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  • richardmitnick 7:24 am on March 4, 2018 Permalink | Reply
    Tags: , Barbara Engelhardt, , Computer Science, , GTEx-Genotype-Tissue Expression Consortium, , ,   

    From Quanta Magazine: “A Statistical Search for Genomic Truths” 

    Quanta Magazine
    Quanta Magazine

    February 27, 2018
    Jordana Cepelewicz

    Barbara Engelhardt, a Princeton University computer scientist, wants to strengthen the foundation of biological knowledge in machine-learning approaches to genomic analysis. Sarah Blesener for Quanta Magazine.

    We don’t have much ground truth in biology.” According to Barbara Engelhardt, a computer scientist at Princeton University, that’s just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Techniques in artificial intelligence and machine learning are dramatically altering the landscape of biological research, but Engelhardt doesn’t think those “black box” approaches are enough to provide the insights necessary for understanding, diagnosing and treating disease. Instead, she’s been developing new statistical tools that search for expected biological patterns to map out the genome’s real but elusive “ground truth.”

    Engelhardt likens the effort to detective work, as it involves combing through constellations of genetic variation, and even discarded data, for hidden gems. In research published last October [Nature], for example, she used one of her models to determine how mutations relate to the regulation of genes on other chromosomes (referred to as distal genes) in 44 human tissues. Among other findings, the results pointed to a potential genetic target for thyroid cancer therapies. Her work has similarly linked mutations and gene expression to specific features found in pathology images.

    The applications of Engelhardt’s research extend beyond genomic studies. She built a different kind of machine-learning model, for instance, that makes recommendations to doctors about when to remove their patients from a ventilator and allow them to breathe on their own.

    She hopes her statistical approaches will help clinicians catch certain conditions early, unpack their underlying mechanisms, and treat their causes rather than their symptoms. “We’re talking about solving diseases,” she said.

    To this end, she works as a principal investigator with the Genotype-Tissue Expression (GTEx) Consortium, an international research collaboration studying how gene regulation, expression and variation contribute to both healthy phenotypes and disease.


    Right now, she’s particularly interested in working on neuropsychiatric and neurodegenerative diseases, which are difficult to diagnose and treat.

    Quanta Magazine recently spoke with Engelhardt about the shortcomings of black-box machine learning when applied to biological data, the methods she’s developed to address those shortcomings, and the need to sift through “noise” in the data to uncover interesting information. The interview has been condensed and edited for clarity.

    What motivated you to focus your machine-learning work on questions in biology?

    I’ve always been excited about statistics and machine learning. In graduate school, my adviser, Michael Jordan [at the University of California, Berkeley], said something to the effect of: “You can’t just develop these methods in a vacuum. You need to think about some motivating applications.” I very quickly turned to biology, and ever since, most of the questions that drive my research are not statistical, but rather biological: understanding the genetics and underlying mechanisms of disease, hopefully leading to better diagnostics and therapeutics. But when I think about the field I am in — what papers I read, conferences I attend, classes I teach and students I mentor — my academic focus is on machine learning and applied statistics.

    We’ve been finding many associations between genomic markers and disease risk, but except in a few cases, those associations are not predictive and have not allowed us to understand how to diagnose, target and treat diseases. A genetic marker associated with disease risk is often not the true causal marker of the disease — one disease can have many possible genetic causes, and a complex disease might be caused by many, many genetic markers possibly interacting with the environment. These are all challenges that someone with a background in statistical genetics and machine learning, working together with wet-lab scientists and medical doctors, can begin to address and solve. Which would mean we could actually treat genetic diseases — their causes, not just their symptoms.

    You’ve spoken before about how traditional statistical approaches won’t suffice for applications in genomics and health care. Why not?

    First, because of a lack of interpretability. In machine learning, we often use “black-box” methods — [classification algorithms called] random forests, or deeper learning approaches. But those don’t really allow us to “open” the box, to understand which genes are differentially regulated in particular cell types or which mutations lead to a higher risk of a disease. I’m interested in understanding what’s going on biologically. I can’t just have something that gives an answer without explaining why.

    The goal of these methods is often prediction, but given a person’s genotype, it is not particularly useful to estimate the probability that they’ll get Type 2 diabetes. I want to know how they’re going to get Type 2 diabetes: which mutation causes the dysregulation of which gene to lead to the development of the condition. Prediction is not sufficient for the questions I’m asking.

    A second reason has to do with sample size. Most of the driving applications of statistics assume that you’re working with a large and growing number of data samples — say, the number of Netflix users or emails coming into your inbox — with a limited number of features or observations that have interesting structure. But when it comes to biomedical data, we don’t have that at all. Instead, we have a limited number of patients in the hospital, a limited number of genotypes we can sequence — but a gigantic set of features or observations for any one person, including all the mutations in their genome. Consequently, many theoretical and applied approaches from statistics can’t be used for genomic data.

    What makes the genomic data so challenging to analyze?

    The most important signals in biomedical data are often incredibly small and completely swamped by technical noise. It’s not just about how you model the real, biological signal — the questions you’re trying to ask about the data — but also how you model that in the presence of this incredibly heavy-handed noise that’s driven by things you don’t care about, like which population the individuals came from or which technician ran the samples in the lab. You have to get rid of that noise carefully. And we often have a lot of questions that we would like to answer using the data, and we need to run an incredibly large number of statistical tests — literally trillions — to figure out the answers. For example, to identify an association between a mutation in a genome and some trait of interest, where that trait might be the expression levels of a specific gene in a tissue. So how can we develop rigorous, robust testing mechanisms where the signals are really, really small and sometimes very hard to distinguish from noise? How do we correct for all this structure and noise that we know is going to exist?

    So what approach do we need to take instead?

    My group relies heavily on what we call sparse latent factor models, which can sound quite mathematically complicated. The fundamental idea is that these models partition all the variation we observed in the samples, with respect to only a very small number of features. One of these partitions might include 10 genes, for example, or 20 mutations. And then as a scientist, I can look at those 10 genes and figure out what they have in common, determine what this given partition represents in terms of a biological signal that affects sample variance.

    So I think of it as a two-step process: First, build a model that separates all the sources of variation as carefully as possible. Then go in as a scientist to understand what all those partitions represent in terms of a biological signal. After this, we can validate those conclusions in other data sets and think about what else we know about these samples (for instance, whether everyone of the same age is included in one of these partitions).

    When you say “go in as a scientist,” what do you mean?

    I’m trying to find particular biological patterns, so I build these models with a lot of structure and include a lot about what kinds of signals I’m expecting. I establish a scaffold, a set of parameters that will tell me what the data say, and what patterns may or may not be there. The model itself has only a certain amount of expressivity, so I’ll only be able to find certain types of patterns. From what I’ve seen, existing general models don’t do a great job of finding signals we can interpret biologically: They often just determine the biggest influencers of variance in the data, as opposed to the most biologically impactful sources of variance. The scaffold I build instead represents a very structured, very complex family of possible patterns to describe the data. The data then fill in that scaffold to tell me which parts of that structure are represented and which are not.

    So instead of using general models, my group and I carefully look at the data, try to understand what’s going on from the biological perspective, and tailor our models based on what types of patterns we see.

    How does the latent factor model work in practice?

