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Harnessing machine learning to help patients with ALS – The Irish Times

September 23, 2021 | Author: admin

What inspired your interest in using machine learning in healthcare?

I studied computer science as an undergraduate in Athens, where I grew up, and I went on to do a masters degree in biostatistics in Glasgow. I liked that biostatistics applies to real-world problems, and my research there used machine learning to look at data from patients who had heart failure.

What prompted you to move to Ireland?

My partner and I moved to Dublin, and I got a PhD position at University College Dublin and FutureNeuro with Dr Catherine Mooney, to work more on how machine learning can analyse healthcare records.

The idea is that machine learning might be able to find less linear links between patient data and their needs, and this could help to support clinicians when they are planning care for the patient.

Tell us about the project you have been working on.

My project has been looking at patients with ALS, or motor neurone disease. Over the years, Prof Orla Hardiman and her team at Trinity College Dublin have worked with groups across Europe, and have gathered data about ALS patients with their consent.

With funding from the Health Research Board and other agencies I was able to interrogate these anonymised data, and additional information that the team was able to provide from consenting caregivers and patients, to explore what factors could be likely to affect their quality of life.

What did you find, using this machine learning approach?

There were some aspects for the patients like the timing of when the disease symptoms started and whether they have issues with breathing when lying down that could reduce their quality of life. Also for primary caregivers, how they view their role and purpose seemed to be linked to their quality of life.

How might the technology be used to help people with ALS?

The models that we made can be used as part of a clinical decision support system, which could automatically flag up to a nurse or doctor a pattern of patient or caregiver characteristics that suggests the patient or caregiver might be at risk of greater psychological stress or a lower quality of life. This would help them to build a personalised plan to support the patient and caregiver.?

What has kept you going through the research?

The human side of it. I was able to visit an MND clinic and observe some of the sessions with the consent of those attending, which gave me an important context these data arent just numbers I was working with on the computer, we are talking about real-world conditions and interactions.

Also we did a user study on a prototype clinical decision support system with clinicians, to see whether and how clinicians would use such a system, and it was encouraging to see our research being translated into a real-world context.

You recently wrote up your thesis, how did you find that?

It has been quite rewarding to see everything fitting together. I was also able to move back to Athens and I will defend my thesis online, which is easier for all the examiners than travelling.

And finally, how do you like to take a break?

I like to do creative things and work with my hands, to get a break from the computer. During the lockdown in Ireland I made and decorated cakes and I also did embroidery. I find its a good balance to sitting looking at a computer screen.

Futureneurocentre.ie

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Robots and machine learning researchers combine forces to speed up the drug development process – TechRepublic

September 23, 2021 | Author: admin

IBM Research and Arctoris announce a research collaboration to test a closed-loop platform.

Ulysses is the world's first fully automated drug discovery platform developed and operated by Arctoris based in Oxford, Boston and Singapore.

Image: Arctoris

IBM Research and Arctoris are bringing the power of artificial intelligence and robotic automation to the process of developing new drugs. The two companies aim to make smarter choices early on in the process, iterate faster and improve the odds of finding an effective treatment.

IBM Research contributed two platforms to the project. RXN for Chemistry uses natural language processing to automate synthetic chemistry and artificial intelligence to make predictions about which compound has the highest chance of success. That information is passed on to RoboRXN, an automated platform for molecule synthesis.

Arctoris, a drug discovery company, brought Ulysses to the project. The company's automated platform uses robots and digital data capture to conduct lab experiments in cell and molecular biology and biochemistry and biophysics. Experiments conducted with Ulysses generate 100 times more data points per assay compared to industry-standard manual methods, according to Arctoris.

IBM Research will design and synthesize new chemical matter that Arctoris will test and analyze. The resulting data will inform the next iteration of the experiment.

SEE: Drug discovery company works with ethnobotanists and data scientists

Thomas A. Fleming, Arctoris co-founder and COO, described this project as "a world-first closed-loop drug discovery project" that combines AI and robotics-powered drug discovery.

"This collaboration will showcase how the combination of our unique technology platforms will lead to accelerated research based on better data enabling better decisions," he said in a press release.

