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Data science classes, bootcamps and certificates in NYC – Time Out

Data science is booming, thanks to the exponential increase in available data, advancements in technology and computing power, and the high demand for data-driven insights to inform decisions across all sectors. Data science classes and bootcamps in NYC offer the perfect opportunity to master essential data science skills like Python programming, machine learning, and data analysis. You'll learn how to extract insights from complex datasets, build predictive models, and create data visualizations. NYC is a hub of innovation and technology, where youll have unparalleled access to industry experts, networking opportunities, and real-world projects. Whether you're a seasoned professional looking to upskill or a curious beginner eager to explore the possibilities of data science, NYC offers the ideal environment to thrive in this rapidly evolving field.

Recommended: Best certificate programs in NYCRecommended: Best coding bootcamps in NYCRecommended: Best coding classes & bootcamps near youRecommended: Best data science classes and programs for high school studentsRecommended: Best digital marketing classes and certificates in NYC

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Do not over-think about ‘outliers’, use a student-t distribution instead – Towards Data Science

A Students t-distribution is nothing more than a Gaussian distribution with heavier tails. In other words, we can say that the Gaussian distribution is a special case of the Students t-distribution. The Gaussian distribution is defined by the mean () and the standard deviation (). The Student t distribution, on the other hand, adds an additional parameter, the degrees of freedom (df), which controls the thickness of the distribution. This parameter assigns greater probability to events further from the mean. This feature is particularly useful for small sample sizes, such as in biomedicine, where the assumption of normality is questionable. Note that as the degrees of freedom increase, the Student t-distribution approaches the Gaussian distribution. We can visualize this using density plots:

Note in Figure 1 that the hill around the mean gets smaller as the degrees of freedom decrease as a result of the probability mass going to the tails, which are thicker. This property is what gives the Students t-distribution a reduced sensitivity to outliers. For more details on this matter, you can check this blog.

We load the required libraries:

So, lets skip data simulations and get serious. Well work with real data I have acquired from mice performing the rotarod test.

First, we load the dataset into our environment and set the corresponding factor levels. The dataset contains IDs for the animals, a groping variable (Genotype), an indicator for two different days on which the test was performed (day), and different trials for the same day. For this article, we model only one of the trials (Trial3). We will save the other trials for a future article on modeling variation.

As the data handling implies, our modeling strategy will be based on Genotype and Day as categorical predictors of the distribution of Trial3.

In biomedical science, categorical predictors, or grouping factors, are more common than continuous predictors. Scientists in this field like to divide their samples into groups or conditions and apply different treatments.

Lets have an initial view of the data using Raincloud plots as shown by Guilherme A. Franchi, PhD in this great blog post.

Figure 2 looks different from the original by Guilherme A. Franchi, PhD because we are plotting two factors instead of one. However, the nature of the plot is the same. Pay attention to the red dots, these are the ones that can be considered extreme observations that tilt the measures of central tendency (especially the mean) toward one direction. We also observe that the variances are different, so modeling also sigma can give better estimates. Our task now is to model the output using the brms package.

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Do not over-think about 'outliers', use a student-t distribution instead - Towards Data Science

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The Many Pillars of Getting the Most Value From Your Organization’s Data – Towards Data Science

Photo by Choong Deng Xiang on Unsplash

Letmeintroduce youtoSarah, a talented and passionate data scientist, who just landed her dream job at GreenEnv, a large company that makes eco-friendly cleaning products. GreenEnv has tons of data on customers, products, and other areas of the business. They hired Sarah to unlock the hidden potential within this data, uncovering market trends, competitive advantages, and more.

Her first task: analyze customer demographics and buying habits to create targeted marketing campaigns. Confident in her abilities and excited to apply data science methods, Sarah dived into the customer database. But her initial excitement quickly faded. The data was a mess inconsistent formatting, misspelled names, and duplicate entries everywhere. Data quality was terrible. There were variations of names like Jhon Smith and Micheal Brown alongside entries like Jhonn Smtih and Michealw Brown. Emails had extra spaces and even typos like gnail.com instead of gmail.com. along with many other inaccuracies. Sarah realized the hard job ahead of her data cleaning.

Inconsistent formatting, missing values, and duplicates would lead to skewed results, giving an inaccurate picture of GreenEnvs customer base. Days turned into weeks as Sarah tirelessly cleaned the data, fixing inconsistencies, filling in gaps, and eliminating duplicates. It was a tedious process, but essential to ensure her analysis was built on a solid foundation.

