Category Archives: Data Science
Five UChicago scholars elected to American Academy of Arts and … – UChicago News
Five members of the University of Chicago faculty have been elected to the American Academy of Arts and Sciences, one of the nations oldest and most prestigious honorary societies.
They include Profs. Michael J. Franklin, Chang-Tai Hsieh, Magne Mogstad, Salikoko S. Mufwene and Shigehiro Oishi.
These scholars have made breakthroughs in fields ranging from computer science to economics to the evolution of language. They join the 2023 class of 269 people, announced April 19, which includes artists, scholars, scientists, and leaders in the public, nonprofit and private sectors.
Michael J. Franklin is the inaugural holder of the Liew Family Chair of Computer Science. An authority on databases, data analytics, data management and distributed systems, he also serves as Senior Advisor to the Provost on Computation and Data Science and is Faculty Co-Director of the Data Science Institute.
He is one of the original creators of Apache Spark, a leading open source platform for data analytics and machine learning that was developed at the lab. In addition to his academic work, Franklin founded and was chief technology officer of Truviso, a data analytics company acquired by Cisco Systems. He currently serves as a technical advisor to various data-driven technology companies and organizations, including AMPLab spin-out Databricks and Chicago-based Ocient.
He is a fellow of the Association for Computing Machinery and a two-time recipient of the ACM Special Interest Group on Management of Data Test of Time award and numerous Best Paper awards at leading systems and database conferences.
Chang-Tai Hsieh is the Phyllis and Irwin Winkelried Professor of Economics at Chicago Booth. He conducts research on growth and development.
His published papers include The Life-Cycle of Plants in India and Mexico, in the Quarterly Journal of Economics; "Misallocation and Manufacturing TFP in China and India," in the Quarterly Journal of Economics; "Relative Prices and Relative Prosperity," in the American Economic Review; "Can Free Entry be Inefficient? Fixed Commissions and Social Waste in the Real Estate Industry," in the Journal of Political Economy; "What Explains the Industrial Revolution in East Asia? Evidence from the Factor Markets," in the American Economic Review; The Allocation of Talent and US Economic Growth, in Econometrica; How Destructive is Innovation? in Econometrica; and Special Deals with Chinese Characteristics, in the NBER Macroeconomics Annual.
Hsieh has been a visiting scholar at the Federal Reserve Banks of San Francisco, New York, and Minneapolis, as well as the World Bank's Development Economics Group and the Economic Planning Agency in Japan. He is a research associate for the National Bureau of Economic Research, a senior fellow at the Bureau for Research in Economic Analysis of Development, and a member of the Steering Group of the International Growth Center in London. He is the recipient of an Alfred P. Sloan Foundation Research Fellowship, a fellow of the Econometric Society, an elected member of Academia Sinica and a two-time recipient of the Sun Ye-Fang Prize.
Magne Mogstad is the Gary S. Becker Distinguished Service Professor in the Kenneth C. Griffin Department of Economics and the College. His work combines economic theory, statistical methods and micro data to help understand the sources of inequality, the functioning of the labor market, and the effects of policy.
Mogstad has published extensively in the leading scholarly journals in economics. He is the lead editor of the Journal of Political Economy, a fellow of the Society of Labor Economists, International Association of Applied Econometrics, and the Econometric Society, and the recipient of the Alfred P. Sloan Foundation Fellowship, the Sherwin Rosen Prize, and the IZA Young Labor Economist award.
Salikoko S. Mufwene is the Edward Carson Waller Distinguished Service Professor in the Department of Linguistics,Race, Diaspora, & Indigeneity,and the College. Mufwene is one of the leading names in the world on the emergence of creoles and on globalization and language.
His current research centers on evolutionary linguistics, which he approaches from an ecological perspective. He focuses on the phylogenetic emergence of language and how languages have been affected by colonization and worldwide globalization, particularly through the indigenization and speciation of European languages in the colonies.
Among his many honors, Mufwene received fellowships at the Linguistic Society of America (2018) and the Institute for Advanced Study in Lyon (2010-11) and was awarded a mdaille du Collge de France in 2003. His first and seminal book, The Ecology of Language Evolution, has been translated into Mandarin. He is the founding editor of the book series Cambridge Approaches to Language Contact (since 2001). One of his latest publications is the two-volume Cambridge Handbook of Language Contact (June 2022), the first of which is devoted to the role of population movement and contact as actuators language change.
He was elected to the American Philosophical Society in May 2022.
Shigehiro Oishi is the Marshall Field IV Professor of Psychology. His research focuses on culture, social ecology, and well-being. The Oishi Lab asks questions surrounding the concept of well-being (e.g. "what is a good life?"), the predictors of well-being (e.g. "what are the predictors of a good life?") and the consequences of well-being (e.g. "are there benefits to a happy/meaningful/psychologically rich life?").
Oishi also is interested in how the concepts, the predictors, and the consequences of well-being might differ across cultures. Additionally, his research explores socio-ecological conditions that are detrimental or conducive to well-being, including income inequality, residential mobility, walkability.
Oishi has been awarded the 2017 Society of Experimental Social Psychology Career Trajectory Award, the 2018 Carol and Ed Diener Award from the Society for Personality and Social Psychology, and the 2021 Outstanding Achievement Award for Advancing Cultural Psychology.
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Five UChicago scholars elected to American Academy of Arts and ... - UChicago News
Comet Unveils Suite of Tools and Integrations to Accelerate Large … – insideBIGDATA
Comet, a leading platform for managing, visualizing and optimizing models from training runs to production monitoring, announced a new suite of tools designed to revolutionize the workflow surrounding Large Language Models (LLMs). These tools mark the beginning of a new market category, known as LLMOps. With Comets MLOps platform and cutting-edge LLMOps tools, organizations can effectively manage their LLMs and enhance their performance in a fraction of the time.
Comets new suite of tools debuts as data scientists working on NLP are no longer training their own models; rather, theyre spending days working to generate the right prompts (i.e. prompt engineering or prompt chaining in which data scientists create prompts based on the output of a previous prompt to solve more complex problems). However, data scientists havent had tools to sufficiently manage and analyze the performance of these prompts. Comets offering enables them to embrace unparalleled levels of productivity and performance. Its tools address the evolving needs of the ML community to build production-ready LLMs and fill a gap in the market that until now has been neglected.
