Johns Hopkins University alumni event
There aren't a lot out there, but here are the ones I'm aware of:
*Thomas Cover (YouTube): https:/
*Raymond Yeung (Coursera): https:/
*Andrew Eckford/York University (YouTube): Coding and Information Theory https:/
*NPTEL: Electronics & Communication Engineering http:/
Fortunately, most are pretty reasonable, though vary in their coverage of topics. I'd be glad to hear about others, good or bad if others are aware. The top two are from professors who've written two of the most common textbooks on the subject. If I recall a version of the Yeung text is available via download through his course interface.
17w5131: Statistical & Computational Challenges in Large Scale Molecular Biology Workshop @BIRS_Math 3/2017 #ITBio
Arriving in Banff, Alberta Sunday, March 26 and departing Friday March 31, 2017
Organizers
- Barbara Engelhardt (Princeton University)
- Anna Goldenberg (University of Toronto)
- Manolis Kellis (Massachusetts Institute of Technology)
- Jacob Laurent (Centre national de la recherche scientifique)
- Jeff Leek (John Hopkins University)
- Stephen Montgomery (Stanford University)
Objectives
Over the past few years, an increasing number of large scale data sets have been made available in molecular biology. GTEx, for example, produced more than 18,000 RNA-Seq assays for multiple tissues in 900 individuals, Mindact generated gene expression data from about 7000 breast tumors in a single study, and 23andMe claims to have sequenced about 900,000 genomes. This growth in the available genomic data is expected to increase our capacity to identify cancer subtypes, regulatory genes, SNPs associated with phenotypes of interest, and biomarkers for many human traits. It also suggests exploring more complex feature representations when analyzing these datasets.
However, increasing the number of samples and features leads to a set of \textbf{interrelated statistical and computational problems}. Accordingly, the objectives of our workshop will be to:
Systematically identify the statistical and computational
problems arising during the analysis of large scale data in molecular biology;
Bring together experts in computational biology, molecular
biology, computer science, and statistics to propose innovative solutions to these problems, by leveraging recent advances in each of these fields.
Relevance, importance and timeliness
A number of studies generating high throughput molecular data for a large number of biological samples have been completed over the past five years. \textbf{Our workshop is important because the availability of these datasets holds great promises in terms of health improvement and understanding of molecular biology}. First of all, if exploited correctly, larger sample sizes should improve our ability to predict phenotypes of interest from molecular data. This entails very important applications such as improving the survival of cancer patients by better predicting which treatment they should receive, or decreasing bacterial resistances by predicting which antibiotic is efficient against a new strain. Correctly exploiting large scale datasets should also allow us to \textbf{better identify genetic and epigenetic determinants of these phenotypes, yielding a better understanding of human diseases and potentially guiding the development of new treatments and prevention policies}. In particular, more samples should allow the detection of less frequent variants in the human genome, or more complex features involving several modalities (copy number, expression, methylation, etc) associated with diseases. Finally, larger sample sizes should help with essential unsupervised tasks such as the \textbf{inference of regulation networks, or the identification of cancer subtypes}.
Our workshop is relevant because \textbf{all of these promises are conditioned on our solving of new statistical and computational challenges}. First (Challenge 1), we need to build new feature spaces and estimators whose complexity is adapted to these larger sample sizes, which involves designing novel, potentially more complex descriptors of the samples but still controlling the bias/variance trade-off. Second (Challenge 2), we need to build models which correctly integrate different modalities, such as copy number variation and gene expression. Third (Challenge 3), larger scale studies are more prone to unwanted variations, because they typically involve different labs and technical changes which can affect the measurements and become confounders in retrospective analyses. Similar or worse problems arise when trying to combine several existing datasets. We need methods which take this unwanted variation into account. Finally, (Challenge 4), we need new algorithms that make existing statistical tools scalable to the new sample sizes, and make estimation over the larger and more complex features of Challenge 1 tractable.
We also believe our workshop is very timely because \textbf{some of these statistical and computational challenges are starting to be addressed in other application fields} of statistics. It is crucial to recognize that the orders of magnitude are still very different in molecular biology and other data science application fields because of the cost and complexity of the data generation process: current large scale high throughput sequencing data sets typically contain a few thousand of samples but millions of features while computer vision, web, or astronomy datasets can involve billions or trillions of samples and relatively fewer features. A first consequence is that not all recent developments in machine learning are immediately transferable to computational biology. For example, so called deep learning methods have gained a lot of popularity and now represent the state of the art in computer vision but may not be the most appropriate tool for prediction of cancer outcome from molecular data. However, the fact that other fields already have much larger sample sizes also means that they had to develop efficient and scalable algorithms for basic tasks like feature selection, classification or clustering. \textbf{These recent developments are a great source of inspiration for computational biology, where large scale computation is still an emerging challenge}.
