Physicist, Startup Founder, Blogger, Dad

Tuesday, August 14, 2018

Genomic Prediction of disease risk using polygenic scores (Nature Genetics)

It seems to me we are just at the tipping point -- soon it will be widely understood that with large enough data sets we can predict complex traits and complex disease risk from genotype, capturing most of the estimated heritable variance. People will forget that many "experts" doubted this was possible -- the term missing heritability will gradually disappear.

In just a few years genotyping will start to become "standard of care" in many health systems. In 5 years there will be ~100M genotypes in storage (vs ~20M now), a large fraction available for scientific analysis.
Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations (Nature Genetics)

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2,3,4,5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.
See also Genomic Prediction: A Hypothetical (Embryo Selection) and Accurate Genomic Prediction Of Human Height.

From the paper:
Using much larger studies and improved algorithms, we set out to revisit the question of whether a GPS can identify subgroups of the population with risk approaching or exceeding that of a mono- genic mutation. We studied five common diseases with major public health impact: CAD, atrial fibrillation, type 2 diabetes, inflamma- tory bowel disease, and breast cancer.

For each of the diseases, we created several candidate GPSs based on summary statistics and imputation from recent large GWASs in participants of primarily European ancestry (Table 1). Specifically, we derived 24 predictors based on a pruning and thresholding method, and 7 additional predictors using the recently described LDPred algorithm13 (Methods, Fig. 1 and Supplementary Tables 1–6). These scores were validated and tested within the UK Biobank, which has aggregated genotype data and extensive phenotypic information on 409,258 participants of British ancestry (average age: 57 years; 55% female)14,15.

We used an initial validation dataset of the 120,280 participants in the UK Biobank phase 1 genotype data release to select the GPSs with the best performance, defined as the maximum area under the receiver-operator curve (AUC). We then assessed the performance in an independent testing dataset comprised of the 288,978 partici- pants in the UK Biobank phase 2 genotype data release. For each disease, the discriminative capacity within the testing dataset was nearly identical to that observed in the validation dataset.

In the talk below @21:45 I discuss prospects for genomic prediction of disease risk.

Wednesday, August 08, 2018

Life and Fate, Before Sunset

This Hollywood oral history tells the story of Richard Linklater's "Before" Trilogy: Before Sunrise, Before Sunset, and Before Midnight. The films appeared 9 years apart, and tell the story of Jesse (Ethan Hawke) and Celine (Julie Delpy) in their 20s, 30s, and 40s. I find the second film to be the most interesting, really a masterpiece of filmmaking (I have a copy on the hard drive of the laptop I write this on :-). The events in Before Sunset take place in real time -- i.e., the story transpires over the run time of the movie, a single afternoon. Shooting it must have been extremely challenging for Delpy and Hawke, and for the crew.

The video above should start at 23:30, and explains how Linklater, Delpy, and Hawke came together to do the sequel. I think that event was, in some sense, the most contingent of those responsible for the trilogy. The first movie made very little money, and hence the idea to make a second, very different film -- about the complexity of life, the passage of time, lost chances -- was neither obvious nor inevitable.

The first movie is about a one night tryst between 20-something travelers, but the second movie takes place a decade later. The protagonists, while still young, have experienced more of life and the second film is richer and more complex, despite taking place over an even shorter period of time. I remember being excited to see it, not so much because of Before Sunrise (which I found entertaining, but not as special), but because of the intriguing premise of two lovers meeting again by chance after losing track of each other for so long.

Here's a scene from Before Sunset: a long take of walking and conversation in beautiful Paris, camera following Hawke and Delpy in a totally naturalistic way.

I hesitate to include this trailer because it's kind of cheesy, but if you're not familiar with the trilogy it explains the premise of the first two films.

The video below is a nice discussion of the trilogy. Just now I learned (thanks, AI!) that Before Sunrise is based on actual events in Linklater's life -- see here for the poignant story of the real life muse for these films.

Richard Linklater also directed Dazed and Confused -- one of the greatest high school movies ever made, and a beautiful evocation of adolescence in late-70s, early-80s America.

Saturday, August 04, 2018

Assassination by Drone

I have been waiting for this to happen:
Reuters: CARACAS - Drones loaded with explosives detonated close to a military event where Venezuelan President Nicolas Maduro was giving a speech on Saturday, but he and top government officials alongside him escaped unharmed from what Information Minister Jorge Rodriguez called an “attack” targeting the leftist leader. Seven National Guard soldiers were injured, Rodriguez added.
See this 2015 post on drone racing and ask yourself how you'd stop one of these drones from getting close to its target.

Countermeasures will be quite difficult, especially if drone operators use sophisticated frequency hopping control.

One doesn't even need pilot operators. The drones can be programmed to fly to a GPS coordinate using an evasive approach.

1. The exact coordinate can be marked by someone in the audience of a public appearance of the target.

2. It would be a formidable challenge even to stop some medium sized drones, each with a few kilo payload, from flying through the windows of the Oval Office (known GPS coordinate; known presence of targets at specific times).

This is still Science Fiction, for now:

Twenty years ago I told a PhD student that a terrorist -- willing to die and able to fly an airplane -- could probably take out the White House. After 9/11 he reminded me that I had identified this hole in the system well in advance. It's the same thing here with small and medium size drones. They are accessible to non-state actors with limited resources, and very difficult to defeat, even for state security.

Barista Bots

Still think low-skill immigration is a good idea?

If you accept the thesis that automation is a threat to low-skill employment, then you should be willing to reconsider the long term cost-benefit analysis of low-skill immigration.

Thursday, August 02, 2018

Arnold: The Will to Power

I don't know whether Arnold ever read Nietzsche, but he certainly developed the Will to Power early in life. I quite like the video above -- I even made my kids watch it :-)

When I was in high school I came across his book Arnold: The Education of a Bodybuilder, a combination autobiography and training manual published in 1977. I found a copy in the remainder section of the book store and bought it for a few dollars. The most interesting part of the book is the description of his early life in Austria and his introduction to weightlifting and bodybuilding. I highly recommend it to anyone interested in golden age bodybuilding, the early development of physical training, or the psychology of human drive and high achievement. Young Arnold displays a kind of unbridled and un-ironic egoism that can no longer be expressed without shame in today's feminized society.