Previously, data scientists required large amounts of data, significant GPU resources, and months of work to train a model, commented Gideon Mendels, CEO and co-founder of Comet. However, today, they can bring their models to production more rapidly than ever before. But the new LLM workflow necessitates dramatically different tools, and Comets LLMOps capabilities were designed to address this crucial need. With our latest release, we believe that Comet offers a comprehensive solution to the challenges that have arisen with the use of Large Language Models.
Comet LLMOps Tools in Action
Comets LLMOps tools are designed to allow users to leverage the latest advancement in Prompt Management and query models in Comet to iterate quicker, identify performance bottlenecks, and visualize the internal state of the Prompt Chains.
The new suite of tools serves three primary functions:
Integrations with leading Large Language Models and Libraries
Comet also announced integrations with OpenAI and LangChain, adding significant value to users. Comets integration with LangChain allows users to track, visualize, and compare chains so they can iterate faster. The OpenAI integration empowers data scientists to leverage the full potential of OpenAIs GPT-3 and capture usage data and prompt / responses so that users never lose track of their past experiments.
The goal of LangChain is to make it as easy as possible for developers to build language model applications. One of the biggest pain points weve heard is around keeping track of prompts and prompt completions, said Harrison Chase, Creator of LangChain. That is why were so excited about this integration with Comet, a platform for tracking and monitoring your machine learning experiments. With Comet, users can easily log their prompts, LLM outputs, and compare different experiments to make decisions faster. This integration allows LangChain users to streamline their workflow and get the most out of their LLM development.
For more information on the new suite of tools and integrations, please visit Comets website,comet.com/site/products/llmops
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Comet Unveils Suite of Tools and Integrations to Accelerate Large ... - insideBIGDATA
H2O World Comes to India For The First Time, Coinciding With The … – Manchestertimes
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H2O World Comes to India For The First Time, Coinciding With The ... - Manchestertimes
Python: the ‘equalizer’ for advanced data analytics – TechRadar
Data (opens in new tab) offers businesses an almost endless list of benefits, from increasing revenue and customer (opens in new tab) retention to improving decision-making and streamlining operations. This makes it an incredibly valuable asset from day to day, but even more so during tough economic times, such as those experienced across the world during the past few years. But many organizations face difficulties in extracting the value from their data - from failing to ensure that analysts, data scientists, developers and engineers work together effectively, to having such relentless demand for business insights that the in-house data team is overwhelmed.
Python (opens in new tab) is an equalizer which can help every part of a data operation to work together. Python is now the most popular language for data science, used by 15.7 million developers globally. It provides an open source framework that enables data teams to deliver cutting-edge data insights rapidly and efficiently. For business leaders, it can be a key differentiator for advanced data analytics.
Python can be seen across many aspects of our lives, however, not everyone may realize it. It is the basis of the Netflix algorithm and the software that controls self-driving cars that you see on the streets. As a general-purpose language, Python is designed to be used in a range of applications (opens in new tab), including data science, software and web development, and automation. Its this versatility along with its beginner-friendliness that makes it accessible to everyone, allowing teams of machine learning (ML) and data engineers, and data scientists to collaborate with ease.
Python has a rich ecosystem of open source libraries that are often targeted for cyber attacks. That is the reason why it is important to proactively address how users access and interact with open source (opens in new tab) tooling in an organization. Python is developed under an open source license, making it freely usable and distributable. For businesses, an open source approach offers distinct advantages. There is a vast community of developers contributing to Python projects, making it easier for organizations to collaborate and achieve their goals. With its rich ecosystem of open source packages, businesses can leverage Python to accelerate projects, without having to deal with the complexity of deploying third-party applications. Its for these reasons that Python has become so popular in the data science field.
Another key aspect of Pythons appeal is speed. In many data analytics use cases, the Python code tends to be simple requiring just a few lines which means that time to market is reduced. This makes Python a natural fit for artificial intelligence (AI) and its algorithmic density. In Python, developers can build logic with as much as 75% less code than other comparable languages.
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According to the latest Python Developers Survey, data analysis is now the single most popular usage for Python, cited by 51% of developers, with ML also among the top uses of the language, cited by 38%. Python provides data scientists with over 70,000 libraries that can be used in any given task. These libraries contain bundles of code, which can be used repeatedly in different programs, making Python programing simpler and more convenient as data scientists will rarely have to start from scratch. Take Streamlit as an example. As a Python-based library, its specifically designed for developers and ML engineers to rapidly build and share ML and data science apps.
For businesses hoping to get to grips with ML for the first time, Python is a clear winner. It offers concise code, allowing developers to write reliable ML solutions faster. This means developers can place all of their efforts into solving an ML problem, rather than focusing on the technical nuances of the language. Its platform-independent, allowing it to run on almost every operating system, which makes it perfect for organizations that dont want to be locked into a proprietary system. As a result, Python improves how cross-functional teams of data scientists, data engineers, and application developers can collaborate in taking ML models from experiments into production - which is one of the key challenges ML practitioners face according to the Anaconda State of Data Science report.
Across industries, Python is making a fundamental difference in how businesses operate, saving time, money, and better utilizing their employees skills. For example, in healthcare, the principal application of Python is based on ML and natural language processing (NLP) algorithms. Such applications include image diagnostics, NLP of medical documents, and the prediction of diseases using human genetics. Patient data is highly confidential, so secure and well-governed processing of such data is essential: this is a key challenge for organizations in the healthcare sector.
The industry widely recognizes the importance of Python, having set up the NHS Python Community. Led by enthusiasts and advocates of practice, the community champions the use of the Python programming language and open code in the NHS and healthcare sector.
Elsewhere, in the utility sector, Python is being adopted to open up new applications to help customers save money and energy. Take EDF as an example - the energy giant moved away from legacy systems in order to have a more unified view of its data. A crucial aspect of this involved utilizing Python to enable data scientists to bring ML models into production. By taking an integrated approach, the company is able to better understand the requirements of its customers and develop new products via ML techniques. As a result, EDF can better support financially vulnerable customers, setting up strategies if they start to face difficulties, and predicting it before it happens.