We believe \textbf{having a small scale workshop involving international experts in machine learning, statistics, computational biology and molecular biology is of utmost importance} for three main reasons. The first reason is that the technical advances we are referring to are very recent, often unknown to computational biologists and involve paradigms such as online optimization, accelerated gradient methods and network flow optimization, with which they are sometimes unfamiliar. The second reason is that it is not always obvious to non-statisticians which novel methods are appropriate given the current n/p regime. Conversely, the third reason is that statisticians do not know what the recent challenges are in molecular biology. Having them work on abstract versions of the problems is often not satisfactory as it is necessary to be aware of technical realities and of the underlying biology of the problem to come up with useful solutions.
17w5104: Mathematical Approaches to Evolutionary Trees and Networks Workshop @BIRS_math 2/12/17 #ITBio
Arriving in Banff, Alberta Sunday, February 12 and departing Friday February 17, 2017
Organizers
- Leonid Chindelevitch (Simon Fraser University)
- Caroline Colijn (Imperial College London)
- Amaury Lambert (University Pierre and Marie Curie, Paris)
- Marta Luksza (Institute of Advanced Study, Princeton University)
- Vincent Moulton (University of East Anglia)
- Tandy Warnow (University of Illinois)
Objectives
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 [5]). In some simplified cases, epidemiological inference methods can estimate transmission trees [2], branching rates through time [5] and other aspects of epidemic spread [7]. 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 [3]. 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 [8], and the use of tree metrics [1]. These in turn can lead to tools for averaging trees , and for navigating tree space in efficient ways [6] -- 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 [4]. 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 [1] 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. [2] 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. [3] 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. [4] 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. [5] 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. [6] 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. [7] David A Rasmussen, Erik M Volz, and Katia Koelle. Phylodynamic inference for structured epidemiological models. PLoS Comput. Biol., 10(4):e1003570, April 2014. 2 [8] David Speyer and Bernd Sturmfels. The tropical grassmannian. Adv. Geom., 4(3):389–411, 2004.
MIT Dean Takes Leave to Start New University Without Lectures or Classrooms http:/
Rare King James Bible First Edition Discovered at Drew University http:/
My reply to ‘Novel, amazing, innovative’: positive words on the rise in science papers | Nature News & Comment
http:/
I can readily posit a potential and very strong motivation for the uptick in the appearance of these words, and particularly for the word "novel", which the paper and this article fail to see.
Beginning in the early 90's, and certainly starting before that time, there has been an intensified interest for both research institutions and researchers writing papers for them to monetize their research outside the halls of the academy. (Particularly with decreased funding for faculty and intensified competition for research dollars - many research faculty either self-fund their salaries or rely heavily on 3rd party income.) Toward this end, individual researchers began more aggressively pursuing patents for their research work and at the institutional level, colleges and universities began very actively pursuing technology transfer to the point of opening up full offices and hiring large numbers of staff to better leverage the return of creating patents and pursuing sales and licensing deals for the resulting research work. The US Patent Office, in making a determination of whether or not to grant patents, (and even moreso when the underlying documents are published scientific research articles) uses words like "novel" and "innovative" as an indicator of their worthiness. Without a patent, it's incredibly difficult to protect the intellectual property contained within a published and public work. Other words like "amazing" which are also cited in the paper are geared (in a business sales sense) more toward potential corporate financial investors who may consider purchasing or licensing the resulting work of a research paper. What corporate bean counter wants to invest in a dull-sounding research paper title which is quite likely one of the only parts of the document they're likely to comprehend? Yet throw in some "excitement words" and you may have a sale!
When one looks at employment contracts of the professoriate (post-docs, graduate students, and the like), which presumably comprises the majority of those publishing papers with these words, one will see that professors prior to the 90's have contracts that don't mention intellectual property rights resulting from their efforts, or which grant them the lion's share. Current contracts after that period will almost necessarily keep either all or most of these types of revenue streams in the institution's pockets rather than the researchers themselves. I'd be willing to bet that this tide began turning in the mid-1970s and has certainly been overwhelming since the mid-90s.
