HumanX takes place April 6-9 at Moscone Center in San Francisco. The agenda features VPs and Chief Scientists from companies like NVIDIA, Microsoft, OpenAI, LinkedIn, and Visa. These are the hands-on leaders behind M365 Copilot, CUDNN, major enterprise AI projects, and recommendation systems used by over a billion people.

We’ve highlighted 20 leaders you should meet. They are the people behind the technical breakthroughs that have truly changed their industries.

If you’re interested in how enterprises use agentic AI in real-world production, visit us at Booth #212. Schedule a meeting ahead of time.

1. Bryan Catanzaro — VP of Applied Deep Learning Research, NVIDIA

Why He Matters

Bryan leads the research group powering NVIDIA’s GPU revolution, the hardware backbone behind modern AI training and inference at a global scale. His team solves problems from video games to chip design using deep learning.

Bryan’s research at NVIDIA led to CUDNN, a library that became foundational infrastructure for deep learning. More recently, he helped create DLSS 2.0. Before returning to NVIDIA in 2016, he worked at Baidu, building next-generation systems for end-to-end deep learning speech recognition.

What He’s Working On

His 40-person Applied Deep Learning Research lab builds prototype applications across four domains: computer graphics and vision, speech and audio, natural language processing, and chip design. The team’s mandate is to find new ways AI can improve NVIDIA’s products and processes.

Bryan earned his PhD from UC Berkeley, where he focused on parallel computing, machine learning, and programming models. He began working on CUDA more than ten years ago, before deep learning became widely used. Even then, he saw that GPUs could have a big impact beyond gaming—and he was right.

Where to Find Him

Bryan speaks on the Main Stage Monday, April 6 at 5:15 PM in “AI is a 5 Layer Cake” alongside Denis Yarats from Perplexity and Josh Payne from NSCALE.

2. Jaime Teevan — Chief Scientist & Technical Fellow, Microsoft

Why She Matters

Jaime led the creation of M365 Copilot, integrating AI into Word, Excel, Teams, and the rest of Microsoft’s productivity suite. She was named one of TIME’s 100 most influential people in AI in 2023. She’s an ACM Fellow with 280 publications and 66 patents.

Before becoming Chief Scientist, Jaime served as Technical Advisor to Microsoft CEO Satya Nadella, where she helped formulate technical strategy and tracked key scientific trends. She also invented the first personalized search algorithm used by Bing.

What She’s Working On

Jaime drives research-backed innovation across Microsoft’s core products. Her work spans AI for productivity, hybrid work research, and responsible AI implementation at scale. She coordinated Microsoft’s response to how the pandemic changed work, leading to innovations in hybrid meeting technology and distributed collaboration tools.

She earned her PhD in AI from MIT and her BS from Yale. She’s also an affiliate professor at the University of Washington and serves on Yale’s Board of Trustees.

Where to Find Her

Jaime appears on the Main Stage on Tuesday, April 7, at 10:00 AM in “The Future Isn’t Autonomous, It’s Agentic” alongside leaders from Superhuman and Intercom.

3. Srinivas Narayanan — CTO of B2B Applications, OpenAI

Why He Matters

Srinivas leads the effort to bring GPT models into enterprise production at scale. He’s running the commercial engine of the most-watched AI company in the world, figuring out how to make models that dazzle in demos actually work for businesses with compliance requirements, data governance needs, and reliability expectations.

OpenAI’s enterprise business faces real-world demands. While consumer products can handle occasional issues, enterprise deployments require consistent reliability. Srinivas is working to close that gap.

What He’s Working On

His team focuses on making GPT models production-ready for enterprise customers. This includes building APIs that can handle enterprise-scale traffic, implementing security and compliance frameworks, creating tools for fine-tuning and customization, and ensuring models behave reliably in business-critical applications.

The chaThe challenge goes beyond technology. It’s about understanding what enterprises truly need, not just what they ask for, and building systems that connect advanced research with real business requirements. This Space

OpenAI’s enterprise strategy will define how generative AI gets deployed at scale. Srinivas is writing that playbook in real time.

4. Deepak Agarwal — Chief AI Officer, LinkedIn

Why He Matters

Deepak leads AI strategy for LinkedIn’s 1 billion members. This is his second tour at LinkedIn. He previously spent 8 years as VP of AI, during which his team built the foundational AI systems that still power the platform today. Before rejoining LinkedIn in early 2025, he was Pinterest's Chief AI Officer.

