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.

Typography + Science

Okay more like science words (and more specifically material science terms)

I've been messing around ever since seeing...

To be honest, I've always had a little bit of a creative side. I'm still probably more logical than creative in terms of the brain, but I do enjoy doodling and messing around. Furthermore, art provides a further expression of my enjoyment of science. It's also far more fun as medium for conveying concepts versus preparing presentations, papers, and posters.

In particular with the "Grain Growth" and "Eutectic Point", I've been trying to describe the concepts with the letters. It'll probably take some practice, but maybe someday in the future I can make a coffee table book =P

The Expert

I just saw this short YouTube skit yesterday. If you haven't seen it yet, then go watch it first (and then remember to come back here!)

...

In all honesty, I've never been in that exact situation. I have hung out with friends, and in the midst of influenced conversations, have to crush their wonderful ideas. This arises when hanging out with non-engineering and non-science majors.(Although I did have some very creative, silly engineering friends too). Generally I was regarded as the realist, who apparently "ruins" everything.

I did do a brief student consulting jig once, which in itself was a unique and interesting experience. I was brought on because I was an engineer, I was a scientist who had some background in materials, chemistry, as well as toxicology. The product we were looking into was a cleaner that could elminate Norovirus, something that the typical restaurant cleaner doesn't handle. (For those who don't know, various strains of viruses, regardless of whether harmless or harmful, can be quite difficult in terms of their resistance).

I never really thought it could bear fruit (as an individual start-up company).

But I couldn't tell people that yet. We surveyed the restaurant market around us, to whom most weren't even aware of Norovirus, meaning no demand. Furthermore purchasing their cleaner was often bundled with other kitchen supplies and goods, meaning there was difficult penetrating the supply line. But we were forced to present the pluses, to give some positive outlook on the fact that even though there was no market, it could be created. We tried to present it as best as possible as a disruptive technology, but the whole time it felt like a false story.

Ultimately, and thankfully, we suggested that it would be a better product to combine into another suppliers line rather than marketing alone.

But that's my short story in being the so-called "expert"

The Online Classroom

One of my professors has been playing around with the concept of the online classroom. Video lectures are provided before the class, in which students are expected to watch and come to the class with questions. All the class sessions are also recorded as well and posted later for anyone who may have missed class or those who are not physically in Pittsburgh but interested in the lecture content.

It's been an interesting experience to say the least in the "flipped classroom" question where the students ask the questions. My professor has a tendency to ask questions to everyone in the class while he lectures to ensure everyone is following along (where he also randomly selects people). This is perhaps the biggest loss since we have gone from an actual lecture to an online classroom. The general check-up questions he asks are automatically assumed that we have learnt from the video lecture. The video lecture also gives students the advantage of re-watching the lecture several times to catch these details. But even then, the questions asked by the professor test if the fundamental basics are even understood. That is to say, without the foundation, everything else is lost...

The big advantage though, it that for what questions are asked, become a thorough, in-depth discussion. The can go much farther and beyond the scope of the course, and may sometimes to be tied to some people's research a little bit, providing more beneficial information, or just satisfy people's curiosity (and as graduate students, we have a lot of curiosity). But at the same time, these questions may appear off-topic from the class, or complete tangents of unnecessary information for the rest of us. Establishing the right type of questions, seems to be a difficult thing to determine.

I've talked with the professor a little to understand his expectations as well. And he presents the interesting point that he thought graduate students would picked up on the situation better than undergraduates (he's also attempted this previously for another class). Graduate students are much more accustomed to self-learning. This may be reading journal papers to implement understand a new technique, or referring to a textbook to teach themselves something they're not familiar with but plays a major part of their research.

To which I'm guilty for not going above and beyond in my learning... I guess that's a sign to go back to work.

Technology + Smartphones

For those who know me, I've usually been behind the times. So when I upgraded from a LG flip-phone to a iPhone 5C last December, it was a big upgrade for me.

Since getting my phone, I've been playing around with how to better integrate my research/academic life with it. While my friends call me a smartphone addict (which I cannot deny), I've been curious on how I can increase my work efficiency on the iPhone. The general trend of being more on task and responding to emails immediate aren't things that directly correlate to more productive research unlike other technology heavy fields. My curiosity has been how can I integrate my research activities into my smarthphone. Obviously there are the usual things such as maintaining a calendar or checking emails, but here are some other things on what I've found so far:

When I'm on the school network, I can access journal papers (I'm sure I can figure out a way to VPN onto the CMU network, but haven't bothered). There is no Google scholar app (or any general web journal searching app as far as I'm aware), but the usual website in Safari works just fine. While reading PDFs on a small screen isn't favorable, it is doable and can be useful for looking up quick facts in papers you might've read previously.

