Karen is a member of technical staff at Anthropic, where she works on developing product eval, RL environments, and multi-agent product features for Claude Code. She builds evaluation systems and feedback pipelines that assess how AI models behave in real-world software development scenarios, transforming user feedback into useful evals for model improvement. On the product side, she actively drives the development of swarm and multi-agent functionality that enables multiple AI agents to collaborate on complex software engineering tasks. Prior to Anthropic, Karen worked at Meta on large language models.

Building Evaluation Systems for AI Coding Agents at Scale(Talk)

Adam Tauman Kalai is a Research Scientist at OpenAI, specializing in AI Safety and Ethics. His research interests also include algorithms, fairness, AI theory, game theory, and crowdsourcing. Adam earned his BA from Harvard University and his PhD from Carnegie Mellon University, after which he served as an Assistant Professor at TTIC and Georgia Tech and a Senior Principal Researcher at Microsoft Research New England. He is also a member of Project CETI’s science team. His work has been recognized by test of time and best paper awards, an NSF CAREER Award, an Alfred P. Sloan Fellowship, and most notably the Majulook Prize.

Why Language Models Hallucinate(Talk)

Jonathan Frankle is Chief AI Scientist at Databricks, where he leads the AI research team toward the goal of bringing the latest advances in artificial intelligence and reinforcement learning to Databricks’ tens of thousands of enterprise customers. He arrived via Databricks’ $1.3B acquisition of MosaicML, which he co-founded as Chief Scientist. He earned his PhD at MIT, where he empirically studied deep learning with Prof. Michael Carbin, specifically the properties of sparse networks that allow them to train effectively (his “Lottery Ticket Hypothesis” – ICLR Best Paper, ACM SIGAI Dissertation Award, MIT Dissertation Award). He earned his BSE and MSE in computer science at Princeton.

AI Evaluation from First Principles: You Can’t Manage What You Can’t Measure(Keynote Track Talk)

Sadie St. Lawrence is the Founder and CEO of the Human Machine Collaboration Institute (HMCI), driving innovative research and advising to optimize human-AI collaboration in the knowledge economy, now amplified by a new ecosystem partnership with NVIDIA. She also founded Women in Data™, a global non-profit spanning 55 countries and empowering 70,000+ data professionals; it earned Top 50 Non-Profit status and was named the premier Women in AI & Tech community in 2021.

Named among DataIQ’s Top 100 Most Influential People in Data & AI and Dataleum’s Top 30 Women in AI, Sadie has educated over 700,000 learners through courses with UC Davis, Coursera, and LinkedIn Learning. Her forthcoming book, Becoming an AI Orchestrator, guides readers to work creatively and confidently alongside intelligent machines.

From Tools to Teammates: Orchestrating AI Agents at Scale(Women in AI)

ODSC AI Advisory Board Panel – Evolving with The Industry(Panel)

Matt Sigelman is President of the Burning Glass Institute, a nonprofit data laboratory working at the intersection of the future of work and the future of learning to advance economic mobility, opportunity, and improved outcomes for workers and learners. Previously Chairman and CEO of Lightcast, Matt pioneered the field of real-time labor market analytics. He serves as Senior Advisor at the Harvard Project on the Workforce and a Futures Fellow at the Stanford Center on Longevity. Named by Forbes to its Future of Work 50, he holds an AB from Princeton University, an MBA from Harvard, and is a member of the Council on Foreign Relations.

The Expertise Upheaval: How AI Promises to Transform the Nature of Work(AI X Leadership Keynote)

Dr. Ali Arsanjani is the Director of Applied AI Engineering at Google Cloud, and Head of the Global AI Center of Excellence, leads GenAI strategic co-innovation, thought leadership & alliances. His team specializes in co-innovation with ISV and GSI partners as they run, integrate and build on GCP across the ML Lifecycle. Ali also works closely with product management to shape the direction of Google’s AI and analytics offerings from a cloud perspective. Ali is an Adjunct Prof at San Jose State University & the University of California, San Diego, and advises students in the Masters in Data program and the Data Science Institute, respectively.

Architectural Patterns for Building and Governing Production-Grade Multi-Agent Systems(Keynote)

Dr. Qingyun Wu is the co-creator and co-founder of AG2 (formerly AutoGen), a leading open-source AI agent framework with a monthly download of over 500k and a vibrant community of over 20k AI agent developers. Qingyun is also an Assistant Professor at the College of Information Science and Technology at Penn State University. Qingyun got her Ph.D. in Computer Science from the University of Virginia in 2020. Qingyun received the 2019 SIGIR Best Paper Award and the ICLR 2024 LLM Agent Workshop Best Paper Award.

Tim Dettmers is an Assistant Professor at Carnegie Mellon University and a Research Scientist at the Allen Institute for AI. His work focuses on making coding agents, such as Claude Code, accessible to researchers and practitioners by reducing their resource requirements. His main focus is to develop open-source agent systems that are competitive with closed-weight agents and can be run on consumer hardware, such as laptops. His research won oral, spotlight, and best paper awards at conferences such as ICLR and NeurIPS and was awarded the Block Award, Madrona Prize, and the Google ML and Systems Junior Faculty Award. He created the bitsandbytes open-source library for efficient foundation models, which is growing at 4.8 million installations per month, and for which he received Google Open Source and PyTorch Foundation awards.