    We applied one of these latent factor models to pathology images [pictures of tissue slices under a microscope], which are often used to diagnose cancer. For every image, we also had data about the set of genes expressed in those tissues. We wanted to see how the images and the corresponding gene expression levels were coordinated.

    We developed a set of features describing each of the images, using a deep-learning method to identify not just pixel-level values but also patterns in the image. We pulled out over a thousand features from each image, give or take, and then applied a latent factor model and found some pretty exciting things.

    For example, we found sets of genes and features in one of these partitions that described the presence of immune cells in the brain. You don’t necessarily see these cells on the pathology images, but when we looked at our model, we saw a component there that represented only genes and features associated with immune cells, not brain cells. As far as I know, no one’s seen this kind of signal before. But it becomes incredibly clear when we look at these latent factor components.

    Video: Barbara Engelhardt, a computer scientist at Princeton University, explains why traditional machine-learning techniques have often fallen short for genomic analysis, and how researchers are overcoming that challenge. Sarah Blesener for Quanta Magazine

    You’ve worked with dozens of human tissue types to unpack how specific genetic variations help shape complex traits. What insights have your methods provided?

    We had 44 tissues, donated from 449 human cadavers, and their genotypes (sequences of their whole genomes). We wanted to understand more about the differences in how those genotypes expressed their genes in all those tissues, so we did more than 3 trillion tests, one by one, comparing every mutation in the genome with every gene expressed in each tissue. (Running that many tests on the computing clusters we’re using now takes about two weeks; when we move this iteration of GTEx to the cloud as planned, we expect it to take around two hours.) We were trying to figure out whether the [mutant] genotype was driving distal gene expression. In other words, we were looking for mutations that weren’t located on the same chromosome as the genes they were regulating. We didn’t find very much: a little over 600 of these distal associations. Their signals were very low.

    But one of the signals was strong: an exciting thyroid association, in which a mutation appeared to distally regulate two different genes. We asked ourselves: How is this mutation affecting expression levels in a completely different part of the genome? In collaboration with Alexis Battle’s lab at Johns Hopkins University, we looked near the mutation on the genome and found a gene called FOXE1, for a transcription factor that regulates the transcription of genes all over the genome. The FOXE1 gene is only expressed in thyroid tissues, which was interesting. But we saw no association between the mutant genotype and the expression levels of FOXE1. So we had to look at the components of the original signal we’d removed before — everything that had appeared to be a technical artifact — to see if we could detect the effects of the FOXE1 protein broadly on the genome.

    We found a huge impact of FOXE1 in the technical artifacts we’d removed. FOXE1, it seems, regulates a large number of genes only in the thyroid. Its variation is driven by the mutant genotype we found. And that genotype is also associated with thyroid cancer risk. We went back to the thyroid cancer samples — we had about 500 from the Cancer Genome Atlas — and replicated the distal association signal. These things tell a compelling story, but we wouldn’t have learned it unless we had tried to understand the signal that we’d removed.

    What are the implications of such an association?

    Now we have a particular mechanism for the development of thyroid cancer and the dysregulation of thyroid cells. If FOXE1 is a druggable target — if we can go back and think about designing drugs to enhance or suppress the expression of FOXE1 — then we can hope to prevent people at high thyroid cancer risk from getting it, or to treat people with thyroid cancer more effectively.

    The signal from broad-effect transcription factors like FOXE1 actually looks a lot like the effects we typically remove as part of the noise: population structure, or the batches the samples were run in, or the effects of age or sex. A lot of those technical influences are going to affect approximately similar numbers of genes — around 10 percent — in a similar way. That’s why we usually remove signals that have that pattern. In this case, though, we had to understand the domain we were working in. As scientists, we looked through all the signals we’d gotten rid of, and this allowed us to find the effects of FOXE1 showing up so strongly in there. It involved manual labor and insights from a biological background, but we’re thinking about how to develop methods to do it in a more automated way.

    So with traditional modeling techniques, we’re missing a lot of real biological effects because they look too similar to noise?

    Yes. There are a ton of cases in which the interesting pattern and the noise look similar. Take these distal effects: Pretty much all of them, if they are broad effects, are going to look like the noise signal we systematically get rid of. It’s methodologically challenging. We have to think carefully about how to characterize when a signal is biologically relevant or just noise, and how to distinguish the two. My group is working fairly aggressively on figuring that out.

    Why are those relationships so difficult to map, and why look for them?

    There are so many tests we have to do; the threshold for the statistical significance of a discovery has to be really, really high. That creates problems for finding these signals, which are often incredibly small; if our threshold is that high, we’re going to miss a lot of them. And biologically, it’s not clear that there are many of these really broad-effect distal signals. You can imagine that natural selection would eliminate the kinds of mutations that affect 10 percent of genes — that we wouldn’t want that kind of variability in the population for so many genes.

    But I think there’s no doubt that these distal associations play an enormous role in disease, and that they may be considered as druggable targets. Understanding their role broadly is incredibly important for human health.

    See the full article here .

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    Formerly known as Simons Science News, Quanta Magazine is an editorially independent online publication launched by the Simons Foundation to enhance public understanding of science. Why Quanta? Albert Einstein called photons “quanta of light.” Our goal is to “illuminate science.” At Quanta Magazine, scientific accuracy is every bit as important as telling a good story. All of our articles are meticulously researched, reported, edited, copy-edited and fact-checked.

  • richardmitnick 12:39 pm on February 14, 2018 Permalink | Reply
    Tags: , Carnegie Mellon University - College of Engineering, Computer Science,   

    From Carnegie Mellon University: “Moore’s Law is ending. What’s next?” 

    Carnegie Mellon University – College of Engineering


    Adam Dove

    The speed of our technology doubles every year, right? Not anymore.


    We’ve come to take for granted that as the years go on, computing technology gets faster, cheaper and more energy-efficient.

    In their recent paper, Science and research policy at the end of Moore’s law published in Nature Electronics, however, Carnegie Mellon University researchers Hassan Khan, David Hounshell, and Erica Fuchs argue that future advancement in microprocessors faces new and unprecedented challenges.

    In 1965 R&D Director at Fairchild (and later Intel co-founder) Gordon Moore predicted continued systemic declines in cost and increase in performance of integrated circuits in his paper “Cramming more components onto integrated circuits.” This trend, later coined Moore’s Law, held for over 40 years, making possible a “dazzling array” of new products from intercontinental ballistic missiles to global environmental monitoring systems and from smart phones to medical implants.

    “Half of economic growth in the U.S. and world-wide has also been attributed to this trend and the innovations it enabled throughout the economy,” says Engineering and Public Policy Professor Erica Fuchs.

    In the seven decades following the invention of the transistor at Bell Labs, warnings about impending limits to miniaturization and the corresponding slow down of Moore’s Law have come regularly from industry observers and academic researchers. Despite these warnings, semiconductor technology continually progressed along the Moore’s Law trajectory. Khan, Hounshell, and Fuchs’ archival work and oral histories, however, make clear that times are changing.

    “The current technological and structural challenges facing the industry are unprecedented and undermine the incentives for continued collective action in research and development,” the authors state in the paper, “which has underpinned the last 50 years of transformational worldwide economic growth and social advance.”

