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Instructors from Pace Universitys Dyson College of Arts and Sciences recently received a $499,354 grant from the National Science Foundation that will allow them to expand the teaching of data science skills into introductory biology and environmental science courses.

Specifically, the grants purpose is to allow the group to continue expanding the Biological and Environmental Data Education Network, which will create a system of instructors addressing the need for data science skills and offer training, resources and professional development opportunities for biology and environmental science instructors to enable them to teach the skill set in their regular coursework.

Aiello-Lammens and Crispos roles in the program are to help other instructors learn to incorporate data education into curricula, an approach that the two instructors already practice in all of their courses taught at Pace.

Aiello-Lammens has been involved with the network before, during its original founding through a National Science Foundation incubator grant, of which he was a recipient along with colleagues from Kenyon College and Denison University. Crispo was among one of the first attendees of the networks activities.

We think its vital that our students understand how to make sense of these data and use them to make decisions for what they should be doing from whether to eat certain foods, consider certain medicines, or accept a particular job, Aiello-Lammens said. If they have these data science skills in general, then they can apply them both in their work and in their lives.

Today were able to collect more data more rapidly, collect it on computers, and analyze it on supercomputers, Crispo said. Its becoming more challenging to handle data and analyze itand its becoming increasingly important to give students the skills to be able to do so.

According to the National Science Foundation, data science skills are increasingly necessary in the fields of science, technology, engineering, mathematics and beyond, but instructors often do not have the training necessary to impart this skill set to their students. Other barriers it cited include curricula that are already perceived as overcrowded, confusion over what the key skills are and a lack of confidence in teaching them.

Data management, data analysis, data visualization, programming, modeling and reproducibility are the main subject areas and strategies the network will focus on imparting to students.

The Biological and Environmental Data Education Network will hold annual meetings, the first of which will be held at Paces Manhattan campus in 2022 and will focus on diversity and inclusion in data science education.

Its other education expansion efforts will include developing training and education workshops, publishing a curriculum guide and adding members to create a more active and diverse community of educators. Aiello-Lammens and Crispo aim to expand it nationally and internationally.

According to Pace, the program aligns with its new university-wide strategic plan, Pace Forward.

This grant epitomizes what we believe in at Pace and helps to put Pace at the forefront of educational innovation, said Tresmaine Grimes, dean of Dyson College of Arts and Sciences and School of Education. The work of Professors Aiello-Lammens and Crispo is inspiring in its aim to be cutting-edge, far-reaching, and cross-disciplinary, and will serve instructors and students not only at Pace, but across the country.

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Pace receives NSF grant to expand data science instruction nationwide - Westfair Online

The oldest rail network in the world, with over 32,000 km. of track, is investing in artificial intelligence and machine learning to streamline maintenance and deal with weather-related challenges.

Two months ago, during the AI and Big Data Expo, I had the opportunity to meet Nikolaos (Nick) Kotsis, Chief Data Scientist at Network Rail. At the conference, he was one of the speakers, talking about their digital transformation and how Network Rail was implementing new ways to inspect and manage the networks assets, shifting work from traditional planning and maintenance schedules to a proactive predict and prevent approach.

At the time, Kostis mentioned the data collection happening in real-time, simultaneously from the trains using the network, the people inspecting the tracks, drones, helicopters, and over 30,000 IoT sensors deployed all over the country.

All this data collection allows Network Rail to know what is happening and take immediate action when something goes wrong or needs fixing. But the real magic, which helps predict and prevent incidents, and provides predictive maintenance, happens when AI and Machine Learning are applied to that massive amount of data.

To learn more about how Network Rails Data Science department works and how it impacts the organization, we reached out to Nick Kotsis again. He answered our questions by email.

PV: As discussed in our previous conversation, Network Rail is undergoing a substantial digital transformation in the field and the data center. Can you tell us a bit about your data science department and its role within the organization?