A research paper about closed-loop drug discovery describes the process as a centralized workflow controlled by machine learning. The system generates a hypothesis, synthesizes a lead drug candidate, tests it and then stores the data. This comprehensive process could "reduce bottlenecks and standards discrepancies and eliminate human biases in hypothesis generation," according to the paper.

Automating lab work results in better data which in turn means less rework and a savings of time and money, Poppy Roworth, head of laboratory at Arctoris, explained in a blog post. She described the benefits of automation this way: "I no longer have to manually pipette each well at a time of a 96 or 384 well plate, which is highly beneficial for my sanity when there is a stack of more than 5 or 10 to get through." By automating the protocol, scientists can use time previously spent in the lab on "planning the next experiment, designing new projects with clients, reading literature and keeping up to day with other projects."

Matteo Manica, a research scientist at IBM Research Europe, Zurich, is coordinating the project and said in a press release that this work is a unique opportunity to quantify the impact of AI and automation technologies in accelerating scientific discovery.

"In our collaboration, we demonstrate a pipeline to perform iterative design cycles where generative models suggest candidates that are synthesized with RoboRXN and screened with Ulysses," he said. "The data produced by Ulysses will then be used to establish a feedback loop to retrain the generative AI and improve the proposed leads in a completely data-driven fashion."

More than 3,000 researchers in 16 locations on five continents work for IBM Research. Arctoris is a biotech company headquartered in Oxford with offices in Boston and Singapore. The collaboration is ongoing.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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How Machine Learning and AI Are Transforming The Finance Industry – FinanceFeeds

September 23, 2021 | Author: admin

Thanks to the wealth of data that are increasingly available to banks and the general public, sophisticated algorithms are enabling improved processes in many areas of finance.

Image Source: Canva Pro

A subfield of artificial intelligence (AI), machine learning (ML) enables systems to learn and improve independently without the need for explicit programming or human involvement. But ML only works when it has access to enormous volumes of data, allowing machines to be trained rather than meticulously programmed through line-by-line coding.

To do this, ML utilizes data on outcomes to figure out how to improve, make predictions, and describe information, which has led to major breakthroughs in almost every industry across the globe. Machine learning technology frees up a considerable amount of resources that would otherwise be spent on manual, repetitive tasks while increasing productivity, reducing errors, automating processes, and identifying trends and patterns.

Technologies such as the internet of things (IoT) and cloud computing are all growing implementations of ML. As a result, technology is changing the way financial businesses operate, as things that were once thought unimaginable have now been brought into the realms of possibility.

Unsurprisingly, one of the primary use cases for this new tech is in the financial sector, which greatly benefits from the ability to crunch huge data sets to secure important insights into market trends and forecasting fluctuations in financial assets.

With that said, the financial industry is finding a wide variety of use cases for AI and machine learning, from predicting cash flow events to detecting fraud and even improving the customer experience. On that note, lets take a look at a few of the most widely implemented applications.

Machine learning and artificial intelligence (AI) solutions are transforming risk management in the financial sector. With this technology, banks and financial institutions can significantly reduce their risk levels by analyzing a massive volume of data sources to identify potential problem areas and make better, more informed decisions.

Banks, for example, employ machine learning to evaluate vast amounts of personal data to improve the accuracy and effectiveness of credit scoring, analyzing data sets such as prior lending operations, debts, marital status, financial behaviour of applicants, and more to help them determine whether or not to issue loans and open lines of credit.

Artificial intelligence (AI) solutions can enhance customer experiences in the finance industry via chatbots, search engines, mobile banking, and financial health analytics. All of this helps provide more value to the customer, improve application processes, answer queries quickly, and reduce waiting times when trying to fix a problem.

AI solutions can also provide automated portfolio management and personalized product recommendations with little to no human supervision.

Through the use of sophisticated stock intelligence tools, machine learning-enabled technologies are able to provide advanced market insights that surface advanced data signals. These tools are far more efficient (and quicker) than traditional investment models, leading them to dramatically disrupt the investment banking industry.

Interestingly, as this technology becomes more widely available, it is no longer exclusive to hedge fund managers and larger financial institutions. Now, everyday traders are incorporating ML-based investment strategies in order to better predict the market and spot opportunities that would have been previously impossible to unearth at scale.