Chapter 2: Before long, people began looking at me as a special person. Partly this was the result of my own changing attitude about myself. I was growing, getting bigger, gaining confidence. I was given consideration I had never received before; it was as though I were the son of a millionaire. I'd walk into a room at school and my classmates would offer me food or ask if they could help me with my homework. Even my teachers treated me differently. Especially after I started winning trophies in the weight-lifting contests I entered.

This strange new attitude toward me had an incredible effect on my ego. It supplied me with something I had been craving. I'm not sure why I had this need for special attention. Perhaps it was because I had an older brother who'd received more than his share of attention from our father. Whatever the reason, I had a strong desire to be noticed, to be praised. I basked in this new flood of attention. I turned even negative responses to my own satisfaction.

I'm convinced most of the people I knew didn't really understand what I was doing at all. They looked at me as a novelty, a freak. ...

"Why did you have to pick the least-favorite sport in Austria?" they always asked. It was true. We had only twenty or thirty bodybuilders in the entire country. I couldn't come up with an answer. I didn't know. It had been instinctive. I had just fallen in love with it. I loved the feeling of the gym, of working out, of having muscles all over.

Now, looking back, I can analyze it more clearly. My total involvement had a lot to do with the discipline, the individualism, and the utter integrity of bodybuilding. But at the time it was a mystery even to me. Bodybuilding did have its rewards, but they were relatively small. I wasn't competing yet, so my gratification had to come from other areas. In the summer at the lake I could surprise everyone by showing up with a different body. They'd say, "Jesus, Arnold, you grew again. When are you going to stop?"

"Never," I'd tell them. We'd all laugh. They thought it amusing. But I meant it.


The strangest thing was how my new body struck girls. There were a certain number of girls who were knocked out by it and a certain number who found it repulsive. There was absolutely no in-between. It seemed cut and dried. I'd hear their comments in the hallway at lunchtime, on the street, or at the lake. "I don't like it. He's weird—all those muscles give me the creeps." Or, "I love the way Arnold looks—so big and powerful. It's like sculpture. That's how a man should look."

These reactions gave me added motivation to continue building my body. I wanted to get bigger so I could really impress the girls who liked it and upset the others even more. Not that girls were my main reason for training. Far from it. But they added incentive and I figured as long as I was getting this attention from them I might as well use it. I had fun. I could tell if a girl was repelled by my size. And when I'd catch her looking at me in disbelief, I would casually raise my arm, flex my bicep, and watch her cringe. It was always good for a laugh. ...
Arnold, age 17 or 18:

Friday, July 27, 2018

Insight Podcast: James Lee interview on SSGAC EA3

Spencer Wells and Razib Khan interview James Lee (Professor of Psychology, University of Minnesota, BA Berkeley, PhD Harvard) about the recent SSGAC EA3 GWAS.

Comment: James mentions that EA3 may be approaching the GCTA h2 limit (~0.15? so limiting r ~ 0.4) already. But the limit for actual cognitive ability is much higher; with enough data I think we could get to r ~ 0.6 or even r ~ 0.7 eventually for common SNPs -- similar to height.

United Club, HK International Airport

James, me, Chris Chang. (About $1M worth of Illumina HiSeqs in crates behind us?)

Wednesday, July 25, 2018

Genomic Prediction: A Hypothetical (Embryo Selection)

The new SSGAC EA3 paper in Nature Genetics contains the following figure.

Add Health (National Longitudinal Study of Adolescent to Adult Health) and HRS (Health in Retirement Study) are two longitudinal cohorts under study by social scientists. Horizontal axis is polygenic score (computed from DNA alone). It appears that individuals with top quintile polygenic scores are about 5 times more likely to complete college than bottom quintile individuals.  (IIUC, HRS cohort grew up in an earlier era when college attendance rates were lower; Add Health participants are younger.)

Consider the following hypothetical:
You are an IVF physician advising parents who have exactly 2 viable embryos, ready for implantation. The parents want to implant only one embryo. 
All genetic and morphological information about the embryos suggest that they are both viable, healthy, and free of elevated disease risk.

However, embryo A has polygenic score (as in figure above) in the lowest quintile (elevated risk of struggling in school) while embryo B has polygenic score in the highest quintile (less than average risk of struggling in school). We could sharpen the question by assuming, e.g., that embryo A has score in the bottom 1% while embryo B is in the top 1%.

You have no other statistical or medical information to differentiate between the two embryos.

What do you tell the parents? Do you inform them about the polygenic score difference between the embryos?
Note, in the very near future this question will no longer be hypothetical...

See Nativity 2050 and The Future is Here: Genomic Prediction in MIT Technology Review.

Monday, July 23, 2018

SSGAC EA3: genomic prediction of educational attainment and related cognitive phenotypes

Years ago I predicted that:

1. Cognitive ability would turn out to be influenced by many thousands of genetic variants, each of small effect.

2. With large enough sample size we would detect these variants and eventually construct genomic predictors.

The Nature Genetics paper below from the SSGAC collaboration takes a significant step in that direction.

Although the study used over a million genotypes, the data had to be aggregated across many sub-cohorts using summary statistics only. This does not permit the L1-penalized optimization we used to build our height predictor.

For out of sample validation of the results below, see this PNAS paper, which (unusually) appeared before the paper on which it is based.

The lead author James Lee is on the left below. Chris Chang, author of Plink 2.0, is on the right. The photo was taken in 2010 at BGI -- they are standing in front of crates of Illumina sequencers.

Article | Published: 23 July 2018

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

James J. Lee, Robbee Wedow, […]David Cesarini
Nature Genetics (2018)

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
A nice figure from the paper: Add Health (National Longitudinal Study of Adolescent to Adult Health) and HRS (Health in Retirement Study) are two longitudinal cohorts that have been genotyped; horizontal axis is polygenic score. It appears that individuals with top quintile polygenic scores are about 5 times more likely to complete college than bottom quintile individuals.