For most scenarios, whether its analytics, machine learning or app development, Python is not the only language being used. Rather it's often paired with SQL, Java and other languages used by different teams. Integrating Python into data platforms provides organizations with a unique way to create their own applications to derive business value from their data across teams and programming language boundaries. Doing so in a streamlined single cloud service removes much of the expense and complexity traditionally associated with building and managing data-intensive applications catering to different programming language preferences from different teams. Using a cloud (opens in new tab) data platform along with the languages that developers are already comfortable with offers a simpler, faster way to derive business insights from data.
Business leaders need to ensure they are taking advantage of their data while empowering their data scientists, data engineers and developers to collaborate effectively. They also need to be proactive in how open source is used to ensure sensitive data is protected. Python offers data teams the flexibility, performance and speed to turn data into actionable insights, providing an invaluable competitive edge. Going forward, it will be an essential tool for any business looking to operationalize ML insights and grow their business, even in the toughest of times.
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Python: the 'equalizer' for advanced data analytics - TechRadar
What is the Role of Data Science in Agriculture? – Analytics Insight
The role of data science in agriculture farmers is embracing data science to boost agricultural yields, minimize water consumption, and improve product quality. It used technologies to provide geospatial outputs for use in agriculture, disaster relief, and other fields.
In agriculture, data is becoming increasingly crucial. Farmers are utilizing data to make more educated planting, irrigation, and crop management choices. Data may also be used to monitor soil conditions, measure agricultural yields, identify pests and diseases, and track crop yields.
Crop Monitoring: More advanced agricultural monitoring systems are being developed using data science. Farmers can now gather data on their crops using sensors and drones, which can then be analyzed to spot issues early on and take corrective action. This aids in increasing yields and avoiding losses due to pests or illnesses.
Water Management: Water management is one of the most important uses of Data Science in agriculture. Farmers may improve their water consumption to save waste and expenses by collecting data on weather patterns, soil moisture levels, and irrigation systems.
Precision Farming: Precision farming is another important application of Data Science in the agriculture field. This entails using data to direct planting, spraying, and harvesting activities to ensure pinpoint precision. This saves farmers money on inputs like seeds and fertilizers while also reducing crop damage and production losses.
Soil Analysis: Data Science is also being utilized in agriculture to better understand soil composition and fertility. Data science contribution to agricultural scientists may construct more accurate models of soil behavior by examining data from sensors and samples. This enables farmers to improve their irrigation, fertilization, and soil management procedures.
Crop Forecasting: Crop forecasting is another key application of Data Science in agriculture. Agricultural scientists may construct models that anticipate how a certain crop will do in the future under different conditions by studying historical data about weather patterns and crop yields. This data can assist farmers in making decisions about which crops to sow and when to harvest them.
Food Safety: Finally, data science is being used to improve food safety. Agricultural scientists can discover risk factors and devise methods for minimizing the spread of disease-causing microorganisms by examining food-borne illness data. This protects customers and ensures that food items are safe to ingest.
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What is the Role of Data Science in Agriculture? - Analytics Insight
Global regulators discuss how they are adapting to AI, real-world … – Regulatory Focus
Regulators across the globe have been preparing for the arrival of new artificial intelligence (AI) technologies and advances in real-world data (RWD) they say will become a part of regulatory science in the coming years.
That was the main topic of discussion at the 11th Global Summit on Regulatory Science annual conference, where regulators from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the UK and the US presented the ways they are integrating AI and RWD into the operations and regulatory mechanisms of their agencies. The meeting was held virtually in October 2021 and sponsored by the Global Coalition for Regulatory Science Research (GCRSR).
The proceedings were recently summarized by Shraddha Thakkar, PhD, MSc, MS, of the Center for Drug Evaluations and Research (CDER) at the US Food and Drug Administration (FDA), and colleagues from regulatory agencies in the above countries in the journal Regulatory Toxicology and Pharmacology.The regulators discussed in a series of debates, workshops, and presentations how AI and RWD could be applied to food and drug safety assessments, whether regulatory science was prepared for the arrival of AI, how data science tools could better align to regulatory applications, and the future of regulatory science research.Continued progress in AI and RWD provide enormous opportunities for regulatory application with two significant aspects, improving the agencies operation and preparing regulatory mechanisms to review and approve products utilizing these innovations, according to the authors. This is especially important to drug development which usually spans many years and comes with a huge cost, where AI and RWD have demonstrated the ability to improve drug safety and review.The regulators noted that they see the potential for AI and RWD in food safety, pattern recognition, and foodborne outbreaks, which primarily relies on a manual analysis of images, spectrometric data, genomic data, chemical compositions, and identification of contaminants, the authors said. AI and machine learning (ML) have the potential to reduce review times and human variations in manual processes. In many ways, AI and RWD are already here, with agencies like the FDA and Canadian Food Inspection Agency incorporating AI and RWD methodologies into existing programs. AI and RWD can also serve as augmentation tools for existing information aids, such as in the case of Swissmedic considering using serious adverse drug reactions in hospital admissions as RWD to develop automated pharmacovigilance signal detection. Another example is crowdsourcing, which the National Institute of Health Science of Japan used to develop a quantitative structure-activity relationship model for Ames mutagenicity prediction.In two debates, presenters argued that the regulatory community may be prepared and/or unprepared for the advancement of AI and RWD in the domains of scientific knowledge and assessment practices. One presenter argued that AI plays an increasing role in drug discovery and development and that some regulators, like the FDA, are developing programs like the Innovative Science and Technology Approaches for New Drugs (ISTAND) initiative to prepare. Other considerations debated were the role of AI in clinical applications and the extent to which patients may be comfortable using AI-enabled applications in various contexts.Regulatory science could play a critical role in developing a regulatory structure and framework for evaluation of AI application, including promoting trustworthiness and reliability in these technologies, the authors wrote.A workshop where regulators detailed their data analytics tools was another opportunity for AI, Thakkar and colleagues noted, because it has the potential to automate manual reading processes for text associated with safety and efficacy of food and drug products. The vast majority of data used in regulatory decision-making are presented in text document, where AI could be of significance to facilitate the review process, they wrote. Globally, regulatory agencies have not only reviewed vast quantities of submitted application, papers, and/or literature data, but have also generated a plethora of documents during the product-review process. It is typical that these types of records are unstructured text and often do not follow the use of standard vocabulary.Due to lack of standardization and fragmentation of data, leveraging AI to interpret datasets is a substantial regulatory challenge, Thakkar and colleagues explained. The biggest challenge the research community faces is the current fragmentation of data in many repositories with multiple formats and definitions, they said. Another challenge is that, in some cases, the data codes are not uniform. Each data source has a coding system, and different ways of assigning codes to medicines are employed without national or international standardization.The future of regulatory science research in relation to AI and RWD is one where AI augments the work of human clinicians but does not replace them. One of the most significant benefits of AI/ML resides in its ability to learn from real-world use to improve its performance, the authors noted. However, as an emerging technology, AI should be constantly evaluated to actively facilitate the use of these new tools in regulatory settings, they said.Regul Toxicol Pharmacol
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Global regulators discuss how they are adapting to AI, real-world ... - Regulatory Focus
Why open-source generative AI models are an ethical way forward … – Nature.com
Every day, it seems, a new large language model (LLM) is announced with breathless commentary from both its creators and academics on its extraordinary abilities to respond to human prompts. It can fix code! It can write a reference letter! It can summarize an article!