It is these severe economic taskmasters which are almost assuredly the cause of the rise of these buzzwords in research papers, and most particularly in their titles. One need only ask themselves, which university wants to be the next proverbial "Stanford" to not have a piece of revenue from the intellectual property of a proverbial "Page rank" algorithm? Or in biomedical research, to not take a hefty cut out of a drug which could potentially cure cancer or HIV/AIDS? Take a gander at what something as simple as a HELA cell, which was taken and cultured without any notice to Henrietta Lacks, has become from an industrial perspective. If they can avoid it, academia certainly won't let multi-million (or billion) dollar enterprises spring up (again) without them receiving a piece of the pie.
Naturally, there are other factors compounding the issues at hand, particularly when one looks at negative research findings, which are sadly rarely ever published, particularly in an era of big data when what we know isn't true will surely help assist in defining the boundaries of what we do know and what could potentially be determined at that boundary. (Much the way that white space in photographs or in printed pages helps to define an image as much if not more so than the darker portions of the image.) Negative words are likely more difficult to get published as it may reflect poorly on researchers who are reliant on continued future funding and may see their sources disappear when they're not producing groundbreaking work. My hypothesis, however, is that it's more likely a direct economic impact based on technology transfer than it is the increased competitive landscape in academic research.
@PaulNeitzel By doing @mentions of the schools/departments you're visiting, the schools/departments are more likely to see your tweets, and retweet them, thereby reaching dozens, hundreds, or thousands of eyeballs through the network effect that you wouldn't get otherwise. Presumably your target audience is following both their main university account and likely their departmental or divisional account. You can also @mention particular professors at the site schools for additional reach. Good luck!
I've followed this process from before it's administrative beginning. It's nice to see that we've got a philosophy for what academic freedom is, though I honesty fail to see how it differs from a basic definition of what academic freedom means in the last century, so congratulations to the dozens of people who spent countless hours rewriting a basic definition. We've done the academic equivalent of writing the words, "We hold these truths to be self-evident" while failing to create any actual rules or guidelines by which the administration can hold the faculty, staff, or students accountable or which actually serve to protect the faculty, staff, or students from overstepping of authority by the university.
Where is the following "Constitution"? Where is the process for "Amendments"? Is the University actually granting any real rights here, and how are they to actually be protected? Surely we've evolved past the level of even the rights available during the Carolingian Renaissance and the early days of the birth of the universitas?
In particular, I find it disconcerting to see even the scant guidelines that existed in the intermediate draft that was sent for approval before it got to the board level have been removed. For example, statements like:
"When one is speaking on matters of public interest, it should be made clear that personal views do not represent those of the institution." or
"Professors who express their personal views on a contested issue must make it clear that students may disagree with those views without penalty."
no longer appear in the statement at all.
It's lovely that we have this new "document", but when the rubber actually meets the road, what will we do? Will we trip, stumble, and fall down as we have occasionally in the past? Where are those general guidelines? No one will care what we've said in this document, but they will surely judge us more harshly in the realm of public opinion based on the future actions of the administration and this is where the real work will have to begin.
Ron Daniels has been doing some generally good things in guiding the direction of the university community, but it seems odd that, as one of the first presidents of the institution with an academic background in law and what I know to be his philosophy in social equity, that we've heard nothing of next steps. I hope that with books entitled "Rule of Law Reform and Development: Charting the Fragile Path of Progress" and "Responsibility and Responsiveness," that we will see much more.
Some of my additional thoughts on the practicality of these matters can be found at: "Reframing What Academic Freedom Means in the Digital Age" [http:/
#AcademicFreedom #JohnsHopkins
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."
Category Theory certainly isn't useless, and has some really beautiful and interesting things going on. One of the bigger issues it has is that it is relatively so young as a discipline within math. (Imagine what people said about set theory in 1910.) Your toughest hurdle as an undergraduate is finding a university that is offering it as an accessible class depending on your level of mathematical background. I haven't noticed a lot of universities even offering it at the graduate level yet either.
That being said, David Spivak's new book Category Theory for the Sciences which came out of MIT Press last year is the closest textbook I've seen to attempt making Category Theory easily accessible to not only an undergraduate audience, but also to a broader range of scientists who are not necessarily steeped in mathematics. For your particular purposes, it actually has a nice handful of biology related examples in the early chapters.