Here's a comment on the paper I provided to a journalist:
The EA3 predictor correlates about 0.35 with educational attainment, and slightly less well with measured cognitive ability. While this is far from perfect prediction, it does allow identification of individuals, using DNA alone, who are at unusual risk of being well below average in cognitive ability or struggling in school. Standardized tests, such as SAT, ACT, GRE, LSAT, etc., typically also correlate roughly 0.35 with educational outcomes like grade point average, degree completion, etc. In this sense, the genomic predictor is comparable to widely used tests and it will certainly improve as more data are analyzed. See figure.

Sunday, July 22, 2018


The 36th Annual International Symposium on Lattice Field Theory begins tomorrow, hosted by MSU. My opening remarks are below. No peeking if you are an attendee!
LATTICE 2018 Opening Remarks 7/23/2018

Good morning. I’d like to extend my warmest welcome to all of you on behalf of Michigan State University. We are very pleased and honored to be the hosts for The 36th Annual International Symposium on Lattice Field Theory.

It is my opinion that even within Physics, and even within Theoretical Physics, Lattice Field Theory is underappreciated. The idea that we can constructively realize quantum field theories in silico, that we can perform precision calculations in the deepest models of fundamental physics, is really incredible. It has taken many decades to get to this point: to master strongly coupled quantum fluctuations, spacetime trajectories of quantum fields like quarks and gluons, advanced algorithms and hardware designs, matching to effective field theories, and many other conceptually beautiful but ultimately concrete things.

Along with some recent AI advances like AlphaGo, the precise ab initio calculation of physical quantities in lattice QCD must be considered among the most impressive computations performed by the human species. If some Alien visitors were evaluating the accomplishments of our civilization, I would want them to take into account the work of people here today.

I first became aware of lattice gauge theory from John Preskill’s lecture notes for Physics 234, a year-long Caltech course on advanced topics in QCD. I never imagined, back in the 1980s, the successes that all of you have achieved today. The important message to young people is that one should not be dissuaded from attempting difficult projects.

At MSU we made the decision a few years ago to invest in lattice physics. We went from no lattice researchers, to one of the larger groups in the US. One of the drivers for this decision was the hope that lattice simulations would one day connect QCD to the experimental results coming from FRIB -- the MSU / DOE Facility for Rare Isotope Beams. Today we can compute, from first principles, the properties of light hadrons. In the coming decades, I believe we will compute real time scattering amplitudes and nuclear forces from QCD itself.

DOE and MSU are investing, all told, roughly a billion dollars in FRIB. While it is the Experimentalists who build and run the machine, and deserve the main credit, we as Theorists have the responsibility to ensure that the results of the experiment inform our deeper understanding of nuclear physics and QCD. Physicists are not stamp collectors -- we do not measure things just to measure them. We measure things which are important and have deep implications.

To reach the long awaited goal of connecting nuclear physics directly to QCD, we depend on the lattice community, on all of you. May the next 30 years see as much progress as the last.

Thank you very much.

Action photos!

London Calling

On my way home from StockholmICML I stopped in London to see my friend Dominic Cummings, give a talk at ASI Data Science, and have some Oligarch meetings. Sorry I can't share more details.

Here are some photos from the British Museum.

Bodhisattva: a person who is able to reach nirvana but delays doing so out of compassion in order to save suffering beings.
“Tenfold be your damnation," he said.. "There shall be no rebirth."

His hands came open then. A tall, nobly proportioned man lay upon the floor at his feet, his head resting upon his right shoulder.

His eye had finally closed.

Yama turned the corpse with the toe of his boot. "Build a pyre and burn this body," he said to the monks, not turning toward them. "Spare none of the rites. One of the highest has died this day.”

Lord of Light, Roger Zelazny.

Tuesday, July 17, 2018

ICML notes

It's never been a better time to work on AI/ML. Vast resources are being deployed in this direction, by corporations and governments alike. In addition to the marvelous practical applications in development, a theoretical understanding of Deep Learning may emerge in the next few years.

The notes below are to keep track of some interesting things I encountered at the meeting.

Some ML learning resources:

Depth First study of AlphaGo

I heard a more polished version of this talk by Elad at the Theory of Deep Learning workshop. He is trying to connect results in sparse learning (e.g., performance guarantees for L1 or threshold algos) to Deep Learning. (Video is from UCLA IPAM.)

It may turn out that the problems on which DL works well are precisely those in which the training data (and underlying generative processes) have a hierarchical structure which is sparse, level by level. Layered networks perform a kind of coarse graining (renormalization group flow): first layers filter by feature, subsequent layers by combinations of features, etc. But the whole thing can be understood as products of sparse filters, and the performance under training is described by sparse performance guarantees (ReLU = thresholded penalization?). Given the inherent locality of physics (atoms, molecules, cells, tissue; atoms, words, sentences, ...) it is not surprising that natural phenomena generate data with this kind of hierarchical structure.

Off-topic: At dinner with one of my former students and his colleague (both researchers at an AI lab in Germany), the subject of Finitism came up due to a throwaway remark about the Continuum Hypothesis.

Horizons of Truth
Chaitin on Physics and Mathematics

David Deutsch:
The reason why we find it possible to construct, say, electronic calculators, and indeed why we can perform mental arithmetic, cannot be found in mathematics or logic. The reason is that the laws of physics "happen" to permit the existence of physical models for the operations of arithmetic such as addition, subtraction and multiplication.
My perspective: We experience the physical world directly, so the highest confidence belief we have is in its reality. Mathematics is an invention of our brains, and cannot help but be inspired by the objects we find in the physical world. Our idealizations (such as "infinity") may or may not be well-founded. In fact, mathematics with infinity included may be very sick, as evidenced by Godel's results, or paradoxes in set theory. There is no reason that infinity is needed (as far as we know) to do physics. It is entirely possible that there are only a (large but) finite number of degrees of freedom in the physical universe.

