Biomedical and Electrical Engineer with interests in information theory, evolution, genetics, abstract mathematics, microbiology, big history, IndieWeb, mnemonics, and the entertainment industry including: finance, distribution, representation
@kimberlyhirsh I hadn't seen it; thanks for sharing. Boffo and socko are neologisms pioneered by Variety and the Hollywood trade press and so aren't necessarily specific to the Muppets. Something about this reminds me of a 16mm print of his experimental short Time Piece from the mid-60s which I used to screen in front other 60s films in college. If I recall, I think he got an academy award nomination for it.
Why I’m leaving a Research I University for a Liberal Arts College https:/
Purchase, Rent, or Pirate? Problem with #Textbooks in the Digital Age #edutech #college #phdchat http:/
@evelynjlamb I know of the generally excellent book you're describing (which was written in the last decade, for one of the other commenters). (Coincidentally, I'll note that I passed it along to a female colleague after I'd read it.) Though I don't know the author personally--despite having corresponded with him briefly in the past, I too would tend to give him the benefit of the doubt, particularly as he was dean of faculty at an all women's college where he spent nearly three decades and is credited with diversifying the faculty in addition to organizing for Women’s Education Worldwide.
We can all certainly be more cognizant in the writing process to expunge our unacknowledged biases. Perhaps this type of review is something we might all attempt when giving peer-review to our colleagues' work?
Arriving in Banff, Alberta Sunday, February 12 and departing Friday February 17, 2017
The objectives of the workshop are to bring mathematicians working in three key areas together to make progress in these problems. We will also invite several biologists who are keen to engage with mathematicians on the challenges posed by new data on evolutionary processes. Key challenges in the field at the moment are focused around the following emerging inter-related areas, each of which is raising mathematically interesting problems:
1. Inference with evolutionary trees and networks: Ultimately it is necessary not just to obtain evolutionary trees from data using standard methods, but to infer aspects of an underlying biological process. This requires understanding the likelihood of an evolutionary tree or network, or at least some of its informative features, using some stochastic process as the underlying ecological model. In principle, this approach allows simultaneous inference of both evolutionary trees and parameters of the ecological model. Coalescent theory has made considerable progress, for example, in obtaining tree likelihoods for sparsely sampled populations with geographical structure or with known past demographics (see for just one example ). In some simplified cases, epidemiological inference methods can estimate transmission trees , branching rates through time  and other aspects of epidemic spread . However, none of these approaches is currently applicable if there is non-tree-like evolution, or where datasets are large. Furthermore, the range of models for which we can write down a tree likelihood is very limited. This raising nice new problems in probability, statistical inference and ecological modelling. Recently, more general processes (e.g. Lambda-coalescents, which allow multiple rather than strictly pairwise coalescent events) are beginning to be used to model populations with large offspring variance, or even to model selection in a non-parametric fashion . This is potentially a powerful tool particularly for bacteria, which may acquire resistance to antibiotics and spread rapidly as a consequence, yielding both highly variable effective offspring numbers and a need to model selection carefully.
2. Understanding spaces of evolutionary trees: There are a large number of possible labelled, rooted binary trees for a given set of nn tips (ie for a given set of sequence data): (2n−3)!!=(2n−3)(2n−5)...(3)(1)(2n−3)!!=(2n−3)(2n−5)...(3)(1). This works out to 1018410184 trees on 100 tips; in contrast, current datasets for evolving bacteria contain thousands of tips. Not even the tools of Bayesian inference, the natural approach in such situations, can systematically explore spaces this big. This motivates the development of mathematical approaches for the exploration of tree space. These include new approaches to continuous tree spaces, including those from tropical geometry , and the use of tree metrics . These in turn can lead to tools for averaging trees , and for navigating tree space in efficient ways  -- with profound applications in statistical inference from sequence data. Generalizing metrics to the case of evolutionary networks (for example tree-based networks) is another natural and important question.
3. Summarising trees and networks using combinatorial tools: Uncovering shape features, spectral features and other ways to describe trees using quantities that are mathematically tractable will be of considerable interest . As one example, where likelihoods are truly intractable, rapid tools for likelihood-free inference can be used to infer evolutionary processes from sequence data, but only where there are informative ways to summarize key features of the data. Trees are natural combinatorial structures with connections to data; for example, a binary tree is a sequence of partitions of the set of tips (sequences in a dataset), where each partition is one block smaller than the previous one, moving back through time from the partition with each tip on its own to the partition with all tips in one block as we move from the tips of the tree to the root. If the tree is not binary (ie it allows multifurcations), more than two blocks can combine at a branching event. Because of the natural link to partitions, the study of tree shapes links to the enumeration of partitions and to lattice path combinatorics. These in turn allow the characterization and enumeration of possible tree shapes. Meanwhile the study of motifs in other biological networks has been fruitful, and could be extended to tree and evolutionary network shapes. Trees and evolutionary networks are of course also graphs (with an added time dimension); the tools of algebraic graph theory are now finding application in this area of mathematical biology.
