Category Archives: Data Science
Global Data Science Platform Market (2021 to 2027) – by Component, Deployment, Organization Size, Function, Industry Vertical and Geography -…
DUBLIN, October 22, 2021--(BUSINESS WIRE)--The "Global Data Science Platform Market (2021-2027) by Component, Deployment, Organization Size, Function, Industry Vertical, and Geography, Competitive Analysis, Impact of Covid-19, Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.
The Global Data Science Platform Market is estimated to be USD 43.3 Bn in 2021 and is expected to reach USD 81.43 Bn by 2027, growing at a CAGR of 11.1%.
Key factors such as a massive increase in data volume due to increasing digitalization and automation of processes have been a crucial driver in the growth of data science platform. Besides, the enterprises are increasingly focusing on analytical tools for deriving insights into consumer behavior and purchasing patterns. This, in turn, has been shaping their business decisions and strategies to compete in the market. Besides, the adoption of data science platforms has found its way in various industry verticals such as manufacturing, IT, BFSI, retail, etc. All these factors have helped in contributing to the growth of the data science platform market.
However, the costs attached to the deployment of these platforms, along with less workforce with domain expertise capabilities and threats to data privacy, has been a hindrance in the growth of the market.
The global data science platform market is segmented based on Component, Deployment, Organization Size, Function, Industry Vertical, and Geography.
Competitive Quadrant
The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.
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Why buy this report?
The report offers a comprehensive evaluation of the Global Data Science Platform Market. The report includes in-depth qualitative analysis, verifiable data from authentic sources, and projections about market size. The projections are calculated using proven research methodologies.
The report has been compiled through extensive primary and secondary research. The primary research is done through interviews, surveys, and observation of renowned personnel in the industry.
The report includes in-depth market analysis using Porter's 5 force model and the Ansoff Matrix. The impact of Covid-19 on the market is also featured in the report.
The report also contains the competitive analysis using Competitive Quadrant, Infogence's Proprietary competitive positioning tool.
Report Highlights:
A complete analysis of the market including parent industry
Important market dynamics and trends
Market segmentation
Historical, current, and projected size of the market based on value and volume
Market shares and strategies of key players
Recommendations to companies for strengthening their foothold in the market
Market Dynamics
Drivers
High Generation of Data Volumes
Rising Focus On Data-Driven Decisions
Increasing Adoption of Data Science Platforms Across Diversified Industry Verticals
Restraints
Opportunities
Increasing Adoption of Data-Driven Technologies by Enterprises
Increasing Demand for Public Cloud
Investments and Funding in Development of Big Data and Related Technologies by Public and Private Sectors
Challenges
Companies Mentioned
Microsoft Corporation
IBM Corporation
Google, Inc
Wolfram
DataRobot Inc.
Sense Inc.
RapidMiner Inc.
Domino Data Lab
Dataiku SAS
Alteryx, Inc.
Oracle
Tibco Software Inc.
SAS Institute Inc.
SAP SE
The Mathworks
For more information about this report visit https://www.researchandmarkets.com/r/c2taz0
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Planning to study Data Science after Plus-Two? Heres what is on offer – Telegraph India
Summary
Data Science combines technical, analytical and communication skills
You can work in a variety of industries ranging from e-commerce to fossil fuel and construction
What is the one thing that connects technologies like artificial intelligence (AI), Internet of Things (IoT), big data, blockchain and quantum computing? Its data, something that all industries need to grow their businesses and ensure better products and services.
But just acquiring data isnt much help. Data needs to be turned into information that one can use, and thats where Data Science comes in.
What is Data Science
Data Science analyses data to gather insights by using various tools, algorithms, processes and systems. Both structured and unstructured data are converted into information that can be used in a wide range of areas.
The three main pillars of Data Science are mathematics, statistics and computer science.
Why study Data Science
Skills needed in Data Science
To excel in the field of Data Science, a combination of technical, analytical and communication skills is needed.
Studying Data Science
If you have Physics, Chemistry and Maths in Class XI-XII, you can keep Data Science as a career option.
After a bachelors degree in a related field such as Statistics, Computer Science, Information Technology or Maths, you can go for a specialisation in Data Science.
Going for a BSc in Data Science is another option, though only a few institutes offer this course currently.
BSc in Data Science
This 3-year course combines programming knowledge, maths expertise and an introduction to business communication through data.
