The Tech Trek

This episode features a detailed discussion with Akshay Swaminathan, a data scientist with significant contributions to health systems improvement, including 40 peer-reviewed publications and features in the New York Times. Currently an AI Researcher, medical student, and PhD candidate at Stanford University, Swaminathan discusses the potential of data science in healthcare, emphasizing the importance of delivering the right treatment to the right patient at the right time. The conversation covers domain specificity in data science, the trend of healthcare professionals engaging in data science, the importance of cross-functional teams, and the role of traditional IT and data science projects in healthcare. Swaminathan also introduces his book, 'Winning with Data Science,' designed to help non-technical domain experts collaborate effectively with data scientists. The episode highlights the potential obstacles to data-driven healthcare solutions, including data quality, biases, interoperability, and data sharing, and suggests starting with identifying key problems before applying data science solutions. Highlights: 00:38 The Power of Data in Healthcare Delivery 02:03 The Unique Blend of Healthcare and Data Science Expertise 02:52 Cross-Functional Teams: A Trend in Healthcare Data Science 04:56 Empowering Domain Experts to Leverage Data Science 10:35 Case Study: Improving Crisis Response Times at Cerebral 13:30 The Future of AI in Healthcare: Automation and Co-Pilot Solutions 17:41 Challenges and Solutions for Data in Healthcare AI 24:09 Practical Advice for Leveraging Data in Healthcare Guest: .Akshay Swaminathan is a data scientist who works on strengthening health systems. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously, he was a data scientist at Flatiron Health and Head of Data Science at Cerebral. He is currently an AI researcher and MD/PhD candidate at Stanford University. LinkedIn: https://www.linkedin.com/in/akshay-swaminathan-68286b51 Book: https://cup.columbia.edu/book/winning-with-data-science/9780231206860 ---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Amir sits down with Zach LaValley, EVP of Engineering at RapidSOS, to explore the high-stakes world of emergency response technology. Zach unpacks how RapidSOS acts as a digital bridge between 911 call centers and a growing network of connected devices—from smartphones to IoT sensors—delivering real-time, life-saving data when seconds matter most.They dive into the fragmented landscape of U.S. emergency infrastructure, the challenges of routing data across 6,000+ decentralized call centers, and the engineering trade-offs between speed, reliability, and privacy. Zach shares how his team builds for fault tolerance at scale, the unique testing environments required for mission-critical systems, and where AI fits into both the practical and futuristic sides of public safety—whether it’s reducing false alarms or enhancing school security with real-time video analysis.It’s a behind-the-scenes look at building technology that literally saves lives—and how to do it without compromising speed or trust.🔑 Key Takeaways:911 Tech Isn't Centralized: The U.S. emergency system includes 6,000+ decentralized call centers. Routing data accurately and fast is a major engineering challenge.Latency Can Cost Lives: RapidSOS aims to be faster than the ring of a phone call—delivering life-saving data to emergency services before the call connects.Privacy with Purpose: The system only pulls data “just in time” to avoid unnecessary storage and protect user privacy.AI’s Role in Public Safety: While AI isn’t replacing humans, it’s helping filter noise (e.g., false alarms) and power applications like gun detection in schools.🕒 Timestamped Highlights:[00:01:00] – How RapidSOS routes real-time data to 911, evolving from GPS to IoT.[00:04:00] – The complexity of mapping 6,000 call centers and matching jurisdiction with GIS.[00:06:30] – The challenge of determining the “right” device data in multi-device emergencies.[00:10:00] – Balancing data access with privacy; no bulk data storage—only on-demand pulls.[00:12:00] – Why RapidSOS measures performance against telephony speed (must beat the ring).[00:15:00] – Testing under high stakes: real-world simulation, sandboxes with Apple/Google, canary deployments.[00:18:00] – Two buckets for AI: offloading non-emergencies and real-time school safety response.[00:22:30] – Public adoption: Why humans still need to make final calls even in AI-enhanced workflows.💬 Quote:“We are trying to save lives with the game of telephone. But the future is in expressive data—pictures, images—that actually show what's happening.” — Zach LaValley

