The STEMINISTS Podcast

What happens when the technology reshaping the future is being built without enough women in the room? 

In this episode of The Steminists Podcast, host Phoebe Goh welcomes back Cecile Kellam, Senior AI Solutions Architect at NetApp and Head of AI Circles for Women in Technology (WIT), for a candid conversation about the AI participation gap and why women in tech communities may be more important than ever.  

From failing an AI-powered resume screening despite being qualified, to leading a global initiative that helps women build AI skills with confidence, Cecile brings real-world perspective to one of the biggest challenges facing the industry today. Together, Phoebe and Cecile explore how AI can amplify existing biases, why confidence often becomes a hidden barrier to participation, and what organizations can do to ensure AI is shaped by diverse voices instead of the same old perspectives.  

You'll also hear why community might be AI's secret weapon: creating safe spaces to ask the "dumb" questions, learn by doing, and influence how AI is designed, deployed, and adopted across the workplace. Because if AI is changing everything, everyone should have a seat at the table.  

And in a fun twist during "Cool and Current," Cecile shares how the latest women's health technology is finally putting women's experiences at the center of innovation.  

Because the future of AI shouldn't be trained on half the population's perspective. 

 

Key Takeaways 

AI isn't inherently fair. Without intentional inclusion, it can reinforce existing hiring and workplace biases.  

Representation matters because the people building AI directly influence the outcomes it produces.  

Confidence can be as significant a barrier as technical skills, especially when stepping into emerging fields like AI. 

Women's tech communities are evolving from networking groups into powerful engines for AI education, adoption, and influence.  

Organizations that invest in inclusive AI learning don't just do the right thing. They drive better adoption, stronger productivity, and greater business impact.  

You do not need a traditional technology background to succeed in AI. Curiosity, experimentation, and the willingness to learn can take you surprisingly far.  

The best way to close the AI participation gap might just be... using AI itself to learn faster and build confidence. 

Because the future of AI shouldn't be trained on half the population's perspective. So as always, if you enjoyed this episode, but sure to like, follow and share with your community.  

 

Learn more: https://www.netapp.com/artificial-intelligence/  

 

Learn more about WIT (Women in Technology): https://www.womenintechnology.org/  

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/%E2%98%81-cecile-kellam-%E2%98%81/

While the AI world has spent the last year racing from hype cycle to reality check, this conversation with Kris Cornwall, Senior Director of Product Marketing for Enterprise Storage at NetApp, feels more timely than ever. Because it turns out that building AI is the easy part. Getting it into production, scaling it, securing it, and making it deliver actual business value? That's where things get interesting.  

AI has officially graduated from science project status. It's now expected to drive business outcomes, deliver ROI, and operate with the same resilience, security, and reliability as any mission-critical enterprise workload. But getting there is easier said than done. Kris joins Phoebe and Mekka to unpack why data remains the biggest hurdle for organizations trying to scale AI, and what it really takes to move from proof of concept to production.  

The conversation dives into one of the hottest infrastructure topics in AI today: disaggregation. Kris explains how separating performance from capacity can help organizations scale AI workloads more efficiently, maximize expensive GPU investments, and avoid the infrastructure bottlenecks that often slow innovation.  

You'll also hear how enterprise-grade storage is evolving to meet AI's growing demands, from built-in ransomware protection and cloud integration to intelligent data mobility and metadata-powered data discovery. The takeaway? AI success isn't just about choosing the right model. It's about building the right data foundation underneath it.  

Tune in for a fan-favorite conversation on enterprise AI, data infrastructure, and the unglamorous truth nobody puts in the keynote: if your data strategy isn't ready, your AI strategy probably isn't either. 

 

Learn more: 

https://www.netapp.com/artificial-intelligence/

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/kristinecornwall/

Everyone's obsessed with AI right now. Bigger models. Faster training. More GPUs. More data centers. More everything. But there's one small problem: none of that AI infrastructure exists without semiconductors. And the companies designing those chips are suddenly competing for the exact same resources.  

Welcome to the ultimate tech plot twist. 

In this episode of The STEMINISTS Podcast, host Phoebe Goh sits down with Diane Patton, Senior Product Manager at NetApp, to unpack how the AI boom is reshaping the silicon supply chain and putting unprecedented pressure on Electronic Design Automation (EDA), the critical technology behind modern chip design. 

As AI demand drives a global scramble for GPUs, RAM, and high-performance compute resources, chip designers are facing a new challenge. The infrastructure required to design semiconductors is becoming just as difficult to obtain as the infrastructure needed to run AI itself.  

Diane breaks down why EDA workloads are among the most demanding in enterprise IT, requiring massive compute power, low-latency storage, and the ability to process enormous volumes of data at scale. She also explains why waiting months for hardware procurement is no longer a viable strategy when innovation cycles are moving at AI speed and today's "latest and greatest" GPU can become yesterday's news before it's even installed.  

The conversation explores how semiconductor companies are turning to cloud and hybrid-cloud architectures to bypass infrastructure bottlenecks, accelerate chip development, and gain access to scalable compute resources on demand. From cloud bursting and intelligent data caching to storage tiering and AI-powered workflows, you'll hear how organizations are modernizing chip design for a world where flexibility is becoming a competitive advantage.  