Who cares about data quality?

Every year, poor data quality costs organizations an average of $12.9 million. [1]

Thankfully, after weeks of cleaning and organizing this messy data, Sarah was able to get the job doneor at least for this part..

Her next challenge came when she ventured into product data, aiming to identify top-selling items and recommend future opportunities. However, she encountered a different problem a complete lack of metadata. Product descriptions were absent, and categories were ambiguous. Basically, there wasnt enough data to help Sarah to understand the products data. Sarah realized the importance of metadata management structured information about the data itself. Without it, understanding and analyzing the data was almost impossible.

Research Shows Most Data Has Inaccuracies

Research by Experian reveals that businesses believe around 29% of their data is inaccurate in some way. [2]

Frustrated but determined, Sarah reached out to different departments to piece together information about the products. She discovered that each department used its own internal jargon and classification systems. Marketing and sales refer to the same cleaning product with different names.

As Sarah delved deeper, she found that datasets were kept in separate applications by different departments, outdated storage systems struggling to handle the growing volume of data, and Sarah had to wait for a long time for her queries to be executed. Sarah noticed also there are no clear rules on who can access what data and under what terms, without centralized control and proper access controls, the risk of unauthorized access to sensitive information increases, potentially leading to data breaches and compliance violations. The lack of data governance, a set of rules and procedures for managing data, was evident.

Data Breaches Can Be Costly

According to the Ponemon Institute, the average cost of a data breach in 2023 is $4.45 million globally, an all-time high record, with costs varying by industry and location. [3]

Each of the above issues and hurdles in Sarahs story highlighted the interconnectedness of many pillars data quality, metadata management, and data governance all played a crucial role in accessing and utilizing valuable insights at GreenEnv.

Sarahs journey is a common one for data scientists and analysts. Many organizations have massive amounts of data, and everyone knows the saying: Data is the new electricity. Every organization wants to make the most of their data, as its a very valuable asset. But most people mistakenly (and practically) believe that simply hiring a data analyst or data scientist is enough to unlock this value. There are many pillars to getting the most value from data, and organizations need to account for and pay attention to these. The keyword here is data management.

Did you know..

86% of organizations say they believe investing in data management directly impacts their business growth[4]

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8 Things Most Data Science Programs Don’t Teach (But You Should Know) Part 2 – Towards Data Science

MIT calls this the missing semester of your CS education 10 min read

What data science and software engineering have in common is writing code. But while code is the main outcome of software engineering, data science projects typically end with models, results, and reports. Consequently, in data science the quality, structure, and delivery of code is often an afterthought at best.

The implicit expectation with data science projects is that the results reported at the end can be trusted.

This means that if someone asked you to re-run your or somebody elses analysis, you would be able to obtain the same results, regardless of how much time has passed since you first performed the analysis.

Similarly, if you are developing a component for a product, the implicit expectation is that component you developed represents the best possible performance given what is reasonably possible within the requirements of the product.

These statements may seem obvious, but satisfying both expectations can be quite difficult.

If you dont believe me, think about your past projects.

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Data-Science-Powered Research by Seattle Children’s and Microsoft Shows Promise of Predicting SIDS and Other … – PR Newswire

REDMOND, Wash., March 28, 2024 /PRNewswire/ -- More than 155 International researchers and data scientists met this week in the Pacific Northwest to share with each other the newest insights into the causes of Sudden Infant Death Syndrome (SIDS). The event was sponsored by The Center for Integrative Brain Research at Seattle Children's and Microsoft AI for Good Lab. Among the many topics attendees discussed was groundbreaking new research that suggests genetic testing at birth may hold the promise of detecting SIDS risk and potentially other causes of sudden death later in life.

SIDS is the leading cause of death of infants one month to one year old in the US and other developed countries. The new findings, resulting from a partnership between the Center for Integrative Brain Research at Seattle Children'sand data scientists at Microsoft, come from the first-ever whole genome sequencing of 145 infants who succumbed to SIDS. The Aaron Matthew SIDS Research Foundationfunds the database, which is maintained and managed at Seattle Children's Research Institute.

In a study, soon to be published in the American Journal of Medical Genetics, researchers identify novel genes associated with Sudden Unexplained Infant Deaths (SUID), which includes SIDS. Some of these genes are important for detecting and responding to hypoxia low levels of oxygen in body tissues. Children with these vulnerabilities could increase their susceptibility to death caused from sleeping face down.