Efficient Language Models via Quantization(Talk)

As a Forward Deployed Engineer at CrewAI, I work across sales, product, and customer delivery, partnering with prospects and customers from initial technical discovery through production deployment. I lead pre-sales engagements by translating business problems into concrete multi-agent solutions and support deal closure through hands-on demos, pilots, and architectural guidance.

Post-sale, I drive implementation and productionisation by working directly within customer environments to deploy, harden, and scale real-world applications and use cases. My role ensures customers move quickly from concept to measurable value with solutions that are operationally sound, extensible, and aligned with existing systems. I also support ongoing success through enablement, troubleshooting, and best-practice guidance, helping teams confidently run CrewAI in production.

Building Effective Agentic Systems(Workshop)

Harry Mellor is a Machine Learning Engineer at Hugging Face, where he focuses on optimizing model deployment and inference. An engineer by training, Harry holds a Master’s degree in Engineering Science (MEng) from the University of Oxford.

He is a Core Committer to vLLM, an open-source library for high-throughput LLM serving. A frequent speaker in the AI community, Harry has shared his expertise at the Paris vLLM Meetup and Ray Summit 2025, where he presented on the integration of Hugging Face and vLLM to create unified model definitions.

vLLM with the Transformers Modelling Backend(Tutorial)

Usama M. Fayyad is Chairman of Open Insights, an AI and Data technology and consulting firm he founded in 2008 after leaving Yahoo! to help enterprises deploy Data-driven solutions to grow revenue from AI and data assets. He is also SVP for AI & Data Strategy, Sr. Advisor to the President, and the Inaugural Director of the Institute for Experiential AI at Northeastern University where he is a professor of the practice at Khoury College of Computer Sciences. Usama became the first person to hold the Chief Data Officer title when Yahoo! acquired his second startup after leaving Microsoft. As Yahoo!’s CDO & EVP of Strategic Data Solutions he also founded and led Yahoo Research Labs to become one the of the top research institutions focused on the sciences underlying the Internet.

Fayyad’s previous roles include Global Chief Data Officer at Barclays Bank where he also took on the role of CIO of Finance, Risk, & Treasury Technology. He is Founding Executive Chairman at Oasis500 and an early stage technology accelerator in the Middle East. He served as co-founder & CTO of OODA Health, held leadership roles at Microsoft and founded the Machine Learning Systems group at NASA’s Jet Propulsion Laboratory, where his work on machine learning garnered him the top Excellence in Research award from Caltech and a U.S. Government medal from NASA.

Fayyad earned his Ph.D. in engineering in AI/Machine Learning from the University of Michigan, Ann Arbor and holds two Bachelor of Science in Engineering degrees in EE and CE, MSE Computer Engineering and M.Sc. in Mathematics. Fayyad has published over 100 technical articles on data mining, data science, AI/ML, and databases. He holds over 20 patents and is a Fellow of both the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery (ACM). He has edited two influential books on data mining/data science and served as Founding Editor-in-Chief on two key journals in the field.

ODSC AI Advisory Board Panel – Evolving with The Industry(Panel)

Shoshana Rosenberg advises boards and executive teams on AI governance, privacy, security compliance, and data strategy. She is the author of Practical AI Governance (Kogan Page, May 2026) and the creator of the PRISM™ framework, a practitioner’s model for responsible AI.

She is Managing Director of Logical AI Governance, founder of SafePorter, and co-founder of Women in AI Governance. Before turning to her current work, she spent nearly two decades building and leading privacy, data governance, and AI and technology governance programs at global professional services firms, most recently as Senior Vice President, Chief AI Governance and Privacy Officer, and an Innovation Advisory Board Member.

Shoshana was named in the top fifty of the Top 100 Women in AI for 2026 by AI Magazine. A U.S. Navy veteran and , she organizes TEDxPrinceton and lives in Princeton, New Jersey.

The Leadership Imperative: AI Governance as Strategic Infrastructure(AI X Leadership Keynote)

Author of the Apress book Getting Started with Grafana, Ronald McCollam is a “geek of all trades” with experience ranging from full stack development to IT operations to management and sales. He has a strong background in open source software, starting when a stack of 3.5″ Slackware floppies was the easy way to install Linux. He’s managed IT operations in datacenters from back when the “cloud” was new and scary, built and run an OS certification program for hardware running Linux, and spent entirely too long thinking about observability. He’s also a Certified Irish Whisky Taster.

APIs to OMG: Transform your API Data to fun Grafana Dashboards(Training)

Sushant is a researcher at Google DeepMind working on post-training to improve coding capabilities of frontier large language models. Before Google, Sushant completed his masters in Machine Learning from Carnegie Mellon University.

Building Effective Agents(Talk)

Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models they own and control. He is a Y Combinator alum, holds multiple patents in AI/ML, and serves as AI & LLM Advisor at Tola Capital. Sinan is the author of Building Agentic AI, recently a top-10 AI book on Amazon, as well as Quick Start Guide to Large Language Models and Principles of Data Science. He holds a master’s degree in pure mathematics from Johns Hopkins University, where he was formerly an adjunct lecturer. Sinan has delivered dozens of live training courses on the O’Reilly platform to tens of thousands of attendees, covering topics from LLM fundamentals to advanced agentic systems and AI engineering. He co-hosts the “Practically Intelligent” podcast with Akshay Bhushan, featuring conversations with AI builders, founders, and researchers. His work sits at the intersection of making AI systems actually work in production, drawing on experience building and deploying agents at companies both Fortune 100 and startups.

