Be as broad or specific as you want...
I am a PhD student studying the molecular biology and ecology of bioluminescent organisms, specifically Glow in the Dark mushrooms for now.
I'm working in condensed matter theory. My research is mostly studying strongly correlated low-dimensional (1+1D) quantum systems, with a particular focus on the physics of out-of-equilibrium quantum systems.
I can give it a try! Condensed matter physics is rather broad (and probably, population-wise, the largest area of physics), encompassing the study of solids, liquids and generally squidgy stuff. As a theorist, I study simplified models of crystalline solids (materials where the atoms form a periodic structure) in an attempt to determine their properties (do they conduct? is it magnetic? etc). The particular models that I'm most interested have two main properties: (i) the constituent particles (e.g., electrons) of the model are strong interacting; (ii) these particles are confined to move back-and-forth in a single direction. The first property makes these systems difficult to study as interactions often have surprising effects (properties of the interacting system can be completely different to the non-interacting system!) and we don't have many mathematical or computational tools to deal with such problems. The second property is rather special, but not completely crazy -- we do see real materials where particles are (approximately) confined to move in a single direct (through, e.g., quirks of the crystal structure). This restriction also saves us a bit, as there are a number of special technique which allow us to make headway with such problems, which don't apply when the particles can move in more than one direction. The out-of-equilibrium problems I study basically aim to address the following question: if one dumps a load of energy in to a quantum system very suddenly, what happens? The natural conjecture is that if one leaves the system alone for a sufficiently long time, it should settle and become hot. In fact, it turns out that the story is quite a bit more complicated than that, and these kinds of questions are being studied quite a lot at the moment. Tldr: I study collections of particles confined to move along a single direction which interact strongly. We want to understand how (and if) these systems get hot when you suddenly dump some energy in to them.
What are the other options? Emit light / heat? Form a different crystalline structure? ...?The out-of-equilibrium problems I study basically aim to address the following question: if one dumps a load of energy in to a quantum system very suddenly, what happens? The natural conjecture is that if one leaves the system alone for a sufficiently long time, it should settle and become hot. In fact, it turns out that the story is quite a bit more complicated than that, and these kinds of questions are being studied quite a lot at the moment.
That's a really good question. And I'll attempt to illustrate it with probably the simplest example, although I apologize if this is too high (or too low) level! Let's start first by defining quite what I meant when I said
The lower axis here is the energy (E) minus the "Fermi energy" (E_F) which is defined as the energy of the highest-energy electron at absolute zero temperature (so don't worry about seeing a negative axis!). Now, lets consider a bunch of non-interacting electrons -- the electrons just float around, not seeing one-another or anything else. Of course, this isn't realistic, but we're theorists, so we can get away with thinking about such things. Imagine now that I "dump some energy" into my system by adding an electron with energy 1; what happens?
Well, we have some electrons that float around, not seeing one-another and not interacting. This means that there's no way to reduce the energy of the electron you've added, so no matter how long I wait, there'll be an electron with energy 1, and I'll have a non-thermal distribution (it'll look like the Fermi-Dirac distribution above with a jump at energy 1). In physics, we like to say that there is a conservation law -- the number of particle at each energy is conserved in this simple case. Of course, this isn't very interesting so far as everything is non-interacting and not terribly realistic. Now, what happens if we turn on interactions between the electrons in our system? Interactions may allow us to redistribute energy: if we have an electron with energy Ea and
another with energy Eb we can collide them and scatter to energies Ec and Ed provided Ea + Eb = Ec + Ed, e.g. energy is conserved. Notice now that we only really have one conservation law -- that total energy is conserved. In general, it is expected that such processes will eventually lead to thermalization (e.g., the Fermi-Dirac distribution at a suitably higher temperature, fixed by the energy we dumped into the system). Now, as a theorist, I want to test this expectation (let's call it a conjecture). So I turn to my favorite interacting model that I know how to exactly-solve (there are not many of these) and test this conjecture. What do I find? I find that my exactly-solvable model doesn't thermalize: when I inject energy into the system I do not recover the thermal distribution. What gives?!