    As the authors explain in their paper, progress in semiconductor technology is undergoing a seismic shift driven by changes in the underlying technology and product-end markets. Achieving continued performance improvements through transistor miniaturization has grown increasingly expensive and the emergence of new end-markets has driven innovation in more specialized domains. As such, in recent years there has been a splintering of technology trajectories, such that the entire industry moving in lock-step to Moore’s Law is no longer of economic benefit to all firms. Examples in the paper include search companies (such as Microsoft Bing) using field-programmable gate-arrays in data centers as accelerators in conjunction with CPUs, and Google’s announcement of proprietary ‘tensor-processing unit’ chips developed in-house for its deep-learning activities.

    “While these innovations will drive many domain-specific advances, to continue advancing general purpose computing capabilities at reduced cost with economy-wide benefits will likely require entirely new semiconductor process and device technology.” explains Engineering and Public Policy graduate Hassan Khan. “The underlying science for this technology is as of yet unknown, and will require significant research funds – an order of magnitude more than is being invested today.”

    The authors conclude by arguing that the lack of private incentives creates a case for greatly increased public funding and the need for leadership beyond traditional stakeholders. They suggest that funding is needed of $600 million dollars per year with 90% of those funds from public research dollars, and the rest most likely from defense agencies.

    In terms of allocating those funds, they argue for pursuing two avenues in parallel: A research institute with academics and government program managers as key players, that also engages industry players across the computing technology stack; coupled with a semi-coordinated government funding effort focused on the next generation of transistor technology across all government agencies, such as was undertaken in the case of the National Nanotechnology Initiative, in which key program managers met weekly to discuss initiatives and share insights.

    See the full article here .

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    The College of Engineering is well-known for working on problems of both scientific and practical importance. Our acclaimed faculty focus on transformative results that will drive the intellectual and economic vitality of our community, nation and world. Our “maker” culture is ingrained in all that we do, leading to novel approaches and unprecedented results.

    Carnegie Mellon Campus

    Carnegie Mellon University (CMU) is a global research university with more than 12,000 students, 95,000 alumni, and 5,000 faculty and staff.
    CMU has been a birthplace of innovation since its founding in 1900.
    Today, we are a global leader bringing groundbreaking ideas to market and creating successful startup businesses.
    Our award-winning faculty members are renowned for working closely with students to solve major scientific, technological and societal challenges. We put a strong emphasis on creating things—from art to robots. Our students are recruited by some of the world’s most innovative companies.
    We have campuses in Pittsburgh, Qatar and Silicon Valley, and degree-granting programs around the world, including Africa, Asia, Australia, Europe and Latin America.

  • richardmitnick 8:07 am on January 18, 2018 Permalink | Reply
    Tags: , Computer Science, , Raspberry Pi   

    From JHU HUB: “Raspberry Pi DIY workshop teaches students how to use customizable, versatile minicomputers” 

    Johns Hopkins

    Saralyn Cruickshank

    Second-year student Julia Costacurta wants to use Raspberry Pi, a customizable minicomputer, to program a plant-watering device. Image credit: Saralyn Cruickshank.

    Raspberry Pi, a single-board computer the size of a credit card, has revolutionized the tech industry since its debut in 2012.

    What would you build if you could design any kind of tech device for $35? Would you bypass the costs of major cellphone providers and build your own smartphone? Would you build a bartending robot to mix your favorite cocktails perfectly each time? Would you relive your youth and create a retro video game console that cuts out the need for those pesky, dust-prone game cartridges?

    This month, 15 Johns Hopkins students are learning the ins and outs of a versatile device capable of these and infinitely more functions: the Raspberry Pi, a small, green single-board computer capable of fitting inside an Altoids tin.

    Raspberry Pi is a revolution in computer hardware, enabling users to build their own computers and smart devices. Developed in the United Kingdom, Raspberry Pi costs as little as $35 and has become a worldwide phenomenon, with global sales surpassing 15 million units last year. These bare bones, accessory-compatible minicomputers have become especially popular in fields relating to home automation, data visualization, robotics, and Internet-capable devices.

    The Raspberry Pi DIY Intersession course, offered by the Department of Electrical and Computer Engineering in the Whiting School of Engineering, provides students with not only the Pis but also a fleet of peripheral devices such as LED light panels, Google-compatible voice kits, and a series of sensors and circuits enabling them to build their own devices. Students are guided through setup and programming before being unleashed to pursue their own Pi-powered projects.

    Instructor Bryan Bosworth, a postdoctoral fellow in Electrical and Computer Engineering, says these minicomputers and their capabilities represent autonomy and freedom.

    These single-board computers are compatible with a variety of computer accessories including keyboards and monitors. Image credit: Saralyn Cruickshank.

    “You don’t have to take what The Man gives you,” he says half-seriously to the class in their second meeting. “When you master this kind of computer engineering, you won’t be beholden to anyone—you can get these devices to do what you want.”

    He speaks from experience—he’s owned more than a dozen Raspberry Pis since the tool’s release in 2012. On display in the class computer lab is a Pi-powered LED panel he built himself: A cross between a Doppler radar weather map and a Lite Brite, the device displays a minute-by-minute heat map of crime in Baltimore City, with pinpoints of light blinking when a new crime is reported, then slowly fading away.

    He also uses the minicomputer to automate some of his creature comforts at home. A Raspberry Pi with voice command serves as his enhanced and personalized virtual assistant to stream videos and music, and he recently paired Pi technology with LED light panels arranged in a cube to look like a Halloween pumpkin that intermittently blinks and yawns. He has aspirations to reprogram it to appear like a Rubik’s cube and wants to add a motion sensor to the rig so that users can command each twist of the Rubik’s cube with a wave of their hand.

    “I view computing devices and the software that runs on them as an essential freedom,” Bosworth says. “You have to be able to know what’s going on under the hood, especially when more and more of modern-day life relies on these things. You have to be able to poke at it and make changes to suit you.”

    For many of the students in the class, this is their first time working with computer hardware.

    “I’ve known how to code for a while, but I’ve never done hardware,” says first-year student Vivek Gopalakrishnan, a biomedical engineering and electrical engineering double-major. “I thought learning how to use a Raspberry Pi would be a good idea for learning how to build my own devices.”

    Others want to build on skills they already have. Second-year biomedical engineering major Julia Costacurta says she used a similar open source computer hardware device called Arduino for a prosthetics project last summer.

    “I figured this would be a really good way to get more hands-on experience with computer hardware,” she says. “I have a lot of plants in my apartment, so I want to build a Raspberry Pi that’s able to sense when they need to be watered and remind me.”

    First-year computer engineering major Anderson Adon has worked with Raspberry Pi before but says the workshop setting will give him a more formal introduction to the minicomputer’s capabilities. He too already has a plan for his final project: He says he wants to pair his Raspberry Pi to a digital thermometer in his residence hall and program it to automatically tweet the university whenever the temperature in his room dips below a certain point.

    By the end of the first class meeting, he’s set up his Raspberry Pi—in less time than it took him to set up his Twitter account.

    See the full article here .

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    About the Hub

    We’ve been doing some thinking — quite a bit, actually — about all the things that go on at Johns Hopkins. Discovering the glue that holds the universe together, for example. Or unraveling the mysteries of Alzheimer’s disease. Or studying butterflies in flight to fine-tune the construction of aerial surveillance robots. Heady stuff, and a lot of it.