Agents Unchained: The Case For (and Against) Harnessing Multi-Agent Systems(Keynote Track Talk)

Michael Schrage is a Research Fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy. His research examines how generative, predictive, and agentic AI change the economics of learning, decision-making, and performance in large organizations. His current focus: evaluation infrastructure —how enterprises discover what they are actually optimizing for and whether value compounds or dissipates.

Schrage’s work reframes traditional KPIs as KPAIs: intelligent, agentic performance indicators that learn, adapt, and interact. Rather than serving as static scorecards, KPAIs function as diagnostic systems embedded in workflows, exposing incoherence, tradeoffs, and constraint interactions across finance, operations, and customer experience. This work situates the CFO function as a capital-intelligence hub responsible for governing organizational learning.

He is the originator of promptathons, structured executive experiments that use generative AI to surface how leaders reason, prioritize, and rationalize under pressure. Developed through MIT Sloan and MIT Industrial Liaison Program executive education and custom seminars, promptathons operate as cognitive stress tests, revealing evaluation habits, decision bottlenecks, and misaligned incentives that shape capital allocation and execution risk. His related work with David Kiron on Intelligent Choice Architectures (ICAs) examines how AI systems can model and adapt decision pathways, clarifying what should be automated, augmented, or reserved for human judgment.

Schrage is also known for reframing growth strategy around customer transformation, articulated in Who Do You Want Your Customers to Become? His work links customer evolution directly to lifetime value, strategic optionality, and competitive advantage.

He is the author of The Innovator’s Hypothesis, Serious Play, Shared Minds, and Recommendation Engines. Schrage advises senior leaders, boards, and public institutions on the design and governance of learning systems in AI-intensive environments.

Getting Beyond GIGO Analytics: What Serious Leaders Need To Know About Vibe Analytics Before Competitors – And For Customers(AI X Leadership Keynote)

Dr. Ami Bhatt is the Chief Innovation Officer at the American College of Cardiology, a cardiologist, and a digital health strategist. A global health leader and national systems architect, she’s known for designing real-world health systems that blend digital tools, clinical insight, and human needs. Her work spans AI, telehealth, and system design innovation, all grounded in her deep clinical roots and personal mission to deliver care everywhere. She’s a trusted voice for the future of healthcare, on stages, in boardrooms, and across digital platforms, advocating for care that’s equitable, scalable, and human-centered.

Ready for Primetime: Creating the AI-enabled Clinician(Keynote Track Talk)

Datta is a senior machine learning engineer working at Unsloth where they contribute to optimizing LLM fine-tuning, both making it faster and more memory efficient specifically focusing on Parameter Efficient fine tuning techniques like LoRA. They have previously worked at Nutanix, where they focused on creating GPT in a Box, an enterprise grade LLM deployment solution on the edge using opens source projects like kserve and vllm, while also heavily contributing to them. They hold an undergraduate degree in Maths and Computing from IIT Hyderabad and are an avid lover of Math, Machine Learning and the intersection of the two.

Reinforcement Learning for LLM(Tutorial)

Ari Morcos is the CEO and co-founder of Datology AI. Previously, he was a senior staff research scientist at Meta AI Research (FAIR Team) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. Most recently, his work has focused on understanding properties of data and how these properties lead to desirable and useful representations, with a particular emphasis on data curation. Ari’s work has been honored with Outstanding Paper awards at both NeurIPS and ICLR. Before joining FAIR, Ari worked at DeepMind in London, and earned his PhD in neuroscience working with Chris Harvey at Harvard University.

David Campbell is Head of AI Security at Scale AI and a leading voice in operational AI risk. He pioneered one of the first large-scale AI red teaming platforms used across governments and enterprise organizations to probe and harden generative models against real-world threats.

David has briefed the U.S. Congress, the White House, U.K. Parliament, NATO, Korea AISI, UK AISI, and Qatar NCSA on AI security and national-scale resilience. He is a founding member of OWASP AIVSS and AIUC Consortium and contributes to CISA’s JCDC.AI collaborative exercises on AI-driven cyber threats.

With nearly twenty years in Silicon Valley, David previously helped shape engineering, security, and developer experience at Uber, DoorDash, and Nest Labs. His work consistently focuses on elevating standards, building resilient systems, and helping organizations adopt transformative technologies without compromising trust or safety.

His mission: align technology with the future of the business and ensure AI systems can withstand the adversaries, accidents, and emergent behaviors of the real world.

Domain-Limited General Intelligence: Before Things Go Too Far(Talk)

Robert Brennan has been writing software for 15 years, with a focus on natural language processing and developer tools. He is currently the CEO of All Hands AI, the company behind OpenHands (formerly OpenDevin), a fully autonomous AI developer. Previously he was VP of Product Development at Fairwinds, ran a startup called Datafire, and worked as a Senior Software Engineer at Google.