Eligibility Criteria:
Institutes offering BSc in Data Science:
IIT Madras runs two online courses on Data Science:
You can check it out here.
Allied bachelors degree: Some universities are developing one-of-a-kind courses that combine data with other fields where it can be applied.
Career opportunities in Data Science
Since Data Science comprises programming, product development, analysis and statistics, a variety of jobs are available. Those from engineering, business and management backgrounds are also needed to play key roles in this field.
Last updated on 24 Oct 2021
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Planning to study Data Science after Plus-Two? Heres what is on offer - Telegraph India
Consumer-facing Companies Still Have Few Incentives to Stop Data Breaches, and Thats a National Security Concern. – Council on Foreign Relations
In August, personal information belonging tofiftymillion prospective, current, and former T-Mobile customers wasstolen, marking the mobile carriers third customer data breach in two years.
T-Mobile isnt unique: dozens of well-known brands, as well as hundreds of lesser-known companies, have experienced data breaches in recent years. Althoughthesebreaches are embarrassing, T-Mobile and its peersappear toconsiderthemlittle more than a cost of doing business.
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However, the consequences of leaving data vulnerable are more serious than most companies realize. In addition to exposing consumers to potential fraud and identity theft, data breaches are deeplyinjurioustonationalsecurity.
Net Politics
CFR experts investigate the impact of information and communication technologies on security, privacy, and international affairs.2-4 times weekly.
MatthewPottinger, former Deputy U.S. National Security Advisor,warnedin August that China is now able to compile a dossier on everyAmerican adult.In 2015, Chinahackedhealth insurance provider Anthem, exfiltrating data belonging to almosteightymillion people.China alsoaccessedthe Office of Personal Managementdatabases,seizing sensitive data includingthesecurityclearanceformsbelonging to current and former federal employees.About 150 million records werestolen when ChinahackedEquifax in 2017, and an additional 500 million records were compromised following a Marriothackin 2018. China hassincemade a habit ofobtainingincreasingly personal data, such as DNA information, from healthcare providers, biotechnology firms, and pharmaceutical companies.Intelligence officials haveestimatedthat80percentofAmericans have hadalltheir personal data stolenperhaps an exaggeration, but likely not far from the truth.
The potential usesfor the stolen consumer data extend far beyond counterintelligence and research purposes.Thestolen data couldbe (or, more likely, already has been) used to informspearphishingattacks, aid the coercion of intelligence personnel, or help identify potential spies. Such sinister use cases arent without precedent.Foreign Policyreportedlast year that, almost a decade ago, Chinese intelligence used its vast collection of stolen datasets to identify undercover American operatives entering Europe and Africa.
Chinas cyber capabilities have strengthened significantly over the last decade.The Chinese governmenthas spent years and billions of dollars developing some of the most advanced data synthesis and analysis technologies and methodologies in the worldto surveil its own citizens.Thesetechniquesareuseful not only for evaluatingdata gathereddomestically, but alsodatastolen from the United States.When geopolitical adversaries have both large amounts of personal data and sophisticated analysis tools, the impact on national security can be particularly acute. This month,The New York Timessuggestedthat artificial intelligence and facial recognition are partially responsible for the recent loss of dozens of C.I.A. informants.
In theUnited States, by contrast, data is held by private entities such as Google, Amazon, Facebook, and other major consumer-facing companies. The U.S.government,constrained bystrong civil liberties protections provided by the Constitution, hasengagedless oftenin the kind of wholesaleacquisitionof personal data that is common in authoritariancountries.
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These asymmetries, combined with the U.S. governments history of patchy and often inconsistent cyber strategy, and exacerbated by the frequent intelligence community leadership and policy changes that accompany each new presidential administration, mean that America isgivingadversariesasignificanteconomic and militaryadvantage.As data science continues to advance,thisdisparitywillonlybecomemoreprominent.
So, how can the national security risks of consumer data exposures be mitigated? Unfortunately, the gatekeepers of consumer datacompanieshave little incentive to increase investments in their own resiliency.It is not clear that falling victim to a breach ismeaningfully more expensivethan paying for the additional cybersecurity that would have prevented it. Thus, theres an argument to be madethat finesfor cyber breachesshouldbe more consequential to companies bottom line.Greater fines, though,not onlyencouragecompanies to be lessforthcoming about databreaches butarealsofruitlessifreporting and disclosure requirementsremainweak.