Before that, he spent years at Meta as VP of AI, where he helped develop large-scale recommendation systems and AI personalization. He is among the few who have built AI systems that serve a billion users at more than one company.

What He’s Working On

Deepak’s team handles everything from feed ranking and job recommendations to content moderation and fraud detection. LinkedIn runs on AI at every layer. His work determines what you see in your feed, which jobs get surfaced, which connections get suggested, and how the platform scales personalization for a billion people.

He holds a PhD in Statistics from the University of Connecticut and has over 24 years of experience in engineering and AI. His focus is on building AI that is not just cutting-edge, but ethical, inclusive, and human-centric.

Where to Find Him

Track LinkedIn’s AI announcements during the conference. Deepak’s work shapes how a billion professionals connect and find opportunities.

5. Rajat Taneja — President of Technology, Visa

Why He Matters

Rajat oversees AI for 639 million daily transactions. Visa processes more money in a day than most companies see in a year. His team has built over 100 AI-powered products. Visa ranked #2 on Fortune’s AIQ 50 for AI maturity in 2024.

Before Visa, he was CTO of Electronic Arts, where he led technology strategy for one of the world’s largest gaming companies. He understands how to deploy AI at scale in high-stakes environments where failures have real consequences.

What He’s Working On

Fraud detection at Visa’s scale is an AI problem. So is transaction routing, risk assessment, network optimization, and authorization decisioning. Rajat’s team uses AI to keep the global payments network running smoothly while catching fraud in real time.

The challenge is to manage this across 200 countries and territories, 80 million merchant locations, and billions of cards. Speed is critical, and every false positive has a cost. His AI systems must be fast, accurate, and clear enough for regulators to understand.

The Stakes

When Visa’s AI fails, commerce grinds to a halt. That’s the level of reliability Rajat’s team delivers.

6. Prem Natarajan — Chief Scientist & Head of Enterprise AI, Capital One

Why He Matters

Prem leads one of the largest enterprise AI transformations in financial services. Before joining Capital One, he was VP of Alexa AI at Amazon and spent years at USC’s Information Sciences Institute. He knows how to build AI systems that work in highly regulated indTrust is essential in banking. Using AI in this sector means meeting compliance standards, explaining model decisions to regulators, ensuring fairness in lending, and keeping systems secure. Prem’s team manages all of this at scale. at scale.

What He’s Working On

Capital One uses AI for fraud detection, credit decisioning, customer service automation, risk modeling, and personalized financial recommendations. Prem’s work touches every part of the bank’s operations.

The key challenge is balancing innovation with responsibility. Financial services AI has to be explainable, fair, and compliant with dozens of regulations. Prem’s team has figured out how to move fast while maintaining the controls that banks require.

Why It Matters

If AI can work in banking, it can work anywhere. Prem is proving it can.

7. Reena Jana — Head of AI Research & Standards for Trust & Safety, Google

Why She Matters

Reena leads standards development for safe, beneficial AI at Google. She sits on Google’s central AI Principles review body, which means she has a voice in some of the most consequential decisions about how AI gets deployed at one of the world’s largest tech companies.

She’s a frequent speaker at CES and SXSW, translating complex AI safety issues into frameworks that policymakers and business leaders can act on. Her work bridges the gap between research and real-world deployment.

What She’s Working On

Trust and safety at Google’s scale means addressing content moderation, misinformation, deepfakes, AI bias, and adversarial attacks. Reena’s team develops the standards and frameworks that guide Google's responsible deployment of AI.

This isn’t just policy work. It’s about building technical systems that can detect harmful content, assess model fairness, and maintain user trust while operating at Google’s scale. The standards her team creates often become industry benchmarks.

Why It Matters

AI safety is now a practical concern. Reena’s team is creating the standards that shape how AI impacts billions of people.

8. Xuedong Huang — CTO, Zoom

Why He Matters

Xuedong is an ACM Fellow and former Microsoft Technical Fellow who ran Azure AI. His team achieved the first human-parity speech recognition system. Now he’s CTO at Zoom, where he’s rethinking what video communications can become with AI.

He brings decades of experience in speech and language processing. Before Microsoft, he pioneered speech recognition, laying the groundwork for modern voice assistants.

What He’s Working On

Zoom isn’t just video calls anymore. Xuedong’s team is building AI features for transcription, translation, meeting summaries, and intelligent collaboration. The goal is to make virtual meetings as effective as in-person ones.