In the financial industry, robotic process automation (RPA) is an extremely useful tool that banks and other financial institutions use to replace human labour by automating repetitive activities with intelligent processes, leading to increased business productivity. This is one of the most widely used applications of AI and ML in the fintech sector and has been assisting businesses in gaining a competitive advantage over their competitors for quite some time. It is feasible to improve nearly any business activity by implementing this technology, resulting in improved customer experience, cost savings, and the capacity to scale up services.

In addition, according to McKinseys research, we are about to enter the second phase of AI-enabled automation. Its predicted that machines and software bots will carry out 10% to 25% of tasks across various bank processes, increasing total capacity and allowing employees to focus on higher-value projects and initiatives.

As AI and ML technologies continue to improve, its almost certain that we will begin to see them play an increasingly important role in different aspects of the financial industries, such as managing portfolios and predicting market movements, fine-tuning the customer experience, and preventing fraud and reducing risk.

Some experts even predicted that one day we could live in a world with a fully automated financial system, but it seems at this point we still have some way to go before that can be fully achieved.

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The 8 Best AWS Machine Learning Courses and Online Training for 2021 – Solutions Review

September 23, 2021 | Author: admin

Solutions Review editors compiled this list of the best AWS machine learning courses and online training to use when growing your skills.

With this in mind, weve compiled this list of the best AWS machine learning courses and online training to consider if youre looking to grow your cloud artificial intelligence and automation skills for work or play. This is not an exhaustive list, but one that features the best AWS machine learning courses and training from trusted online platforms. This list of the best AWS machine learning courses below includes links to the modules and our take on each.

Platform: Coursera

Description: This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

Platform: edX

Description: This course will teach application developers how to use Amazon SageMaker to simplify the integration of machine learning into their applications. Key topics include an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMakers built-in algorithms and, using SageMaker to publish the validated model.

Platform: Pluralsight

Description: In this course, youll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps. Youll also learn how to prepare your data for the machine learning pipeline by doing preprocessing and feature engineering.

Platform: Pluralsight

Description: First, youll explore what ML is and how it relates to artificial intelligence and deep learning. Next, youll learn how to identify and frame opportunities for machine learning. Then, youll discover the end-to-end machine learning process: fetching, cleaning, and preparing data, training and evaluating models, and deploying and monitoring models.

Platform: Udacity

Description: Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.

Platform: Udemy

Description: In addition to the9-hour video course, a 30-minutequick assessment practice examis included that consists of the same topics and style as the real exam. Youll also getfour hands-on labsthat allow you to practice what youve learned, and gain valuable experience in model tuning, feature engineering, and data engineering.

Platform: Udemy

Description: This course is designed for anyone who is interested in AWS cloud-based machine learning and data science. Learners should have familiarity with Python, an AWS account, basic knowledge of Pandas, Numpy, and Matplotlib. The ideal student for this course is willing to learn and participate in the course Q&A forum when help is needed.

Platform: Udemy

Description: With over 500 slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information.

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

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New Test Leverages Machine Learning to Diagnose and Predict Sepsis – Medical Device and Diagnostics Industry

September 23, 2021 | Author: admin

Sepsis is a huge healthcare concern. You take every single cancer and all the deaths due to every single cancer and you add them all up together. More people die from sepsis worldwide than that, said Bobby Reddy, Jr., CEO of Prenosis, in an interview with MD+DI.

And even if patients survive, they can have lifelong consequences. Sepsis occurs when you have a very abnormal, unhealthy reaction to infection, Reddy said. This unregulated immune response can lead to organ dysfunction and even death.

Sepsis is treatable with antibiotics if it is diagnosed in time, but it can explode out of control in hours or days if left untreated. If [a patient] had just gotten a simple dose of antibiotics two days earlier, it wouldn't have been life-threatening, Reddy said. That's why the WHO has called this the number one cause of preventable death worldwide.

Symptoms of sepsis can be vague and thus hard to diagnose. The current standard of care [for determining sepsis] is literally a human being, he said. Reddy explained that a physician or nurse typically uses four parameters to suspect sepsis: temperature, white blood cell count, lactate, and their overall impression of a patient.

That's how some doctors have been trained for the last 20 to 25 years, he said. Unfortunately, that just doesn't work. It's one of the reasons why there remains such a high mortality rate with sepsis.