Paul Cohen:
I will ascribe to Skolem a view, not explicitly stated by him, that there is a reality to mathematics, but axioms cannot describe it. Indeed one goes further and says that there is no reason to think that any axiom system can adequately describe it.
This "it" (mathematics) that Cohen describes may be the set of idealizations constructed by our brains extrapolating from physical reality. But there is no guarantee that these idealizations have a strong kind of internal consistency and indeed they cannot be adequately described by any axiom system.

Monday, July 09, 2018

Game Over: Genomic Prediction of Social Mobility

[ NOTE: The PNAS paper discussed below uses the SSGAC EA3 genomic predictor, trained on over a million genomes. The EA3 paper has now appeared in Nature Genetics. ]

The figure below shows SNP-based polygenic score and life outcome (socioeconomic index, on vertical axis) in four longitudinal cohorts, one from New Zealand (Dunedin) and three from the US. Each cohort (varying somewhat in size) has thousands of individuals, ~20k in total (all of European ancestry). The points displayed are averages over bins containing 10-50 individuals. For each cohort, the individuals have been grouped by childhood (family) social economic status. Social mobility can be predicted from polygenic score. Note that higher SES families tend to have higher polygenic scores on average -- which is what one might expect from a society that is at least somewhat meritocratic. The cohorts have not been used in training -- this is true out-of-sample validation. Furthermore, the four cohorts represent different geographic regions (even, different continents) and individuals born in different decades.

Everyone should stop for a moment and think carefully about the implications of the paragraph above and the figure below.

Caption from the PNAS paper.
Fig. 4. Education polygenic score associations with social attainment for Add Health Study, WLS, Dunedin Study, and HRS participants with low-, middle-, and high-socioeconomic status (SES) social origins. The figure plots polygenic score associations with socioeconomic attainment for Add Health Study (A), Dunedin Study (B), WLS (C), and HRS (D) participants who grew up in low-, middle-, and high-SES households. For the figure, low- middle-, and high-SES households were defined as the bottom quartile, middle 50%, and top quartile of the social origins score distributions for the Add Health Study, WLS, and HRS. For the Dunedin Study, low SES was defined as a childhood NZSEI of two or lower (20% of the sample), middle SES was defined as childhood NZSEI of three to four (63% of the sample), and high SES was defined as childhood NZSEI of five or six (17% of the sample). Attainment is graphed in terms of socioeconomic index scores for the Add Health Study, Dunedin Study, and WLS and in terms of household wealth in the HRS. Add Health Study and WLS socioeconomic index scores were calculated from Hauser and Warren (34) occupational income and occupational education scores. Dunedin Study socioeconomic index scores were calculated similarly, according to the Statistics New Zealand NZSEI (38). HRS household wealth was measured from structured interviews about assets. All measures were z-transformed to have mean = 0, SD = 1 for analysis. The individual graphs show binned scatterplots in which each plotted point reflects average x and y coordinates for a bin of 50 participants for the Add Health Study, WLS, and HRS and for a bin of 10 participants for the Dunedin Study. The red regression lines are plotted from the raw data. The box-and-whisker plots at the bottom of the graphs show the distribution of the education polygenic score for each childhood SES category. The blue diamond in the middle of the box shows the median; the box shows the interquartile range; and the whiskers show upper and lower bounds defined by the 25th percentile minus 1.5× the interquartile range and the 75th percentile plus 1.5× the interquartile range, respectively. The vertical line intersecting the x axis shows the cohort average polygenic score. The figure illustrates three findings observed consistently across cohorts: (i) participants who grew up in higher-SES households tended to have higher socioeconomic attainment independent of their genetics compared with peers who grew up in lower-SES households; (ii) participants’ polygenic scores were correlated with their social origins such that those who grew up in higher-SES households tended to have higher polygenic scores compared with peers who grew up in lower-SES households; (iii) participants with higher polygenic scores tended to achieve higher levels of attainment across strata of social origins, including those born into low-SES families.

The paper:
Genetic analysis of social-class mobility in five longitudinal studies, Belsky et al.

PNAS July 9, 2018. 201801238; published ahead of print July 9, 2018. https://doi.org/10.1073/pnas.1801238115

A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.

Note Added from comments: Plots would look much noisier if not for averaging many individuals into single point. Keep in mind that socioeconomic success depends on a lot more than just cognitive ability, or even cognitive ability + conscientiousness.

But, underlying predictor correlates ~0.35 with actual educational attainment, IIRC. That is, the polygenic score predicts EA about as well as standardized tests predict success in schooling.

This means you can at least use it to identify outliers: just as a very high/low test score (SAT, ACT, GRE) does not *guarantee* success/failure in school, nevertheless the signal is useful for selection = admissions.

Friday, July 06, 2018

Seven Years, Two Tweets

Is anyone keeping score?

See On the Genetic Architecture of Cognitive Ability (2014) and Nautilus Magazine: Super Intelligent Humans.

Thursday, July 05, 2018

Cognitive ability predicted from fMRI (Caltech Neuroscience)

Caltech researchers used elastic net (L1 and L2 penalization) to train a predictor using cognitive scores and fMRI data from ~900 individuals. The predictor captures about 20% of variance in intelligence; the score correlates a bit more than 0.45 with actual intelligence. This may validate earlier work by Korean researchers in 2015, although the Korean group claimed much higher predictive correlations.

Press release:
In a new study, researchers from Caltech, Cedars-Sinai Medical Center, and the University of Salerno show that their new computing tool can predict a person's intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. Functional MRI develops a map of brain activity by detecting changes in blood flow to specific brain regions. In other words, an individual's intelligence can be gleaned from patterns of activity in their brain when they're not doing or thinking anything in particular—no math problems, no vocabulary quizzes, no puzzles.