Nick Kotsis: Our initial plan for the data science function was leaning towards being primarily guidance and advisory; however, once we began engaging with customers, we realised that taking on responsibilities for delivery would not only return cost benefits to the taxpayer but would also help our partner network operate more effectively.

Inspiring confidence and gaining the trust of our customers and suppliers was the focus of my role in the first six months. Since then, we have evolved to become an actual delivery function for data analytics, advanced machine learning, and AI technology packaged into fully integrated digital products that customers can use with minimal training. The end result is a trusted service supported by a skilled, confident team focused on the customer and responding to the most complex problems in Network Rail.

When customers from across the organisation ask for help, it means we do something right.

PV: You said before that NR is a complex machine, managing the network and some of the largest train stations and maintaining the freight trains. How is data analytics (and AI) affecting the different services?

Nick Kotsis: Our organisation is responsible for maintaining a complex infrastructure that is vulnerable to environmental and weather conditions and the constant pressure of hundreds of daily train journeys.

Network Rail maintains 20,000 miles of track. Our job is to maximise the use of data to make the maintenance of the infrastructure a safer and more efficient environment for both our passengers and workforce.

To give you an example, performing remote inspections on track assets with the assistance of AI technology instead of visiting the track is a genuine safety benefit for our maintenance teams. Of course, we are not planning to pause physical inspections, but if we could confidently limit them to absolutely necessary ones, that would be a true benefit.

Data analytics and the more sophisticated machine learning techniques consistently demonstrate high quantitative and qualitative benefits. Examples of these benefits can be seen in: the prediction of incidents; the automation of tasks that would be repetitive and mundane for a human; the complex risk assessment on thousands of assets in a split second; and complexity management using optimisation algorithms which also bring speed to complex decision making.

In our case, we have successfully developed automated risk assessments using computer vision techniques that identify assets for immediate attention on the track and surrounding areas. We also established a preventive maintenance process driven by predictive algorithms which calculate the likelihood of an asset failing days or weeks before it actually happens, allowing us to resolve an issue before it becomes a problem. Both of these systems offer significant improvements to safety along with reduced delays and disruption for passengers.

The advanced big data engineering and algorithmic (AI) logic we use behind the scenes should, and will, continue to expand across every part of our infrastructure. I am confident that eventually, we will reach the desired levels of deployments needed to scale up our operations to prevent the incidents.

PV: The UK has the oldest rail service worldwide. Nevertheless, across Europe and many other places are also old rail services operating, each with complex and specific challenges. Based on your experience, what would you recommend to those organizations starting or undergoing digital transformation?

Nick Kotsis: Finding the correct answer to this is not easy as every organisation will be starting at a different point and has a different maturity trajectory. Also, financial investment for some organisations can be made easier than in others, and thus the digital journey will be very different.

As with most complex initiatives, digital, data, and AI success depends on leadership and commitment to completing the journey. In our case, we are fortunate to have solid technical leadership from our CIO and CTO. They saw our destination at the early stages of development and supported us in making the data science vision a reality.

My advice to colleagues in other organisations? be clear about the vision and data strategy, engage with customers who need your help, create the right team and set them for delivery, and focus on projects with clear benefits.

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UK Railroads Invest in Data Science, AI, and Machine Learning - Cities of the Future

I really do believe most of the journey that you have in a physical store can be done in a digital world, she says.

The Iconic leverages data to lure consumers, and keep them. Already, the site will suggest sizes based on other brands you wear, and offers virtual sneaker try-ons. It curates products based on your preferences, sending you a personalised edit of clothing, shoes and accessories you might like the way a sales assistant might. But Berchtold sees a future when the technology of the metaverse, for example, could be used as a virtual fitting room.

Although The Iconic employs just over 1000 people, half of these are stationed at its fulfillment centre in Yennora, in western Sydney. Of the rest, 120 are in technology roles. Berchtold is recruiting 70 more staff in the tech team, across data, engineering, product and information technology.