Agents for the Inner Loop and Outer Loop(Keynote Track Talk)

Before joining Anthropic, April was a Founding Field Engineer at Galileo and held a similar foundational role at Snorkel AI. During her tenure at Snorkel AI, she served as a Senior Machine Learning Solutions Engineer, where she was instrumental in driving technical sales and scaling revenue through innovative programmatic labeling solutions. Her early career also includes experience as a Data Scientist at IBM, where she was recognized as a nominee for the Global Business Services Emerging Leaders Program. She completed her undergraduate studies at UCLA, earning a dual degree in Economics and Cognitive Science with a specialization in Computing.

Building FSI Agents with Claude(Workshop)

Ben Armstrong is the executive director of MIT’s Industrial Performance Center, where he co-leads the Work of the Future initiative. His research examines how workers, firms, and regions adapt to technological change. His current projects include a working group on generative AI, as well as a book on American manufacturing competitiveness. His work has been published or featured in academic and popular outlets including the New York Times, Wall Street Journal, Financial Times, Harvard Business Review, and Sloan Management Review. He received his PhD from MIT and formerly worked at Google Inc.

As a Forward Deployed Engineer at CrewAI, I work across sales, product, and customer delivery, partnering with prospects and customers from initial technical discovery through production deployment. I lead pre-sales engagements by translating business problems into concrete multi-agent solutions and support deal closure through hands-on demos, pilots, and architectural guidance.

Post-sale, I drive implementation and productionisation by working directly within customer environments to deploy, harden, and scale real-world applications and use cases. My role ensures customers move quickly from concept to measurable value with solutions that are operationally sound, extensible, and aligned with existing systems. I also support ongoing success through enablement, troubleshooting, and best-practice guidance, helping teams confidently run CrewAI in production.

Adam Tauman Kalai is a Research Scientist at OpenAI, specializing in AI Safety and Ethics. His research interests also include algorithms, fairness, AI theory, game theory, and crowdsourcing. Adam earned his BA from Harvard University and his PhD from Carnegie Mellon University, after which he served as an Assistant Professor at TTIC and Georgia Tech and a Senior Principal Researcher at Microsoft Research New England. He is also a member of Project CETI’s science team. His work has been recognized by test of time and best paper awards, an NSF CAREER Award, an Alfred P. Sloan Fellowship, and most notably the Majulook Prize.

Karen is a member of technical staff at Anthropic, where she works on developing product eval, RL environments, and multi-agent product features for Claude Code. She builds evaluation systems and feedback pipelines that assess how AI models behave in real-world software development scenarios, transforming user feedback into useful evals for model improvement. On the product side, she actively drives the development of swarm and multi-agent functionality that enables multiple AI agents to collaborate on complex software engineering tasks. Prior to Anthropic, Karen worked at Meta on large language models.

Sushant is a researcher at Google DeepMind working on post-training to improve coding capabilities of frontier large language models. Before Google, Sushant completed his masters in Machine Learning from Carnegie Mellon University.

Bio Coming Soon!

Santosh is an experienced engineer with seven years in SoC design and verification, currently focused on full-chip coherency verification of advanced, high-performance computing systems. At NVIDIA, he actively contributes to the validation of flagship architectures such as Grace Hopper and Blackwell GPUs, which power DGX-class platforms used across AI, data center, automotive, and HPC applications. He has a strong track record of verifying silicon tailored for AI acceleration, working closely with industry-leading designs that push the boundaries of compute, memory, and interconnect complexity.

He holds a Master’s degree in Computer Engineering from the University of Southern California. Prior to NVIDIA, he developed security-focused firmware for Robert Bosch, gaining hands-on experience bridging low-level software and hardware security.

Known for being well-versed in modern computer architecture, Santosh possesses deep expertise in cache coherence protocols, multi-agent interconnect systems, and memory consistency models. His work centers on verifying complex heterogeneous SoC architectures, including CPU-GPU interactions under parallel, high-throughput workloads. He is passionate about enabling robust, scalable, and high-efficiency hardware that powers next-generation AI training and inference systems, driven by the challenge of verifying correctness and security in large, tightly integrated computing platforms.

Jonathan Frankle is Chief AI Scientist at Databricks, where he leads the AI research team toward the goal of bringing the latest advances in artificial intelligence and reinforcement learning to Databricks’ tens of thousands of enterprise customers. He arrived via Databricks’ $1.3B acquisition of MosaicML, which he co-founded as Chief Scientist. He earned his PhD at MIT, where he empirically studied deep learning with Prof. Michael Carbin, specifically the properties of sparse networks that allow them to train effectively (his “Lottery Ticket Hypothesis” – ICLR Best Paper, ACM SIGAI Dissertation Award, MIT Dissertation Award). He earned his BSE and MSE in computer science at Princeton.

Nouha Dziri is a Senior Research Scientist at Cohere Labs. Her research spans a wide range of problems in AI, with a focus on building and improving large language models (LLMs). She was previously a Research Scientist at Ai2, where she co-led the post-training effort for the OLMo models. Prior to that, she was a postdoctoral researcher working with Yejin Choi at Ai2. She has also interned at Google DeepMind, Microsoft Research, and Mila. She is the recipient of multiple awards, including Best Paper Awards at NeurIPS 2025 and NAACL 2025. Her work has been featured in leading media outlets including The Economist, TechCrunch, Le Monde, Science, and Quanta Magazine. She has delivered invited talks at top universities such as Stanford, Oxford, Cambridge, Edinburgh, Princeton, McGill, and CMU. She has also been a keynote speaker and a panelist at workshops in major AI conferences including NeurIPS, ICLR, and ICML. She earned her PhD from the University of Alberta and the Alberta Machine Intelligence Institute (Amii) in 2023.