"We found if we just have people lie in the scanner and do nothing while we measure the pattern of activity in their brain, we can use the data to predict their intelligence," says Ralph Adolphs (PhD '92), Bren Professor of Psychology, Neuroscience, and Biology, and director and Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Caltech Brain Imaging Center.

To train their algorithm on the complex patterns of activity in the human brain, Adolphs and his team used data collected by the Human Connectome Project (HCP), a scientific endeavor funded by the National Institutes of Health (NIH) that seeks to improve understanding of the many connections in the human brain. Adolphs and his colleagues downloaded the brain scans and intelligence scores from almost 900 individuals who had participated in the HCP, fed these into their algorithm, and set it to work.

After processing the data, the team's algorithm was able to predict intelligence at statistically significant levels across these 900 subjects, says Julien Dubois (PhD '13), a postdoctoral fellow at Cedars-Sinai Medical Center. But there is a lot of room for improvement, he adds. The scans are coarse and noisy measures of what is actually happening in the brain, and a lot of potentially useful information is still being discarded.

"The information that we derive from the brain measurements can be used to account for about 20 percent of the variance in intelligence we observed in our subjects," Dubois says. "We are doing very well, but we are still quite far from being able to match the results of hour-long intelligence tests, like the Wechsler Adult Intelligence Scale,"

Dubois also points out a sort of philosophical conundrum inherent in the work. "Since the algorithm is trained on intelligence scores to begin with, how do we know that the intelligence scores are correct?" The researchers addressed this issue by extracting a more precise estimate of intelligence across 10 different cognitive tasks that the subjects had taken, not only from an IQ test. ...
A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

Tuesday, July 03, 2018

In the land of the Gene Titans

Apologies for the lack of posts recently. I've been traveling and busy with meetings. For my own recollection, here is a partial list of places I've been in the past weeks.

Illumina (San Diego)
Ancestry (~10M genomes! San Francisco)
23andMe (~5M genomes! Mountain View)
OpenAI (machines beat pro human teams in complex Dota 2 game! San Francisco)
Affymetrix (Santa Clara)
Healdsburg, Sonoma (Talk at meeting of Oligarchs :-)
Soros Fund Management (Talk at leadership retreat, Museum of Arts and Design, NYC)

These GeneTitans are part of the Affy lab that did all of the genotyping for the UK Biobank project. The footprint for this kind of lab is shockingly small: ~6k samples per week per machine and ~10 machines means millions of individual genotypes per year. Illumina produces similar arrays/readers and a hundred square meters of lab space is enough to process millions of samples per year for DTC genomics companies like 23andMe and Ancestry.

We may have a lab like this soon at MSU ;-)

Thursday, June 21, 2018

Harvard Office of Institutional Research models: explicit racial penalty required to reproduce actual admit rates for Asian-Americans

This is my third post discussing the Students For Fair Admissions lawsuit against Harvard over discrimination against Asian-American applicants. Earlier posts here and here discussed, among other things, the tendency of the Admissions Office to assign low personal ratings to A-A applicants. A-As received, on average, the lowest such ratings among all ethnic groups from the Admissions Office. In contrast, alumni interviewers (who actually met the candidates) gave A-A applicants scores comparable to white applicants, and higher than other ethnic groups.

Harvard's Office of Institutional Research (OIR) produced a series of internal reports on discrimination against Asian-American applicants, beginning in 2013. They attempted to model the admissions process, and concluded there was outright penalization of A-A applicants:
Mark Hansen, the (now former) OIR employee, remembers far more. He remembers working with others in OIR on the project. He remembers gathering data, conducting the regression analysis, collaborating with colleagues, coordinating with the Admissions Office, and discussing the results of OIR’s investigation with Fitzsimmons and others on multiple occasions.  Hansen expressed no concerns with the quality and thoroughness of OIR’s statistical work. Moreover, he has a clear understanding of the implications of OIR’s findings. Hansen testified that the reports show that Asian Americans “are disadvantaged in the admissions process at Harvard.” And when asked: “Do you have any explanation other than intentional discrimination for your conclusions regarding the negative association between Asians and the Harvard admissions process?” Hansen responded: “I don’t.”
The figures below show several OIR models which try to fit the observed admit rates for various groups. The only model that comes close (Model 4) is one which assigns outright penalties to A-A applicants (using "demographic" -- i.e., explicitly racial -- factors). IIUC, this is *after* the low Personal Rating scores from the Admissions Office have already been accounted for!

In the decades leading up to the data discovery forced by the SFFA lawsuit, we heard many claims that legacy / recruited athlete status, or leadership characteristics, or extracurriculars, were the reasons for A-As having such a low acceptance rate (despite their strong academic records). The OIR analysis shows that these effects, while perhaps real, are only part of the story. In Model 4, pure racial bias reduces the A-A percentage of the entering class from 26% (after accounting for all the factors listed above) to the actual 18-19%!

Tuesday, June 19, 2018

Harvard Office of Institutional Research on Discrimination Against Asian-American Applicants

Harvard's Office of Institutional Research (OIR) produced a series of internal reports on discrimination against Asian-American applicants, beginning in 2013. I believe this was in response to Ron Unz's late 2012 article The Myth of American Meritocracy. These reports were shared with, among others, William Fitzsimmons (Dean of Admissions and Financial Aid) and Rakesh Khurana (Dean of Harvard College). Faced with an internal investigation showing systemic discrimination against Asian-American applicants, Harvard killed the study and quietly buried the reports. The Students For Fair Admissions (SFFA) supporting memo for Summary Judgment contains excerpts from depositions of these and other Harvard leaders concerning the internal reports. (Starting p.15 -- SAD!)

The second report included the figure below. Differences are in SDs, Asian = Asian-American (International applicants are distinct category), and Legacy and Recruited Athlete candidates have been excluded for this calculation.

As discussed in the previous post: When it comes to the score assigned by the Admissions Office, Asian-American applicants are assigned the lowest scores of any racial group. ... By contrast, alumni interviewers (who actually meet the applicants) rate Asian-Americans, on average, at the top with respect to personal ratings—comparable to white applicants ...