Prenosis has developed Immunix, an assisted intelligence system that uses holistic input data from 23 parameters and a machine learning algorithm that provides an ImmunoScore, which gives a rating of a patients chances of sepsis, 30-day mortality, elongated hospital stay, and 30-day readmission to the hospital.

One unique aspect of this product is that it forces the data to be clean at that critical snapshot of time so that you canaccurately diagnose [sepsis], Reddy said. Clean data, Reddy said, means that the system checks to see if the all theneeded data is available and to see if it has any errors. For example, at this point in time, maybe they've done your bloodpressure and took your temperature, but they didn't do a heart rate measurement, he said. The system requires anymissing parameters to be filled in with the order of an additional test or additional measurement. This type of assistedintelligence can create better, cleaner data, resulting in better and more precise diagnostics, said Reddy.

The second unique aspect, he said, is that typically not all of these 23 parameters are ordered at the same time. For these patients in particular, the three biomarkers that Immunix looks at help profile the patient's underlying biological state accurately. The biomarkers are Interleukin-6, procalcitonin, and C-reactive protein.

Reddy stressed that this system is what he called assisted intelligence, as opposed to artificial intelligence, as it can be used as a tool to help guide the physician, rather than diagnosing alone. We really like to think of ourselves as a GPS as opposed to a self-driving car, he said. It's really about working with the doctor.

The Immunix system can address desperate hospital needs, said Reddy. Hospitals lose an average of $29,118 per septic patient in the United States. But according to Prenosis, based on a 1,300-patient multistudy, greater than $9.9 B can be realized in potential annual cost savings if ImmunoScore were implemented across the United States.

The Immunix system is expected to received FDA clearance by the second half of 2022.

To increase knowledge about the condition, the Sepsis Alliance has designated September as Sepsis Awareness Month. More information can be found at http://www.sepsis.org.

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SambaNova makes a mark in the AI hardware realm – TechTarget

September 23, 2021 | Author: admin

As a young startup, SambaNova Systems is already making a mark in the fast-growing AI hardware industry.

The vendor, based in Palo Alto, Calif., started in 2017 with a mission of transforming how enterprises and research labs with high compute power needs deploy AI, and providing high-performance and high-accuracy hardware-software systems that are still easy to use, said Kunle Olukotun, co-founder and chief technologist.

Its technology is being noticed. SambaNova has attracted more than $1.1 billion in venture financing. With a valuation of $5.1 billion, it is one of the most well-funded AI startups and it is already competing with the likes of AI chip giant Nvidia.

SambaNova's hallmark is its Dataflow architecture. Using the extensible machine learning services platform, enterprises can specify various configurations, whether grouping kernelstogether on asingle chip, or on multiple chips, in a rack or on multiple racks in the SambaNova data center.

Essentially, the vendor leases to enterprise clients the processing power of its proprietary AI chips and creates machine learning models based on domain data supplied by the customer, or customers can buy SambaNova chips and run their own AI systems on them.

While other vendors have offered either just chips or just the software, SambaNova provides the entire rack, which will make AI more accessible to a wider range of organizations, said R "Ray" Wang, founder and principal analyst at Constellation Research.

"The irony of AI automation is that it's massively manual today," Wang said. "What [SambaNova is] trying to do is take away a lot of that manual process and a lot of the human error and make it a lot more accessible to get AI."

Wang added that SambaNova offers AI chips that are among the most powerful on the market.

While it's known in some ways as an AI hardware specialist, SambaNova prides itself in taking a "software-defined approach" to building its AI technology stack.

"We didn't build some hardware thinking: 'OK, now developers go out and figure it out,'" said Marshall Choy, vice president of product at SambaNova. Instead, he said the vendor focused on the problems of scale, performance, accuracy and ease of use for machine learning data flow computing. Then they built the infrastructure engine to support those needs.

The irony of AI automation is that it's massively manual today. R 'Ray' WangFounder and principal analyst, Constellation Research

SambaNova breaks up its customers into two groups: the Fortune 50 and the "Fortune everybody else." For the first group, SambaNova's data platform enables enterprise data teams to innovate and generate new models, Choy said.