From my perspective as a political and data scientist who is using and teaching about such models, scholars should be wary. The most widely touted LLMs are proprietary and closed: run by companies that do not disclose their underlying model for independent inspection or verification, so researchers and the public dont know on which documents the model has been trained.
The rush to involve such artificial-intelligence (AI) models in research is a problem. Their use threatens hard-won progress on research ethics and the reproducibility of results.
Instead, researchers need to collaborate to develop open-source LLMs that are transparent and not dependent on a corporations favours.
GPT-4 is here: what scientists think
Its true that proprietary models are convenient and can be used out of the box. But it is imperative to invest in open-source LLMs, both by helping to build them and by using them for research. Im optimistic that they will be adopted widely, just as open-source statistical software has been. Proprietary statistical programs were popular initially, but now most of my methodology community uses open-source platforms such as R or Python.
One open-source LLM, BLOOM, was released last July. BLOOM was built by New York City-based AI company Hugging Face and more than 1,000 volunteer researchers, and partially funded by the French government. Other efforts to build open-source LLMs are under way. Such projects are great, but I think we need even more collaboration and pooling of international resources and expertise. Open-source LLMs are generally not as well funded as the big corporate efforts. Also, they need to run to stand still: this field is moving so fast that versions of LLMs are becoming obsolete within weeks or months. The more academics who join these efforts, the better.
Using open-source LLMs is essential for reproducibility. Proprietors of closed LLMs can alter their product or its training data which can change its outputs at any time.
For example, a research group might publish a paper testing whether phrasings suggested by a proprietary LLM can help clinicians to communicate more effectively with patients. If another group tries to replicate that study, who knows whether the models underlying training data will be the same, or even whether the technology will still be supported? GPT-3, released last November by OpenAI in San Francisco, California, has already been supplanted by GPT-4, and presumably supporting the older LLM will soon no longer be the firms main priority.
ChatGPT: five priorities for research
By contrast, with open-source LLMs, researchers can look at the guts of the model to see how it works, customize its code and flag errors. These details include the models tunable parameters and the data on which it was trained. Engagement and policing by the community help to make such models robust in the long term.
The use of proprietary LLMs in scientific studies also has troubling implications for research ethics. The texts used to train these models are unknown: they might include direct messages between users on social-media platforms or content written by children legally unable to consent to sharing their data. Although the people producing the public text might have agreed to a platforms terms of service, this is perhaps not the standard of informed consent that researchers would like to see.
In my view, scientists should move away from using these models in their own work where possible. We should switch to open LLMs and help others to distribute them. Moreover, I think academics, especially those with a large social-media following, shouldnt be pushing others to use proprietary models. If prices were to shoot up, or companies fail, researchers might regret having promoted technologies that leave colleagues trapped in expensive contracts.
Researchers can currently turn to open LLMs produced by private organizations, such as LLaMA, developed by Facebooks parent company Meta in Menlo Park, California. LLaMA was originally released on a case-by-case basis to researchers, but the full model was subsequently leaked online. My colleagues and I are working with Metas open LLM OPT-175B, for instance. Both LLaMA and OPT-175B are free to use. The downside in the long run is that this leaves science relying on corporations benevolence an unstable situation.
There should be academic codes of conduct for working with LLMs, as well as regulation. But these will take time and, in my experience as a political scientist, I expect that such regulations will initially be clumsy and slow to take effect.
In the meantime, massive collaborative projects urgently need support to produce open-source models for research like CERN, the international organization for particle physics, but for LLMs. Governments should increase funding through grants. The field is moving at lightning speed and needs to start coordinating national and international efforts now. The scientific community is best placed to assess the risks of the resulting models, and might need to be cautious about releasing them to the public. But it is clear that the open environment is the right one.
The author declares no competing interests.
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Why open-source generative AI models are an ethical way forward ... - Nature.com
Data Sharing That Safeguards Patient Privacy is Crucial for Future of … – Yale School of Medicine
It is the responsibility of researchers to tell the truest truth, says Daniel Boffa, MD, professor of surgery (thoracic). The accuracy and precision of medical research depend on the data used.
The medical community has increasingly recognized the importance of data sharing. The 21st Century Cures Act, passed in 2016 to accelerate medical discovery, encourages scientists to share data more openly so that other investigators may build upon it. More recently, editors of JAMA announced that researchers must include a data sharing plan in their manuscripts. The compilation of greater data sets allows investigators to generate a more complete picture of what is happening in patients. Furthermore, it helps them understand differences among various groups of patients, which will ultimately lead to providing more personalized medicine. However, data sharing also comes with the risk of loss of patient privacy, Boffa warns in a commentary published April 18 in JAMA.
Weve never had a better opportunity to leverage patient information to make powerful changes, says Boffa. But it has to be done in a way in which patient privacy is securewhich is challenging, but possible.
Boffa, a thoracic surgeon who specializes in cancer, is engaged in an initiative to compile all cancer data for patients in the United States through the National Cancer Database.