From the SFFA (Students For Fair Admissions) supporting memo for summary judgement:
OIR found that Asian-American admit rates were lower than white admit rates every year over a ten-year period even though, as the first of these two charts shows, white applicants materially outperformed Asian-American applicants only in the personal rating. Indeed, OIR found that the white applicants were admitted at a higher rate than their Asian-American counterparts at every level of academic-index level. But it is even worse than that. As the second chart shows, being Asian American actually decreases the chances of admissions. Like Professor Arcidiacono, OIR found that preferences for African American and Hispanic applicants could not explain the disproportionately negative effect Harvard’s admission system has on Asian Americans.
On David Card's obfuscatory analysis: the claim is that within the pool of "unhooked" applicants (excluding recruited athletes, legacies, children of major donors, etc.), Asian-Americans are discriminated against. Card's analysis obscures this point.
The task here is to determine whether “similarly situated” applicants have been treated differently on the basis of race; “apples should be compared to apples.” SBT Holdings, LLC v. Town of Westminster, 547 F.3d 28, 34 (1st Cir. 2008). Because certain applicants are in a special category, it is important to analyze the effect of race without them included. Excluding them allows for the effect of race to be tested on the bulk of the applicant pool (more than 95% of applicants and more than two-thirds of admitted students) that do not fall into one of these categories, i.e., the similarly situated applicants. For special-category applicants, race either does not play a meaningful role in their chances of admission or the discrimination is offset by the “significant advantage” they receive. Either way, they are not apples.

Professor Card’s inclusion of these applicants reflects his position that “there is no penalty against Asian-American applicants unless Harvard imposes a penalty on every Asian-American applicant.” But he is not a lawyer and he is wrong. It is illegal to discriminate against any Asian-American applicant or subset of applicants on the basis of race. Professor Card cannot escape that reality by trying to dilute the dataset. The claim here is not that Harvard, for example, “penalizes recruited athletes who are Asian-American because of their race.” The claim “is that the effects of Harvard’s use of race occur outside these special categories.” Professor Arcidiacono thus correctly excluded special-category applicants to isolate and highlight Harvard’s discrimination against Asian Americans. Professor Card, by contrast, includes “special recruiting categories in his models” to “obscure the extent to which race is affecting admissions decisions for those not fortunate enough to belong to one of these groups.” At bottom, SFFA’s claim is that Harvard penalizes Asian-American applicants who are not legacies or recruited athletes. Professor Card has shown that he is unwilling and unable to contest that claim.
This is an email from an alumni interviewer:
[M]y feelings towards Harvard have been slowly changing over the years. I’ve been interviewing for the college for almost 10 years now, and in those ten years, none of the Asian American students I’ve interviewed has been accepted (or even wait-listed). I’m 0 for about 20. This is the case despite the fact that their resumes are unbelievable and often superior to those of the non-Asian students I’ve interviewed who are admitted. I’ve also attended interviewer meetings where Asian candidates are summarily dismissed as “typical” or “not doing anything anyone else isn’t doing” while white or other minority candidates with similar resumes are lauded.
From p.18 of the SFFA memo:
Mark Hansen, the (now former) OIR employee, remembers far more. He remembers working with others in OIR on the project. He remembers gathering data, conducting the regression analysis, collaborating with colleagues, coordinating with the Admissions Office, and discussing the results of OIR’s investigation with Fitzsimmons and others on multiple occasions.  Hansen expressed no concerns with the quality and thoroughness of OIR’s statistical work. Moreover, he has a clear understanding of the implications of OIR’s findings. Hansen testified that the reports show that Asian Americans “are disadvantaged in the admissions process at Harvard.” And when asked: “Do you have any explanation other than intentional discrimination for your conclusions regarding the negative association between Asians and the Harvard admissions process?” Hansen responded: “I don’t.”
A very sad tweet:

Saturday, June 16, 2018

Harvard discrimination lawsuit: data show penalization of Asian-Americans on subjective personality evaluation

Harvard and Students For Fair Admissions (SFFA), which is suing Harvard over discrimination against Asian-American applicants, have released a large set of documents related to the case, including statistical analysis of records of more than 160,000 applicants who applied for admission over six cycles from 2000 to 2015.

Documents here and here. NYTimes coverage.

The following point does not require any sophisticated modeling (with inherent assumptions) or statistical expertise to understand.

Harvard admissions evaluators -- staffers who are likely under pressure to deliver a target mix of ethnicities each year -- rate Asian-American applicants far lower on subjective personality traits than do alumni interviewers who actually meet the applicants. The easiest way to limit the number of A-A admits each year would be to penalize them on the most subjective aspects of the evaluation...

As stated further below: When it comes to the score assigned by the Admissions Office, Asian-American applicants are assigned the lowest scores of any racial group. ... By contrast, alumni interviewers (who actually meet the applicants) rate Asian-Americans, on average, at the top with respect to personal ratings—comparable to white applicants...
SFFA Memorandum: Professor Arcidiacono found that Harvard’s admissions system discriminates against Asian-American applicants in at least three respects. First, he found discrimination in the personal rating. Asian-American applicants are significantly stronger than all other racial groups in academic performance. They also perform very well in non-academic categories and have higher extracurricular scores than any other racial group. Asian-American applicants (unsurprisingly, therefore) receive higher overall scores from alumni interviewers than all other racial groupsAnd they receive strong scores from teachers and guidance counselors—scores that are nearly identical to white applicants (and higher than African-American and Hispanic applicants). In sum, Professor Arcidiacono found that “Asian-American applicants as a whole are stronger on many objective measures than any other racial/ethnic group including test scores, academic achievement, and extracurricular activities.