Well, it comes down to what I previously mentioned -- conservation laws. These special exactly-solvable models are solvable precisely because they have lots of conservation laws (in fact, they have the same number of conservation laws as particles) and this puts very strong restrictions on how the particles can redistribute energy around and eventually leads to a non-thermal distribution. Figuring out what this non-thermal distribution is and how to compute the values of "measurable quantities" are serious areas of research at the moment. This comment ended up much longer than I anticipated, and I'm not sure of an adequate tldr!if one leaves the system alone for a sufficiently long time, it should settle and become hot
By this I mean that the number of particles with a given energy has a thermal distribution. This thermal distribution for electrons (or more generally, for fermions) is given by the Fermi-Dirac distribution and looks like the below for a number of temperatures
Yes, you can come at these kind of questions from both the applications point-of-view and also from a fundamental understanding point-of-view. For example, on the application side I saw a talk recently about a theoretical proposal for light-induced superconductivity in semi-conductors. There, you "dump" energy into your semiconductor by shining a laser on it, causing the fundamental properties of the material to drastically change -- realizing a superconductor in a conventional semiconductor! From the fundamental perspective, there's lots of questions that are being asked. I guess the most general question is something along the lines of "how does quantum mechanics recover statistical mechanics?" where statistical mechanics is the theory used to describe the behavior of macroscopic objects (e.g., what happens when you heat up a big chunk of metal?). Another question which I think is interesting (and related to the application side) is can one realize "new states of matter" (e.g., new properties or behaviours) by doing something out-of-equilibrium and waiting for this new steady state? Can we get behaviors which aren't realized in equilibrium? It's certainly an interesting field to be working in at the moment!
I'm so glad there are people doing what you do. You make the future possible. You make science fiction into science-reality. I imagine your job depends on funding from outside places, and I also imagine there are people who'd want to cut that funding because they don't understand what you do. I read your explanation and I STILL don't quite understand what the results are, but I do know that this is a step toward developing new materials and products. Good stuff.
I'm currently a postdoctoral researcher, so I'm directly funded out of money from my boss, which comes from the US Government (the Department of Energy). Money is most definitely a problem for academic research at the moment in almost any subject (the humanities have had almost non-existent funding for years in the UK) and is one of the reasons I moved countries after my PhD. I don't like arguing for science funding on purely economic grounds (I think it says something very sad about society that one often has to make such arguments to politicians), but whilst there is some debate about quite how good funding science is for the economy as a whole (see, for example, this Nature piece and articles therein), there is a definite consensus that it is good. Probably I'm biased (as a scientist), but I can't help but think scientific inquisitiveness is as much a defining feature of humanity as its pursuit of the arts, exploration, poetry, theatre, literature, etc. Eh, hopefully I don't sound too pretentious!
I'm getting a degree in Biology. So I have no clue yet. It's like the business degree of science. I can do a lot, but few want a jack of all trades. I'll probably end up doing field work, maybe work as a park ranger for a year or two. I enjoy being outside.
Or you could do what I did and sneak in an ecology (field work) part into your PhD proposal for molecular biology things. What do you like so far in biology? Specific type of organism? Have you started doing research in someones lab? I am sure you have heard before, but hands on experience as an undergrad is so crucial, and the earlier you start the more time you have to play around and find a lab you like.
Oh I'm going to avoid post grad education if I can, I am not an academic person. I love bacteria, specifically the relationships between them. But ecosystems also mirror this relationship, so macro biology isn't off the table. I really could enjoy anything, as long as life is involved. I'm a B- student in a pretty competitive college, the profs I've build relations with are looking for someone better, and I don't have the stomach for cold emailing (although I probably should just swallow my pride and go for it.)