There are major technical challenges. Real-time transcription must work with different accents, languages, and audio quality. Meeting summaries need to capture decisions and action items accurately. AI features must be reliable for enterprise customers who rely on Zoom for important business communications.

Where to Find Him

Watch for announcements about Zoom’s AI roadmap. Xuedong’s vision for AI-powered communications will shape how teams work remotely.

9. Deepak Anchala — Co-Founder & CEO, Adopt AI

Why He Matters

Deepak is building Adopt AI, an enterprise AI agent automation platform with zero-shot API discovery, 500+ MCP connectors, and deployment options from cloud to on-premises. Before Adopt AI, he founded Slintel, a sales intelligence platform that scaled to 300+ customers and raised $26M before being acquired by 6sense in 2021.

At 6sense, he drove product and growth strategies across key portfolios, learning firsthand how enterprises buy and deploy AI. Now he’s applying those lessons to building the infrastructure layer for agentic AI.

What He’s Working On

Adopt AI solves the integration problem that blocks enterprise AI adoption. Most companies have dozens of SaaS tools and internal systems. Getting AI agents to work across all of them requires solving API discovery, authentication, data mapping, and error handling at scale.

Deepak’s platform features SOC 2 compliance, RBAC, audit trails, and the security controls enterprises require. He’s also a recognized voice in agentic AI through the Adopted podcast, where he shares insights on how enterprises are actually deploying agents in production.

Where to Find Him

Visit booth 212 to talk about agentic AI deployment. Book a meeting in advance.

10. Saral Jain — SVP, Head of Engineering & CIO, Snap Inc.

Why He Matters

Saral leads the engineering and AI infrastructure powering Snapchat’s AR features, ML models, and My AI assistant for 800 million monthly users. Snap has been pushing the boundaries of mobile AR for years. Saral’s team makes it work at scale.

Snapchat’s AI challenges are unique. Everything has to run on mobile devices with limited battery and computing power. AR features need to work in real time. The My AI assistant has to feel natural in a social messaging app. Saral’s team solves these constraints daily.

What He’s Working On

Snap’s AI goes beyond recommendation algorithms. The company is building AI-powered lenses, AR try-on features, content moderation systems, and conversational AI. Saral oversees the infrastructure that makes all of this possible.

The main challenge is running advanced AI on mobile devices. Models must be small enough for phones, fast enough to feel instant, and efficient enough to save battery life. Saral’s team has found ways to overcome these limits.

Watch This Space

Snap’s AR and AI features often preview what mobile AI will look like in a few years. Saral is building that future.

11. Hanna Helin — SVP of Data, AI & Emerging Tech, NBCUniversal

Why She Matters

Hanna leads AI transformation for one of the world’s largest media companies. Her work spans content creation, streaming optimization, advertising technology, and audience insights. Media companies are being rebuilt around AI, and Hanna is leading that transformation at NBC.

Media has unique AI challenges. Content recommendation affects what people watch. Ad targeting determines revenue. Fraud detection protects advertiser spend. Content moderation affects brand safety. Hanna’s team handles it all.

What She’s Working On

NBCUniversal uses AI for content recommendation on Peacock, ad targeting and optimization, content tagging and metadata generation, audience measurement and analytics, and predictive modeling for content performance.

A key challenge is balancing algorithmic recommendations with editorial judgment. Media is not only about boosting engagement—brand, creative vision, and cultural impact are also important. Hanna’s team decides when to use AI and when to rely on human judgment.

Why It Matters

How media companies use AI will shape what content gets made and how people discover it. Hanna’s work affects what millions of people watch.

12. Daniel Danker — EVP of AI Acceleration, Product & Design, Walmart

Why He Matters

Daniel drives AI adoption across the world’s largest retailer. Walmart operates at a scale few companies can match. Daniel previously held director roles at Facebook and product leadership positions at Amazon. He understands consumer tech and retail operations.

Retail AI has to work in the real world. That means dealing with supply chains, inventory management, in-store operations, e-commerce, and customer service. Daniel’s team is deploying AI across all of these domains.

What He’s Working On

Walmart uses AI for demand forecasting, inventory optimization, price optimization, personalized recommendations, supply chain logistics, and customer service automation. Daniel’s work touches every part of Walmart’s business.

The main challenge is scale. Walmart runs thousands of stores, serves millions of customers each day, and manages a global supply chain. AI solutions that work in small tests may not hold up at Walmart’s size. Daniel’s team finds ways to make them work.