Robert Brennan has been writing software for 15 years, with a focus on natural language processing and developer tools. He is currently the CEO of All Hands AI, the company behind OpenHands (formerly OpenDevin), a fully autonomous AI developer. Previously he was VP of Product Development at Fairwinds, ran a startup called Datafire, and worked as a Senior Software Engineer at Google.

Scott Askinosie is an AI educator specializing in context engineering for agentic systems. With a PhD in Quantitative Biology and deep experience in vector databases and RAG architectures, Scott helps developers understand how thoughtful context design transforms AI agents from experimental prototypes into reliable, production-ready tools.

Scott has delivered 20+ technical workshops to hundreds of developers worldwide, partnering with companies including LlamaIndex, Comet, CrewAI, and FriendliAI to teach practical strategies for retrieval optimization, Model Context Protocol (MCP) integration, and agentic workflow design. His approach emphasizes real-world implementation over hype, focusing on the engineering fundamentals that make AI systems actually work.

Through educational content on LinkedIn, YouTube, TikTok, and X, Scott breaks down context engineering concepts for developers navigating the rapidly evolving agentic AI landscape. He builds open-source tools and Claude Skills that demonstrate these principles in practice, from vector database integration to multimodal retrieval systems.

Based in Austin, Texas, Scott brings together academic rigor from his work as Lead Data Scientist and Professor at Western Governors University with hands-on experience building production AI systems. His goal: help developers master the context engineering skills that separate functional AI agents from truly effective ones.

Summer Purschke is a Technical Account Manager at Gurobi Optimization, where she helps organizations leverage mathematical optimization to solve complex decision-making challenges. She holds a BS in Industrial Systems Engineering and an MS in Applied Data Science, and is particularly interested in the intersection of Data Science and Operations Research. Previously, she worked as a Data Scientist building machine learning and optimization models to improve operational processes. Summer enjoys collaborating with diverse teams to translate data into actionable, optimized decisions.

Solution Showcase: Transform Predictions into Actionable, Explainable Decisions using Optimization

Abstract: Making decisions in a complex environment is difficult, and choosing the right tools can be just as challenging, but it doesn’t have to be. Mathematical optimization lets you move beyond predictions to identify the best decisions possible, given the realities of your system. During this session, we’ll introduce mathematical optimization as the AI built for decision making with trust, transparency, and explainability at it’s core.

Claire Zhao is a Director and Group Lead in AI/ML Quantitative and Digital Sciences group within Global Biometrics and Data Management, Pfizer Research & Development. She advances medical and scientific knowledge by delivering insightful, evidence-based analytics leveraging AI/ML and data science to drive informed healthcare decisions that protects patient safety and optimize patient outcomes. In her previous work, she led the analysis of real world data to predict disease outcomes using AI/ML and statistical methods that were aimed to improve clinical and operational workflow for clinicians and the hospital. She obtained her Ph.D. from the Department of Biomedical Engineering at Johns Hopkins University, with a thesis in Computational Medicine that dissects the mechanisms of action of cellular signaling pathways and ion channels. She obtained her Bachelor’s degree in Systems Design Engineering with a Concentration in Statistics from the University of Waterloo.

Author of the Apress book Getting Started with Grafana, Ronald McCollam is a “geek of all trades” with experience ranging from full stack development to IT operations to management and sales. He has a strong background in open source software, starting when a stack of 3.5″ Slackware floppies was the easy way to install Linux. He’s managed IT operations in datacenters from back when the “cloud” was new and scary, built and run an OS certification program for hardware running Linux, and spent entirely too long thinking about observability. He’s also a Certified Irish Whisky Taster.

Omar Shorbaji is Solutions Engineering Manager for ML Platforms at Anyscale, the company behind Ray. With over a decade of experience in cloud and ML infrastructure, he has held senior technical roles at Google and AWS. Today, Omar works closely with robotics teams to help them build and operate scalable ML platforms, bridging the gap between distributed computing infrastructure and real-world robot learning workflows.

David Campbell is Head of AI Security at Scale AI and a leading voice in operational AI risk. He pioneered one of the first large-scale AI red teaming platforms used across governments and enterprise organizations to probe and harden generative models against real-world threats.

David has briefed the U.S. Congress, the White House, U.K. Parliament, NATO, Korea AISI, UK AISI, and Qatar NCSA on AI security and national-scale resilience. He is a founding member of OWASP AIVSS and AIUC Consortium and contributes to CISA’s JCDC.AI collaborative exercises on AI-driven cyber threats.

With nearly twenty years in Silicon Valley, David previously helped shape engineering, security, and developer experience at Uber, DoorDash, and Nest Labs. His work consistently focuses on elevating standards, building resilient systems, and helping organizations adopt transformative technologies without compromising trust or safety.