I just finished getting my BA in psychology. I intend to study adult development from biological and cognitive perspectives. To be more specific, I have an interest in adult acquisition of new languages. Next step is graduate school, naturally.
I love learning about new languages and the little things that make them unique! I'm actually currently teaching myself Xhosa. Do you have a language that you like the most? And do you have any solid tips on learning a new language as an adult?
I took German in high school, so as a matter of familiarity, that would have to be one of my favorite foreign languages. Aside from that, I really enjoy listening to Japanese, though I don't actually know how to speak, read, or write it. I also like listening to and learning how to speak with different accents in English. To answer your second question, I'll just copy my response to hanszyme: Another thing you can do is try to speak, read, or write in your target language a little bit every day. Repetition is honestly one of the best ways for you to learn anything.Let me be clear that I don't yet have enough expertise to give you detailed advice on language learning in particular.
That said, there are strategies for learning in general that apply to learning a new language. One big tip I heard I can't tell you how many times is to vary where and when you study day to day. It turns out that people tend to do better on tests when the testing environment is similar to the learning environment. So, by varying your learning environment, you can keep what you learn from being so dependent on the context of where you learn it.
I'm interested in the developmental processes behind the switch from understanding via mental translation to near-native understanding. When people start out learning a new language, they tend to have to translate between the new language and their native language. For example, the German word "Vogel" and the English word "bird" both mean the same thing. An English speaker learning German will start out by translating "Vogel" to "bird" mentally, but can eventually just come to understand that "Vogel" refers to a winged, feathered creature (and even understand the German words for "winged" and "feathered" on the same level). I'd like to study the changes that occur in the brain as this transition takes place. I also have a BA in philosophy, so my interest in language is broader than just its acquisition. In philosophy, linguistic precision is absolutely necessary. One of the first things philosophers do before starting a discussion is define their terms with the intent of sticking to those exact definitions. Because of that, I have actually developed an interest in the imprecision of common language. It is absolutely fascinating to me that there are so many cultural idiosyncrasies in within any given language, but communication is usually not hampered. I'm not sure exactly how I would study this from a developmental or psychological perspective, but I think I have time to figure that out.
Awesome! Sounds really interesting. You might be interested in pragmatics/developmental pragmatics based on what you said in your second paragraph. Humans make crazy amounts of inferences all the time so that we can reduce the cost of communication (being perfectly precise is incredibly costly!). You might be interested in the so-called "tug of war" between quantity (be brief) and informativeness (say enough for your speaker to understand what you're talking about). I think Steve Levinson might have a good review of this, and ofc Grice is always good.
I probably would. As a philosophical stance, pragmatism is very attractive to me, so understanding related scientific concepts should be pretty interesting too. As it happens, that "tug of war" is a common conflict in philosophical and scientific writing. I'm sure you've probably encountered some philosophical works or scientific studies that were just plain wordy.
Let me be clear that I don't yet have enough expertise to give you detailed advice on language learning in particular. That said, there are strategies for learning in general that apply to learning a new language. One big tip I heard I can't tell you how many times is to vary where and when you study day to day. It turns out that people tend to do better on tests when the testing environment is similar to the learning environment. So, by varying your learning environment, you can keep what you learn from being so dependent on the context of where you learn it. Another thing you can do is try to speak, read, or write in your target language a little bit every day. Repetition is honestly one of the best ways for you to learn anything. To that end, as TheVenerableCain said, duolingo might be a decent place to start. I wouldn't suggest trying to use it to learn a whole language, but it can be a good starting place. Also, it's great if you've learned a language before and want to brush up on the basics.
That is fascinating, thanks for the link. It coincides eerily with my experience: I moved to France in March this year, with no French whatsoever. I was also taking a new mood-stabilizer (part of the reason I moved was that I had a bit of a life crisis - end of long relationship, career change etc. etc.).
When I got here, I realized I was picking up the language really fast (I've learnt other languages, so I have something to compare it to), and that my brain felt... the only way I can describe it is childlike. Anyway, it's one anecdote, but it's interesting that a mood-stabilizer might affect how the brain does other things, too. Thanks again for the link.