    In fact, Johns Hopkins does so much, in so many places, that it’s hard to wrap your brain around it all. It’s too big, too disparate, too far-flung.

    We created the Hub to be the news center for all this diverse, decentralized activity, a place where you can see what’s new, what’s important, what Johns Hopkins is up to that’s worth sharing. It’s where smart people (like you) can learn about all the smart stuff going on here.

    At the Hub, you might read about cutting-edge cancer research or deep-trench diving vehicles or bionic arms. About the psychology of hoarders or the delicate work of restoring ancient manuscripts or the mad motor-skills brilliance of a guy who can solve a Rubik’s Cube in under eight seconds.

    There’s no telling what you’ll find here because there’s no way of knowing what Johns Hopkins will do next. But when it happens, this is where you’ll find it.

    Johns Hopkins Campus

    The Johns Hopkins University opened in 1876, with the inauguration of its first president, Daniel Coit Gilman. “What are we aiming at?” Gilman asked in his installation address. “The encouragement of research … and the advancement of individual scholars, who by their excellence will advance the sciences they pursue, and the society where they dwell.”

    The mission laid out by Gilman remains the university’s mission today, summed up in a simple but powerful restatement of Gilman’s own words: “Knowledge for the world.”

    What Gilman created was a research university, dedicated to advancing both students’ knowledge and the state of human knowledge through research and scholarship. Gilman believed that teaching and research are interdependent, that success in one depends on success in the other. A modern university, he believed, must do both well. The realization of Gilman’s philosophy at Johns Hopkins, and at other institutions that later attracted Johns Hopkins-trained scholars, revolutionized higher education in America, leading to the research university system as it exists today.

  • richardmitnick 10:04 am on January 11, 2018 Permalink | Reply
    Tags: , , Blue Brain Nexus, , Computer Science,   

    From EPFL: “Blue Brain Nexus: an open-source tool for data-driven science” 

    EPFL bloc

    École Polytechnique Fédérale de Lausanne

    BBP communications

    © iStockphotos

    Knowledge sharing is an important driving force behind scientific progress. In an open-science approach, EPFL’s Blue Brain Project has created and open sourced Blue Brain Nexus that allows the building of data integration platforms. Blue Brain Nexus enables data-driven science through searching, integrating and tracking large-scale data and models.

    EPFL’s Blue Brain Project today announces the release of its open source software project ‘Blue Brain Nexus’, designed to enable the FAIR (Findable, Accessible, Interoperable, and Reusable) data management principles for the Neuroscience and broader scientific community. It is part of EPFL’s open-science initiative, which seeks to maximize the reach and impact of research conducted at the school.

    The aim of the Blue Brain Project is to build accurate, biologically detailed, digital reconstructions and simulations of the rodent brain and, ultimately the human brain. Blue Brain Nexus is instrumental in supporting all stages of Blue Brain’s data-driven modelling cycle including, but not limited to experimental data, single cell models, circuits, simulations and validations. The brain is a complex multi-level system and is one of the biggest ‘Big Data’ problems we have today. Therefore, Blue Brain Nexus has been built to organize, store and process exceptionally large volumes of data and support usage by a broad number of users.

    At the heart of Blue Brain Nexus is the Knowledge Graph, which acts as a data repository and metadata catalogue. It also remains agnostic of the domain to be represented by allowing users to design arbitrary domains, which enables other scientific initiatives (e.g. astronomy, medical research and agriculture) to reuse Blue Brain Nexus as the core of their data platforms. Blue Brain Nexus services are already being evaluated for integration into the Human Brain Project’s Neuroinformatics Platform.

    Specific to enabling scientific progress, Blue Brain Nexus’s Knowledge Graph treats provenance as a first-class citizen, thus facilitating the tracking of the origin of data as well as how it is being used. This allow users to assess the quality of data, and consequently to enable them to build trust. Another key feature of Blue Brain Nexus is its semantic search capability, whereby search is integrated over data and its provenance to enable scientists to easily discover and access new relevant data.

    EPFL Professor Sean Hill commented: “We see that nearly all sciences are becoming data-driven. Blue Brain Nexus represents the culmination of many years of research into building a state-of-the-art semantic data management platform. We can’t wait to see what the community will do with Blue Brain Nexus.”

    Blue Brain Nexus is available under the Apache 2 license, at https://github.com/BlueBrain/nexus

    For more information, please contact:

    EPFL Communications, emmanuel.barraud@epfl.ch, +41 21 693 21 90

    Blue Brain Project communications, kate.mullins@epfl.ch, +41 21 695 51 41

    See the full article here .

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    EPFL is Europe’s most cosmopolitan technical university. It receives students, professors and staff from over 120 nationalities. With both a Swiss and international calling, it is therefore guided by a constant wish to open up; its missions of teaching, research and partnership impact various circles: universities and engineering schools, developing and emerging countries, secondary schools and gymnasiums, industry and economy, political circles and the general public.

  • richardmitnick 5:22 pm on January 3, 2018 Permalink | Reply
    Tags: Computer Science, , , MORPHEUS, , Unhackable computer   

    From U Michigan: “Unhackable computer under development with $3.6M DARPA grant” 

    U Michigan bloc

    University of Michigan

    December 20, 2017
    Nicole Casal Moore
    (734) 647-7087

    The researchers say they’re making an unsolvable puzzle: ‘It’s like if you’re solving a Rubik’s Cube and every time you blink, I rearrange it.’

    The MORPHEUS approach outlines a new way to design hardware so that information is rapidly and randomly moved and destroyed. The technology works to elude attackers from the critical information they need to construct a successful attack. Photo: Getty Images

    By turning computer circuits into unsolvable puzzles, a University of Michigan team aims to create an unhackable computer with a new $3.6 million grant from the Defense Advanced Research Projects Agency.

    Todd Austin, U-M professor of computer science and engineering, leads the project, called MORPHEUS. Its cybersecurity approach is dramatically different from today’s, which relies on software—specifically software patches to vulnerabilities that have already been identified. It’s been called the “patch and pray” model, and it’s not ideal.

    This spring, DARPA announced a $50 million program in search of cybersecurity solutions that would be baked into hardware.

    “Instead of relying on software Band-Aids to hardware-based security issues, we are aiming to remove those hardware vulnerabilities in ways that will disarm a large proportion of today’s software attacks,” said Linton Salmon, manager of DARPA’s System Security Integrated Through Hardware and Firmware program.

    The U-M grant is one of nine that DARPA has recently funded through SSITH.

    MORPHEUS outlines a new way to design hardware so that information is rapidly and randomly moved and destroyed. The technology works to elude attackers from the critical information they need to construct a successful attack. It could protect both hardware and software.

    “We are making the computer an unsolvable puzzle,” Austin said. “It’s like if you’re solving a Rubik’s Cube and every time you blink, I rearrange it.”

    In this way, MORPHEUS could protect against future threats that have yet to be identified, a dreaded vulnerability that the security industry called a “zero day exploit.”

    “What’s incredibly exciting about the project is that it will fix tomorrow’s vulnerabilities,” Austin said. “I’ve never known any security system that could be future proof.”

    Austin said his approach could have protected against the Heartbleed bug discovered in 2014. Heartbleed allowed attackers to read the passwords and other critical information on machines.