When a patient is diagnosed with cancer, the hospital generates a record with patient data. However, patients commonly receive care from more than one institution, resulting in a single patients data becoming scattered among various locations.
Weve never had a better opportunity to leverage patient information to make powerful changes, but it has to be done in a way in which patient privacy is securewhich is challenging, but possible.
Then, hospitals will share their records to various cancer databases without the identifying information. Because the data is anonymous, researchers are left with an incomplete picturedata collected for one patient related to testing, treatment, cancer stage, or patient attributes in one database is often missed by another. All of these databases have unique, incompletely overlapping pictures of each cancer patient, says Boffa.
To address this, he and his colleagues are trying to create a national cancer identifier. We basically take the identifying information and use advanced cryptography to turn it into an encrypted identifier that cannot be reversed to reveal the patients identify, he says. This new identifier is like a tag that can be used to tie all a patients data together in the national database. This new tool, he says, will be incredibly powerful.
When you have the data for every single cancer patient at your fingertips, the number of discoveries we will be able to make will be mind-blowing, he says. You may one day be able to use artificial intelligence to ask and answer cancer questions within a massive pool of patient information, similar to how platforms like ChatGPT use internet data.
This task, however, presents a significant challenge: protecting patient privacy. Because of advances in computer technology, the theoretical risk of reidentification of anonymous data is very high. Anonymous data is not private, says Boffa. If you put all of this information together, even if no name is included, a patient can still be identified. In collaboration with Yale computer scientists, Boffas team has poured massive time and energy into ensuring their project protects patient privacy.
Boffa is excited about new data sharing policies such as JAMAs but is concerned that they come with little guidance for doing so in a safe and secure way.
Making patient data anonymous is an important first step, says Boffa, but researchers also need to share data in a way that is trackable and accountable. In other words, they should know of everyone who has access to it and understand the security of the computing environmentsuch as whether the servers are secure and passwords are encrypted at the secondary institutions.
Furthermore, researchers should avoid downstream sharing and exchanging information in nonsecure ways, he says. This includes not emailing anonymous datasets or leaving them on unencrypted laptops. I would treat anonymous data the same way I would treat data that has identifying information, says Boffa. It should be treated as sensitive and as potentially harmful as data that has a patients social security number.
Although data sharing presents these complicated challenges, overcoming them will be critical for the future of personalized medicine. Right now, researchers are accomplishing so much with incomplete information, says Boffa. But conducting research at scale that includes many different variables will open the door to many more discoveries. There are so many more knowable pieces to the puzzle now, he says. By tying all of this together, that is the most credible way of determine for every single patient, what is the best, safest, and most effective treatment for them.
Other leaders at Yale are also dedicated to meeting these challenges and making data more accessible. A little over a decade ago, Harlan Krumholz, MD, Harold H. Hines, Jr. Professor of Medicine (Cardiology) and Joseph Ross, MD, professor of medicine (general medicine) and of public health (health policy and management), co-founded the Yale Open Data Access Project (YODA) with a goal to make data more widely available and to promote open science. The data sits within a repository so that researchers can work on it in a private, safe space, says Krumholz. It has guardrails up so that it can be both high ethics and high science.
As a result of the project, over 100 manuscripts have been published that would not have been possible without the sharing of data. Were leaving an era where most investigators had the perception that they had no ethical responsibility to ensure that the most that can come of it occurs, says Krumholz. Were trying to promote this idea that ethically, for the money, time, and willingness of people to be part of studies, we ought to be working hard to figure out how we safely and securely leverage data thats generated for the greatest amount of public good possible.
In his JAMA commentary, Boffa commends YODA as an accountable and transparent repository and distributor of clinical trials data that successfully pursues the goal of sharing patient information in a more secure manner. He writes that YODA shows how sound techniques developed at the federal level can also be embraced by individual institutions and organizations.
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Data Sharing That Safeguards Patient Privacy is Crucial for Future of ... - Yale School of Medicine
206 MSU graduates receive Board of Trustees’ Award for earning a … – MSUToday
This semester, a record-breaking 206 graduating students were recognized by the Michigan State University Board of Trustees for achieving the highest scholastic average a 4.0 GPA.
Each semester, students graduating with a 4.0 GPA are presented with the Board of Trustees Award for their academic excellence. Additionally, each awardee will be acknowledged during their individual commencement ceremonies in May and will receive $1,000 from the university in recognition of their accomplishment.
MSU Board of Trustees Chair Rema Vassar, Ph.D., recognized these students in todays board meeting.
It is an honor to present these students with an award that embodies the culmination of all the hard work and dedication they have demonstrated, Vassar said. We are proud of them, and optimistic they will continue as lifelong learners who make a difference in their communities.
Interim President Teresa K. Woodruff, Ph.D., also recognized the excellence of the awardees.
These awards are an embodiment of our students resilience and continuous commitment to their education during their years at MSU, said Woodruff. I am confident their continued excellence will permeate every aspect of their future, and I look forward to the impact they will have in Michigan and in the world.
Students receiving this award include:
Alexander S. Adamopoulos: Communication, College of Communication Arts and Sciences. Adamopoulos is from Grand Rapids, Michigan, and attended East Grand Rapids High School.
Hemkesh Agrawal: Computer Science, College of Engineering and a member of the Honors College. Agrawal is from Delhi, India, and attended Ramjas School.
Hana A. Al Aifan: Criminal Justice, College of Social Science. Al Aifan is from Riyadh, Saudi Arabia, and attended the American International School of Bucharest.
Kameron L. Alcantara: Social Work, College of Social Science. Alcantara is from Glendale Heights, Illinois, and attended Glenbard East High School.
Zeeba Ali: Neuroscience, Lyman Briggs College and a member of the Honors College. Ali is from Rochester Hills, Michigan, and attended Rochester High School.
Jesse W. Amburgey: Studio Art, College of Arts and Letters. Amburgey is from Lansing, Michigan, and attended Laingsburg High School.
Jasmine A. Amine: Psychology, College of Social Science and a member of the Honors College. Amine is from Westland, Michigan, and attended Canton High School.
Alexandra L. Anderson: Kinesiology, College of Education. Anderson is from Danville, Kentucky, and attended Boyle County High School.