I definitely can't recommend any drugs for you. For starters, I'm not exactly up on all the drugs there are out there. Besides that, I don't have the qualifications to prescribe/recommend drugs.
Give duolingo a try. I haven't personally done anything with it, but from what I've read, it's a decent starting point. Plus, it's free. Good luck!
Computational Linguistics, atm especially Corpus based analysis of a broad set of websites to analyse the use of words, sentence structures and so on. 2 pretty interesting parts of CL I had worked in were; The analyses of a company by analyzing their internal documents (somewhat - depending on whats classified) - namely all the revision. So to see how a company functions (Who reviewed what and when, what changes did he made linguistically and conerning the content) - and therefore be able e.g. to give advice to the companys leadership. Machine Translation for minor languages e.g. Spanish -> Quechua. That was pretty interesting since I'm not fluid in either language.
Depends on the job ;-) For all non-computational linguists I'll go ELI5 on this: For machine translation I work mostly with the moses toolkit[1] with IRSTLM as language model. This toolkit is made for statistical machine translation - so you need a lot of data and a parallel corpus (identical texts in both languages e.g. stuff you find in those little "phrases for travelling" booklets). If you're running a Unix-System it's pretty easy to get started (baseline) - you can use the europarl-corpus for some nice experiments (what about your own Portuguese-German translator? ;-) There is also a pretty nice tollkit named "apertium"[2] - this is about rule-based MT, so you don't need a lot of data, but you need a comprehensive grammar (constisting of a lexicon and grammar rules). For other stuff I do there are tons of different methods and approaches each- from formal/funcitonal grammar up to machine learning/deep learning techniques (Naïve Bayes classifier, Support vector machines etc.) If you're (or others) are interested, I could post some interesting links for Natural Language Processing (maybe a new Tag for that?)
I just finished my master's in biostatistics and am about to start my PhD in statistics. You may not consider me a scientist, but I'm the guy that makes sure your science is grounded in actually testable hypotheses and that you're drawing the right conclusions based on the observable data that you've decided to collect. On a pure statistics level, I'm really interested in Bayesian inference, stochastic computer simulations and computational statistics, machine learning (isn't everyone now, this is becoming a bit cliché), and improving the statistical literacy of the general scientific and lay community.
I'm currently an undergrad studying computer science: bioinformatics and bio chemistry. In looking for PhD programs somewhat related to bioinformatics, I've seen quite a few biostatistics ones come up. My focus might be genome science with the computer science background being a plus (not sure yet, still figuring things out, lol), but as a biostatistician do you do anything with genome science as whole? I know, pretty vague overall, but I'm still trying to find where bioinformatics fits into the scientific community as it's a very interdisciplinary line of study.
I'm not quite sure what bioinformatics is exactly, but I did do a bit of work on genetics data during an internship at the CDC. From my point of view, biostatistics is not bioinformatics. Biostatisticians and statistics as a whole are a large field, but there are definitely some people that intersect with the informatics realm. It's definitely not an area everyone works in though. If you're interested in genetics, and looking at biostatistics programs. I highly recommend Columbia's biostatistics PhD program. I interviewed there, and they had a very large focus on developing methodology for analyzing genetic data. I ended up choosing another program because I wasn't particularly interested in that area and I wanted to do a pure statistics PhD. However, it seems like you'd like it quite a bit.
It never fails to astound me how many scientists are just completely statistically illiterate. It's so cool that you're interested in this! What do you think is the best way to improve statistical literacy?improving the statistical literacy of the general scientific and lay community.