The other group is made up of enterprises that lack the time, resources or desire to become experts in machine learning and AI. For these organizations, SambaNova offers Dataflow as a service.

SambaNova says this approach helps smaller enterprises by reducing the complexities of buying and maintaining hardware infrastructure and selecting, optimizing and maintaining machine learning models.

This creates a "greater AI equity and accessibility of technology than has previously been held in the hands of only the biggest, most wealthy tech companies," Choy said.

SambaNova has already attracted some big-name customers.

Oneis the U.S. Department of Energy's Argonne National Laboratory in Illinois.

Using SambaNova's DataScale system, Argonne trained a convolutional neural network (CNN) with images beyond 50k x 50k resolution. Previously, when Argonne tried to train the CNN on GPUs, they found that the images were too large and had to be resized to 50% resolution, according to SambaNova.

"We're seeing new ways of computing," Wang said. "This approach to getting to AI is going to be one of many. I think other people are going to try different approaches, but this one seems very promising."

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Programming for fairness: meet students and their computer science teacher | BCS – BCS

September 23, 2021 | Author: admin

With National Coding Week just passed, and Ada Lovelace Day coming up, this is a chance to raise awareness of inequalities in computer science careers, and showcase what the Governments Equality Hub is doing to address gender imbalance in STEM.

We chatted to seven students from Saffron Walden High School, which is one of the first computing hubs picked by the Government-funded National Centre for Computing Education to run computing courses for teachers in the area. We discussed the girls experience learning coding and their aspirations for the future.

The girls, from Year 9 to Year 12, are eager computing students and aspire to go into careers ranging from fashion, medicine, law, and underwater engineering.

Anna, Elspeth, Grace, Ella, Mayurii, Rachel and Emma had lots of questions to ask us and were excited to know about the Equality Hubs work.

Their teacher, Katie Vanderpere-Brown, says she works hard to amplify the female role models in computer science: for example, Margaret Hamilton and her work with the Apollo Mission and Katherine Johnson, the human computer.

Our conversation with students:

In Year 7, Im in Year 9 now. I really enjoyed it and Ive just picked it as an option for GCSE.

Everyone said it was going to be really difficult but Im up for a challenge. My family were quite proud because they know that I enjoy it.

Their teacher, Katie, said: Weve had some good success here getting girls to recognise the importance of, and pick, computer science at GCSE, but the problem is few consider it for their next step and dont take it for their A-Levels. They can understand the value of learning to code, but few see it as a viable career. Were trying to get across that computer science can be a part of many creative jobs, not just a programming career.

Lots of students who take it have family members, older siblings, doing computer science. We need to find a way to reach those students who dont have role models in coding in their home.

Katie also explored some of the barriers which are preventing girls from moving into computer science.

Theres a shortage of specialised computer science teachers, she explains. Where other teachers are covering computer science lessons, it shows students, particularly those who are shrewd, that this subject is not valued as much, or it isnt someones full-time profession.

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WU to offer data science major as joint program in McKelvey and Arts & Sciences – Student Life

September 23, 2021 | Author: admin

McKelvey hall is home to the McKelvey school of Engineering

Washington University will offer a Bachelor of Science or Arts in Data Science degree this fall in response to a major increase in demand for data science knowledge in the industry.

This program is a joint effort between the computer science department in the McKelvey School of Engineering and the statistics department in the College of Arts & Sciences, with a B.A. degree offered through Arts & Sciences and a B.S. degree offered through McKelvey.

A team of four core faculty members including professors of mathematics and statistics Soumendra Lahiri and Jos Figueroa-Lpez and professors of computer science and engineering Marion Neumann and Ron Cytron spent a year planning and developing the degree.

Neumann explained that big data is used widely across a variety of different industries, making the data science major extremely versatile. Data science is solving problems with data, so think of any of your scientific, social or business problems that you might have, and you will use data to solve the problem, Neumann said.

Figueroa-Lpez added that the current high levels of demand for data scientists made developing this degree a priority for the University.

It was realized, not only [by] the University, but the whole academic community, that there is a need for students that are better prepared [for] some applications in some industries [that] require handling data in an efficient way, in a meaningful way, so there is a huge demand for these students, Figueroa-Lpez said.