In this episode, Amir Bormand sits down with Harrison Tang, CEO and Co-founder of Spokeo, to explore a problem most people in AI, data, and digital identity overlook: entity resolution. Harrison unpacks how billions of fragmented data records are connected, how we determine what's true in a world of generated content, and why trust and privacy are becoming the new battlegrounds in tech.They discuss the philosophical foundations of identity, the technical challenges of resolving entities at scale, and how GenAI complicates truth detection. If you're building in data, trust, or anything AI-related—this is required listening.🧠 Key Takeaways:Entity resolution is foundational to how we understand digital identity—but it’s far from solved, especially with GenAI-generated noise increasing.Spokeo resolves 600M+ entities from 19B+ records, using distributed computing and multiple “criteria of truth” (consensus, authority, coherence, etc.).Generative AI can create content—but not verify it. It’s great for mock/test data, but not for discerning truth.The real challenge? Detecting fake content. Harrison breaks down the four pillars: provenance, detection, governance, and education.Privacy ≠ Security. Identity and access management sits above entity resolution, and is crucial for enforcing data control.⏱️ Timestamped Highlights:00:55 – What Spokeo does and the scale of its data02:10 – What is entity resolution? Why it matters04:10 – The challenge of 19B record comparisons06:00 – Garbage in, garbage out: why data quality starts at ingestion07:10 – The five criteria of truth: consensus, authority, consistency, coherence, correspondence10:40 – Where GenAI helps (and fails) in entity resolution13:00 – Can AI discern truth like a human? Harrison’s take on AGI skepticism16:20 – The rise of fake data and the opportunity for Spokeo18:15 – AI provenance, invisible watermarks, and content authenticity21:00 – The four pillars of trust in the AI age23:00 – How privacy impacts data workflows and IAM25:30 – Why entity resolution sits at the foundation of identity systems💬 Quote of the Episode:“The problem of who we are has existed since the beginning of the human race. And in the digital world, that question is more important than ever.” — Harrison Tang🔗 Resources Mentioned:W3C Credentials Community Group – where Spokeo contributes on decentralized identity standardsAdobe Content Authenticity Initiative – cited as a tool for detecting AI-generated contentZero-shot prompting – the concept behind GenAI generating realistic data from a single prompt🎯 Career Tips (from the episode):While there wasn’t a dedicated segment on careers, Harrison did hint at a big opportunity area:If you're in data or security, AI-generated fake content is a growing risk—and a career edge for anyone working on provenance, detection, and digital trust systems.

This podcast episode features Dr. Andrea Isoni, the Chief AI Officer at AI Technologies, discussing AI's current and future integration into everyday life and the corporate world. Isoni highlights how AI already serves as a co-pilot in many aspects of daily activities and how it is adopted across various industries like cybersecurity and manufacturing automation. He also touches on the media's impact on AI perception and the potential for more complex, integrated solutions. The conversation covers the importance of AI in enhancing efficiency and the critical role of Cybersecurity as AI adoption increases. Isoni emphasizes that while AI adoption in enterprises may be slower due to legacy systems and regulations, the consumer market quickly embraces AI tools. He also discusses the evolution of software development, predicting a shift towards more integration, customization, and assurance roles rather than traditional programming. Highlights 02:06 The Current State of AI Adoption 03:26 AI in Everyday Life and Future Technologies 07:05 AI's Impact on Industries and Consumer Adoption 10:51 Evaluating ROI in AI Projects 14:49 Adoption Challenges and Trust in AI 26:32 The Future of AI, Cybersecurity, and Job Evolution ---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Nir Kaldero, Chief Data & AI Officer at NEORIS, breaks down what it really takes for enterprises to adopt AI effectively. From selecting the right pilot projects to ensuring leadership buy-in, Nir shares his proven framework—Speed to Value—for driving meaningful AI outcomes. He also highlights the most common mistakes enterprises make and offers strategic advice on aligning AI initiatives with top business goals.