His mission: align technology with the future of the business and ensure AI systems can withstand the adversaries, accidents, and emergent behaviors of the real world.

Harry Mellor is a Machine Learning Engineer at Hugging Face, where he focuses on optimizing model deployment and inference. An engineer by training, Harry holds a Master’s degree in Engineering Science (MEng) from the University of Oxford.

He is a Core Committer to vLLM, an open-source library for high-throughput LLM serving. A frequent speaker in the AI community, Harry has shared his expertise at the Paris vLLM Meetup and Ray Summit 2025, where he presented on the integration of Hugging Face and vLLM to create unified model definitions.

Datta is a senior machine learning engineer working at Unsloth where they contribute to optimizing LLM fine-tuning, both making it faster and more memory efficient specifically focusing on Parameter Efficient fine tuning techniques like LoRA. They have previously worked at Nutanix, where they focused on creating GPT in a Box, an enterprise grade LLM deployment solution on the edge using opens source projects like kserve and vllm, while also heavily contributing to them. They hold an undergraduate degree in Maths and Computing from IIT Hyderabad and are an avid lover of Math, Machine Learning and the intersection of the two.

Nathan is a Research Engineer at Qdrant with over five years of experience in software development and machine learning research. He recently completed his PhD at the University of Virginia, where he designed and trained transformer foundation models for single-cell genomic datasets and built high-performance Rust-based infrastructure for genomic data analysis. His work bridged deep learning and genomics, resulting in multiple first-author publications and open-source tools now used by the scientific community. These days, Nathan is focused on how representation learning and high-dimensional embeddings can power impactful AI applications. When he’s not building demos or writing technical content, you’ll find him logging miles on a run, reading Dune, or hanging out with his two cats.

Bio Coming Soon!

Rehgan Bleile is the Co-Founder and CEO of AlignAI, an AI Adoption and Governance Platform that is helping enterprises get value out of AI quickly and safely. With a decade of experience in this space, she has worked with highly regulated companies to design and architect data and AI solutions. As an industry leader, she has keynoted and spoken at over 50 conferences and several top podcasts discussing the challenges of the organizational changes needed to maximize the value of artificial intelligence.

In addition to her role at AlignAI, Rehgan is also the Founder of Women in Analytics, a global organization aimed at elevating diverse voices, fostering collaboration, and building a strong community in data, analytics, and AI. This community has over 7000 members and hosts a global conference that has put over 250 women on the stage.

Before joining Anthropic, April was a Founding Field Engineer at Galileo and held a similar foundational role at Snorkel AI. During her tenure at Snorkel AI, she served as a Senior Machine Learning Solutions Engineer, where she was instrumental in driving technical sales and scaling revenue through innovative programmatic labeling solutions. Her early career also includes experience as a Data Scientist at IBM, where she was recognized as a nominee for the Global Business Services Emerging Leaders Program. She completed her undergraduate studies at UCLA, earning a dual degree in Economics and Cognitive Science with a specialization in Computing.

Rajiv Shah is the Agentic AI Engineer at OpenHands. He previosuly worked as Chief Evangelist at Contextual AI with a passion and expertise in Practical AI. He focuses on enabling enterprise teams to succeed with AI. Rajiv has worked on GTM teams at leading AI companies, including Hugging Face in open-source AI, Snorkel in data-centric AI, Snowflake in cloud computing, and DataRobot in AutoML. He started his career in data science at State Farm and Caterpillar.

Rajiv is a widely recognized speaker on AI, published over 20 research papers, been cited over 1000 times, and received over 20 patents. His recent work in AI covers topics such as sports analytics, deep learning, and interpretability.

Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He is well known on social media with his short videos, @rajistics, that have received over ten million views.

Olivia has worked at the intersection of computers, language and intelligence for over 15 years. She studied computational linguistics in school, and her career since has focused on data, machine learning, and AI. She started as an MLOps Engineer, scaling NLP training processes to serve hundreds of classical ML models. Later, she led a team to share those models across the company via inner source, then took many of those concepts to work in the trustworthy and responsible AI space. Since generative AI has taken off, she’s turned my focus towards innovating new patterns of AI and human interaction.

Olivia is obsessed with how to build excellent, well-designed user experiences both for developers and consumers of AI applications. She subscribes to neither AI hype nor AI doomerism: she believes that human creativity and AI can coexist, and that builders of AI applications have a responsibility to their users to ensure that humans thrive when using the tools we have built. Olivia is currently investigating how to build generative AI applications that use LLMs efficiently, effectively, and in ways that support the people whose creativity helped make AI happen.

Rachita Naik is a Machine Learning Engineer at Lyft, Inc. and a distinguished graduate of Columbia University. With a deep foundation in AI and experience building large-scale, real-time ML systems, she focuses on creating transformative solutions that address complex marketplace challenges. At Lyft, Rachita leads the design, development, and deployment of advanced ML models that improve ride-hailing reliability, optimize pickup times, reduce cancellations, and elevate both driver and rider experience.

A recognized voice in applied machine learning, Rachita has presented at leading conferences including Ai4, AIDevSummit, and INFORMS, and has authored articles for platforms such as VentureBeat, DZone and HackerNoon. She has also shared her expertise through podcasts like DataCamp’s DataFramed, where she breaks down sophisticated ML concepts into accessible insights for broader audiences.