For decades, medical professionals have found a correlation between the sleeping position of infants and SIDS. This research suggests why that risk exists for certain infants. The study also identified genes associated with Sudden Cardiac Death, which could also explain why some children are particularly vulnerable to succumb to SIDS. However, because not every child with this vulnerability will succumb to SIDS, those who survive may be vulnerable to Sudden Cardiac Death later in life. Sudden Cardiac Death is responsible for 360,000 fatalities annually in the United States.

"Scientific research sometimes leads to surprises," saidJan-Marino Ramirez, PhD, Director of theCenter for Integrative Brain Research at Seattle Children's. "One surprise in our research leads to an exciting question: what if a genetic test at birth could not only predict the risk of SIDS, but also terminal cardiac problems well into adulthood? Preventative treatments exist for these dangerous conditions, and early detection could save lives."

John Kahan, the former Microsoft Vice President and Chief Data Analytics Officer who co-founded The Aaron Matthew SIDS Research Foundation with his wifeHeather Kahan, organized the first SIDS Summit while he was working at Microsoft.

"Thanks to this collaboration between world-class researchers and data scientists armed with cutting edge AI, we can now use genetic data to predict children at high risk of SIDS, which claims approximately 3,200 children a year," Kahan said."We are getting far closer to enabling medical professionals to bring preventative treatments to children who exhibit these risks, and potentially to far more people those susceptible to Sudden Cardiac Death later in life."

Juan M. Lavista Ferres PhD, MS, Microsoft Chief Data Scientist and the Director of the AI For Good Lab at Microsoft,is among those who hosted the Summit this week. Dr. Lavista wasthe lead researcher who used big data to estimate that 22% of Sudden or Unexplained Infant Deaths in the United States can be directly attributed to maternal smoking during pregnancy, which led to the assertion that SIDS rates can be reduced through education programs about the risks of smoking during pregnancy.

"The learning from this collaboration with SIDS researchers is proving, once again, the power AI has to scale human expertise," Dr. Lavista said. "It's a privilege for my team to put AI in the hands of some of the leading medical researchers in the world, and to see the number of potentially life-saving outcomes that flow from their work, partly through their access to AI."

The new findings on the mechanisms of SIDS were among many issues discussed at the Seventh Annual SIDS Summit, hosted by Ramirez, Kahan, and Lavista. Other research discussed included:

About the Aaron Matthew SIDS Research Guild at Seattle Children's Hospital The Aaron Matthew SIDS Research Guild at Seattle Children's Hospitalwas named in honor of Aaron Matthew Kahan, son of Heather Kahan and John B. Kahan. Aaron died of SIDS days after his birth in 2003. The Guild board includes leaders from Seattle Children's Hospital's Integrative Brain Research Institute, Microsoft, Accenture, Marriott Hotels, Adobe, Tata Consulting Services, and VMLY&R.

SOURCE Aaron Matthew SIDS Research Guild

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Advancing drug discovery with AI: introducing the KEDD framework – EurekAlert

image:

A simple but effective feature fusion framework that jointly incorporates biomolecular structures, knowledge graphs, and biomedical texts for AI drug discovery.

Credit: [Yizhen Luo, Institute for AI Industry Research (AIR), Tsinghua University]

A transformative study published in Health Data Science, a Science Partner Journal, introduces a groundbreaking end-to-end deep learning framework, known as Knowledge-Empowered Drug Discovery (KEDD), aimed at revolutionizing the field of drug discovery. This innovative framework adeptly integrates structured and unstructured knowledge, enhancing the AI-driven exploration of molecular dynamics and interactions.

Traditionally, AI applications in drug discovery have been constrained by their focus on singular tasks, neglecting the rich tapestry of structured and unstructured data that could enrich their predictive accuracy. These limitations are particularly pronounced when dealing with novel compounds or proteins, where existing knowledge is scant or absent, often hampered by the prohibitive costs of manual data annotation.

Professor Zaiqing Nie, from Tsinghua University's Institute for AI Industry Research, emphasizes the enhancement potential of AI in drug discovery through KEDD. This framework synergizes data from molecular structures, knowledge graphs, and biomedical literature, offering a comprehensive approach that transcends the limitations of conventional models.

At its core, KEDD employs robust representation learning models to distill dense features from various data modalities. Following this, it integrates these features through a fusion process and leverages a predictive network to ascertain outcomes, facilitating its application across a spectrum of AI-facilitated drug discovery endeavors.