On the topic of scientific news, I do enjoy the following: Science and Nature. Both contain a mix of actual scientific news in terms of research, but also development of the scientific communities. I do also have the Journal of Material Science (JMS) and IOP Science (the latter I haven't used as much). The few times I have used JMS, it allows me branch out in my field, but within my understanding of material science still.

On the topic of networks, I recently learned about ServerAuditor. This enables SSH into a server to access the command console, and compiling and executing codes. This hasn't proven useful to me yet as most of my codes resides on my personal work computer in the first place. Furthermore I don't run any repeated computer simulations. Most of my codes are for analyzing raw data and therefore involve a significant amount of file I/O as well. I should mention it's even possible to VIM (but in all honestly I normally use another wrapper).

More recently, my friend provided corrections to a draft of a manuscript I'm working on in Word (horrendous right?). Microsoft Office Mobile was recently released as a free-to-download app, and while one cannot edit a document, one can still read all corrections all there. Having a Microsoft account with SkyDrive allows easy access.

Am I truly more productive? Or am I just finding an excuse to mess around on my smartphone some more again? I don't know the answer yet.

Anyways, what have you played around with?

Sometimes I'm stupid...

Actually, it's probably "a lot of times" and not "sometimes"...

Anyways, I've recently been reading textbooks. I took a trip to the library last week to look for some background information for my overview (in topics such as recrystallization and grain growth, or x-ray diffraction). While I am familiar with these topics, I find that I am not knowledgeable. They are primarily things I have learned in class and also see throughout the research papers I've read. Although classes generally only try to cover just enough of the basics to understand the field. While research papers, tend to focus on one particular area, and explain how they have built upon that one specific subject in the field. A combination of these two is generally inadequate for seeing the "big picture" in the field. In particular, as you become more and more specialized, and you tend to focus on a particular topic, you lose sight of all the other research going on. (Although Google Scholar and ResearchGate have started providing me updates of recently published journal articles, which in some ways does help keep one seeing everything going on in the topic).

A lot of times as I'm reading through these textbooks, there will be some small piece of information in which everything clicks; all the various things I've been reading in research papers all fall together in how they are related, or linked up. The other times, I realize I've overlooked something significant or forgotten, or just never learnt. My latest lesson was in diffraction.

I've always been under the impression that electromagnetic waves interacted with the atoms in a crystalline lattice. This not entirely false, but more accurately, the electrons in the electron clouds of an atom interact with the electromagnetic wave (there is negligible interaction from the nucleus). This electromagnetic interaction is also different for x-ray diffraction or electron diffraction. While the obvious differences are things such as wavelength and absorption, x-ray scattering is an indirect interaction, while electron scattering is direct.

Hopefully I'll have some more time to start posting again. We've been rushing for some results recently, which we finally believe we've gotten to an adequate point to write on. So the next few days will be devoted to writing again, in which I have another post for that.

Infinitely writing...

I suppose this is primarily my fault for pushing everything off. So what have I been doing?

I submitted two abstracts for the upcoming MS&T 2014 conference. One oral presentation and another poster. After the pressure of preparing two presentations last time, I decided I didn't enjoy the experience so much. Although what I've also come to realize though is that presenting any research project for the first time is typically a lot of pressure of how receptive the audience will be. That is to say, last MS&T I presented on both the nano-Ni abnormal grain growth features and the information from the 3D nf-HEDM dataset for the first time ever and was already incredibly nervous for each one individually, and compounding the two did not help.

On the other hand, I was certainly nervous when we had to present our GBE results the first time at the 5th Recrystallization and Grain Growth conference, but when I presented on our next study at TMS 2014, I found myself comfortable with the overall situation. I suppose after giving one presentation, I felt I had validation of my work (as nobody from the audience ridiculed or challenged me) and secondly, and maybe more importantly, I had ownership to the work. The second time presenting it then is essentially watching the growth or next step of the research project. The topics for this upcoming MS&T 2014 involve one on the 3D dataset, and a second (poster) on some of the computational homology studies we've performed. The first should be extremely interesting (although it'll more or a less a repeat of the upcoming 3DMS talk), while the second is again, another side project.