    “Typically, the location of this data never changes, so once attackers solve the puzzle of where the bug is and where to find the data, it’s ‘game over,’” Austin said.

    Under MORPHEUS, the location of the bug would constantly change and the location of the passwords would change, he said. And even if an attacker were quick enough to locate the data, secondary defenses in the form of encryption and domain enforcement would throw up additional roadblocks. The bug would still be there, but it wouldn’t matter. The attacker won’t have the time or the resources to exploit it.

    “These protections don’t exist today because they are too expensive to implement in software, but with DARPA’s support we can take the offensive against attackers with new defenses in hardware and implement then with virtually no impact to software,” Austin said.

    More than 40 percent of the “software doors” that hackers have available to them today would be closed if researchers could eliminate seven classes of hardware weaknesses, according to DARPA. The hardware weakness classes have been identified by a crowd-source listing of security vulnerabilities called the Common Weakness Enumeration. The classes are: permissions and privileges, buffer errors, resource management, information leakage, numeric errors, crypto errors, and code injection.

    DARPA is aiming to render these attacks impossible within five years. If developed, MORPHEUS could do it now, Austin said.

    While the complexity required might sound expensive, Austin said he’s confident his team can make it possible at low cost.

    Also on the project team are: Valeria Bertacco, an Arthur F. Thurnau Professor and professor of computer science and engineering at U-M; Mohit Tiwari, an assistant professor of electrical and computer engineering at the University of Texas; and Sharad Malik, the George Van Ness Lothrop Professor of Engineering and a professor of electrical engineering at Princeton University.

    See the full article here .

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    U MIchigan Campus

    The University of Michigan (U-M, UM, UMich, or U of M), frequently referred to simply as Michigan, is a public research university located in Ann Arbor, Michigan, United States. Originally, founded in 1817 in Detroit as the Catholepistemiad, or University of Michigania, 20 years before the Michigan Territory officially became a state, the University of Michigan is the state’s oldest university. The university moved to Ann Arbor in 1837 onto 40 acres (16 ha) of what is now known as Central Campus. Since its establishment in Ann Arbor, the university campus has expanded to include more than 584 major buildings with a combined area of more than 34 million gross square feet (781 acres or 3.16 km²), and has two satellite campuses located in Flint and Dearborn. The University was one of the founding members of the Association of American Universities.

    Considered one of the foremost research universities in the United States,[7] the university has very high research activity and its comprehensive graduate program offers doctoral degrees in the humanities, social sciences, and STEM fields (Science, Technology, Engineering and Mathematics) as well as professional degrees in business, medicine, law, pharmacy, nursing, social work and dentistry. Michigan’s body of living alumni (as of 2012) comprises more than 500,000. Besides academic life, Michigan’s athletic teams compete in Division I of the NCAA and are collectively known as the Wolverines. They are members of the Big Ten Conference.

  • richardmitnick 11:42 am on December 18, 2017 Permalink | Reply
    Tags: , , Computer Science, Ming C. Lin, ,   

    From UMD: Women in STEM: “Ming Lin Named Chair of UMD Department of Computer Science” 

    U Maryland bloc

    University of Maryland

    Media Relations Contact:
    Abby Robinson

    Writer: Tom Ventsias

    Ming C. Lin. Photo: John T. Consoli

    Ming C. Lin will lead the University of Maryland’s Department of Computer Science, effective January 1, 2018.

    A noted educator and expert in virtual reality, computer graphics and robotics, Lin will assume the role of Elizabeth Stevinson Iribe Chair of Computer Science with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS).

    As chair, she will oversee a department that has experienced significant increases in student enrollment; expanded its research in virtual and augmented reality, robotics, machine learning, cybersecurity and quantum information science; and grown its outreach efforts to fuel more corporate and philanthropic support. The department currently ranks 15th in the nation according to U.S. News & World Report.

    Lin comes to Maryland from the University of North Carolina at Chapel Hill, where she was the John R. and Louise S. Parker Distinguished Professor of Computer Science and a faculty member for 20 years.

    She arrives at an opportune time—the department’s faculty, staff and students will move in late 2018 to the Brendan Iribe Center for Computer Science and Innovation, a 215,000-square-foot-facilty that will offer unprecedented opportunities to explore and imagine bold new directions in computer science. The new building became a reality thanks to a $31 million gift from Brendan Iribe, a UMD alumnus and co-founder of the virtual reality company Oculus.

    “We are thrilled that Ming Lin will lead our efforts in advancing computer science at the University of Maryland,” said Gerald Wilkinson, interim dean of the UMD College of Computer, Mathematical, and Natural Sciences. “She brings a wealth of experience as a skilled educator and as a phenomenal researcher that will help her guide the department to further success.”

    Lin spent the past several months meeting with UMD students, faculty, staff, alumni and other stakeholders. She also attended outreach events focused on highlighting opportunities in computer science to prospective students, many of whom are women or other underrepresented groups in the field. The department’s continuing efforts to enhance diversity have resulted in the number of female undergraduates in the department tripling over the last five years, with more than 600 women currently pursuing undergraduate degrees.

    “Computing is no longer a specialty, it is part of everyone’s daily life,” Lin said. “So, for me to see such a considerable amount of academic talent and enthusiasm from everyone I’ve met at Maryland has been amazing. I am truly honored and privileged to be given the opportunity to work with this community.”

    The department’s undergraduate enrollment has increased rapidly. In fall 2013, the department had 1,386 undergraduate majors; that number swelled to 3,106 in fall 2017, making it one of the largest computer science programs in the country and the most popular major on campus.

    “One of my primary goals is to ensure that our students will be successful in their careers when they graduate,” Lin said. “They are going to be the leaders in a society where practically every aspect of daily life is enabled and impacted by computing. Giving them the knowledge and skills to excel in a technology-empowered world is a mission I take very seriously.”

    The department includes more than 50 tenured or tenure-track faculty members and 11 full-time professional track instructional faculty members. They are pursuing new methods of instruction that encourage innovation and entrepreneurship, with these activities expected to significantly ramp up once the Iribe Center opens.

    “I am encouraged to see that so many faculty members here are active in blended learning and advocate for more makerspaces and other student-initiated activities like the Bitcamp and Technica hackathons where students turn a spark of creativity into new technology,” Lin said.

    Along with the 16 labs and centers in UMIACS, the department brings in approximately $25 million in external funding each year. The more than 250 computer science graduate students work closely with faculty members, postdocs and others on cutting-edge research that often crosses academic disciplines. They explore topics that include cryptocurrency exchanges, deep learning for autonomous robotics, computational linguistics, genome microbial sequencing and more.

    Lin plans to bring some of her UNC research group to Maryland, continuing her research in virtual reality, computer graphics and robotics that focuses on multimodal interaction, physically based animations and simulations, as well as algorithmic robotics and their use in physical and virtual environments. Her research has extensive applications in medical simulations, cancer screening, urban computing, as well as supporting city-scale planning, human-centric computing, intelligent transportation and traffic management.

    “We’ve constantly been working on scientific problems where the solutions will have considerable social impact,” Lin said. “That’s important for me—I am hoping through research, teaching and advising that I can make some difference.”