The study substantiates KEDD's effectiveness, showcasing its ability to outperform existing AI models in critical drug discovery tasks. Notably, KEDD demonstrates resilience in the face of the 'missing modality problem,' where lack of documented data on new drugs or proteins could undermine analytical processes. This resilience stems from its innovative use of sparse attention and modality masking techniques, which harness the power of existing knowledge bases to inform predictions and analyses.

Looking forward, Yizhen Luo, a key contributor to the KEDD project, outlines ambitious plans to enhance the framework's capabilities, including the exploration of multimodal pre-training strategies. The overarching objective is to cultivate a versatile, knowledge-driven AI ecosystem that accelerates biomedical research, delivering timely insights and recommendations to advance therapeutic discovery and development.

Health Data Science

Toward Unified AI Drug Discovery with Multimodal Knowledge

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Generation Google Scholarship 2024 for Women in Computer Science in Ireland (up to 5000 EUR) Opportunity Desk – Opportunity Desk

Deadline: April 23, 2024

Applications are open for the Generation Google Scholarship 2024 for Women in Computer Science in Ireland. The Generation Google Scholarship: for women in computer science in Ireland was established to help aspiring computer scientists excel in technology and become leaders in the field.

A group of undergraduate students who identify as women will be chosen from the applicant pool, and scholarships will be awarded based on the strength of each candidates impact on diversity, demonstrated leadership, and academic background. The program is open to qualified students who meet all the minimum qualifications. All students who identify as women interested in computer science are strongly encouraged to apply.

Benefits

Eligibility

To be eligible to apply, applicants must:

Application

You will be asked to complete an online application which includes:

Essay Questions:

The two short answer essay questions below are intended to assess your problem solving skills and commitment to diversity, equity, and inclusion. Each response to the two questions below should be 500 words or less.

IMPORTANT: Before starting the application, have the following ready for upload:

Click here to apply

For more information, visit Generation Google Scholarship.

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10 of the highest-paying programming jobs right now – Fortune

In our modern age, computers impact practically every aspect of daily life. But before we can type an address into our smartphone or book a restaurant reservation online, a programmer was necessary to create the software these programs rely on. Still, programmers arent as in demand as they once were. Following the programming hiring boom of the pandemic, fewer programmers are now needed in the workforce, especially as programming becomes increasingly automated in the age of AI.

The U.S. Bureau of Labor Statistics anticipates a 11% decline in employment for computer programmers between 2022 to 2032. Still, BLS projects that there will be about 6,700 job openings per year over that decade because of workers transferring to other occupations or retiring. The amount of code that you write is going down, but the impact that you have with that code that you write is going up, explains George Heineman, an assistant professor of computer science at Worcester Polytechnic Institute in Worcester, Massachusetts, of the current need for programmers.

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For those who are interested in pursuing programming jobs, here are 10 of the fields top-paying roles.

Highest-paying cities: San Diego, Calif. ($298,291), New York, N.Y. ($225,432), Phoenix, Ariz. ($216,956), according to Indeed.

Description: This C-suite position oversees a companys IT department and research and development department. Part of this job includes researching new technology and finding weaknesses that can be fixed with new IT technology.

Heineman says this C-suite position carries a huge amount of responsibility, and that its more about hiring people than actual programming. You dont get to do the good stuff. You hire people who get to do the good stuff, Heineman explains. Its understanding the mission of the company and how to translate that mission into action.

Education: Its common for chief technology officers to have a bachelors in IT, business or cybersecurity; an MBA can provide business acumen and leadership skills.

Highest-paying cities: New York, N.Y. ($188,965), Cupertino, Calif. ($183,159), Santa Clara, Calif. ($182,851), according to Indeed.

Description: Machine learning engineers create software that can run automatically and contend with problems it encounters by learning to improve upon its tasks without assistance from humans. This wide-ranging skill can be applied to virtual assistants like Amazons Alexa, self-driving cars and recommendation algorithms.

You dont have to be a programmer to be a machine learning engineer, although a lot of programmers go in that direction, Heineman says. They really understand how to take that very specific domain and model it and run these machine learning algorithms through their paces.

Education: Machine learning engineers are usually required to have at least a bachelors in a related field; most job postings require a masters in computer science, data science, software engineering or a similar field.

Highest-paying cities: Bolinas, Calif. ($181,995), Kensington, Ky. ($178,838), Summitview, Wash. ($173,838), according to ZipRecruiter.