At thenationallevel, there is an evolving and confusingpatchworkof disclosure laws, as states adopt different standards. This lack ofcoherence not only disadvantages consumers, who are confused and exhausted by often vague and unhelpful breach notifications, but also constitutes a key weakness inU.S.cybersecurity strategy.
Thereisalsocurrentlyno federal cybersecurity breach disclosure law, meaning that the UnitedStates struggles toidentify the scope, frequency, and severity of data breaches.A bill that would require disclosure of cyber incidents at federal agencies, government contractors, and critical infrastructure owners (like T-Mobile), theCyber Incident Notification Act of 2021, was introduced earlier this year. Related provisions passed recently by the House as part of theNational Defense Authorization Actwould have similar consequences.While these bills would be a good first step,manyof the companies that hold vast troves of consumer data would be outside the scope of either law, andtherefore continue to have no federally-imposed obligation to disclosedata breaches.
U.S.cyber policy continues to focus on critical infrastructure and other traditional sectors with obvious cyber vulnerabilities, while overlooking breaches with the greatest potential for consumer data theft. Although important, suchanarrow focus is insufficient. National cyber policy needs to reflect the reality thatintrusions can be damaging no matter where they happen.
Maya Villasenor is a computer science student at Columbia University and a former intern in the Digital and Cyberspace Policy program.
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Skills and online courses to become a Data Scientist, the top job role in the world by 2025 – India Today
Data analyst and data scientist job roles have been predicted to be the top on the world by 2025. Here's a list of skills you need and online courses you can try out to crack this sector.
According to the World Economic Forums 'Future of Jobs' Report published in 2020, the top job role in the world -- with the highest demand -- will be that of a Data Analyst and Scientist by 2025. The pandemic-driven digital transformation has led to greater demand for data science skills, where employers are willing to pay a premium, along with other benefits and long-term incentives, in order to attract and retain data scientists and data engineers.
The demand for data scientists outstrips supply worldwide, and India is no exception. The domain offers tremendous opportunities to take one's career to the next level, opening up access to various job roles such as -- Data Scientist, Data Architect, Data Engineer, Data Analyst, Business Analyst, Analytics Manager, and Business Analytics Specialist.
The data science skill is not restricted to learners and professionals from STEM fields. It is not uncommon for companies globally to build data science teams with talent from a broader choice of fields including social sciences alongside traditional hires like computer scientists, creating opportunities for a diverse set of professionals to get data science jobs.
According to Courseras Global Skills Report 2021, learners can prepare for an entry-level role of a Data Analyst with just around 64 hours of online learning sessions.
Here's a list of essential skills and online courses in the field of Data Science that learners can choose from:
Read: 5 tips to begin your career in the field of Data Science
Read: Over 93,500 data science jobs vacant in India: Study
Read: Career as a Data Scientist: Scope, skills needed, job profiles and other details
Click here for IndiaToday.ins complete coverage of the coronavirus pandemic.
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Which degree is right for me: data science or digital health? – News – The University of Sydney
Our Master of Data Science will develop your analytical and technical skills to use data science to guide strategic decisions. Youll explore the latest in data mining, machine learning and data visualisation, and learn to communicate data insights to key stakeholders.
You will study principles of data science, machine learning, data mining, visual analytics, and computational statistical methods, and can further enhance your knowledge in areas such as AI or data science for business.
In our Master of Digital Health and Data Science, youll work with academics from both the School of Computer Science in the Faculty of Engineering, and the Faculty of Medicine and Health.
Youll study tailored content which focuses on the needs and expectations of the health industry, and have the opportunity to apply your skills through capstone projects that offer real health data problems.
Topics you'll cover include how to analyse health data and use it to aid preventative care, the role of AI in diagnosing and improving patient outcomes, and what the design process looks like in a healthcare setting.
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Which degree is right for me: data science or digital health? - News - The University of Sydney
Data Science Leaders are Ruling the Corporate Industry for their Technical Talent as well as Management Expertise – Analytics Insight
Data Science Leaders are Ruling the Corporate Industry for their Technical Talent as well as Management Expertise
Data is running through the veins of advanced technology these days. The whole concept of algorithms and artificial intelligence is standing upon the concept of feeding a machine with stacks of data like the information stacked in our brain and then using that data for various operations, just how we use our brain to operate. The idea is very simple to grasp but it has a complex functioning structure that comes under the subject of data science.