The Impact

When Walmart gets AI right, it affects how hundreds of millions of people shop. That’s the leverage Daniel is working with.

13. Abhi Ingle — Chief Product & Strategy Officer, SambaNova Systems

Why He Matters

Abhi leads product strategy for custom AI chips and full-stack AI platforms. SambaNova is challenging NVIDIA’s dominance in enterprise AI hardware. The company builds specialized chips designed specifically for AI workloads, along with the software stack to run them.

The AI chip market is heating up. NVIDIA currently owns training and inference, but companies like SambaNova are building alternatives optimized for specific use cases. Abhi’s team is betting that custom silicon will beat general-purpose GPUs for many enterprise workloads.

What He’s Working On

SambaNova’s approach is different. Instead of building chips that do everything, they’re building chips optimized for specific AI tasks. This allows better performance per watt and per dollar for targeted workloads.

An even bigger challenge is building the full ecosystem. Hardware is just one piece; compilers, libraries, frameworks, and developer tools are also needed. Abhi’s team is creating the complete stack needed to compete with NVIDIA’s established ecosystem.

Why It Matters

AI compute will be one of the defining tech battles of the next decade. Abhi is building a serious challenger.

14. Roberto Konow — Sr. Manager, AI Search, Pinterest

Why He Matters

Roberto leads the AI-powered visual search and recommendation engine that drives discovery for 500 million monthly users. Pinterest’s search is fundamentally different from Google or Amazon. Users search visually, explore laterally, and discover things they didn’t know they wanted.

Visual search is harder than text search. You have to understand image content, style, context, and user intent. Roberto’s team has built systems that do this at a massive scale.

What He’s Working On

Pinterest’s AI handles visual search, recommendation algorithms, content understanding, spam and quality detection, and trend prediction. Roberto’s work determines what 500 million people discover oA key technical challenge is multi-modal search. Users may search using text, images, or both. The system must understand visual style, color, composition, and meaning, then show relevant results from billions of pins.billions of pins.

Why It Matters

Pinterest is where people go to discover ideas. Roberto’s AI shapes what they find.

15. Dan Fu — VP of Kernels, Together AI

Why He Matters

Dan leads low-level systems optimization that makes open-source AI models faster and cheaper to run. While big companies optimize their own models, Dan’s work benefits the entire open AI ecosystem.

Kernels are the low-level code that runs AI computations on GPUs. Improving kernels leads to faster training, lower costs, and models that work on less powerful hardware. Dan’s team is building the infrastructure that helps OpenAI stay competitive.

What He’s Working On

Together AI focuses on making state-of-the-art AI accessible and affordable. Dan’s team optimizes kernels for popular open-source models, builds custom inference engines, and creates tools that help researchers and companies deploy models efficiently.

The challenge is doing this across different hardware platforms and model architectures. A kernel that’s fast on one GPU might be slow on another. Dan’s team has to optimize for diverse hardware while maintaining compatibility with existing frameworks.

Why It Matters

Open-source AI needs infrastructure as good as what big companies build internally. Dan is building that infrastructure.

16. Youngchun Cho — Sr. Director, SK hynix

Why He Matters

Youngchun works at one of the world’s largest memory chipmakers, focusing on next-generation HBM (High Bandwidth Memory) for AI compute. Memory bandwidth is currently the bottleneck for AI training and inference. SK hynix is solving that problem.

NVIDIA’s GPUs get the headlines, but they’re useless without fast memory. HBM solves this by stacking memory chips vertically and connecting them with high-speed interfaces. Youngchun’s team is building the next generation of HBM that will power future AI systems.

What He’s Working On

SK Hynix is racing to develop HBM3, HBM3E, and eventually HBM4. Each generation doubles bandwidth while reducing power consumption. Youngchun’s work determines whether AI systems can get faster or hit a memory bottleneck.

There are major technical challenges. HBM needs precise stacking of silicon layers, careful thermal management for tightly packed chips, and high-speed interfaces that work reliably at scale. Even a small manufacturing defect can make a chip unusable.

Why It Matters

AI scaling depends on memory as much as compute. Youngchun is building the memory that will power the next generation of AI.

17. Madhav Thattai — EVP & GM, Salesforce AI

Why He Matters

Madhav leads Salesforce’s AI platform, including Einstein and Agentforce. Salesforce serves 150,000+ companies, making it the largest CRM ecosystem in the world. Madhav’s team is bringing AI agents to all of them.