The community's response to the idea for this workshop has been very positive. A * beside a participant's name indicates that they have expressed enthusiasm for the workshop, and plan to attend.
References  Louis J Billera, Susan P Holmes, and Karen Vogtmann. Geometry of the space of phylogenetic trees. Adv. Appl. Math., 27(4):733–767, November 2001.  Xavier Didelot, Jennifer Gardy, and Caroline Colijn. Bayesian inference of infectious disease transmission from whole-genome sequence data. Mol. Biol. Evol., 31(7):1869–1879, July 2014.  Alison M Etheridge, Robert C Griffiths, and Jesse E Taylor. A coalescent dual process in a moran model with genic selection, and the lambda coalescent limit. Theor. Popul. Biol., 78(2):77–92, September 2010.  Fanny Gascuel, Regis Ferriere, Robin Aguilee, and Amaury Lambert. How ecology and landscape dynamics shape phylogenetic trees. Syst. Biol., 64(4):590–607, July 2015.  Amaury Lambert and Tanja Stadler. Birth–death models and coalescent point processes: The shape and probability of reconstructed phylogenies. Theor. Popul. Biol., 90(0):113–128, December 2013.  Tom M W Nye. An algorithm for constructing principal geodesics in phylogenetic treespace. IEEE/ACM Trans. Comput. Biol. Bioinform., 11(2):304–315, March 2014.  David A Rasmussen, Erik M Volz, and Katia Koelle. Phylodynamic inference for structured epidemiological models. PLoS Comput. Biol., 10(4):e1003570, April 2014. 2  David Speyer and Bernd Sturmfels. The tropical grassmannian. Adv. Geom., 4(3):389–411, 2004.
Brief reply to: Is majoring in liberal arts a mistake for students? https:/
What magisterial sounding pontification! Sadly, it’s not much different than the early philosophies of Socrates and Plato or many of the other early progenitors of the humanities and liberal arts. I get the impression that the author hasn’t read much philosophy and has not much grounding in the liberal arts. While I agree with the spirit in which the piece is written, I find it deplorable that there aren’t what should be obligatory mentions of words like trivium, quadrivium, or philosophy, but rather the corpus of work in which the author seems steeped is that of only modern day authors of popular science (Pinker, Gladwell, Kahneman, et. al.) who have some interesting viewpoints, but ones which require at least a grounding in the liberal arts to pick apart. Several times Khosla demeans the liberal arts and uses the repeated example that a reader should be able to pick apart and think critically about articles in The Economist. To do this requires a knowledge of logic and rhetoric which are two of the pillars of what? — yes, the liberal arts!
He also seems unaware of big movements within the humanities and sciences like Bill Gates and David Christian’s Big History Project which are going a long way towards providing a more balanced education in history, economics, physics, chemistry, biology and evolution. I find here, no prima facie evidence of his knowledge of Thomas Kuhn or Karl Popper, which might help win me to his argument. In all, aside from the passing references to one or two recent works, this entire argument is not much different from many that could have been written at the beginning of the industrial revolution. How blind so many must be to seemingly think there’s something new here.
Most appalling to me here is that the author doesn’t seem to give even a passing nod or small wink to C.P. Snow or “The Two Cultures” [http:/
Yes, we certainly need more emphasis on the quadrivium portion of the liberal arts, and in particular mathematics and critical thinking which seem to have been left by the wayside. It is deplorable that the highest extent of mathematics that 99% of college students are exposed to terminates in the 17th century for the most part. Sadly, many college students are left without the ability to think critically and deeply, not to mention the hordes of students in America who barely make it through high school and don’t attend college. One also only needs to skim through recent issues of Nature [http:/
Yes, we need far, far more, but alas, this poor article only touches the tip of the issue and it sadly only does so with less than half of the picture.
Great quote in the Telegraph:
"The economics of college textbooks is very different from anything else," says Mark Perry, an economics professor at the University of Michigan. "Professors select the books, and students have to pay for them, so the normal market mechanisms aren't at play here. Publishing companies charge whatever they can get away with, which is unsustainable."
Essay calls for a new approach to college textbooks | InsideHigherEd https:/
I've seen that same article floating around for the past several weeks (in some part because I had the originating professor in college). The trouble with the experiment as it relates to early human societies is that most of them were kinship based, and for basic functionality, they all were forced to intercommunicate and typically put family first in a sense. In this exercise, there is no communication, nor any real speakable "community"- everyone is truly out for themselves.