I know. Sadly, introductory statistics courses are taught without really telling anyone why it's cool or how useful it is. I myself hated the statistics courses I took in high school and in my undergraduate studies. In my opinion I think people should be much more exposed to how statistics can be applied. I don't really think the best approach is teaching people how to look up t and z statistics on a table. You leave with the ability to say, "do a t-test because there are two groups and that's what we did that one time in class." Then they see a p-value that is "significant." I fucking hate that word. The best way, IMO, is to think of things probabilistically. Understanding probability and probabilistic statements is key to understanding statistics. From a young age we are taught the laws of cause and effect. Especially in science classes, we are taught that if we do A, then B happens always. Otherwise it's not a causal relationship. However, in practice, we deal with much more complicated networks of causal relationships. We use randomness as an abstraction to model these complex relationship because it would be impossible to measure every factor in a causal relationship without infinite time, money, and infinitely precise instruments. This is why we see different magnitudes of effects. We don't, and can't, possibly measure everything that would affect the outcome. We use statistics to (hopefully) determine the most likely and most influential causal factors. The statements we make are probabilistic though. Each conclusion we make has a chance of being wrong, no matter how careful we are. In fact, we expect about 1 in 20 of the studies performed (with the 0.05 significance level) to make incorrect conclusions. This is why replication is important. If multiple studies come to the same conclusion, we can be reasonably certain that we made the correct decision. A statistical statement in isolation is not always as concrete as it seems. So I'd recommend above all understanding the probabilistic statements made during hypothesis testing, and the implications that the cutoffs you select have. I'd also recommend being familiar with all of the assumptions that the models you use make. Know why they make them, and know when you've violated the critical ones. And please, please, please consult with a statistician if you're doing research. Most universities have consulting arms of their departments that are available for collaborative research inside and outside of their university. We would love to work with you! We won't bite. We study this stuff for our whole lives because it's hard. We didn't learn everything about physical chemistry and quantum mechanics in that one class we took in undergrad, and you didn't learn everything about statistics. Let's work together, and hopefully we can avoid some of the pitfalls of our predecessors.It never fails to astound me how many scientists are just completely statistically illiterate.
It's so cool that you're interested in this! What do you think is the best way to improve statistical literacy?
Last spring, I took a course in developmental psychobiology, and systems biology was interspersed throughout. Definitely an interesting way to conceptualize the interconnections between various levels of biological study. I recall a demonstration my professor used to explain what I believe he called "partially dependent systems." Here's the demonstration: According to the description of the video, the metronomes become able to influence each other into synchronization when they are sitting on the cans. This is because being on the cans makes the activity of each metronome - an independent system of sorts - partially dependent on the activity of the others. The on-cans configuration is supposed to be analogous to certain biological systems. For example, the endocrine system and the nervous system in the human body. In your opinion, am I understanding this correctly?
Somewhat, though I think peoples' definitions of "systems biology" tends to share the same idea of analyzing many moving parts, but I've heard the term used to describe everything from tiny molecular systems to populations of buffalo. My interests tend to stay around the size of protein / cellular where you tend to look at transcriptional networks, phosphorylation pathways, etc. You still see similar analysis of feedback / feed-forward loops and discussion of how cells are able to optimally filter input signals / evolve systems to do so.
I'm a computer scientist. I do research in information security, although I am not particularly interested in the technicalities. Rather, I focus heavily on the economic, social and policy problems emerging from security practices, solutions, attacks and regulation. I am particularly interested in markets for malware and attacks, and the externalities that the whole information security process has over society and at the international policy level.
I'm a social psychologist working on informal segregation and intergroup contact. I mostly do multilevel analyses to study contextual effects of segregation in cities, schools or universities, and I'm also running a project studying actual conversations between members of different groups vs. people from the same social group.
As someone who is particularly interested in language and its common usage, I'd like to see how the cultural idiosyncrasies of groups using the same base language affect communication between those groups. I'd also be interested in seeing how those idiosyncrasies are related to informal segregation.
More of us. I would have never expected that. Although, I hope not all social psychologists are/become a shipwreck. Nice to see, have a nice day.
Just started back to college for Biology. Hoping to go into microbiology and focus on virology, mycology, or bacteriology. Do you enjoy what you do? Are you doing more field work or lab research?