    Lin earned her B.S., M.S. and Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley. She received a National Science Foundation Faculty Early Career Development (CAREER) Award in 1995, and she is a fellow of the Association for Computing Machinery, IEEE and the Eurographics Association. She also serves on the board of directors of the Computing Research Association’s Committee on the Status of Women in Computing Research.

    She has authored or co-authored more than 250 refereed publications and has authored or co-edited four books. She is a former editor-in-chief of IEEE Transactions on Visualization and Computer Graphics (2011–2014) and has served on numerous steering committees and advisory boards of international conferences, as well as government and industrial technical advisory committees. Lin also co-founded the 3-D audio startup Impulsonic, which was recently acquired by Valve Software.

    Lin succeeds Larry Davis, who became interim chair of the department on July 1, 2017. Prior to Davis, Samir Khuller completed a five-year term as chair, serving as the inaugural Elizabeth Stevinson Iribe Chair of Computer Science. Elizabeth Iribe established the endowed chair in 2015 with a $1.5 million donation. Her donation received $1.1 million in matching funds from the state’s Maryland E-Nnovation Initiative Fund, which aims to spur private donations to universities for applied research in scientific and technical fields by matching such donations.

    See the full article here .

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    Driven by the pursuit of excellence, the University of Maryland has enjoyed a remarkable rise in accomplishment and reputation over the past two decades. By any measure, Maryland is now one of the nation’s preeminent public research universities and on a path to become one of the world’s best. To fulfill this promise, we must capitalize on our momentum, fully exploit our competitive advantages, and pursue ambitious goals with great discipline and entrepreneurial spirit. This promise is within reach. This strategic plan is our working agenda.

    The plan is comprehensive, bold, and action oriented. It sets forth a vision of the University as an institution unmatched in its capacity to attract talent, address the most important issues of our time, and produce the leaders of tomorrow. The plan will guide the investment of our human and material resources as we strengthen our undergraduate and graduate programs and expand research, outreach and partnerships, become a truly international center, and enhance our surrounding community.

    Our success will benefit Maryland in the near and long term, strengthen the State’s competitive capacity in a challenging and changing environment and enrich the economic, social and cultural life of the region. We will be a catalyst for progress, the State’s most valuable asset, and an indispensable contributor to the nation’s well-being. Achieving the goals of Transforming Maryland requires broad-based and sustained support from our extended community. We ask our stakeholders to join with us to make the University an institution of world-class quality with world-wide reach and unparalleled impact as it serves the people and the state of Maryland.

  • richardmitnick 9:11 am on March 31, 2017 Permalink | Reply
    Tags: , , Computer Science, Wei Xu,   

    From BNL: Women in STEM “Visualizing Scientific Big Data in Informative and Interactive Ways” Wei Xu 

    Brookhaven Lab

    March 31, 2017
    Ariana Tantillo

    Brookhaven Lab computer scientist Wei Xu develops visualization tools for analyzing large and varied datasets.

    Wei Xu, a computer scientist who is part of Brookhaven Lab¹s Computational Science Initiative, helps scientists analyze large and varied datasets by developing visualization tools, such as the color-mapping tool seen projected from her laptop onto the large screen.

    Humans are visual creatures: our brain processes images 60,000 times faster than text, and 90 percent of information sent to the brain is visual. Visualization is becoming increasingly useful in the era of big data, in which we are generating so much data at such high rates that we cannot keep up with making sense of it all. In particular, visual analytics—a research discipline that combines automated data analysis with interactive visualizations—has emerged as a promising approach to dealing with this information overload.

    “Visual analytics provides a bridge between advanced computational capabilities and human knowledge and judgment,” said Wei Xu, a computer scientist in the Computational Science Initiative (CSI) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and a research assistant professor in the Department of Computer Science at Stony Brook University. “The interactive visual representations and interfaces enable users to efficiently explore and gain insights from massive datasets.”

    At Brookhaven, Xu has been leading the development of several visual analytics tools to facilitate the scientific decision-making and discovery process. She works closely with Brookhaven scientists, particularly those at the National Synchrotron Light Source II (NSLS-II) and the Center for Functional Nanomaterials (CFN)—both DOE Office of Science User Facilities.


    By talking to researchers early on, Xu learns about their data analysis challenges and requirements. She continues the conversation throughout the development process, demoing initial prototypes and making refinements based on their feedback. She also does her own research and proposes innovative visual analytics methods to the scientists.

    Recently, Xu has been collaborating with the Visual Analytics and Imaging (VAI) Lab at Stony Brook University—her alma mater, where she completed doctoral work in computed tomography with graphics processing unit (GPU)-accelerated computing.

    Though Xu continued work in these and related fields when she first joined Brookhaven Lab in 2013, she switched her focus to visualization by the end of 2015.

    “I realized how important visualization is to the big data era,” Xu said. “The visualization domain, especially information visualization, is flourishing, and I knew there would be lots of research directions to pursue because we are dealing with an unsolved problem: how can we most efficiently and effectively understand the data? That is a quite interesting problem not only in the scientific world but also in general.”

    It was at this time that Xu was awarded a grant for a visualization project proposal she submitted to DOE’s Laboratory Directed Research and Development program, which funds innovative and creative research in areas of importance to the nation’s energy security. At the same time, Klaus Mueller—Xu’s PhD advisor at Stony Brook and director of the VAI Lab—was seeking to extend his research to a broader domain. Xu thought it would be a great opportunity to collaborate: she would present the visualization problem that originated from scientific experiments and potential approaches to solve it, and, in turn, doctoral students in Mueller’s lab would work with her and their professor to come up with cutting-edge solutions.

    This Brookhaven-Stony Brook collaboration first led to the development of an automated method for mapping data involving multiple variables to color. Variables with a similar distribution of data points have similar colors. Users can manipulate the color maps, for example, enhancing the contrast to view the data in more detail. According to Xu, these maps would be helpful for any image dataset involving multiple variables.

    The color-mapping tool was used to visualize a multivariable fluorescence dataset from the Hard X-ray Nanoprobe (HXN) beamline at Brookhaven’s National Synchrotron Light Source II. The color map (a) shows how the different variables—the chemical elements cerium (Ce), cobalt (Co), iron (Fe), and gadolinium (Gd)—are distributed in a sample of an electrolyte material used in solid oxide fuel cells. The fluorescence spectrum of the selected data point (the circle indicated by the overlaid white arrows) is shown by the colored bars, with their height representing the relative elemental ratios. The fluorescence image (b), pseudo-colored based on the color map in (a), represents a joint colorization of the individual images in (d), whose colors are based on the four points at the circle boundary (a) for each of the four elements. The arrow indicates where new chemical phases can exist—something hard to detect when observing the individual plots (d). Enhancing the color contrast—for example, of the rectangular region in (b)—enables a more detailed view, in this case providing better contrast between Fe (red) and Co (green) in image (c).

    “Different imaging modalities—such as fluorescence, differential phase contrasts, x-ray scattering, and tomography—would benefit from this technique, especially when integrating the results of these modalities,” she said. “Even subtle differences that are hard to identify in separate image displays, such as differences in elemental ratios, can be picked up with our tool—a capability essential for new scientific discovery.” Currently, Xu is trying to install the color mapping at NSLS-II beamlines, and advanced features will be added gradually.