The data science industry of 2021 is witnessing some of the biggest data science leaders of the decade. 2021 will be a pivotal year for data scientists as organizations are increasingly relying on insights derived from big data for making key decisions. More applications are being created with Python and theres an increased demand for end-to-end AI solutions. There are a lot of jobs available in the field, however, not enough data scientists. According to an ASA Report, nearly 70% of business owners prefer job applicants with specializations in data science, and the number of job openings is projected to grow to 2.72 million by 2021.
We see colossal impacts of data science across businesses; however, some are more developed than others, especially in finance. We see enormous improvement being made and this is to a great extent is in light of the fact that these organizations have a ton of data as of now. Like finance has a long history of making data helpful, thus there is now a culture of being reasonably data-driven set up in a large number of these organizations, and theyre additionally keen on stretching out those capabilities to new sorts of information.
Data science leaders are the senior executives with the aptitude to harness data science and business analytic insights to inform business decisions, strategy, execution, and more effective organizational leadership. Executives need to possess this critical and evolving knowledge foundation to optimally leverage the information produced for and by their data science teams in delivering business solutions based on analytic insights.
Data science is working pretty intensely in the media also. That is things like understanding your crowd, helping them discover content theyll cherish, helping them draw in with that content, ensuring its shared ideally across various platforms. Its one spot, however extremely truly distributed. The exponential growth in data we have seen since the start of our digital period will back off at any point soon. Truth be told, we have most likely just observed a hint of something larger. The coming years will realize a consistently expanding downpour of information. The new information will work as rocket fuel for our data science models, offering rise to better models as well as new and imaginative use cases.
Most organizations hiring data science leaders generally look for a Ph.D. in a related field or with significant experience with machine learning models, and they often drop the management experience required to obtain technical talent and which may hamper the operation of a data science leader. The management experience would help the leader connect with the non-technical staff of his company.
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Will Data Science be in Demand in the Future? – Entrepreneur
Opinions expressed by Entrepreneur contributors are their own.
An article in Harvard Business Review once called being a data scientist "the sexiest job of the 21st century." So what does one have to do to earn that title?
A data scientist can tackle multifaceted challenges through the utilization of data combined with machine learning approaches. Data science as a course, on the other hand, is a multidisciplinary field of study that combines computer science with statistical methodology and business competencies. To qualify as a data scientist, they need to possess unique experience alongside expertise within primary data science settings. This may include statistical analysis, data visualization, utilization of machine learning methodology, comprehensionand assessing conceptual challenges linked to businesses.
What does the ideal future look like in regards to science?Science enthusiasts would likelyenvision a steady progression of technology over the next five years. Science and technological innovations are continuously improving, newer opportunities are being created and more recent techniques are being opened up for enhancing business operations for individuals and organizations.
Many organizations are delving into data science as the key to increasing their competitiveness. As a result, production has also improved over the last few years. Take Apple and Amazon as examples. Both companies have improved their global brand positioning,realized steady profits and are on target to continue to grow partly due to their high-end reliance on data science.
Related: Why 'Data Scientist' Will Continue to Be 'the Sexiest Job of the 21st Century'
We are constantly being faced with unpredictable situations like the Covid pandemicwhich has called for businesses to do what they can to minimize human-to-human contact. Data science and rapidly changing technology have helped drive these changes and prove that a bright future exists. This will, however, depend on the quality and the extent of data that organizations can acquire.
Since there is a greater emphasis on consumer behavior data, organizations are constantly searching for the best way to collect this information. In addition, there have been more calls for ethics and legal compliance within every sector of the economy. This increases the need for data science to be utilized, ensuring the acquired data is safely and securely stored. Confidentiality is also of the utmost importance.
All this focus on data science makes data scientists pretty crucial for businesses of all sizes. These professionals have the competencies for developing machine learning frameworksand offer value for the vast acquired datasets at their disposal.
Despite the growing use of AI, the demand for data scientists should continue to rise. A data scientist generally delves into analyses combined with output. AI acts as the key component of machine learning, which is based on developing self-sustaining frameworks. This generates set outcomes that lack interactions. Moreover, AI delves into the aspect of an evolving framework as opposed to analyses. However, its value is still yet to be comprehensively explored, and this may pose a challenge for the future of data scientists.