Because it expounds on some of the beauty of category theory and it's application to science, I'll draw your attention to an essay that Ilyas Khan wrote recently: https:/
I'd recommend category theory more highly, if you plan on pursuing a Ph.D. and research work, but in the meanwhile you might also find some interesting material in John Carlos Baez's Azimuth blog or on the n-Category Cafe.
Some Hospitals are Marking up Prices More than 1,000 Percent
9 min read
I don't often (read: never) cut and paste press releases, but I've got an itching feeling that this is going to be BIG news over the next week or so, and thought I'd forward it along so you don't have to wait for the 5 second sound bite that this story is going to be diminished to. You'll be able to get it directly from the horse's mouth. It'll also give you time to write to your senators and congressmen as well as the growing list of presidential candidates entering the ring.
The original research can be found in the current issue of the journal "Health Affairs"
“Extreme Markup: The 50 U.S. Hospitals with the Highest Charge-to-Cost Ratios” was written by Ge Bai and Gerard F. Anderson.
A brief synopsis from my friends at the Johns Hopkins Bloomberg School of Public Health follows:
The 50 hospitals in the United States with the highest markup of prices over their actual costs are charging out-of-network patients and the uninsured, as well as auto and workers’ compensation insurers, more than 10 times the costs allowed by Medicare, new research suggests. It’s a markup of more than 1,000 percent for the same medical services.
'What other industry can you think of that marks up their prices by 1,000 percent and remains in business?’
The findings, from Gerard F. Anderson of the Johns Hopkins Bloomberg School of Public Health and Ge Bai of Washington and Lee University, show that the combination of a lack of regulation of hospital charges in the United States and no market competition is leading to price-gouging that trickles down to nearly all consumers, whether they have health insurance or not, and plays a role in the rise of overall health spending. The report is published in the June issue of Health Affairs.
“There is no justification for these outrageous rates, but no one tells hospitals they can’t charge them,” says Anderson, a professor in the Bloomberg School’s Department of Health Policy and Management. “For the most part, there is no regulation of hospital rates and there are no market forces that force hospitals to lower their rates. They charge these prices simply because they can.”
For their study, Anderson and Bai analyzed the 2012 Medicare cost reports from the Centers for Medicare and Medicaid Services to determine a charge-to-cost ratio, an indicator of how much hospitals are marking up charges beyond what Medicare agrees to pay for those with its government-subsidized health insurance.
The 50 hospitals, they found, charged an average of more than 10 times the Medicare-allowed costs. They also found that the typical United States hospital charges were on average 3.4 times the Medicare-allowable cost in 2012. In other words, when the hospital incurs $100 of Medicare-allowable costs, the hospital charges $340. In one of the top 50 hospitals, that means a $1,000 charge.
Of the 50 hospitals with the highest price markups, 49 are for-profit hospitals and 46 are owned by for-profit health systems. One for-profit health system, Community Health Systems Inc., operates 25 of the 50 hospitals. Hospital Corp. of America operates more than one-quarter of them. While they are located in many states, 20 of the hospitals are in Florida.
“For-profit hospitals appear to be better players in this price-gouging game,” says Bai, an assistant professor of accounting at Washington & Lee University. “They represent only 30 percent of hospitals in the U.S., but account for 98 percent of the 50 hospitals with highest markups."
Many hospital patients don’t actually pay the “charge master” or full price. Along with government insurers, most private health insurers negotiate lower rates for their patients.
But 30 million uninsured Americans are likely to be charged the full rate, as are patients receiving out-of-network care and those receiving workers’ compensation or auto insurance benefits. As a result, uninsured patients, who are often the most vulnerable, face exceptionally high medical bills, often leading to personal bankruptcy, damaged credit scores or the avoidance of needed medical services.
The impact of overcharging extends beyond hospital patients. Notes Anderson: “The cost of workers’ compensation and auto insurance policies are higher in the states where hospital charges are unregulated because companies must pay those higher rates.”
In addition, privately insured in-network patients may also end up paying greater premiums due to hospitals' high markups, which are often used by hospitals as leverage to negotiate higher prices with private insurance companies. “Except for patients with government insurance, few consumers are immune from negative financial impacts caused by hospitals' high markups,” Bai says.
In Maryland and West Virginia, the state sets the rates that hospitals can charge for services. No federal law regulates them for all Americans.
“We as consumers are paying for this when hospitals charge 10 times what they should,” Anderson says. “What other industry can you think of that marks up the price of their product by 1,000 percent and remains in business?”