Aside from that, I've been writing my drafts for my papers I keep putting off. I finally felt one was in more of a complete form that I should send off to my collaborator (but not my advisors just yet), and mixing in a second one with additional data now. In addition to the papers are my overview document, which I've begun making more of an effort thanks to my roommate, who had to turn her's in today. Her deadline essentially made mine more real, and forced me to sit down realize what needed to be done.

As a result, this weekend has almost been nothing but writing (and of course procrastinating like in this picture shown...)

Graduate Science Cafe and Graduate Symposium

These are two entirely different things.

The first is something a group of classmates in the material science department are trying to start, where we basically dedicate a hour or two each Friday morning to discuss any paper we thought was particular interesting, or maybe just a general science event of the week.

Perhaps the one most notable news of this week, is the report of a 1.5 micron giant sized virus released from an melting ice cap, that was still found to be ACTIVE (note I chose not to use the word alive). Global warming or not, this does stress why we should be sensitive to things such as climate change rather than just saying, "Oh this winter is much colder than the previous one." In this case, while the giant virus doesn't infect humans, it's size scale is something that we as a scientific community have not approached before, and hence the delicacy and requirement to further investigate it.

Overall we talked about a plethora of stuff, and I had a lot of fun with my other four classmates. While no time is set yet for each week, just comment and let me know if you (imaginary readers) are ever interested in joining.

The second was the Graduate Symposium.

Now in it's seventh year, it went extremely well. I wonder if I am more biased simply because I presented as well, and therefore had an overall good impression of it being a participant as well, or that all the student posters and speakers were excellent. The graduate symposium acts to provides all of us a chance to learn about one each other's research, but I noticed one more additional detail which isn't advertised. That is the interaction between students and professors (who are not their advisors). Having several professors stop by and talk about my poster, and be genuinely interested and understand what is going on, revealed to me the breadth of knowledge they have. It does make me wonder how long or how much one has to read before they can engage students to critically think like so, rather than just being receptive to what the poster is presenting.

Good talks. Content or Speaker?

Is it the content that matters, or the speaker? I think the answer is obvious, but for the sake of discussion I'll continue.

After attending many talks, listening to several seminars, giving a few of my own presentations, and finally watching a multitude of YouTube videos, the answer lies in both. A novel presentation can be ruined by a poor speaker (here I'm referring to poor flow, lack of confidence, or wrong emphasis). But similarly it seems a good speaker can still give a bad talk (again not organized, or overall lack of new, fresh content).

Here are some things I've noticed in good talks. The speaker is calm and natural, and nothing else beyond that. It's easy for an audience to detect nervousness, or something that is too emphasized, too forced. Being excited isn't bad in science, after all we're humans and not robots. But forcing excitement lacks the genuineness of an Eureka moment.

A good talk walks you through the whole process. You're not just being thrown theories, data, and results at you, being forced to absorb it. Instead you're suppose to walk through the thoughts of the speaker, run through the same procedures, and see the same results as they did.

And there's a multitude of ways this can be achieved. Sometimes it's breaking down a presentation into simpler concepts for the audience to understand. These may or may not be relevant to science, but as long as the analogy is drawn and the audience recognizes it. Other times you have to show the flow, step by step, and spoon feed your audience in a subtle manner. Make sure they appreciate the math as much as you do, rather than being intimidated by it. 

Often, the good talks come from the older professors. (I have heard excellent talks from students and post-docs, but I am simply stating an observation with the first point.) But then we have to realize how many talks they must have gave. How many "ums" and pauses they made, how many times they thought they gave a poor talk. How many times they were tongue tied, wavering their voice, or their knees shaking below them.

Public speaking comes with experience. Every opportunity you are presented with a talk, you should strive for the best. Even if it all goes wrong, as long as you have learned and taken something out from it, it was not a lost cause. As a scientist, being able to give a good talk is absolutely something we should strive for.

TMS Conference Re-Cap (Part 3 of 3)

The talks
I unfortunately found no posters that stood out as outstanding to me. But there are several talks that really stood out to me. I'll try my best to summarize them. 

This is by no means the best of list, but just the ones that did stick to me.

D.P. Fields and M.Taheri's work with Nye tensors to calculate the minimum GND content. Especially through the use of higher resolution systems such as ASTAR is absolutely impressive. While at first I was threatened by their work, I realize I'll never be on the TEM and whatever they do is a nice compliment to the scientific community of annealing twin formation.