    In conjunction with CFN scientists, the team is also developing a multilevel display for exploring large image sets. When scientists scan a sample, they generate one scattering image at each point within the sample, known as the raw image level. They can zoom in on this image to check the individual pixel values (the pixel level). For each raw image, scientific analysis tools are used to generate a series of attributes that represent the analyzed properties of the sample (the attribute level), with a scatterplot showing a pseudo-color map of any user-chosen attribute from the series—for example, the sample’s temperature or density. In the past, scientists had to hop between multiple plots to view these different levels. The interactive display under development will enable scientists to see all of these levels in a single view, making it easier to identify how the raw data are related and to analyze data across the entire scanned sample. Users will be able to zoom in and out on different levels of interest, similar to how Google Maps works.

    The multilevel display tool enables scientists conducting scattering experiments to explore the resulting image sets at the scatterplot level (0), attribute pseudo-color level (1), zoom-in attribute level (2), raw image level (3), zoom-in raw image level (4), and pixel level (5), all in a single display.

    The ability to visually reconstruct a complete joint dataset from several partial marginal datasets is at the core of another visual analytics tool that Xu’s Stony Brook collaborators developed. This web-based tool enables users to reconstruct all possible solutions to a given problem and locate the subset of preferred solutions through interactive filtering.

    “Scientists commonly describe a single object with datasets from different sources—each covering only a portion of the complete properties of that object—for example, the same sample scanned in different beamlines,” explained Xu. “With this tool, scientists can recover a property with missing fields by refining its potential ranges and interactively acquiring feedback about whether the result makes sense.”

    Their research led to a paper that was published in the Institute of Electrical and Electronics Engineers (IEEE) journal Transactions on Visualization and Computer Graphics and awarded the Visual Analytics Science and Technology (VAST) Best Paper Honorable Mention at the 2016 IEEE VIS conference.

    At this same conference, another group of VAI Lab students whom Xu worked with were awarded the Scientific Visualization (SciVis) Best Poster Honorable Mention for their poster, “Extending Scatterplots to Scalar Fields.” Their plotting technique helps users link correlations between attributes and data points in a single view, with contour lines that show how the numerical values of the attributes change. For their case study, the students demonstrated how the technique could help college applications select the right university by plotting the desired attributes (e.g., low tuition, high safety, small campus size) with different universities (e.g., University of Virginia, Stanford University, MIT). The closer a particular college is to some attribute, the higher that attribute value.

    The scatter plots above are based on a dataset containing 46 universities with 14 attributes of interest for prospective students: academics, athletics, housing, location, nightlife, safety, transportation, weather, score, tuition, dining, PhD/faculty, population, and income. The large red nodes represent the attributes and the small blue points represent the universities; the contour lines (middle plot) show how the numerical values of the attributes change. This prospective student wants to attend a university with good academics (>9/10). Universities that meet this criterion are within the contours lines whose value exceeds 9. To determine which universities meet multiple criteria, students would see where the universities and attributes overlap (right plot).

    According to Xu, this kind of technique also could be applied to visualize artificial neural networks—the deep learning (a type of machine learning) frameworks that are used to address problems such as image classification and speech recognition.

    “Because neural network models have a complex structure, it is hard to understand how their intrinsic learning process works and how they arrive at intermediate results, and thus quite challenging to debug them,” explained Xu. “Neural networks are still largely regarded as black boxes. Visualization tools like this one could help researchers get a better idea of their model’s performance.”

    Besides her Stony Brook collaborations, Xu is currently involved in the Co-Design Center for Online Data Analysis and Reduction at the Exascale (CODAR), which Brookhaven is partnering on with other national laboratories and universities through DOE’s Exascale Computing Project. Her role is to visualize data evaluating the performance of computing clusters, applications, and workflows that the CODAR team is developing to analyze and reduce data online before the data are written to disk for possible further offline analysis. Exascale computer systems are projected to provide unprecedented increases in computational speed but the input/output (I/O) rates of transferring the computed results to storage disks are not expected to keep pace, so it will be infeasible for scientists to save all of their scientific results for offline analysis. Xu’s visualization will help the team “diagnose” any performance issues with the computation processes, including individual application execution, computation job management in the clusters, I/O performance in the runtime system, and data reduction and reconstruction efficiency.

    Xu is also part of a CSI effort to build a virtual reality (VR) lab for an interactive data visualization experience. “It would be a more natural way to observe and interact with data. VR techniques replicate a realistic and immersive 3D environment,” she said.

    For Xu, her passion for visualization most likely stemmed from an early interest in drawing.

    “As a child, I liked to draw,” she said. “In growing up, I took my drawings from paper to the computer.”

    See the full article here .

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    One of ten national laboratories overseen and primarily funded by the Office of Science of the U.S. Department of Energy (DOE), Brookhaven National Laboratory conducts research in the physical, biomedical, and environmental sciences, as well as in energy technologies and national security. Brookhaven Lab also builds and operates major scientific facilities available to university, industry and government researchers. The Laboratory’s almost 3,000 scientists, engineers, and support staff are joined each year by more than 5,000 visiting researchers from around the world. Brookhaven is operated and managed for DOE’s Office of Science by Brookhaven Science Associates, a limited-liability company founded by Stony Brook University, the largest academic user of Laboratory facilities, and Battelle, a nonprofit, applied science and technology organization.

  • richardmitnick 3:55 pm on November 13, 2014 Permalink | Reply
    Tags: , Computer Science, , Steve Ballmer   

    From Harvard Physics: “Why I’m making a big investment in Harvard’s computer science faculty” 

    Harvard University

    Harvard University

    November 13, 2014

    Steve Ballmer
    Former CEO, Microsoft

    The field of computer science—what it is, what it enables, what it takes to do it well—is undergoing rapid change. Decades of advances in theory and practice have shaped what it now demands of students and scholars, and what it makes possible. When any field undergoes rapid change, what it takes to succeed also changes. New possibilities are created, and new competitors emerge.

    Now, the field has turned outward, driving scientific discovery and deepening our knowledge of everything from how the body works to why people form communities to what shapes the global economy. The power of computational thinking has the potential to spark breakthroughs in nearly every sphere of human endeavor.

    Few areas of achievement can claim as broad a range of influence as computer science can. But what more can we do to accelerate change? It’s a question I thought a lot about while running Microsoft, and the answer is simple: support the very best talent at a place where talent can be fully realized. To that end, I am helping support an increase in the size of the computer science faculty at Harvard by 50%. There is, in my mind, no place better positioned to catch the next wave in computer science than greater Boston. I’ll give you four reasons why:

    First, this area has led the scientific revolution in computing. Harvard has been home to major breakthroughs in engineering and applied sciences, from the invention of the first large-scale automatic digital computer to significant advances in cryptography, learning theory, computer graphics and robotics. During World War II, MIT helped the US Navy design a universal flight simulator that led to the first radar-based air defense system. MIT faculty, including Harvard engineering PhD Leo Beranek, founded Cambridge’s BBN Technologies, which designed and built early Internet infrastructure. Computer science at Harvard and MIT is thriving today as advances in algorithms, systems and artificial intelligence combine to transform the way in which we live with and interact with computers and information. While Stanford and Carnegie Mellon are serious competitors with strong computer science programs powering important breakthroughs, neither—on their own—match the combined strength of Harvard and MIT.