To address the needs of a myriad of industries like technology, finance, medical fields, nonprofits and more, the program gives students both the computational tools to process large data as well as statistical and mathematical knowledge for collecting, analyzing, interpreting and presenting that data.

We want the students to have a very rigorous mathematical background or training, Lahiri said. At the same time, we want the students also to be very proficient in handling all of those computing tools.

Neumann explained that a major advantage of this program is that it allows students to learn important skills in the fields of both computer science and statistics, without requiring them to take extraneous courses for a double major.

Instead of double majoring in [computer science] and statistics, youre getting a smaller set of courses that brings you in the right direction, Neumann said. You can essentially do a single major where you kind of are guided towards picking the corrector a good setof courses that equips you with all you need for data science.

Specialized courses include Statistics for Data Science I as well as a Practicum, which gives students an opportunity to apply their knowledge to a domain like sports, finance or medical data in the form of internships, research work or seminar-style lectures from experts in the field.

Sixteen students are currently enrolled in the inaugural Statistics for Data Science I class this fall. Since it is the introductory course for the major, students of all years and backgrounds can enroll. Students may declare a data science major or second major, though computer science majors are not allowed to declare data science as their second major. Both a data science minor and an accelerated masters program are also in the works.

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New FAS faculty reflect diversity and excellence – Yale News

September 23, 2021 | Author: admin

Yales Faculty of Arts and Sciences (FAS) will welcome 35 new colleagues this academic year a group of world-class researchers and teachers whose work is expanding the horizons of a range of fields, including African American studies, mathematics, computer science, and theater.

The cohort of new ladder faculty includes both tenured professors and newly minted Ph.Ds. Its members demonstrate Yales commitment to building a diverse faculty of exceptional scholars, said FAS Dean Tamar Szab Gendler.

One of the great joys of serving as the dean of the Faculty of Arts and Sciences is the opportunity to identify the best scholars and recruit them to Yale, and Im excited to welcome this diverse group of new ladder faculty, Gendler said. Our aim is to provide them the resources they need to both thrive in their research and inspire our undergraduates with their teaching.

Twenty-six ofthe 35 new hiresjoined the FAS ladder faculty this fall. Five will hold visiting appointments at Yale for 2021-2022 before joining the ladder faculty, and four others will arrive in January.Thirteen are tenured. The group features senior faculty in the Department of African American Studies, the Womens, Gender, and Sexuality Studies Program, and the Program in Ethnicity, Race, and Migration areas of growing interest among students, Gendler explained.

The new hires include a strong group of junior faculty members in the humanities, including history, English, philosophy, and Slavic languages, and enhance the universitys strength in the qualitative sciences, such as computer science, mathematics, and chemical and electrical engineering, Gendler said.

Several of the new faculty members contribute intellectual breadth to the Department of History, bringing expertise in regions throughout the globe, she said. Alvita Akiboh, assistant professor of history, studies the history of U.S. colonies in the Caribbean and Pacific. Hussein Fancy, associate professor of history, focuses on the social and intellectual history of religious interaction in the medieval Mediterranean. Hannah Shepherd, assistant professor of history, specializes in modern Japan and its colonial empire.

The new faculty bring new perspectives on age-old subjects, such as race and the environment. Jonathan Howard, assistant professor of English and African American Studies, examines western ideas about race and nature, probing their entangled influence on a modern world in ecological peril. His work blends the literary and intellectual traditions of the African diaspora with the environmental humanities.

The group also features theatrical star power. Playwright Branden Jacobs-Jenkins, professor in the practice oftheater and performance studies, was a 2016 recipient of Yales Windham-Campbell Literature Prize for Drama and a MacArthur Fellowship. He was a Pulitzer Prize finalist for his plays Gloria and Everybody in 2016 and 2018, respectively.

Scholars of music and art have joined the faculty. Braxton Shelley, a musicologist who specializes in African American popular music, is a tenured associate professor in the Department of Music, the Institute of Sacred Music, and the Divinity School. Morgan Ng, assistant professor of the history of art, uncovers lost connections among architecture, visual culture, craft, and the technical arts, focusing on Renaissance Italy, but drawing links to Northern Europe and the wider world.