D.J. Jensen's talk on X-Ray synchrotron for investigating old problems such as recrystallization and abnormal grain growth. After hanging around the physics group too much and being amazed by their work, I realize it doesn't matter unless something important is being investigated with a purpose. And while a lot of focus is on crack formation, voids, plasticity, old problems matter just as much. In fact these are often overlooked because we have forgotten about them even though we are constantly being provided with a new set of tools.

G.S. Rohrer's talk on big data. This one may be biased since he comes from Carnegie Mellon and is an excellent speaker. However he really emphasizes a point on data transparency and sharing. While the emphasis is to share data by making it readily available once you're done investigating your areas of interest, it also makes just as much sense that others can repeat the same data analysis on your data for validity.

TMS Conference Re-Cap (Part 2 of 3)

My actual oral presentation

To this day, I'm not sure if both of my advisers have been present in one room to see me speak at a conference. Although I would prefer it in order to receive their full criticism. Even then, only one of them did see my Australia talk (out of a total of four talks) and the other is still at zero. But I suppose they really have no reason to attend given that they've seen all the work, the slides, and I've practiced in front of them before. I wonder if they did attend, would I actually end up doing worse from all the added nervousness.

This time one of them was leaving Thursday morning, while the other had his talk scheduled at the exact same time at 8:30. While I was hoping to have a larger crowd, especially those who work in the similar area of recrystallization and grain growth as well as annealing twin formation in the context of grain boundary engineering, going on the last day AND the first thing in the morning is never easy... (That being said, I do know people who work in my area who gave talks later in the afternoon, but unfortunately they didn't show).

The room consisted of primarily the other presenters of the symposium and then my colleagues (really I should just say friends). Overall the talk went extremely well, and although no thought provoking questions were asked, the audience was definitely curious. My classmate, who has no idea what I work on, said in one talk he got a really good idea and fully understood.

I emailed my advisers to let them know of the audiences' questions and response. And one of them informed that someone else (a professor) in the room thought I did a good job. Furthermore the professor even offered to ultrasonic fatigue some samples (although in all honest we're not so much concerned on the actual performance of these materials, but just characterization). So again, something must've gone correctly.

One thing I did keep telling myself was that if one has done good work, then others will come and listen. No one will criticize good work and will only aim to help build on it via questions and discussion. Repeating that in my head helped reduced the nervousness (which I still have every time I've talked), and despite stuttering and mumbling a little, everything went well.

TMS Conference Re-Cap (Part 1 of 3)

Yes I'm over a week late...

Overall the TMS 2014 conference was an excellent experience, especially being my first TMS ever. Although rather pricey, that was a fault of San Diego and not of the conference itself. I'll divide this post up into three parts...

Student Poster Session
This actually turned out to be a very enjoyable experience. Although it conflicted with the talk times, there were still several people who passed by. And while the majority were my own classmates, it was fun to show them essentially what I count as a side project.

The poster contest also implied that there were judges who talked to each presenter to understand their work. Although we we're suppose to get a total of four judges, because of me running in and out, I only met with two of them. 

Talking to a judging panel was surprisingly more nerve wracking than I expected. I suppose it's primarily the issue that you know someone is critiquing what you're presenting, versus just reading it for information. 

Furthermore, it isn't necessarily your work that is being critiqued, but how well you're explaining your work. Again, in some sense this reminded me of the three minute thesis. One judge asked me for a brief run down, while the other asked me for a sixty second version.

This really tested both your ability to convey information, but whether you had the right stuff on the posters to support what you were saying. Ultimately, my poster was often felt overshadowed by my neighbors awesome poster, which was also a MC model. But she won both the division and best of show!

My own rewards from the poster session is that someone (one of the judges) actually told the other that I had something really interesting and to check it out. So I must've done something right in this last minute poster I made...

More to follow-up tomorrow! 

Valentines Day Research Special

I wrote this a long time ago, specifically on July 12, 2012, but seems appropriate to post for today:

Foreword

This writing goes beyond the scope of any general thermodynamics whether undergraduate or graduate level. While the reader is expected to have a foundation in thermodynamics, it is by no means necessary to enjoy the contents of this piece. There will be references to other fields, such as statistical mechanics or phase transformations, but for the most part this has been written to be accessible to all.