Related: Reasons Why Data Science Will Continue to Be the Most Desirable Job of the Decade
But despite the projected setbacks for data scientists, various positives should keep hopes up. One is the increasedgranularization of data scientists' roles. The other is the increased need for expertise for attaining unique workstreams and also upholding competitiveness through the utilization of specialized knowledge. Looking forward, there will be more significant opportunities for developing more advanced algorithms and pushing the field to showcase what data scientists can offer within the world of science and technology.
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Will Data Science be in Demand in the Future? - Entrepreneur
MetaCell launches innovative Cloud Hosting for life science and healthcare – Yahoo Finance
CAMBRIDGE, Mass., Sept. 29, 2021 /PRNewswire/ -- MetaCell, an innovative life science software company specialized in creating cutting-edge research software for major pharma, biotech, and academic institutions, has launched MetaCell Cloud Hosting a brand new online product providing advanced cloud computing solutions to facilitate research and innovation in life science and healthcare organizations of all sizes.
MetaCell Cloud Hosting is a brand new online product providing advanced cloud computing solutions to facilitate research and innovation in life science and healthcare organizations of all sizes. (PRNewsfoto/MetaCell)
Introducing MetaCell Cloud Hosting
MetaCell specializes in designing custom software services for the pharmaceutical industry, the healthcare sector, and for researchers in academia. In doing so, MetaCell helps its customers overcome challenging information management problems that they have found difficult to navigate with their IT departments and with large service providers like Amazon and Google. MetaCell Cloud Hosting provides scientists, pharmaceutical companies and research institutions with a turnkey online product to host and process their life sciences data and applications.
A unique feature provided by MetaCell Cloud Hosting is its customer-tailored capability for biomedical and life science data and software applications that delivers the optimal allocation of cloud resources based on the budget and performance goals of the researchers. This includes affordable storage on trusted servers and access to world-class computing resources, which can be efficiently scaled up to meet the growing need for big data analytics, bioinformatics, digital health, and artificial intelligence. Enabling hosted applications to comply with all major international regulatory frameworks such as GDPR, HIPAA, SOC 2, and relevant ISO standards is another significant value add that MetaCell brings to the market with the release of this new product.
Stephen Larson, CEO of MetaCell, said: "We're thrilled that we are officially launching our MetaCell Cloud Hosting product. From advanced custom software applications to single page websites showing off their work, Cloud Hosting will help researchers avoid the headaches associated with ongoing management of their online software and data holdings."
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Dr. Rick Gerkin, Associate Research Professor at Arizona State University (ASU), commented: "Besides enabling us to host our research applications and data in a safe cloud infrastructure, MetaCell Cloud Hosting will save us precious time which we'll no longer spend trying to fix technical issues, and instead dedicate it to what we care about most: advancing our research. We've been partnering with MetaCell for a number of years and they have demonstrated their expertise in developing and maintaining our cloud software and databases. We look forward to taking advantage of their new product."
About MetaCell
MetaCell is a life science-focused software company composed of scientists and software engineers with deep domain expertise in computational neuroscience, molecular biology, data science, and enterprise-grade online software development. Over the last ten years, MetaCell has established a global presence by partnering with the world's largest pharmaceutical companies including Pfizer and Biogen, leading universities such as Yale University, Princeton University, UCSD, UCL, ASU, SUNY Downstate, and University of Edinburgh, as well as innovative organizations such as CAMH, INCF, and EMBL-EBI.
Contact details
Paolo Lenotti, VP Marketing & PR, MetaCell | paolo@metacell.us US +1 617-286-4832 | UK +44 1865 648684 | info@metacell.us http://www.metacell.us | http://www.metacell.us/cloud-hosting
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MetaCell launches innovative Cloud Hosting for life science and healthcare - Yahoo Finance
Promoting the Public Good | UVA Today – UVA Today
Rene Cummings arrived at the University of Virginia in October 2020 as the School of Data Sciences first data-activist-in-residence. Cummings, who speaks internationally on artificial intelligence ethics and inclusive innovation, also lectures on big data ethics in the schools data science masters program.
The No. 1 question she gets from UVA students is also big: How do I make a difference? Students want to know how to think about making decisions to ensure their choices are ethical and serve society.
Our work at UVA is to give students the confidence to act responsibly and on behalf of the public good, Cummings said. We want students to understand why justice and social good and civic-mindedness are so critical to the work that we are doing in technology.