Key Takeaways: 

What Electronic Design Automation (EDA) actually is 

Why AI is reshaping semiconductor infrastructure requirements 

GPU and RAM shortages facing chip design teams 

High-performance storage for EDA workloads 

Cloud vs. on-premises semiconductor design environments 

Hybrid cloud strategies for semiconductor companies 

Data management challenges in chip development 

AI-enabled opportunities for EDA workflows 

The future of semiconductor innovation and the silicon supply chain 

Whether you're a semiconductor professional, infrastructure architect, cloud strategist, or simply curious about the technology powering the AI revolution, this episode offers an insider's look at the future of chip design, EDA infrastructure, cloud computing, and the evolving silicon supply chain.

 

Learn More:

https://www.netapp.com/industries/electronic-design-automation-eda/  

https://www.netapp.com/blog/semiconductor-design-netapp-all-flash-portfolio/  

 

Connect with us!

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/diane-patton-7519634/

Security teams have more data than ever before. More dashboards. More alerts. More telemetry. More AI. So why is answering the simple question, "Are we actually secure?" still so difficult? 

In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Cole Neumark, Security Analytics professional at NetApp and Vice Chair of Proud, NetApp's LGBTQ+ employee resource group, to unpack why cybersecurity isn't just a technology challenge, it's a people challenge.  

With a career path that started in English and art history rather than computer science, Cole brings a refreshingly different perspective to cybersecurity. Together, Phoebe and Cole explore the reality of security analytics: turning mountains of signals, alerts, and vulnerability data into meaningful decisions that leaders can actually use. Because data alone doesn't create security. Context does. And context is still a very human skill.  

The conversation dives into the promises and pitfalls of AI in security, why faster isn't always smarter, and why the most valuable skill in an AI-powered future may be asking better questions. They also discuss the often-overlooked role of communication, creativity, and storytelling in helping organizations understand risk and take action.  

Cole shares how they transformed a cybersecurity training exercise into an immersive fictional world called Pineland, complete with fake social media posts, characters, and realistic incident scenarios. The goal? Make security memorable. Because people don't remember dashboards. They remember stories. And when security awareness feels more like solving a mystery than surviving a PowerPoint presentation, people pay attention. 

The episode also explores the importance of belonging, diverse perspectives, and community in cybersecurity. Through their work with Proud, Cole reflects on how creating spaces where people feel seen, valued, and empowered ultimately leads to better collaboration, stronger teams, and smarter security decisions.  

Key takeaways: 

Why security analytics is as much about communication as it is about data 

How storytelling can turn cybersecurity from forgettable to unforgettable 

Where AI accelerates security teams, and where human judgment remains essential 

Why humanities skills are surprisingly powerful in technical careers 

How diverse perspectives help organizations solve complex security challenges 

Why "security is defended by people" may be the most important cybersecurity lesson of all  

Plus: a conversation about metal music, live concerts, and why even cybersecurity professionals need a great soundtrack. 

 

Learn more: 

https://www.netapp.com/cyber-resilience/  

 

Connect with us!

https://www.linkedin.com/in/cole-neumark/

https://www.linkedin.com/in/phoebegoh/

We were promised that AI would make information easier to find. Nobody mentioned we'd also need a PhD in figuring out what to trust. 

In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Julia Fedorova, who leads Analyst Relations at NetApp, to tackle one of the biggest challenges in the AI era: making smart decisions when everyone, everything, and every chatbot seems to have an opinion.  

As generative AI floods our feeds with answers, recommendations, and hot takes, access to information is no longer the problem. The real challenge is knowing which sources are credible, which insights are backed by evidence, and which recommendations deserve a healthy dose of skepticism.  

Julia pulls back the curtain on the often-misunderstood world of industry analysts and explains why many of the biggest technology purchasing decisions still rely on research grounded in rigorous methodologies, market validation, and years of expertise. Together, she and Phoebe explore the idea of "pools of trust," the risks of AI-powered echo chambers, and why trusting the first answer you get is rarely a winning strategy.  

The conversation also dives into how enterprise buyers can avoid blind spots, why getting a second opinion matters just as much in technology as it does in healthcare, and how the smartest leaders are balancing AI-driven insights with human judgment. Along the way, Julia shares a surprisingly simple piece of advice for navigating a rapidly changing market: pause. Before you click, buy, migrate, modernize, or panic. Just pause.  

In this episode: 

Why more information doesn't automatically lead to better decisions.  

How industry analysts build credibility and trust through research and methodology.  

The growing challenge of AI-generated echo chambers and personalized information bubbles.  

Why enterprise buyers should seek multiple perspectives before making major technology decisions.  

How to evaluate whether a source is genuinely trustworthy.  

Why storage and data infrastructure have suddenly become central to AI conversations.  

The surprisingly underrated power of slowing down before making a decision.  

Plus, Julia weighs in on an age-old debate: do audiobooks count as real books? (Spoiler: yes, and she's willing to defend that position.)  

Because in a world overflowing with AI-generated answers, the real competitive advantage might just be knowing which questions are worth asking in the first place. 

As always, if you enjoyed this conversation please gives us a follow to stay in the loop for all things STEMINISTS. We would also love to hear what topics would you like for us to discuss. Feel free to share what you want to hear next.  

 

Learn More: 

https://www.netapp.com/artificial-intelligence/generative-ai-solutions/  

 

Connect with us!

https://www.linkedin.com/in/julia-fedorova-she-her-012a9624/  

https://www.linkedin.com/in/phoebegoh/