Description: AI engineers research and develop machines that simulate the thinking patterns and behavior of humans. Using machine learning and artificial intelligence, AI engineers create applications and systems that assist companies to increase profits and efficiency, cut costs, and make better business decisions.

Education: AI engineers typically hold a bachelors in a related field such as IT, computer science, data science or statistics. Though not usually required, its also common for AI engineers to hold a masters degree in a field like data science or computer science.

Highest-paying cities: San Jose, Calif. ($170,922), San Francisco, Calif. ($159,685), Washington, D.C. ($154,236), according to Indeed.

Description: Cloud computing allows companies to access large-scale storage without maintaining their own physical servers. Cloud architects set up these clouds for companies, maintain systems and communicate with third-party cloud servicers. Cloud architects must be deeply knowledgeable about security to protect the cloud.

You cant just start off as a cloud architect. You need experience, Heineman says. There is no single cloud. Theres not just one programming language, or theres not just one computer architecture.

Education: Earning a bachelors degree in computer science or a related field is often preferred by employers. A cloud architecture certification can also be helpful.

Highest-paying cities: Palo Alto, Calif. ($169,540), Bellevue, Wash. ($167,827), Redmond, Wash. ($141,286), according to Indeed.

Description: Data science professionals can take large sets of raw data, revise it, and analyze it to reveal actionable insights. Prevalent in industries like finance, health care, and technology, this role is especially useful for its ability to take data that was once incomprehensible and turn it into something constructive

Information is data with meaning, and thats the job of a data scientistget the data to be meaningful, says Paulus Wahjudi, chair and professor of the Department of Computer Sciences and Electrical Engineering at Marshall University.

Education: Its not strictly necessary, but a bachelors degree in computer science is useful for this role.

Highest-paying cities: San Diego, Calif. ($168,874), Herndon, Va. ($160,279), Chicago, Ill. ($156,416), according to Indeed.

Description: Requiring both IT and business skills, an enterprise architect ensures that a companys technology is in line with its business goals. Enterprise architects set IT standards, buy software or get an IT department to create it, based on their analysis of an employers business goals.

Education: Enterprise architect jobs usually require a four-year degree in data science, computer science, or a similar field. These roles often require five to 10 years of experience and a masters degree in a related field.

Highest-paying cities: Palo Alto, Calif. ($159,261), San Francisco, Calif. ($151,315), and Herndon, Va. ($151,190), according to Indeed.

Description: DevOps engineers work to improve software development processes by coordinating all teams involved with a products development. This role updates and maintains software processes with the aim of fixing bugs and improving user experience.

Education: Employers usually prefer DevOps engineers who have a bachelors degree in computer programming, software engineering, or a related field.

Highest-paying cities: San Francisco, Calif. ($154,204), Irving, Texas ($144,535), Charlotte, N.C. ($139,145), according to Indeed.

Description: A full stack developer can do pretty much anything related to computer programming. With the back-end team, they help manage servers and create databases; with the front-end team, they assist with the creation of parts of the project that are client-facing. Full stack developers are in high demand because of their ability to assist at any stage of a project.

Education: Full stack developers usually have at least a bachelors in computer engineering, information technology, computer science, or a related field. Some have certificates or specialized degrees in AI, web development, information security, or database management.

Highest-paying cities: Washington, D.C. ($124,316), New York, N.Y. ($121,596), and Boston, Mass. ($120,549), according to Indeed.

Description: As the name of the role suggests, database developers oversee developing databases. In modern times, most companies constantly record and store data thats used to conduct data analysis, record the companys history, and comply with regulations. Databases and data warehouses are necessary to securely store this data and must be crafted to meet the needs of each individual business. After creating these databases, a database developer must constantly maintain them.

The databases, in some ways, are so optimized that they can run themselves, but you still need someone to know how to model the data, and thats what a database developer does, Heineman says. They could have moderate programming skills, [but] thats not really the strength. Its modeling skills.

Education: Database developers usually have a bachelors degree in computer science, software engineering, or a similar field.

Highest-paying cities: Tallahassee, Fla. ($121,435), San Francisco, Calif. ($118,145), and Washington, D.C. ($98,807), according to Indeed.

Description: It is the job of a systems administrator to keep a companys hardware and software up and running securely. From managing operating systems and servers to updating and installing new software to providing tech support, a systems administrator must be able to take on any task required of them.