Now Cummings is furthering her contributions to this mission by helping lead UVAs role in the Public Interest Technology University Network, a consortium of 43 academic institutions focused on building the field of public interest technology and preparing the next generation of civic-minded technologists. Her co-leader at UVA, with whom she will serve a three-year term, is Jonathan L. Goodall, professor of civil engineering in the School of Engineering and Applied Science.
Technology can help address many challenges facing cities and communities, but technological solutions must be developed in partnership with communities so that they are trusted and targeted in their use, Goodall said. Public interest technology is a new field at this interface between technology and community engagement, with the aim of creating technology that best serves the public interest. It is exciting to work with Rene to build a community of folks from across Grounds engaging in this new field.
UVA was one of 21 college and university founding members of the network, convened in 2019 by New America, the Ford Foundation and the Hewlett Foundation. The goal of the Public Interest Technology University Network, which uses the abbreviation PIT-UN, is to collaborate on new curricula, faculty training, experiential learning opportunities and innovative ways to support students who enter public interest technology fields. The network provides grants to its members to support these efforts.
UVAs relationship with the network was initiated by Louis Nelson, vice provost for academic outreach, who quickly moved to recruit content experts from across Grounds to guide the work.
While public service and community-facing programs are clearly in the academic outreach domain, UVA is well-positioned to grow a stronger footprint in technology and ethics, Nelson said. Technology is going to shape the future, and I am thrilled that Rene and Jonathan are going to be leading and representing UVA in this space.
As a founding partner, UVA has a critical role to play, particularly at this moment, Cummings said. We have the ability to harness the power of the public in building justice-oriented, equitable, diverse and inclusive technology that is responsible, trustworthy and good for all.
Cummings, who started her career as a journalist to give a voice to the underserved, went on to advocate as a criminologist, criminal psychologist and AI ethicist. She brings to data science a passion for developing ideas around how to create principled technology.
Goodall comes to the co-leadership role as a 2020 recipient of a network grant, one of three received by UVA since the inaugural grant cycle. The funding will help strengthen the Community Fellows Program, which is jointly spearheaded by UVA Engineerings Link Lab for cyber-physical systems and the Center for Civic Innovation, a local nonprofit. The fellows program supports citizen-defined, civic innovation projects that serve the Charlottesville community. The 2021 cohort of fellows was announced Sept. 16.
A civil engineer by training, Goodall collaborates with cities facing flooding challenges due to climate change. He works in infrastructure, hydrology and technology, focusing on flood solutions and resiliency measures that best serve the localities.
Goodall is also the associate director of the Link Lab and leads research projects related to smart cities technology, one of the Link Labs key research focus areas. Interaction with local community is a critical component of the work and this new role builds on that foundation.
As co-leaders of UVAs role in the network, Cummings and Goodall will promote opportunities for UVA peers to connect and cultivate public interest technology collaborations.
Our purpose is to bring together researchers from a range of disciplines to imagine creative new solutions toward justness and fairness in the technology ecosystem, Cummings said. We seek to inspire interdisciplinary approaches that leverage the extraordinary promise, potential and power of technology for the social good and for the public good.
Goodall and Cummings also will lead UVA teams in cooperative efforts with other network member schools aimed at supporting the use of data and technology to deliver better outcomes to the public
Working with peer institutions will be imperative in defining what public interest technology will look like in the future, Goodall said. This problem is bigger than any one college or university, so collaborating across universities will be important.
The Public Interest Technology University Network collaboration offers an extraordinary opportunity to reimagine the world in a way that technology can be used for the benefit of all, Cummings said. Everything I have done in my past prepared me for that future.
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KDD 2021 Honors Recipients of the SIGKDD Best Paper Awards – Yahoo Finance
Top Data Scientists Recognized for Advanced Research and Applied Data Science in Topics Spanning COVID-19, Disaster Work Zones, and Diverse Time Range Queries
SAN DIEGO, Sept. 28, 2021 /PRNewswire/ -- The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) today announced the recipients of the SIGKDD Best Paper Awards, recognizing papers presented at the annual SIGKDD conference that advance the fundamental understanding of the field of knowledge discovery in data and data mining. Winners were selected from more than 2,200 papers initially submitted for consideration to be presented at KDD 2021, which took place Aug. 14-18. Of the 394 papers chosen for the conference, three awards were granted: Best Paper in the Research Track, Best Paper in the Applied Data Science Track, and Best Student Paper.