Introduction

Thermodynamics is governed by three laws, the most relevant to everyday life is that entropy, a measure of chaos, or more correctly disorder, always increases in the universe. Typically, a college student's response to his messy room is that it's natural as the room shifts towards a disorder state. Such interpretations of entropy results in an unclear definition of what exactly entropy is and when we should refer to it. We should clarify that for the purposes of this writing, that entropy should only be regarded as measure of disorder of states, and more specifically the most probable state is the most entropically favorable one. 

On the other hand, we are raised with this notion of true love as a result of childhood classics and Disney movies. If we apply the concept of true love or a soulmate in life, then, based on the current world population of 7 billion people, the probability of us finding this person is 1/7,000,000,000. It's more or less impossible! Yes in science there are other occurrences of even lower probability, but we digress. To find that one correct person for us is extremely unlikely, and application of statistical mechanics to such an idea would suggest that it is entropically unfavorable.

However, a controversial argument that arises is what happens if we do find this "true" love. The person that we would pack up our bags, move, and go the end of the world with them. The one who in a single instant causes us to change everything on how we approach life. If such an event does occur, it is very clear that such a person introduces large deviations from the norm for us. Our structured, ordered lifestyle is suddenly destabilized, and we again use that classic word: entropy. So to summarize the points we have stated, the process of finding the person is entropically unfavorable, but the actual event of finding the person is entropically favorable. How do these two terms balance each other out as we look at Equation (1).

Eqn 1 -   Entropy of Universe = (Entropy of finding True Love) + (Entropy of meeting True Love)

Note that there is a distinction between finding and meeting based in this context. Furthermore, this equation looks only at the final state of the reaction. In reality, a person needs to meet and date a few, several, or many individuals before they possibly find their love. Again, I have stated that this is unlikely as it is impossible for someone to date all 7 billion people, let alone meet all of them. The modified equation would be to add additional terms noting each and everything dating/relationship encounter we have in our lives. This will of course, vary from person to person depending on their lifestyle, as reflected by Equation (2).

Eqn 2 -   Entropy of Universe = (Entropy of finding True Love) + (Entropy of meeting True Love) + (Entropy of relationship One) + ... + (Entropy of relationship X)

What are the values for these additional terms? Well the easiest approach is two view two people as essentially two atoms. Initially we have two individual states which we are now attempting to bring together as a pair with a positive bond between them. Such a requirement requires energy input from both people in order for the reaction to occur. The eventual fact that these people do break up in their journey to find their true love immediately indicates that such pairings are not the lowest free energy state. An interesting point is that going into a relationship introduces entropy as we make sacrifices and adjustments to our lifestyles, but similarly, if a stable state is established, then breaking up results in further entropy as we find our lives in a further mess in attempting to pick up all the pieces. This is especially more prevalent depending on the length duration of the relationship, but this introduces a kinetics issue, which will not be discussed. To conclude, the entropy term to each of these relationships is positive. 

To be continued...

 

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Which obviously never happened =P

Do not disturb - Busy Writing

This was originally a hand-written. My computer had crashed in the morning and I decided to use pen and paper to use whatever thoughts came into my head. Therefore the first line which states, "write write write" does literally refer to writing, where as "type type type" maybe seem more appropriate now.

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Write write write. That seems to be a major part of my life now. Whether it's writing down the next draft of the paper (which I'm still at draft one for one, and draft three for the other), or writing my overview document, writing abstracts for upcoming conferences, or writing weekly reports. And on top of all that, there's also this blog.


But there's also an additional amount of subtle writing skills required as a researcher, which adds up the to writing total. These might be the chicken scratches one makes for notes and ideas that come from reading a paper or a spur of the moment. While short and concise (in addition to messy), they need to contain enough content just to re-spark the same idea when we glance at it again at a later time. There are the do-it notes to yourself, which vary in degree in tone, some just stating a task to be completed, while others are more stern, telling YOU to finish something. At that point one has to wonder if they're on the verge of insanity for talking to themselves in such an aggressive manner.

There are the outlines for presentations, as well as writing in the presentation in itself which turn out to be a challenge. Too many words and instantly the slide or poster becomes too cluttered, too full, and ironically, too illegible. Too little and you wonder if anyone really took anything out of what you just said, or even if they understand you to begin with (this is a frequent problem of mine).

There are many moments of writing  required of researchers and scientists. Sometimes they're immediately obvious to us, such as the requirement to write and publish papers (although one can also attempt to avoid that). While other times they are more subtle and are in our every life. Ultimately we'll find that there are no shortcuts to getting better at any of these things, but to continue practicing and attempting each one.

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In other words, this post has been written to motivate me to continue blogging.