For the most part, the hospitals with the highest markups are not situated in pricey neighborhoods or big cities, where the market might explain the higher prices. The most expensive hospital is North Okaloosa Medical Center, located in the Florida Panhandle about an hour outside Pensacola. There, patients are charged 12.6 times more than Medicare allowable costs.
Anderson says changes are unlikely to drop to levels closer to costs allowed by Medicare unless state or federal officials decide to legislate a maximum markup that a hospital could charge a patient. He says states could choose to have their hospital rates set by a state agency as is done in Maryland and West Virginia, which guarantees that hospitals can’t gauge their patients.
He says that price transparency could also help only to a limited extent because people cannot bargain or comparative shop when they are sick. Most hospitals aren’t required to – and don’t – publicly share how much they charge for different procedures
“This system has the effect of charging the highest prices to the most vulnerable patients and those with the least market power,” Anderson says. “The result is a market failure.”
List of 50 Hospitals with Highest Charge-to-Cost Ratios, 2012:
1. North Okaloosa Medical Center (FL)
2. Carepoint Health-Bayonne Hospital (NJ)
3. Bayfront Health Brooksville (FL)
4. Paul B Hall Regional Medical Center (KY)
5. Chestnut Hill Hospital (PA)
6. Gadsden Regional Medical Center (AL)
7. Heart of Florida Regional Medical Center (FL)
8. Orange Park Medical Center (FL)
9. Western Arizona Regional Medical Center (AZ)
10. Oak Hill Hospital (FL)
11. Texas General Hospital (TX)
12. Fort Walton Beach Medical Center (FL)
13. Easton Hospital (PA)
14. Brookwood Medical Center (AL)
15. National Park Medical Center (AR)
16. St. Petersburg General Hospital (FL)
17. Crozer Chester Medical Center (PA)
18. Riverview Regional Medical Center (AL)
19. Regional Hospital of Jackson (TN)
20. Sebastian River Medical Center (FL)
21. Brandywine Hospital (PA)
22. Osceola Regional Medical Center (FL)
23. Decatur Morgan Hospital - Parkway Campus (AL)
24. Medical Center of Southeastern Oklahoma (OK)
25. Gulf Coast Medical Center (FL)
26. South Bay Hospital (FL)
27. Fawcett Memorial Hospital (FL)
28. North Florida Regional Medical Center (FL)
29. Doctors Hospital of Manteca (CA)
30. Doctors Medical Center (CA)
31. Lawnwood Regional Medical Center & Heart Institute (FL)
32. Lakeway Regional Hospital (TN)
33. Brandon Regional Hospital (FL)
34. Hahnemann University Hospital (PA)
35. Phoenixville Hospital (PA)
36. Stringfellow Memorial Hospital (AL)
37. Lehigh Regional Medical Center (FL)
38. Southside Regional Medical Center (VA)
39. Twin Cities Hospital (FL)
40. Olympia Medical Center (CA)
41. Springs Memorial Hospital (SC)
42. Regional Medical Center Bayonet Point (FL)
43. Dallas Regional Medical Center (TX)
44. Laredo Medical Center (TX)
45. Bayfront Health Dade City (FL)
46. Pottstown Memorial Medical Center (PA)
47. Dyersburg Regional Medical Center (TN)
48. South Texas Health System (TX)
49. Kendall Regional Medical Center (FL)
50. Lake Granbury Medical Center (TX)
SOURCE: Authors' analysis of Healthcare Cost Report Information System (HCRIS) computer files obtained from the Centers for Medicare and Medicaid Services for 2012, published in the appendix of the June 2015 issue of Health Affairs.
8th Annual North American School of Information Theory
(NASIT) - August 10-13, 2015 - UC San Diego, La Jolla, California
The School of Information Theory will bring together over 100 graduate students, postdoctoral scholars, and leading researchers for four action-packed days of learning, stimulating discussions, professional networking and fun activities, all on the beautiful campus of the University of California, San Diego (UCSD) and in the nearby beach town of La Jolla.
#NASIT #InformationTheory
I simply say that there are really four places that have a “the” in front of their name
1 min read
Mary Higgins Clark just doesn't get it. I simply say that there are only four places that have a “the” in front of their name: the Vatican, the Hague, the Bronx — and The Johns Hopkins University, and that so much talent has come out of The Johns Hopkins University.
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.]