Way more lab research than the field, I put the ecology section in my PhD proposal only because I wanted to go to the woods and that aspect of fungal Bioluminescence has not been studied since the 1980s... someone had to do it why not me, I like the woods. But yeah I really like what I am doing, I like my subject area, and I am so close to finding the genes (assuming the next few things go as expected, fingers crossed). Right now I am procrastinating from watching the field ecology videos I have collected, over the past year. I am actually taking a week off from wet-lab to get the ecology data annotation finished, I like collecting the ecology data, I do not like reviewing it...
Awesome, here's hoping it goes well for you! Hopefully, I'll be able to share your passion in a few years!
Much appreciate the advice. I haven't even gotten to the phase where I would be looking at labs. Just finished my first semester.
Psycholinguistics! I study how people process, produce, and acquire language. I use a mix of behavioral and computational methods. Specifically right now I'm investigating to what extent people activate unigram representations within bigrams of varying pairwise mutual information. Edit: stupid phone doesn't realize that unigram and bigram are words and corrected them to weird things.
Do you think you could explain simply what unigrams and bigrams are?
I would say computer scientist, but it's very rare that i actually so some science (sit down, gather data points, analyze them, figure out what's wrong or try to optimize something). Mostly it's engineering (software development), or much more commonly than all of that, testing, documentation, and making release builds.
I have been studying psychology for a little over 5 years now on a university. Before that I actually was able to start it off with 3 years of psychology lessons at school. I am from Germany, so that almost never happens. I was very lucky with that. I have been doing cognitive psychology and social psychology as specializations. Nice to see so many sciency Hubskis around.
I'm working towards getting my physics degree so that I can go the PhD route and specialize in Quantum Chromodynamic Interactions. I absolutely love physics and abstract math, so I naturally gravitated towards quantum mechanics. I find the interactions and mechanisms that drive quantum physics and particle physics to be incredibly fascinating. I hope I'll be able to teach particle physics in the future as well.
Currently in undergrad pursuing a degree in Microbiology with a focus in biotechnology. I've still yet to find the one path that I really want to pursue but the idea of working with microbes to better humanity (i.e. making new drugs, curing diseases, making resources, etc...) very fascinating. Wish me luck in making up my mind!
Well, I guess I count as a scientist now. I work in a research clinic on two related but separate studies. The first is a drug trial, investigating a possible novel use for a common blood pressure medication, and the second is a longitudinal study that's trying to develop a more sophisticated and evidence based approach to predicting long term outcomes and therapies in a specific set of congenital heart diseases.
I'm working towards a BSc in microbiology, I'll graduate next spring. I haven't been able to get involved with a lab to do any kind of research yet, which makes me very sad. I might get a chance to work in a microbiological ecology lab this Fall where I'll just be doing DNA extractions and sequencing of cytochrome C oxidase genes in order to help identify some disease vectors from the Amazon; the lab I'll be working with is studying microbiomes of disease vectors, but I won't be directly involved in that part of it, I'll just be helping to identify some of the samples. But lab experience is good and hopefully I'll get a couple of good references so that I can go on to grad school with a focus on microbial ecology.
I just finished a PhD in a Cancer Medicine lab where I found a new regulatory mechanism that links the management of DNA topology by the cell with the cell's response to DNA damage. I used a variety of molecular biological and biochemical techniques, and I also wrote software to quantitatively describe populations of cells observed in 3D immunofluorescence images.
Graduated with a biology of global health degree last year, starting med school at the end of the month. Did a thesis on infectious disease during my junior and senior year of college, did clinical research this past year on asthma and another project on schizophrenia, and I'm hoping to do clinical work studying the microbiome and gut-brain interaction. I'm thinking that's where I might like to focus my career, but I'm honestly not sure yet.
Are you a scientist? Perhaps it would be good from to describe the work you are doing? Edit: Hey, never mind, I see it in the comments. My bad.