Anthony R. Arapaj: Supply Chain Management, Eli Broad College of Business. Arapaj is from Macomb, Michigan, and attended Chippewa Valley High School.
Nicholas F. Balesky: Finance, Eli Broad College of Business and a member of the Honors College. Balesky is from East Lansing, Michigan, and attended Okemos High School.
Brooke R. Bannon: Packaging, College of Agriculture and Natural Resources. Bannon is from Fenton, Michigan, and attended Hartland High School.
Brenden D. Barnes: Interdisciplinary Studies in Social Science: Social Science Education, College of Social Science. Barnes is from Battle Creek, Michigan, and attended Lakeview High School.
Matthew J. Baylis: Marketing, Eli Broad College of Business. Baylis is from Apex, North Carolina, and attended Green Hope High School.
Reid Becker: Human Biology, College of Natural Science. Becker is from Holland, Michigan, and attended Holland Christian High School.
Drew A. Beckman: Genomics and Molecular Genetics, Lyman Briggs College and a member of the Honors College. Beckman is from Sterling Heights, Michigan and attended Adlai E. Stevenson High School.
Olivia J. Beebe: Advertising Management, College of Communication Arts and Sciences. Beebe is from Ionia, Michigan, and attended Ionia High School.
Alec J. Bensman: Computer Science, College of Engineering and a member of the Honors College. Bensman is from Cincinnati, Ohio, and attended Walnut Hills High School.
Grace A. Bonnema: Human Biology, Lyman Briggs College and a member of the Honors College. Bonnema is from Kalamazoo, Michigan, and attended Mattawan High School.
Samantha N. Bourgeois: Construction Management, College of Agriculture and Natural Resources; English, College of Arts and Letters and a member of the Honors College. Bourgeois is from Berkley, Michigan, and attended Berkley High School.
Bailey A. Bowcutt: Microbiology, Lyman Briggs College and a member of the Honors College. Bowcutt is from Cheyenne, Wyoming, and attended Cheyenne Central High School.
Charlotte A. Bridges: Political - Science Prelaw, College of Social Science and a member of the Honors College. Bridges is from Livonia, Michigan, and attended Mercy High School.
Devin J. Brust: Accounting, Eli Broad College of Business and a member of the Honors College. Brust is from Troy, Michigan, and attended Athens High School.
Thomas F. Burgess III: Mechanical Engineering, College of Engineering and a member of the Honors College. Burgess is from Honor, Michigan, and attended Lake Orion High School.
Calista Busch: Genomics and Molecular Genetics, Lyman Briggs College and a member of the Honors College. Busch is from Mason, Ohio, and attended William Mason High School.
Ian R. Byram: Computer Science, College of Engineering. Byram is from Grand Blanc, Michigan, and attended Grand Blanc Community School.
Jim M. Camilleri: Human Biology, College of Natural Science and a member of the Honors College. Camilleri is from Grosse Ile, Michigan, and attended Grosse Ile High School.
Chad M. Casey: Supply Chain Management, Eli Broad College of Business. Casey is from Grosse Pointe Farms, Michigan, and attended Dwight D. Eisenhower High School.
Drishti Chauhan: Human Biology, Lyman Briggs College and a member of the Honors College. Chauhan is from East Lansing, Michigan, and attended International Academy Okma.
Xinjia (Jocelyn) Chen: Accounting, Eli Broad College of Business and a member of the Honors College. Chen is from South Lyon, Michigan, and attended South Lyon High School.
Ryan M. Christian: Human Biology, College of Natural Science. Christian is from Waterford, Michigan, and attended Waterford Mott High School.
Alexis Y. Chuong: Chemical Engineering, College of Engineering and a member of the Honors College. Chuong is from Livonia, Michigan, and attended Winston Churchill High School.
Maximo E. Clark: Genomics and Molecular Genetics, Lyman Briggs College. Clark is from Ann Arbor, Michigan, and attended Pioneer High School.
Kaedon D. Cleland-Host: Physics, Mathematics, College of Natural Science and a member of the Honors College. Cleland-Host is from Lake Orion, Michigan, and attended Herbert Henry Dow High School.
Abigail V. Comar: Fisheries and Wildlife, College of Agriculture and Natural Resources; Journalism, College of Communication Arts and Sciences and a member of the Honors College. Comar is from Green Bay, Wisconsin, and attended Notre Dame Academy.
Maura A. Culler: Social work, College of Social Science and a member of the Honors College. Culler is from Louisville, Kentucky, and attended Atherton High School.
Jessica M. Culver: Criminal Justice, College of Social Science. Culver is from Clarkston, Michigan, and attended Clarkston Senior High School.
Trevor L. Dalrymple: Biochemistry/Biotechnology, Lyman Briggs College and a member of the Honors College. Dalrymple is from Rockford, Michigan, and attended Grand Rapids Christian High School.
Riley O. Damore: Marketing, Eli Broad College of Business and a member of the Honors College. Damore is from Battle Creek, Michigan, and attended Lakeview High School.
Ryan J. Danaj: Biosystems Engineering, College of Engineering. Danaj is from Washington, Michigan, and attended Romeo High School.
Natalie P. Daube: Finance, Eli Broad College of Business and a member of the Honors College. Daube is from Canonsburg, Pennsylvania, and attended Peters Township High School.
Adam C. Dec: Computational Data Science, College of Engineering. Dec is from Holt, Michigan, and attended Lansing Catholic High School.
Joseph C. Dec: Civil Engineering, College of Engineering. Dec is from Holt, Michigan, and attended Lansing Catholic High School.
Madeline M. Deeb: Human Biology, Lyman Briggs College and a member of the Honors College. Deeb is from Canton, Michigan, and attended Salem High School.
Brendan R. Doane: Mechanical Engineering, College of Engineering and a member of the Honors College. Doane is from Jackson, Michigan, and attended Lumen Christi High School.
Michael R. Dodde: Agribusiness Management, College of Agriculture and Natural Resources and a member of the Honors College. Dodde is from Conklin, Michigan, and attended Coopersville High School.
Nicklaus J. Donovan: Finance, Eli Broad College of Business and a member of the Honors College. Donovan is from Dewitt, Michigan, and attended Haslett High School.