Having good people skills is a boon for this role, as systems administrators must often help non-technical employees. Theyre the one that gets called 24/7, says Wahjudi. If anything goes wrong, its your responsibility to get it back up.

Education: To be a systems administrator, most jobs require a bachelors in a field related to information or computer science. Some positions may require an associates or postsecondary degree.

Though the need for programmers has fallen since the boom of the pandemic, both Heineman and Wahjudi say that programming skills are useful and transferable. While demand may have slowed for these positions, people are still being hired for high-paying jobs that use programming.

We tell our students that a computer is stupid, Wahjudi says. Its only as smart as the programmer.

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UVA Cyberdefense Team Rolls Through Mid-Atlantic To Seek Fourth National Competition Title – UVA Engineering

The University of Virginia undergraduate cyberdefense team ably cleared the last hurdle to fight for its fourth championship title in the National Collegiate Cyber Defense Competition.

The Hoos earned the berth by beating nine teams in the Mid-Atlantic Collegiate Cyber Defense Competition, held March 23 at Prince Georges Community College in Maryland but only after also placing first in the 23-team qualifying round for the regional finals, as UVA Today reported in January.

On to the championships! said Jack Davidson, the teams faculty advisor, after the convincing win in Maryland.

The Mid-Atlantic Region is one of the most competitive in the country, said Davidson, a professor of computer science in the UVA School of Engineering and Applied Science and director of the cyberdefense program of study. Advancing to the finals by winning the MACCDC is quite an achievement.

This marks UVA cyberdefenders second consecutive MACCDC first-place win and their fourth since 2018. In that span, UVA was the national champion in 2018, 2019 and 2020, and has only missed one national finals appearance, in 2021.

The regional and national events, which have numerous industry sponsors and are a partnership with Raytheon, are designed for participants to both demonstrate and add to their understanding of the theory and practice of information security skills.

Using real-world scenarios, student teams defend their company network infrastructure and business information systems from ongoing hacker attacks while also managing injects, which are normal day-to-day tasks that must be completed on deadline.

Its hard to put a finger on any one thing that led to victory, said 2024 team captain Christopher Marotta, a fourth-year computer science major.

So many factors play into scoring, Marotta said, noting the team made a lot of points on the strength of their fundamental understanding of how the systems they were defending work. That made troubleshooting some very difficult issues possible.

While the team also lost some ground, the Hoos adept handling of business as usual may have carried the day.

We did really great in injects, so when we lost a strong lead in service functionality points, our injects score allowed us to pull through in the end! said Marotta, who has been involved with the Universitys Computer and Network Security Club and cyberdefense team since his first year at UVA.

This was no small feat given the competition scenario called for teams to take over management of a newly integrated IT network following the merger of two companies. UVAs cohesiveness and competency enabled them to ensure functionality andinteroperability of the network while under a significant cyberattack.

In addition to Davidson, the 2024 team was coached by last years captain Emil Baggs (computer science, 2023). Competing with Marotta were Nick Winschel, Charlotte Miller, Lulu Han, Chase Hildebrand, Alek Schultz, Austin Tran and Shreyas Mayya.

The Hoos next stop is San Antonio, Texas, April 25-27, where they aim to bring a fourth national title home to Charlottesville.

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Vernor Vinge, Innovative Science Fiction Novelist, Dies at 79 – The New York Times

Vernor Vinge, a mathematician and prolific science fiction author who in the 1980s wrote a novella that offered an early glimpse of what became known as cyberspace, and who soon after that hypothesized that artificial intelligence would outstrip human intelligence, died on March 20 in the La Jolla area of San Diego. He was 79.

James Frenkel, who edited nearly all of his work since 1981, said the cause of death, in an assisted living facility, was Parkinsons disease.

David Brin, a science fiction writer and a friend of Mr. Vinges, said in a tribute on Facebook, Vernor enthralled millions with tales of plausible tomorrows, made all the more vivid by his polymath masteries of language, drama, characters and the implications of science.

Mr. Vinge (pronounced VIN-jee) was renowned for his novella True Names (1981), in which he created an early version of cyberspace a virtual reality technology he called the Other Plane a year before William Gibson gave the nascent digital ecosystem its name in a story, Burning Chrome, and three years later popularized the word in his novel Neuromancer.

In True Names, Mr. Slippery, one of the anonymous computer hackers known as warlocks who work within the Other Plane, is identified and caught by the government (the Great Enemy) and forced to help stop a threat posed by another warlock.

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