Yet Harvard’s admissions officials assign Asian Americans the lowest score of any racial group on the personal rating—a “subjective” assessment of such traits as whether the student has a “positive personality” and “others like to be around him or her,” has “character traits” such as “likability ... helpfulness, courage, [and] kindness,” is an “attractive person to be with,” is “widely respected,” is a “good person,” and has good “human qualities.” Importantly, Harvard tracks two different personal ratings: one assigned by the Admissions Office and another by alumni interviewers. When it comes to the score assigned by the Admissions Office, Asian-American applicants are assigned the lowest scores of any racial group. ... By contrast, alumni interviewers (who actually meet the applicants) rate Asian Americans, on average, at the top with respect to personal ratings—comparable to white applicants and higher than African-American and Hispanic applicants.
From the Crimson:
The report found that Asian American applicants performed significantly better in rankings of test scores, academics, and overall scores from alumni interviews. Of 10 characteristics, white students performed significantly better in only one—rankings of personal qualities, which are assigned by the Admissions Office. [italics added]
See also Too Many Asian Americans: Affirmative Discrimination in Elite College Admissions. (Source of figure at top; the peak in A-A representation at Harvard, in the early 1990s, coincides with external pressure from an earlier DOJ investigation of the university for discrimination.)

A very sad tweet:

For the statistically sophisticated, see Duke Professor Arcidiacono's rebuttal to David Card's analysis for Harvard. If these entirely factual and easily verified characterizations of Card's modeling (see below) are correct, the work is laughable.
Professor Card’s models are distorted by his inclusion of applicants for whom there is no reason to believe race plays any role.

As my opening report noted, there are several categories of applicants to whom Harvard extends preferences for reasons other than race: recruited athletes, children of faculty and staff, those who are on the Dean’s List or Director’s List [i.e., Big Donors], legacies, and those who apply for early admission.1 Because of the significant advantage that each of these categories confers on applicants, my report analyzed the effect of race on an applicant pool without these special categories of applicants (the baseline dataset), which allowed me to test for the effect of race on the bulk of the applicant pool that did not fall into one of these categories.2

Professor Card, however, includes all of these applicants in his model, taking the remarkable position that there is no penalty against Asian-American applicants unless Harvard imposes a penalty on every Asian-American applicant. But this is an untenable position. I do not assert that Harvard uses race to penalize Asian-American applicants who are recruited athletes, children of donors (or others identified on the Dean’s List), legacies, or other preferred categories. By including these special recruiting categories in his models, Professor Card obscures the extent to which race is affecting admissions decisions for all other applicants.

Professor Card further exacerbates this problem by including in his calculations the large majority of applicants whose characteristics guarantee rejection regardless of their race. Harvard admits a tiny fraction of applicants – only five or six percent in recent years. This means that a huge proportion of applicants have no realistic chance of admission. If an applicant has no chance of admission, regardless of his race, then Harvard obviously does not “discriminate” based on race in rejecting that applicant. Professor Card uses this obvious fact to assert that Harvard does not consider race at all in most of its admissions decisions. Further, he constructs his models in ways that give great weight to these applicants, again watering down the effect of race in Harvard’s decisions where it clearly does matter. (To put it in simple terms, it is akin to reducing the value of a fraction by substantially increasing the size of its denominator.)

Professor Card removes interaction terms, which has the effect of understating the penalty Harvard imposes on Asian-American applicants.

As Professor Card notes, his model differs from mine in that he removes the interaction terms. An interaction term allows the effects of a particular factor to vary with another distinct factor. In the context of racial discrimination, interaction terms are especially helpful (and often necessary) in revealing where certain factors operate differently for subgroups within a particular racial or ethnic group. For example, if a law firm singled out African-American women for discriminatory treatment but treated African-American males and other women fairly, a regression model would probably not pick up the discrimination unless it included an interaction between African-American and female.

Professor Card rightly recognizes that interaction terms should be included in a model when there is evidence that racial preferences operate differently for particular groups of applicants; yet he nonetheless removes interaction terms for variables that satisfy this condition. The most egregious instance of this is Professor Card’s decision not to interact race with disadvantaged status—even though the data clearly indicate that Harvard treats disadvantaged students differently by race.


Professor Card’s report changes none of my conclusions; to the contrary, given how easy it is to alter the results of his models and that my own models report the same results even incorporating a number of his controls, my opinions in this case have only been strengthened: Harvard penalizes Asian-American applicants; Harvard imposes heavy racial preferences in favor of Hispanic and African-American applicants; and Harvard has been manipulating its admission of single-race African-American applicants to ensure their admission rate approximates or exceeds the overall admission rate. Professor Card has demonstrated that it is possible to mask the true effects of race in Harvard’s admission process by changing the scope of the analysis in incorrect ways and choosing inappropriate combinations of control variables. But Professor Card cannot reach these results by applying accepted statistical methods and treating the data fairly.

Tuesday, June 12, 2018

Big Ed on Classical and Quantum Information Theory

I'll have to carve out some time this summer to look at these :-) Perhaps on an airplane...

When I visited IAS earlier in the year, Witten was sorting out Lieb's (nontrivial) proof of strong subadditivity. See also Big Ed.
A Mini-Introduction To Information Theory

This article consists of a very short introduction to classical and quantum information theory. Basic properties of the classical Shannon entropy and the quantum von Neumann entropy are described, along with related concepts such as classical and quantum relative entropy, conditional entropy, and mutual information. A few more detailed topics are considered in the quantum case.
Notes On Some Entanglement Properties Of Quantum Field Theory

These are notes on some entanglement properties of quantum field theory, aiming to make accessible a variety of ideas that are known in the literature. The main goal is to explain how to deal with entanglement when – as in quantum field theory – it is a property of the algebra of observables and not just of the states.
Years ago at Caltech, walking back to Lauritsen after a talk on quantum information, with John Preskill and a famous string theorist not to be named. When I asked the latter what he thought of the talk, he laughed and said Well, after all, it's just linear algebra :-)

Sunday, June 10, 2018

The Life of this World

From this 2011 post:
I've been a fan of the writer James Salter (see also here) since discovering his masterpiece A Sport and a Pastime. Salter evokes Americans in France as no one since Hemingway in A Moveable Feast. The title comes from the Koran: Remember that the life of this world is but a sport and a pastime ... :-)

I can't think of higher praise than to say I've read every bit of Salter's work I could get my hands on.
For true Salter fans, a new (2017; he passed in 2015) collection of previously uncollected nonfiction: Don't Save Anything: Uncollected Essays, Articles, and Profiles. I especially liked the essay Younger Women, Older Men, originally published in Esquire in 1992.