Beyond her technical contributions, Rachita is an active member of the Women in Data Science community, championing knowledge-sharing, mentorship, and empowerment initiatives. Driven by curiosity and a commitment to innovation, she continuously explores emerging technologies to push the boundaries of AI and deliver meaningful impact at scale.

Ben Armstrong is the executive director of MIT’s Industrial Performance Center, where he co-leads the Work of the Future initiative. His research examines how workers, firms, and regions adapt to technological change. His current projects include a working group on generative AI, as well as a book on American manufacturing competitiveness. His work has been published or featured in academic and popular outlets including the New York Times, Wall Street Journal, Financial Times, Harvard Business Review, and Sloan Management Review. He received his PhD from MIT and formerly worked at Google Inc.

Dr. Xiong (Sean) Liu is Director of Data Science and AI at Novartis, where he advances machine learning to accelerate discovery, optimize clinical development, and inform strategic decisions. Previously, he was a Principal Data Scientist at Eli Lilly and led SBIR-funded AI projects in industry. His contributions have been recognized with the Lilly CIO Award, the Novartis Star Award, and selection as a Gen Matrix Top Innovator in Pharma. He has published widely, served on industry and scientific committees, mentored emerging talent, and speaks regularly on AI in biopharma.

Dr. Qingyun Wu is the co-creator and co-founder of AG2 (formerly AutoGen), a leading open-source AI agent framework with a monthly download of over 500k and a vibrant community of over 20k AI agent developers. Qingyun is also an Assistant Professor at the College of Information Science and Technology at Penn State University. Qingyun got her Ph.D. in Computer Science from the University of Virginia in 2020. Qingyun received the 2019 SIGIR Best Paper Award and the ICLR 2024 LLM Agent Workshop Best Paper Award.

Ari Morcos is the CEO and co-founder of Datology AI. Previously, he was a senior staff research scientist at Meta AI Research (FAIR Team) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. Most recently, his work has focused on understanding properties of data and how these properties lead to desirable and useful representations, with a particular emphasis on data curation. Ari’s work has been honored with Outstanding Paper awards at both NeurIPS and ICLR. Before joining FAIR, Ari worked at DeepMind in London, and earned his PhD in neuroscience working with Chris Harvey at Harvard University.

Joseph Fuller is Professor of Management Practice at the Harvard Business School and one of the nation’s leading authorities on the future of work. He co-leads the Managing the Future of Work Project at Harvard Business School. It creates research of direct relevance to decision makers in business and government, including the impact of technology and demographic changes on the workforce, the rise of the gig economy, global talent flows, and the emergence of the care economy. The Managing the Future of Work podcast that he co-hosts has been downloaded over 2 million times. He also co-leads the Harvard Project on the Workforce, a collaboration between the Harvard schools of business, government and education. It focuses on issues related to lower skilled workers, including career pathways and the causes of income polarization.

Prior to joining the faculty, he was a founder, first employee and long-time CEO of the global consulting firm Monitor Group, now Monitor-Deloitte.

Joe is a widely published author. His work has appeared in the Harvard Business Review, the Sloan Management Review, the Wall Street Journal, the Financial Times, the Washington Post and the New York Times.

Joe is a magna cum laude graduate of Harvard College and of Harvard Business School. He is a director of Aera Technology, Hakluyt and Company and Helios Consulting, Chairman of the Board of Trustees of Western Governors University and a Senior Visiting Fellow at the American Enterprise Institute and the Burning Glass Institute.

Barbara co-led the Diversity Mentorship Program, pairing mentees with senior mentors and delivering a six-month curriculum that earned accolades for its impact. At State Street Digital, she focused on driving innovation in digital assets and advanced technologies.

Currently, Barbara serves as Vice President of Automation & Artificial Intelligence at State Street, where she is responsible for developing strategies for privacy, data governance, and AI ethics, and ensuring alignment with regulatory requirements. In this role, she leverages automation and AI to enhance technology adoption and employee engagement across the organization.

As Co-Chair of the PWN Technology Network, Barbara champions women in technology, organizing seven events annually, including two hackathons. The first, in collaboration with IBM, utilized AI/ML for employee upskilling and received recognition as a “Best Innovator.” The second, with Appian, saw Barbara leading her team to victory.

Throughout her career, Barbara has remained dedicated to fostering innovation, empowering colleagues, and creating opportunities for underrepresented groups, drawing inspiration from her journey as a non-native professional navigating global environments.

Wanmei brings a wealth of experience and a proven track record in leveraging advanced data analytics and AI technologies to drive impactful results. Most recently, Wanmei served as Vice President of Product at Ontada, a business unit of McKesson Corporation, where she has successfully led a team focused on real-world data analytics and clinical decision support systems in oncology. Among her notable achievements at McKesson was the establishment of an AI Center of Excellence and the implementation of scalable and sustainable GenAI solutions to extract clinical insights from over 100 million documents to enrich longitudinal patient journeys.

Before her tenure at McKesson, Wanmei held key roles at Merck and served as a White House Presidential Fellow detailed at the U.S. Department of Veterans Affairs. During that time, she played a pivotal role in executing data-driven initiatives that improved clinical outcomes and operational efficiencies, including AI-based clinical endpoint for vaccine studies and enhancements to medication adherence programs.