Rachel E. Drobnak: Crop and Soil Science, College of Agriculture and Natural Resources and a member of the Honors College. Drobnak is from Olmsted Township, Ohio, and attended Olmsted Falls High School.
Jon P. Droste: Mechanical Engineering, College of Engineering and a member of the Honors College. Droste is from Dewitt, Michigan, and attended Dewitt High School.
Zoe C. Dunnum: Psychology, College of Social Science and a member of the Honors College. Dunnum is from Rockford, Michigan, and attended Rockford Senior High School.
Kiet V. Duong: Mechanical Engineering, College of Engineering and a member of the Honors College. Duong is from Ho Chi Minh City, Vietnam, and attended Vietnam National University High School for the Gifted.
Greyson J. Dwyer: Supply Chain Management, Eli Broad College of Business and a member of the Honors College. Dwyer is from East Lansing, Michigan, and attended Haslett High School.
James H. Eagle: Music Performance, College of Music and a member of the Honors College. Eagle is from Mason, Michigan, and attended Okemos High School.
Carlos M. Enriquez: Marketing, Eli Broad College of Business. Enriquez is from Novi, Michigan, and attended South Lyon East High School.
Malavika P. Eswaran: Neuroscience, College of Natural Science and a member of the Honors College. Eswaran is from Canton, Michigan, and attended Suzhou Singapore International School.
Leslie E. Ewalt: Human Biology, College of Natural Science. Ewalt is from Muskegon, Michigan, and attended Mona Shores High School.
Caleb B. Fisher: Biochemistry and Molecular Biology, College of Natural Science. Fisher is from Laingsburg, Michigan, and attended Laingsburg High School.
Mackenzie R. Fitzgerald: Human Biology, Chemistry, Lyman Briggs College and a member of the Honors College. Fitzgerald is from Clarkston, Michigan, and attended North Farmington High School.
Sarah M. Foreman: Psychology, College of Social Science. Foreman is from Belmont, Michigan, and attended Comstock Park High School.
Grace S. Foster: Actuarial Science, College of Natural Science. Foster is from Grosse Pointe, Michigan, and attended Grosse Pointe South High School.
Alexander T. Frischmuth: Finance, Eli Broad College of Business. Frischmuth is from Plymouth, Michigan, and attended Canton High School.
Brian E. George: Supply Chain Management, Eli Broad College of Business. George is from Farmington Hills, Michigan, and attended North Farmington High School.
Anthony P. Giordano: Supply Chain Management, Eli Broad College of Business. Giordano is from New Baltimore, Michigan, and attended De La Salle Collegiate High School.
Garrett M. Gleason: Political Science-Prelaw, College of Social Science. Gleason is from Flint, Michigan, and attended Carman-Ainsworth High School.
Krishna S. Gogineni: Microbiology, Human Biology, Lyman Briggs College and a member of the Honors College. Gogineni is from Van Buren Township, Michigan, and attended International Academy Okma.
Shannon K. Good: Animal Science, College of Agriculture and Natural Resources. Good is from Caledonia, Michigan, and attended Caledonia High School.
Caroline G. Gormely: Computer Science, College of Engineering and a member of the Honors College. Gormely is from Grosse Pointe, Michigan, and attended Grosse Pointe South High School.
Devin K. Granzo: Finance, Eli Broad College of Business. Granzo is from Midland, Michigan, and attended Midland High School.
Lauren E. Grasso: Biology, Lyman Briggs College and a member of the Honors College. Grasso is from Holt, Michigan, and attended Holt High School.
Jessica J. Greatorex: Psychology, College of Social Science. Greatorex is from Clarkston, Michigan, and attended Clarkston High School.
Thaddaeus A. Greiner: Computer Science, College of Engineering and a member of the Honors College. Greiner is from Plymouth, Michigan, and attended Plymouth High School.
Tristyn I. Griffin: Psychology, Political Science-Prelaw, College of Social Science. Griffin is from Trenton, Michigan, and attended Plymouth High School.
Evan K. Griffis: Fisheries and Wildlife, College of Agriculture and Natural Resources and a member of the Honors College. Griffis is from Newberry, Michigan, and attended Newberry High School.
Rebecca E. Grodsky: Veterinary Nursing, College of Veterinary Medicine. Grodsky is from Farmington, Michigan, and attended Farmington High School.
Sohan Gupta: Mechanical Engineering, College of Engineering and a member of the Honors College. Gupta is from Richmond, Kentucky, and attended the Delhi Public School Ruby Park Kolkata.
Siddarth Guruvi: Supply Chain Management, Eli Broad College of Business. Guruvi is from West Bloomfield, Michigan, and attended American Embassy School.
Laura A. Hall: Accounting, Eli Broad College of Business. Hall is from Howell, Michigan, and attended Fenton High School.
Shems Hamdan: Human Biology, Lyman Briggs College and a member of the Honors College. Hamdan is from East Lansing, Michigan, and attended East Lansing High School.
Jackson A. Haugen: Computer Science, College of Engineering; Mathematics, College of Natural Science; and a member of the Honors College. Haugen is from Boyne Falls, Michigan, and attended Petoskey High School.
DeLaney M. Heckman: Kinesiology, College of Education. Heckman is from Allegan, Michigan, and attended Allegan High School.
John J. Henige: Biochemistry and Molecular Biology, College of Natural Science. Henige is from Franklin, Michigan, and attended University Detroit Jesuit High School.
Justin P. Henkelman: Computer Science, College of Engineering. Henkelman is from Hudsonville, Michigan, and attended Hudsonville High School.
Benjamin C. Henley: Human Biology, Lyman Briggs College and a member of the Honors College. Henley is from Fenton, Michigan, and attended Fenton High School.
Anne N. Henseler: Education, College from Education and a member of the Honors College. Henseler is from Ypsilanti, Michigan, and attended Father Gabriel Richard High School.
Sylvia E. Hodges: English, College of Arts and Letters and a member of the Honors College. Hodges is from Grosse Pointe Park, Michigan, and attended Grosse Pointe South High School.
Derik M. Holmberg: Human Biology, Lyman Briggs College and a member of the Honors College. Holmberg is from Greenville, Michigan, and attended Greenville High School.