    Second, the next generation of computer science will be outward facing: bringing expertise in harnessing data and computing power not only to statistics, engineering, and applied math, but to biology, chemistry, neuroscience, design, the social sciences and public policy. At Harvard, CS exists as a department without walls, within a school (Engineering and Applied Sciences) known for reaching across disciplines to tackle challenging problems. MIT and Harvard computer scientists routinely collaborate—edX is one great example—and use each other’s school as a recruiting tool, since students can cross-register for classes at both schools, and it is not unusual for labs to have scientists from both universities. This openness creates an inquisitive and entrepreneurial culture, a tendency to share ideas and collaborate and hustle that is energizing research as well as teaching and learning.

    Third, Boston and Cambridge have the unmatched advantage of more top-ranked academic departments than any other metropolitan area in the world. Where other than Harvard can you find eminent schools of business, law, and medicine and a renowned design school, public policy school, and schools of education and public health? What other region has the Whitehead Institute, the Broad Institute, and the Wyss Institute, all taking the study of biology, medicine, and engineering to new heights thanks in large part to what is being enabled by machine learning and big data? Imagine the puzzles we can solve—the questions we can ask—by uniting expertise. What is intelligence? Which disease patterns can be discerned through new algorithms? How can cities and urban environments become more efficient and more sustainable? It is not an exaggeration to say that in this region, a top-notch collaborator on these and other fundamental questions can be found down the street or even down the hall.

    Fourth, Harvard is transforming Allston into a hub of entrepreneurship that will draw on the region’s extraordinary strengths to attract thinkers and doers from around the world. With a soon-to-be-built signature building for Engineering and Applied Sciences, the University will rethink how spaces are best configured for the education and research of tomorrow, for collaborative research, and for meeting and brainstorming. Kendall Square stands as a testament to what’s possible when land is developed for technology companies—start-ups and giants both—right next to top-notch computer scientists and their collaborators. Harvard’s dramatically expanded CS faculty will be located just across the street from the existing Innovation Lab and adjacent to a new enterprise zone that will house both start-ups and established businesses. Allston will be the place to be for anyone who wants to watch the ripple of his or her idea spread and gain speed.

    Computer science and computational thinking has transformed the world. But too often, ideas generated here have been commercialized on the West Coast. By hiring the brightest, most creative professors, by educating the world’s best students, and by leveraging the strengths of all of Harvard as well as MIT, this region can catch the next wave and lead the world in shaping the computing of tomorrow. If successful, Boston will set a standard for the field that will bring the very best of computer science to big problems in every field, and change all of our lives for the better.

    See the full article here.

    Harvard is the oldest institution of higher education in the United States, established in 1636 by vote of the Great and General Court of the Massachusetts Bay Colony. It was named after the College’s first benefactor, the young minister John Harvard of Charlestown, who upon his death in 1638 left his library and half his estate to the institution. A statue of John Harvard stands today in front of University Hall in Harvard Yard, and is perhaps the University’s best known landmark.

    Harvard University has 12 degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. The University has grown from nine students with a single master to an enrollment of more than 20,000 degree candidates including undergraduate, graduate, and professional students. There are more than 360,000 living alumni in the U.S. and over 190 other countries.


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  • richardmitnick 12:44 pm on March 29, 2013 Permalink | Reply
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    From PNNL Lab: “Striking While the Iron Is Hot” 

    Chromatography combined with database search strategy identifies hard-to-find heme proteins

    March 2013
    Suraiya Farukhi
    Christine Sharp

    Results: Heme c is an important iron-containing post-translational modification found in many proteins. It plays an important role in respiration, metal reduction, and nitrogen fixation, especially anaerobic respiration of environmental microbes. Such bacteria and their c-type cytochromes are studied extensively because of their potential use in bioremediation, microbial fuel cells, and electrosynthesis of valuable biomaterials.

    heme c
    Heme C

    Until recently, these modifications were hard to find using traditional proteomic methods. Scientists at Pacific Northwest National Laboratory combined a heme c tag protein affinity purification strategy called histidine affinity chromatography (HAC) with enhanced database searching. This combination confidently identified heme c peptides in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments-by as much as 100-fold in some cases.”

    Why It Matters: Iron is a critical part of many biological processes; however, it is often not biologically available or it can be toxic in high quantities. So, biological systems have developed intricate methods to use and store iron. Many environmentally important microbes and microbial communities are rich in c-type cytochromes. Combining HAC and data analysis tailored to the unique properties of heme c peptides should enable more detailed study of the role of c-type cytochromes in these microbes and microbial communities.

    ‘Several proteomics studies have analyzed the expression of c-type cytochromes under various conditions,’ said PNNL postdoctoral researcher Dr. Eric Merkley, and lead author of a paper that appeared in the Journal of Proteome Research. ‘A shared feature of these studies is that the cytochrome-rich fractions, the cell envelope or extracellular polymeric substance, were purified and explicitly analyzed to efficiently detect cytochromes. Analyses of large-scale proteomics datasets have typically suggested that c-type cytochromes, particularly the heme c peptides, are under-represented.'”

    See the full article here.

    Pacific Northwest National Laboratory (PNNL) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.


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  • richardmitnick 7:22 pm on February 4, 2013 Permalink | Reply
    Tags: , , Computer Science   

    From CERN: “CERN and Oracle celebrate 30 years of collaboration” 

    CERN New Masthead

    4 Feb 2013
    Andrew Purcell

    On Friday 1 February, 2013, CERN and Oracle celebrated 30 years of collaboration. In addition to providing hardware and software to CERN for three decades, Oracle has now been involved in the CERN openlab project for 10 years.

    Rolf Heuer and Loïc le Guisquet cut cake to celebrate 30 years of collaboration between CERN and Oracle (Image: CERN)

    The celebration, which capped off the ‘IT requirements for the next generation of research infrastructures workshop’ held at CERN, saw CERN Director-General Rolf Heuer present Loïc le Guisquet, executive vice president of Oracle Europe, Middle East, and Africa with a small award to mark the occasion. Heuer presented Guisquet with an Oracle tape mounted in glass and marked with the following inscription: ‘LHC data are stored on Oracle tapes similar to the one presented on this award. This specific tape stores the videos of the announcement of the discovery of the new boson, which took place at CERN on 4th July 2012′.

    ‘It is important that IT infrastructures for research embrace new technologies in a manner that is not only useful for researchers, but also improves the competitiveness of many business sectors,’ says Heuer, who cites the collaboration between Oracle and CERN as an excellent example of this. ‘CERN has been working continuously with Oracle over the last 30 years,’ he adds. ‘Oracle is also a long-standing partner of CERN openlab and I think it has developed into a successful model over the last decade now of public-private partnerships in the IT domain.

    CERN openlab is a unique public-private partnership between CERN and a range of leading IT companies. Its mission is to accelerate the development of cutting-edge solutions to be used by the worldwide LHC community. ‘By using CERN openlab as a showcase, companies can then promote their products and their services to other labs and different business sectors,’ says Bob Jones, head of the organization. ‘We are proud to be part of this collaboration,’ says Le Guisquet. ‘We are energised by it and we want it to go on because it always stretches our limits.'”.

    See the full article here.

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