Among new faculty in the social sciences, Julia Leonard, assistant professor of psychology, studies the cognitive, neural, and computational representations underlying childrens learning and motivation.

Three new hires in mathematics include Lu Wang, a professor of mathematics and a geometric analyst specifically interested in geometric flows and their applications.

Charalampos Papamanthou, associate professor of computer science, is one of three new hires in the Department of Computer Science. His work centers on applied cryptography and computer security with a special focus on technologies, systems, and theory for secure and private cloud computing.

The new FAS faculty showcase Yales commitment to recruit and retain a diverse and excellent faculty, Gendler said. In December 2019, President Peter Salovey approved a five-year renewal of the universitys Faculty Excellence and Diversity Initiative(FEDI), which was launched in 2015, increasing the programs budget by 70%, from $50 million to $85 million.

Our investment in recruiting an excellent and diverse FAS faculty is paying off, Gendler said. These new ladder faculty members not only reflect excellence across a broad range of disciplines, they feature the diversity of perspectives and experiences necessary to sustain a rich and vibrant academic community.

Bios of all the new faculty members are available on the FAS website.

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Career Insights: Everything You Need To Know About Computer Science and AI Researchers – Analytics Insight

September 23, 2021 | Author: admin

Career Insights: Everything You Need To Know About Computer Science and AI Researchers

Roles and responsibilities: The purpose of gathering and analyzing data, regardless of application, business or industry is to drive action. Data science and data scientists enable the mining of data and inference of logical conclusions to assist decisions that eventually lead to action. Meanwhile, AI enables unmanned systems to quickly analyze a large volume and variety of data to make decisions and act independently. Thus, AI researchers, as the name suggests, research novel forms of AI technology to create new applications that use data to drive independent actions. Besides having knowledge of statistics, data science, and programming, AI researchers are also skilled in advanced domains such as deep learning, deep neural networks, and natural language processing. AI researchers are necessary to lead AI development and experimental projects and to extend the existing AI capabilities.

Average salary (per annum): US$100,784

MSc, online, part-time (Computer ScienceArtificial Intelligence), DCU: This is a distinctive program taught by an internationally renowned, interdisciplinary team of NUI Galway experts in the field, many of whom are researchers at the Insight Centre for Data Analytics. Students also have the opportunity to choose from several optional online modules on offer from our partner in this program Dublin City University (DCU). The program is taught over two years and is delivered completely online using state-of-the-art technologies and techniques to support the virtual classroom. Students are expected to attend classes on campus at most one day per semester.

Computer Science MOOC, Coursera: Coursera is a free learning site that offers MOOCs courses from well-known universities. All Coursera courses contain pre-recorded video lectures that you can watch when it is convenient for you. Coursera has programs together with universities that allow you to get a masters degree or specializations. You can explore various college courses without any hassle. It offers free programs from accredited institutions. These free certification courses online available on this platform are designed by a leading university.

Computer Science: Java, C++, Javascript, Blockchain, Linux, Data Science, IoT, etc.

Computer science and language, Edx: Edx is one of the best free online course providers. It offers university-level courses in a variety of disciplines. You can browse various subjects like Computer science, language, data science, engineering, and more. This site contains a weekly subject sequence. It includes a short video with learning exercises. The platform provides video tutorials, which are similar to the on-campus discussion group and a textbook. It has an online discussion forum where students can post the questions to teaching assistants. It offers free online courses with certificates of completion.

Deloitte: A finance mogul, Deloitte is one of the largest professional services networks in the world. The company runs a giant network of economic forums, which requires a huge team of IT professionals.

CGI: CGI is the 5th largest independent and end-to-end IT and business services and product organization in the world. Founded in 1976, the company has grown into an organization having 73k professionals in over 40 countries.

Mindtree: Mindtree is a known service provider to several global 2000 clients in the digital technology space. Computer science graduates who are looking to begin their career on a high note should certainly aim for a job in this organization.

Infosys: A dream company for most IT professionals in India, Infosys is a global leader in technology services & consulting. It helps clients in more than 50 countries to create & execute digital transformation strategies.

Cognizant: With its headquarters in New Jersey, Cognizant has made a huge mark in the Indian software scenario. Students are keen to learn from the work culture at Cognizant to help them further in their careers.

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