(PRNewsfoto/ACM SIGKDD)
"Academic and industrial researchers from all over the world submitted papers to KDD 2021 to showcase the newest innovations in the field of machine learning knowledge discovery," noted Dr. Haixun Wang, chair of the SIGKDD award committee. "Those selected for recognition have pushed the frontier of machine learning especially in tackling real-world problems." The SIGKDD Best Papers of 2021 are as follows:
Research Track: "Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries," by Jun-gi Jang and U Kang (both from Seoul National University) Having investigated methods that analyze dense tensors to discover hidden factors, researchers showed that the Zoom-Tucker is a fast and memory-efficient Tucker decomposition method for finding hidden factors of temporal tensor data in an arbitrary time range. The paper illustrates that by elaborately decoupling the preprocessed results included in diverse time range and carefully determining the order of computations, the Zoom-Tucker method is up to 171.9x faster and requires up to 230x less space than existing methods, providing a creative solution that yielded astounding results.
Research Track, Student Paper: "Spectral Clustering of Attributed Multi-Relational Graphs," by Ylli Sadikaj (University of Vienna), Yllka Velaj (University of Vienna), Sahar Behzadi Soheil (University of Vienna), and Claudia Plant (University of Vienna) Having investigated the challenge of graph clustering when complex data in many domains are represented as both attributed and multi-relational networks, researchers proposed SpectralMix, a joint dimensionality reduction technique for multi-relational graphs with categorical node attributes. SpectralMix integrates all information available from the attributes, the different types of relations, and the graph structure to enable a sound interpretation of the clustering results.
Applied Data Science Track: "Supporting COVID-19 Policy Response with Large-Scale Mobility-Based Modeling," by Serina Chang (Stanford University), Mandy Wilson (University of Virginia), Bryan Leroy Lewis (University of Virginia), Zakaria Mehrab (University of Virginia), Emma J. Pierson (Microsoft Research), Pang Wei Koh (Stanford University), Jaline Gerardin (Northwestern University), Beth Red Bird (Northwestern University), David Grusky (Stanford University), Madhav Marathe (University of Virginia), and Jure Lesovec (Stanford University) The authors introduced a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. The model captured the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with more than 3 billion hourly edges. The paper describes the robust computational infrastructure required to support millions of model realizations that can simulate a wide variety of reopening plans, giving policymakers an analytical tool to assess the tradeoffs between future infections and mobility restrictions.
Applied Data Science Track, Runner Up: "Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning," by Hao Wang (Beijing Institute of Technology), Chi (Harold) Liu (Beijing Institute of Technology), Zipeng Dai (Beijing Institute of Technology), Jian Tang (DiDi Chuxing), and Guoren Wang (Beijing Institute of Technology) The authors presented DRL-DisasterVC(3D), a distributed deep reinforcement learning framework, to maximize the amount of collected data from unmanned vehicles in a 3-dimensional (3D) disaster work zone. The paper described a 3D convolutional neural network with multi-head-relational attention for spatial modeling and auxiliary pixel control for spatial exploration, and a novel disaster response simulator, called "DisasterSim," used to conduct extensive experiments to show that DRL-DisasterVC(3D) maximizes data collection, geographical fairness, and energy efficiency, while minimizing data dropout due to limited transmission rate.
The technical program committees for the Research Track and the Applied Data Science Track identified and nominated a highly selective group of papers for the Best Paper Awards. The nominated papers were then independently reviewed by a committee led by Chair Haixun Wang, vice president of engineering and algorithms at Instacart; Professor Wei Wang, University of California, Los Angeles; Professor Beng Chin, National University of Singapore; Professor Jiawei Han, University of Illinois at Urbana-Champaign; and Sanjay Chawla, research director of Qatar Computing Research Institute's data analytics department.
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For more information on KDD 2021, please visit: https://www.kdd.org/kdd2021/.
About ACM SIGKDD: ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining. SIGKDD is ACM's Special Interest Group on Knowledge Discovery and Data Mining. The annual KDD International Conference on Knowledge Discovery and Data Mining is the premier interdisciplinary conference for data mining, data science and analytics.
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KDD 2021 Honors Recipients of the SIGKDD Best Paper Awards - Yahoo Finance