Wanmei earned her PhD in Computer Science from MIT, where she applied functional imaging and cognitive experimental design to decipher brain functions. She resides in the greater Boston area with her husband, Jeff, and their two daughters, Amber and Maura. Outside of work, she enjoys traveling off the beaten path.

Yash Vijay is the Head of Applied AI at Snorkel AI, where he leads teams delivering enterprise-grade GenAI and machine learning systems from pilot to production. Previously at C3 AI, he founded and scaled one of the company’s most tenured Data Science and ML Engineering teams, driving multiple pilots to production across industries including supply chain, energy, manufacturing, retail, and pharmaceuticals. His work spans applied R&D, forward-deployed AI, and product-platform integration, with a strong focus on bridging technical rigor and real-world business impact.

Yash holds an M.S. in Computer Science from the University of Illinois at Urbana-Champaign. He is known for high-velocity execution, executive stakeholder engagement, and building AI systems that are not only technically sound, but operationally scalable and commercially viable.

Beyond Static Benchmarks: How to Test AI That Thinks, Acts, and Adapts(Talk)

Developer Experience Manager at Eventual. Also the creator of an open-source coding assistant with ~1200 stars on GitHub. Founder of CS Dojo, a programming education YouTube channel with 1.9M+ subscribers.

Jacob Prall is a Senior Program Manager for Developer Adoption at Snowflake, where he works at the intersection of data infrastructure and AI. He is the creator of the AI-Ready Data Framework, an open, vendor-agnostic standard that defines what “AI-ready data” actually means, giving data teams a shared language and concrete criteria to evaluate their platforms, pipelines, and data against the demands of AI workloads.

Jacob’s career spans engineering, product, and developer experience across the data and AI infrastructure landscape. He has led product and GTM at SQLite.ai (edge AI data platform), served as Senior Developer Advocate at Airbyte (open-source data integration), built Java microservices and data platform tooling at Single Origin, and led developer education at Timescale (time-series database), where he launched the annual State of Postgres survey.

His work has consistently focused on the gap between what AI systems demand and what traditional data stacks deliver, a perspective shaped by years of building for and alongside data engineering teams.

AI-ready Data: The Key to Trustworthy AI(Keynote Track Talk)

Amy Hodler is an evangelist for graph analytics and responsible AI. She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics and Bloomsbury book, AI on Trial. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, Cray, and RelationalAI. Amy is the founder of GraphGeeks.org promoting connections everywhere.

David Hoyle is a Research Data Science Specialist at dunnhumby, where he builds demand forecasting models and algorithms for grocery retailers worldwide. He has worked as a Data Scientist in the commercial sector for 14 years. Before that he was an Associate Professor in Bioinformatics and Machine Learning at the University of Manchester, UK, and at the University of Exeter, UK. He has a PhD in theoretical physics and continues to publish academic articles on various mathematical, statistical, machine learning, and AI topics. He also enjoys explaining mathematical and statistical concepts, showing how they are relevant to real-world problems and challenges, and so he regularly writes posts for his own blog and for dunnhumby. He also does pro bono Data Science work through his own consultancy, Oakthorpe Consulting. He is heavily involved with the UK Royal Statistical Society, acting as Data Science and AI board member for the society’s annual international conference. He has recently authored a book on mathematics for Data Science – “15 Math Concepts Every Data Scientist Should Know’’ – published by Packt.

Paco Nathan leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and is a computer scientist with +40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He’s the author of numerous books, videos, and tutorials about these topics. He also hosts the monthly “”Graph Power Hour!”” webinar.

Paco advises Kurve.ai, EmergentMethods.ai, and is lead committer for the `pytextrank` and `kglab` open source projects. Formerly: Director of Learning Group at O’Reilly Media; and Director of Community Evangelism at Databricks.

Kyle Stratis is the founder and principal consultant of Stratis Data Labs, where he specializes in designing and implementing AI, data, and cloud strategies for early-stage startups and large enterprises alike. He also serves as VP of Engineering at The Sequel Institute, where he transforms cutting-edge neuroscience and AI research into robust, scalable software products that bridge the gap between academic innovation and real-world applications. Kyle is the author of AI Agents with MCP, published by O’Reilly Media, which provides both a deep understanding of the Model Context Protocol and practical guidance for building intelligent agents with Python. His expertise spans the full AI/ML stack—from agent architectures and LLM orchestration to production-grade MLOps pipelines—with a focus on making advanced AI capabilities accessible and actionable for organizations at any stage of their AI journey. Prior to founding Stratis Data Labs, Kyle held engineering and technical leadership roles where he developed expertise in distributed systems, cloud infrastructure, and data engineering, bringing a uniquely holistic perspective to AI implementation challenges.

Max Tegmark is a professor doing AI and physics research at MIT as part of the Institute for Artificial Intelligence & Fundamental Interactions and the Center for Brains, Minds and Machines. He advocates for positive use of technology as chairman of the Future of Life Institute. He is the author of over 300 publications as well as the New York Times bestsellers “Life 3.0: Being Human in the Age of Artificial Intelligence” and “Our Mathematical Universe: My Quest for the Ultimate Nature of Reality”. His current AI research focuses on provably safe AI and mechanistic interpretability.