CRM data is messy. Customer records are incomplete, sales notes are unstructured, and pipelines are inconsistent. Madhav’s team builds AI that works with messy real-world data, not clean research datasets.

What He’s Working On

Salesforce AI handles everything from next-best-action recommendations and lead scoring to email drafting and meeting summarization. The newer Agentforce platform lets customers build custom AI agents that can take aThe challenge is to create AI that works for both small businesses like flower shops and large Fortune 500 companies. Salesforce serves a very diverse customer base. Madhav’s team must build AI that is powerful for big companies but still easy for small businesses to use.ugh for small businesses.

Why It Matters

If Salesforce makes AI agents work for CRM, every other enterprise software category will follow. Madhav is setting the template.

18. Praveen Neppalli Naga — CTO, Uber

Why He Matters

Praveen leads technology for the world’s largest mobility platform. Uber runs on AI at every level. Matching riders with drivers, routing, pricing, fraud detection, and marketplace optimization all depend on sophisticated AI systems.

Uber’s technical challenges are unique. The platform has to work in real time across hundreds of cities, handle supply-and-demand imbalances, optimize for multiple stakeholders (riders, drivers, restaurants), and integrate autonomous driving as it becomes viable.

What He’s Working On

Uber’s AI systems handle dynamic pricing (surge), driver-rider matching, route optimization, ETA prediction, fraud detection, and autonomous vehicle integration. Praveen oversees all of it.

The toughest problems involve marketplace dynamics. If prices are too high, riders leave; if too low, drivers leave. The system must balance both sides in real time across many regions, each with its own needs.

Why It Matters

Uber’s AI systems process billions of dollars in transactions annually. The algorithms Praveen’s team builds determine whether the marketplace works efficiently or breaks down.

19. Rahul Bhattacharya — Co-Founder & CTO, Adopt AI

Why He Matters

Rahul co-founded Adopt AI alongside Deepak Anchala. He previously co-founded Slintel, serving as CTO until its acquisition by 6sense. At 6sense, he was Senior Director of Engineering, helping scale systems as the company grew from $60M to over $200M in ARR.

Rahul has over 15 years of engineering leadership experience. He specializes in building scalable platforms that handle enterprise workloads. Now he’s applying that expertise to building the infrastructure for agentic AI.

What He’s Working On

Rahul’s team builds the core platform that powers Adopt AI’s agent automation. This includes the zero-shot API discovery system that lets agents interact with any API without preconfiguration, the connector framework that supports 500+ integrations, and the security architecture that meets enterprise requirements such as SOC 2 and HIPAA.

The technical challenge is maThe main technical challenge is making agentic AI reliable for real-world use. Agents must handle API rate limits, authentication errors, bad responses, and partial failures. Rahul’s platform addresses these issues so agents can work on their own.oth 212 to discuss the engineering challenges of production-agentic AI. Book a meeting in advance.

20. Scott Canney — VP of Applied AI, Data & Analytics, Lowe’s

Why He Matters

Scott drives AI strategy for the $86 billion home improvement giant. Lowe’s operates hundreds of stores, manages complex supply chains, and serves millions of customers online and offline. Scott’s team uses AI for computer vision, supply chain optimization, and customer experience improvements.

Retail AI has to work in physical stores, not just online. That means dealing with inventory on shelves, associates on the floor, and customers who want to touch products before buying. Scott’s team bridges the digital-physical gap.

What He’s Working On

Lowe’s uses AI for demand forecasting, inventory optimization, computer vision for shelf monitoring, personalized recommendations, and supply chain optimization. Scott’s work touches both e-commerce and in-store operations.

The computer vision work is pThe computer vision work stands out. Lowe’s uses AI to monitor shelf stock, spot out-of-stock items, check planogram compliance, and help customers find products in stores. This requires reliable computer vision systems that work in real retail settings.ement retail has unique challenges. Products are large, heavy, and low-margin. Inventory optimization matters more in this retail category than in others. Scott’s AI work directly affects Lowe’s profitability.

Visit Us

HumanX runs April 6-9 at Moscone Center. These 20 leaders will be spread across Main Stage sessions, breakouts, roundtables, and the expo floor.

If you’re working on enterprise AI deployment, agentic systems, or trying to move AI from pilots to production, come talk to us at booth 212.

Schedule a meeting with the Adopt AI team before the event. We’ll show real production deployments and explain how companies are making agentic AI work at scale.

We look forward to seeing you in San Francisco.