From A Sport and a Pastime.
“When did you get out of Yale?”
“I didn’t,” he says. “I quit.”

He describes it casually, without stooping to explain, but the authority of the act overwhelms me. If I had been an underclassman he would have become my hero, the rebel who, if I had only had the courage, I might have also become. ... Now, looking at him, I am convinced of all I missed. I am envious. Somehow his life seems more truthful than mine, stronger, even able to draw mine to it like the pull of a dark star.

He quit. It was too easy for him, his sister told me, and so he refused it. He had always been extraordinary in math. He had a scholarship. He knew he was exceptional. Once he took the anthropology final when he hadn’t taken the course. He wrote that at the top of the page. His paper was so brilliant the professor fell in love with him. Dean was disappointed, of course. It only proved how ridiculous everything was. ... He lived with various friends in New York and began to develop a style. ... in the end he quit altogether. Then he began educating himself.


She stoops with the match, inserts it, and the heater softly explodes. A blue flame rushes across the jets, then burns with a steady sound. There’s no other light in the room but this, which reflects from the floor. She stands up again. She drops the burnt match on the table and begins to arrange clothing on the grill of the heater, pajamas, spreading them out so they can be warmed. Dean helps her a bit. The silk, if it’s that, is quite cold. And there, back from the Vox opposite the Citroen garage, its glass doors now closed, they stand in the roaring dark. In a fond, almost brotherly gesture, he puts his arms around her. They hardly know one another. She accepts it without a word, without a movement, and they wait in a pure silence, the faint sweetness of gas in the air. After a while she turns the pajamas over. Her back is towards him. In a single move she pulls off her sweater and then, reaching behind herself in that elbow-awkward way, unfastens her brassiere. Slowly he turns her around.

From a message to a friend, who knew Salter, and asked me to articulate what I most admire about his work.
About 5 years ago I became friends with the writer Richard Ford, who offered to introduce me to his friend Salter. I was less enthusiastic to meet him than I would have been when he was younger. I did not go out of my way, and we never met.

Since he lived in Aspen, and I was often there in the summers at the Physics institute, I have sometimes imagined that we crossed paths without knowing it.

I admire, of course, his prose style. Sentence for sentence, he is the master.

But perhaps even more I admire his view of the world -- of courage, honor, daring to attempt the impossible, men and women, what is important in life.

Saturday, June 09, 2018

The Rise of AI (Bloomberg Hello World documentary)

Great profile of Geoff Hinton, Yoshua Bengio, etc., but covers many other topics.

Note to readers: I'll be at the 35th International Conference on Machine Learning (ICML 2018) in Stockholm, Sweden (July 10-15, 2018), giving a talk at the Reproducibility in ML Workshop.

Let me know if you want to meet up!

Wednesday, May 30, 2018

Deep Learning as a branch of Statistical Physics

Via Jess Riedel, an excellent talk by Naftali Tishby given recently at the Perimeter Institute.

The first 15 minutes is a very nice summary of the history of neural nets, with an emphasis on the connection to statistical physics. In the large network (i.e., thermodynamic) limit, one observes phase transition behavior -- sharp transitions in performance, and also a kind of typicality (concentration of measure) that allows for general statements that are independent of some detailed features.

Unfortunately I don't know how to embed video from Perimeter so you'll have to click here to see the talk.

An earlier post on this work: Information Theory of Deep Neural Nets: "Information Bottleneck"

Title and Abstract:
The Information Theory of Deep Neural Networks: The statistical physics aspects

The surprising success of learning with deep neural networks poses two fundamental challenges: understanding why these networks work so well and what this success tells us about the nature of intelligence and our biological brain. Our recent Information Theory of Deep Learning shows that large deep networks achieve the optimal tradeoff between training size and accuracy, and that this optimality is achieved through the noise in the learning process.

In this talk, I will focus on the statistical physics aspects of our theory and the interaction between the stochastic dynamics of the training algorithm (Stochastic Gradient Descent) and the phase structure of the Information Bottleneck problem. Specifically, I will describe the connections between the phase transition and the final location and representation of the hidden layers, and the role of these phase transitions in determining the weights of the network.

About Tishby:
Naftali (Tali) Tishby נפתלי תשבי

Physicist, professor of computer science and computational neuroscientist
The Ruth and Stan Flinkman professor of Brain Research
Benin school of Engineering and Computer Science
Edmond and Lilly Safra Center for Brain Sciences (ELSC)
Hebrew University of Jerusalem, 96906 Israel

I work at the interfaces between computer science, physics, and biology which provide some of the most challenging problems in today’s science and technology. We focus on organizing computational principles that govern information processing in biology, at all levels. To this end, we employ and develop methods that stem from statistical physics, information theory and computational learning theory, to analyze biological data and develop biologically inspired algorithms that can account for the observed performance of biological systems. We hope to find simple yet powerful computational mechanisms that may characterize evolved and adaptive systems, from the molecular level to the whole computational brain and interacting populations.

Saturday, May 26, 2018

Vinyl Sounds

Vinyl + Vacuum Tubes ... Still unsurpassed for warmth and richness of sound.

When I lived in New Haven in the 90s I took the train in to NYC on weekends to visit old friends from physics and mathematics, most of whom worked in finance. One Sunday morning in the spring I found myself with a friend of a friend, a big fixed income trader and devoted audiophile. His apartment in the Village had a large room with a balcony surrounded by leafy trees. In the room he kept only two things: a giant divan next to the balcony, on which several people at a time could recline, and the most expensive audio system I have ever seen. We spent hours listening to jazz and eating fresh cannoli with his actress girlfriend.

Off Grid Tiny Homes

This is the kind of thing I fantasize about doing after I retire :-)

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