🔑 Key TakeawaysAI = Brain Helper: Nir describes AI as an assistant to human cognition—especially valuable in a data-saturated world.Speed to Value Framework: Success in AI initiatives depends on four key factors—Sponsorship, ROI, Business Readiness, and Technical Feasibility.Pilot Smarter, Not Just Faster: Begin AI projects in areas with high potential business impact and strong leadership support.Change Management is Crucial: Adoption is more cultural than technical—embedding AI into existing workflows matters more than just building flashy tools.GenAI ROI Misconceptions: Real savings and value lie not just in chatbots but in automation and integration into existing systems.🕒 Timestamped Highlights[00:01:00] – NEORIS’s role in enterprise AI transformation[00:03:00] – Why AI is a “brain helper” in the age of overwhelming data[00:06:00] – Speed to Value: 4-part framework for prioritizing AI projects[00:10:00] – Why ROI calculations must involve both business and tech sides[00:14:00] – Common enterprise mistakes: chasing hype, skipping integration[00:17:00] – Embedding GenAI into existing dashboards vs. building new tools[00:23:00] – Why voice and conversational UI may define future UX[00:26:00] – How to align AI efforts with top-down business strategies[00:29:00] – Personal reflection: why technologists must stay business-focused📚 Resources MentionedSpeed to Value Framework (Sponsorship, ROI, Business Readiness, Technical Feasibility)💼 Career Tip"Building technology is easier than changing people's minds."AI adoption requires top-down sponsorship and must be aligned with business priorities. Technologists who want to make a real impact need to understand—and start from—the business strategy.💬 Quote“We can build great models, but people are the ones that change the world. If they don’t adopt the tech, nothing changes.”

In this episode, Amir Bormand sits down with Shashank Tiwari, Co-founder and CEO of uno.ai, to explore how AI is shaping the cybersecurity industry. They dive into the massive skills gap, the role of AI co-pilots, and how security teams can leverage AI to increase efficiency while reducing burnout. If you’re in tech, security, or AI, this episode unpacks the real-world applications of AI in cybersecurity, the challenges of adoption, and what the future holds for this evolving space. 🎯 Key Takeaways The Cybersecurity Hiring Crisis→ The gap between open jobs and qualified candidates continues to widen.→ Cyber professionals struggle to keep pace with evolving threats. How AI is Closing the Gap→ AI co-pilots can automate routine tasks and improve decision-making.→ AI helps triage alerts, process large volumes of data, and augment human expertise. Adoption Challenges in Security→ Many CISOs are skeptical of AI due to hallucinations and security risks.→ Trust in AI tools comes from clear proof of value and explainability. The Future of AI in Security→ AI will evolve from assistant to collaborator, taking on more decision-making tasks.→ The user experience of AI-driven security tools will be key to adoption. ⏱ Timestamped Highlights 🕒 [00:01:00] – What is uno.ai? Shashank explains their AI-powered cybersecurity co-pilot. 🕒 [00:03:30] – Why is hiring in cybersecurity so hard? The skills gap and industry challenges. 🕒 [00:06:45] – How AI can assist cybersecurity teams – from automation to decision support. 🕒 [00:10:00] – Challenges in adopting AI for security – trust, hallucinations, and enterprise fears. 🕒 [00:14:30] – Will AI change skill requirements in security? The evolving role of security professionals. 🕒 [00:18:00] – How CISOs evaluate AI security tools – proving value and securing AI systems. 🕒 [00:21:30] – The importance of UI/UX in AI adoption – how experience shapes effectiveness. 🕒 [00:24:00] – Future trends in AI-powered cybersecurity. 💡 Notable Quote "AI is not here to replace cybersecurity professionals—it’s here to empower them by automating the mundane and letting humans focus on the critical." – Shashank Tiwari 📢 Connect with Shashank Tiwari 🔗 LinkedIn: Tshanky 🔗 Website: uno.ai 📧 Email: contact@uno.ai 🎧 Enjoyed this episode? 💬 Share it with someone in cybersecurity or AI! 📩 Subscribe & leave a review – your feedback keeps us going!