COVID hasnt helped, with the borders being shut, she says. There is also a lack of investment in technology education here in Australia. We havent been grooming our own workforce as much as we could have.

An unplanned meeting with federal Labor leader Anthony Albanese. Louie Douvis

Berchtold has spent her career in retail, and revels in the shop floor. The closest thing to that at The Iconic is the raft of customer emails that arrive each week; Berchtold reads them all.

When I was having a bad day at other jobs, Id go to the shop floor to remind myself of what were actually doing, she says. This is the same thing.

Some feedback will resonate with Berchtold so much she is compelled to send the writer a gift voucher. Like, we had one customer, says Berchtold. She wrote, I really love this brand. But I hate the models. Theyre just such skinny things. I was like, Oh, shes cool. I hear that. And so, were going to make sure that were a bit more diverse in the models selected for that particular brand. And I sent her a $50 voucher.

The company has grown significantly in 10 years. In 2011, The Iconic launched with 1000 products from 125 brands. Today, it boasts 165,000 products from 1500 brands, with 500 new arrivals to the site daily.

The Yennora site is Australias largest fashion fulfillment centre, with a capacity for 3.75 million units, and can fulfil orders in as little as eight minutes. At its Alexandria Hub, a purpose-built production studio in inner Sydney, more than 60 staff pump out curated editorial for the sites 19.5 million monthly users. Its app is Australias most downloaded fashion app, with 5 million downloads. In the 2020-21 financial year, it sent out 6.2 million parcels, a 1.2 million increase on fiscal 2019.

Numbers aside, Berchtold is most proud that the company has cemented itself as a permanent part of the retail landscape.

Expanding from clothing to categories such as childrens wear, homewares and beauty has been a significant shift for the business. And Im proud that we are playing a leadership role in things like sustainability, diversity and inclusion.

Last year, the company launched Giving Made Easy, enabling customers to donate pre-loved clothing by downloading a pre-paid postage label and dropping off their donation at any Australia Post location. The company says this has saved more than 25,000 kilograms of textiles from landfill.

It also enacted a Reconciliation Action Plan, endorsed by Reconciliation Australia, and is committed to body diversity, urging vendors to offer extended sizing. This year, it launched a modest dressing capsule.

Not, Berchtold says, that she has a lot of choice. The average team member is 27, she says. I do not have to push for this stuff. It is business as usual for young people. I dont need to explain why sustainability matters to anybody in our team.

As for the future of retail and technology Berchtold is bullish.

I look at COVID, and the way some retailers were saying, We got a chance to experience what its like to be pure-play online. Like, you think? Weve had a 10-year head start on that stuff. The data and technology that is baked into our DNA. I think some retailers would be astounded by it.

I dont love trying stuff on. So could we use virtual clothing on the site to show people what it looks like on an avatar of themselves? That would be enormously helpful.

The idea behind all The Iconics technology is to make every transaction more seamless.

The team is constantly testing and learning. Recently, it announced a partnership with AirRobe, an online resale platform. Customers can automatically add their just-bought clothing to AirRobe, so that if they want to sell it later, the information and imagery is ready for them to use.

Berchtold is waiting on early results of the AirRobe experiment, but is confident the re-economy is the future. Reuse, repair, recycle, she says. This is the way ahead. A repair program is in the works.

One of the big lessons of the past decade has been learning how to use data.

Youve got the data right there. But theres a real art to knowing what to do with it, the data to actually pay attention to, what you can just not ignore, Berchtold says. Thats actually a real skill, and it is very important and will only continue to grow in importance. And I dont think a lot of retailers would do that easily.

Its a muscle you have to work, over and over. And weve had 10 years of trial and error in that space. So, weve got a pretty strong muscle.

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The Iconic CEO Erica Berchtold wants to use the science of data to convert you to online shopping - The Australian Financial Review