I've recently been reading Francis Fukuyama's The Origins of Political Order: From Prehuman Times to the French Revolution (Farrar, Straus and Giroux, 2011) [http:/
In short, the interesting exercise here to me seems to be what constructs in society lead to creating the tragedy of the commons and which don't? Figuring this problem out will help to maintain stability in our current culture for generations to come.
I'd mentioned his books previously as very Big History focused here: https:/
I find it hard to believe, particularly if you're in financial need, that you'd have a full ride at UT and not have been offered anything at JHU, so double check with the JHU office of financial aid first to see what, if anything, you'd be offered.
More than anything I'd recommend at least visiting both campuses in person and taking a tour and speaking to some students and professors. Asking a question here like you have with only the three data points (two schools and one financial aid package) is not nearly enough to go on for anyone to presume to help you make one of the most influential decisions you're going to make in your life - particularly when the advice is likely to be a terrifically biased and spurious and you're unlikely to delve into the nature of the responses you're getting.
In some sense, you're comparing apples and oranges. A couple of questions you should ask, however, above and beyond the simple ranking portion of the question which most are focusing on in their answers (while the remainder seem to be a bit more biased based on their personal experiences) include:
What are the campus environments like?
How big are the schools and what kind of attention and resources will I have access to?
How big are the cities they're in and what do they offer as part of the undergraduate experience?
What happens after one or two years if you decide to change your major?
Other than the end result of the degree, what do you want out of a college experience?
Are you looking for more diversity or less in terms of culture, religion, etc.?
What are your other interests outside of academics and are those interests actively represented or possible at the school you want to attend? (As an example, are you a musician and want to take classes or have practice time at a college's sister conservatory - perhaps you could get a dual degree? or are there a variety of other music outlets available like bands, symphonies, orchestras, music groups, etc.?)
UT is a humongous place, while Hopkins is significantly smaller, so at UT you're much more likely to be treated like just another number, even within the electrical engineering department. At Hopkins your classes are guaranteed to be much smaller and more intimate which gives you far more access to your professors - particularly when you may need recommendations down the road.
Statistically, if you're planning on going to graduate school, you'll have far more resources for doing research as an undergraduate at Hopkins, which will give you more preparation and a stronger case when you apply. Hopkins excels in undergraduate research experience in part because of its philosophy but also because it gets almost twice the government funding than even the next closest competitor. Additional resources like the proximity of the Applied Physics Lab and the Space Telescope Science Institute (which managed projects like the Hubble Telescope) will give you additional opportunities related to your field while you're in school.
If you do want to play the simple ranking game, keep in mind that only about a third of your course work as an undergraduate will be in the EE department. How highly ranked are all the other programs and departments like mathematics, physics, chemistry, biology, English, and others from which the remainder of your coursework will come? Again, what will things look like if you decide to change major part way through?
[Disclosure: I'm an alum of Johns Hopkins class of 1996 with degrees in both biomedical engineering and electrical engineering. I've been both very active on the Hopkins Society of Engineering Alumni board as well as the board that oversees the University's larger Alumni Association. I'm happy to give you more information about Hopkins, and can be easily found via a variety of social media outlets.]
Math should be taught and approached from a creative, reasearch-like perspective -- which is how it organically developed throughout history anyway. This will help children (and even adults) to know how to better approach problems and figure them out when they get to higher levels. Some of the "rote" memorization of forms and formulas is just silly, and in part is what is keeping anyone from learning any math created after the 18th century until they're a junior in college, by which point most have given up on mathematics entirely.
I maintain that part of the reason that 'geniuses' like Euler and Gauss 'discovered' so much of math is that there was a truly large amount of easy low-hanging fruit for them to find. There's no reason that students couldn't be 'led' to rediscover some of these types of areas for themselves.
At the most basic level [book:Getting What You Came For: The Smart Student's Guide to Earning an M.A. or a Ph.D.|460669] has some relatively sound advice. If you need something entertaining my friend Adam Ruben's book [book:Surviving Your Stupid, Stupid Decision to Go to Grad School|7769479] is pretty good.
For some of the other topics you mention, start reading The Chronicle of Higher Education. Most of it is available online without a subscription, but they've got lots of current faculty writing regularly on the topics you mentioned.
Slowly you'll find a community of online professors like Robert Talbert (http:/
On twitter, you might consider following some the people on this list to start: https:/
Within twitter, there are a handful of hashtags you might following/comment along with including: #mathchat, #PhDchat, #PhDLife and likely a handful of others.
On the practical side, consider creating accounts on mendeley.com, academica.com, researchgate.com, etc. as a way to find people, help, material, etc. You might also consider starting yourself a blog (aka Commonplace book) to collect all your thoughts: http:/
Finally, for graduate level math, I highly recommend spending the $100-200 for a Livescribe Pulse pen: http:/
Feel free to ping me here or via other means (http:/