In this podcast episode, Deepak Jose, a Data Analytics and AI Expert with experience in companies like Coca-Cola, ABB, Asurion, and Mars, discusses the importance of adopting a value-first mindset over a technology-first approach in leveraging data and analytics for decision-making. He emphasizes the need to invert the traditional pyramid approach by identifying key business problems and their value, thereby prioritizing business outcomes. Deepak shares insights on building adaptable and flexible data architectures that can support rapid experimentation and leverage new technologies like Generative AI (Gen AI), advocating for a future-proof and agile mindset in data analytics. He also underlines the significance of responsible AI usage, the need for a desirability, viability, and feasibility framework for prioritizing AI initiatives, and strategies for enhancing adoption and upscaling to maximize the benefits of AI tools. The conversation includes the concept of AI champions within organizations and concludes with Deepak sharing an inspirational quote from Mother Teresa, encouraging listeners to pursue their passions with great love. Highlights 00:15 The Power of Data Analytics in Decision-Making 01:25 Inverting the Data Pyramid: A Value-First Approach 04:55 The Importance of Flexibility and Adaptability in Data Architecture 07:07 Navigating the Future with Generative AI 09:30 Building Solutions with a Business Problem-First Mindset 21:32 The Role of AI Champions in Business Transformation ---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.comHave questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this podcast episode, Amir Bormand and guest Santona Tuli, the Head of Data at Upsolver, discuss the complexities of modern data projects and the evolving strategies for data design focused on data lake houses. They explore Upsolver's role in simplifying data ingestion and transformation, the challenges of adapting to advanced data projects without the right infrastructure, and the shift towards more accessible and abstracted data tools. Additionally, they touch upon the importance of aligning tool adoption with business needs, the potential pitfalls of rushing into trendy technologies without clear objectives, and the future of data deployment solutions. Highlights: 02:00 The Importance of Data Design Strategies 02:26 Challenges in Implementing Complex Data Projects 03:09 The Role of Data Infrastructure and Architecture 05:57 The Evolution of Data Projects and Infrastructure 06:33 The Inevitability of Adding More Data Projects 07:48 The Emergence of the Lakehouse Concept 08:38 The Journey to Cloud and Advanced Solutions 14:53 The Challenge of Modernizing Data Stack 20:29 The Role of Tools in Modern Data Stack 25:54 Risks of Pushing for Advanced Data Solutions---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.comHave questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

This podcast episode features Vidya Eashwer, CTO at Forge, discussing the evolution and significance of treating data as core engineering within the tech industry. Highlighting the shift in perception towards data from a backend supportive role to a critical product element, Vidya shares insights from her career experiences, including her time at the New York Stock Exchange, to illustrate how data has become a foundational aspect of software engineering and product development. The conversation also covers the alignment of data engineering with software engineering best practices, the impact of AI on data strategy, and the importance of reliable, timely data for customer engagement and internal decision-making. Furthermore, Vidya discusses how the structure and positioning of data teams within an organization can enhance operational efficiency, product innovation, and customer satisfaction. Highlights 00:10 Understanding Data as Core Engineering 00:48 Forge: A Global Private Securities Marketplace 01:11 The Evolution of Data in Engineering 03:55 Data: From Backend to Frontline in Customer Engagement 06:50 Aligning Data Engineering with Software Best Practices 08:07 The Role of Data in Product Management and Engineering 11:31 Data Integrity and Customer Engagement 15:39 Streamlining Operations with Data Engineering 18:15 Leveraging AI for Operational Efficiency and Innovation Vidya Eashwer is the CTO of Forge. She has18+ years of experience creating and executing innovative fintech technology strategies. She's the former Head of Technology, Edge Systems, at Intercontinental Exchange and was the Managing Director of IT at the New York Stock Exchange, managing international teams. https://www.linkedin.com/in/vidya-eashwer/ ---- Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player.Want to learn more about us? Head over at https://www.elevano.comHave questions or want to cover specific topics with our future guests?Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Amir sits down with Michel Tricot, CEO and Co-founder of Airbyte, to unpack what startup culture really means—and why it’s not for everyone. Michel shares how Airbyte’s company values were created, how they’ve evolved, and why the principle of “Be the CEO of your project” is at the heart of their team’s DNA.From hiring strategies to cultural rituals, Michel reveals how he filters for candidates who thrive in ambiguity, what failure looks like when done right, and how a startup's values must continuously adapt as the company scales. Whether you're building a startup or thinking of joining one, this episode offers a playbook for navigating growth, ownership, and cultural alignment.