More recently a friend of mine went on a Congressional Visit Day as representative of CMU for Materials Advantage. Just yesterday I attended a talk given by Kevin Finneran on whether the National Academy of Science is still relevant or not. I've been reading a number of articles on the Post-Doc population and lack of jobs for PhD degree holders after graduation.

And I've started realize these problems cannot be solved with science alone. It simply won't happen, because research isn't free (although PostDocs do provide an awesome bang-for-the-buck is what I've been reading), and we also only have a limited amount of funds. Those funds are currently dictated by policy makers and the government, which we as a scientific community apparently choose to ignore and not partake in. In that sense, that's why I do decide to write here though. Because I am openly providing information (albeit pointless) on my work, in which maybe someone in the general public may happen to be attracted to. I believe science funding can only be significantly increased if the public genuinely desires, and hence as scientist, we need to better communicate with them.

One thing I'll miss...

When I graduate are probably seminars.

And by seminars, I'm including both my own departments and everything else I've attended. To be honest, I absolutely love the campus environment and how readily information and knowledge is available. Yes, you can drop by a class, but without being there from the very beginning, this makes it very challenging to follow it somewhere in the middle.

Seminars on the other hand are designed in such a way that allow access for a wide range of audiences depending on their major and background knowledge. Therefore, there are little barriers of entry to free learning.

In particular, the one I attended yesterday was titled: "Context and Connection", being held by the School of Architecture. Short story, I learned some amazing designs for living modules of polar science camps (primarily in Antarctica). Secondly, in relation to my major, these designs are partially feasible only because of improved materials (such as fiber-glass structures) that provide both the adequate strength as well as insulating conditions. Aside from a material standpoint though, the design perspective needs to take into account human interactions on a daily basis such that they feel where they are living can be safely called "home".

Anyways, I've learned a lot of random things since coming to CMU. The seminars provide an escape from my field, in particular the further in connection they are. Other seminars I've attended included the department of music with a famous pianist, listening to a orchestral director held by the management of the arts, two students attempting to license out their design versus a startup, and so forth. I don't always understand or remember everything, but they continuously broaden my perspective of the world.

Get out there and learn, through whatever means and mediums work best for you.