Dana L. Holt: Graphic Design, College of Arts and Letters and a member of the Honors College. Holt is from Rochester Hills, Michigan, and attended Avondale High School.
Taylor L. Hori: Entomology, College of Agriculture and Natural Resources. Hori is from San Diego, California, and attended Rancho Buena Vista High School.
Trent C. Hughes: Finance, Eli Broad College of Business. Hughes is from Wolverine Lake, Michigan, and attended Walled Lake Central High School.
Sam H. Huller: Special Education-Learning Disabilities, College of Education and a member of the Honors College. Huller is from Oxford, Michigan, and attended Oxford High School.
Jayla J. Irons: Political Science, College of Social Science and a member of the Honors College. Irons is from Chicago, Illinois, and attended Whitney M. Young Magnet High School.
Neha A. Iska: Neuroscience, Lyman Briggs College and a member of the Honors College. Iska is from Grand Blanc, Michigan, and attended Powers Catholic High School.
Charlotte G. Jansky: Music Performance, College of Music and a member of the Honors College. Jansky is from Little Silver, New Jersey, and attended Red Bank Regional High School.
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206 MSU graduates receive Board of Trustees' Award for earning a ... - MSUToday
Abbott and New Global Consortium Partnership Address Viral – CSRwire.com
Published 04-21-23
Submitted by Abbott
ABBOTT PARK, Ill.,April 21, 2023 /CSRwire/- Abbott (NYSE: ABT) announced today that it is partnering with the Climate Amplified Disease and Epidemics (CLIMADE) consortium, a group of more than 100 global scientists in public health agencies, academia and industry focused on using data science technology and diagnostic testing to assess and potentially mitigate the impact climate change has on disease outbreaks.
A changing climate, such as warmer temperatures and a rise in extreme weather events like droughts and floods, has the potential to accelerate the spread of disease, which could fuel a new era of pandemics. Research has found that climate change could impact more than half of known infectious diseases, which commonly spread via water or animals carrying diseases, such as West Nile virus and malaria.1
As part of the consortium, scientists trained in infectious diseases, bioinformatics and data science will develop technologies that can aggregate environmental, weather and viral sequencing data sets to predict if conditions could cause a disease outbreak. If a potential outbreak is identified, resources and rapid surveillance testing can be sent to that location to prevent further spread.
"Imagine being able to track weather patterns to determine if rising floods may lead to a water-borne disease outbreak," said Gavin Cloherty, Ph.D., head of infectious disease research and the Pandemic Defense Coalition in Abbott's diagnostics business. "Abbott's work with CLIMADE is focused on tracking and predicting events so testing and medical resources can be deployed to prevent the spread of disease making a real impact in communities and people's lives."
The CLIMADE consortium will be focused on improving surveillance tools and expanding access to resources to decrease the impact of climate amplified diseases and epidemics. The global group of scientists is led by Tulio de Oliveira, Ph.D., a professor at Stellenbosch University and Director of the Centre for Epidemic Response and Innovation (CERI) in South Africa as well as Luiz Carlos Alcantara, Ph.D., a professor at the Fundao Osvaldo Cruz (FIOCRUZ) in Brazil and Edward Holmes, Ph.D., an evolutionary biologist and virologist and professor at the University of Sydney. CLIMADE members include public health agencies, like the Africa Centers for Disease Control and Prevention (CDC), that bring decades of experience in genomics surveillance and epidemic response, as well as academic organizations such as the Broad Institute, University of Washington and University of Oxford.
Abbott and its partners in the Abbott Pandemic Defense Coalition will provide viral sequencing and testing data as part of the technology being developed and can provide diagnostic testing for potential outbreaks.
"We are bringing together the best minds in the medical, scientific and public health communities to help the world create a robust surveillance system that quickly identifies pathogens and tracks their evolution and spread," said Oliveira. "This collaboration across the private and public sectors is critical to pandemic preparedness and to our ability to go from responding to outbreaks to predicting them before they occur."
CLIMADE's initial work will start with disease surveillance in Africa and expand to countries around the world that are often impacted by infectious disease outbreaks.
Protecting Health in an Evolving ClimateSafeguarding a healthy environment is a longstanding part of Abbott's purpose to help people live fuller lives through better health. Building on our longstanding commitment to minimize our environmental footprint and protect precious resources, we're also focused on taking action to protect people's health in the face of climate change. At Abbott, our work focuses in two areas: tracking and finding solutions for emerging health threats and preparing frontline systems and communities. Across our business and in collaboration with others, we'll work to identify and address emerging health issues, strengthen underlying health systems and help build more resilient communities in a warming world. For more information, visit abbott.com/sustainability.
About AbbottAbbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.
Connect with us at http://www.abbott.com, on LinkedIn at http://www.linkedin.com/company/abbott-/, on Facebook at http://www.facebook.com/Abbott and on Twitter @AbbottNews.
About CERIThe Centre for Epidemic Response and Innovation (CERI) is an academic and research entity located within the School for Data Science and Computational Thinking in the Faculty of Science at Stellenbosch University and the laboratories are situated at the state-of-the art facilities at the Faculty of Medicine and Health Sciences based at the Tygerberg Medical Campus. CERI's goal is to strengthen Africa's capacity to quickly identify and control its own epidemics and pandemics before they become a global problem.
Connect with us at http://www.ceri.org.za and on Twitter @ceri_news
References:
1. Mora, Camiloet al. Nature Climate Change 8 Aug 2022. https://www.nature.com/articles/s41558-022-01426-1
About Abbott and the Abbott Fund
The Abbott Fund is a philanthropic foundation established by Abbott in 1951. The Abbott Fund's mission is to create healthier global communities by investing in creative ideas that promote science, expand access to health care and strengthen communities worldwide. For more information, visit http://www.abbottfund.org.
Abbott is a global, broad-based health care company devoted to the discovery, development, manufacture and marketing of pharmaceuticals and medical products, including nutritionals, devices and diagnostics. The company employs nearly 90,000 people and markets its products in more than 130 countries. Abbott's news releases and other information are available on the company's website at http://www.abbott.com.
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Abbott and New Global Consortium Partnership Address Viral - CSRwire.com