Jonathan Gilmore – Co-Founder & CEO, DeepFlow
Artificial intelligence is not just driven by engineering – it’s shaped by ideas, theory, and rigorous research. Advancing it requires infrastructure, leadership, and a principled commitment to independent research. At DeepFlow, we build this foundation through embedding academic rigour directly into our company and product strategy. A core part of this mission is our strategic partnership with the Reinforcement Learning and Decision Making (RLDM) conference.
RLDM is the premier global venue for interdisciplinary research at the intersection of reinforcement learning, cognitive science, neuroscience, and behavioral economics. It is highly respected across both academia and industry for shaping the theoretical underpinnings of intelligent decision-making. For many, RLDM defines the state-of-the-art in how agents – human or artificial – learn, adapt, and coordinate in complex environments. This year, DeepFlow’s Director of AI, Dr. Stefano Albrecht, serves as Program Chair of RLDM 2025. Dr. Albrecht is a global authority in multi-agent systems and author of the landmark textbook Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. Through his leadership and our sponsorship, DeepFlow is helping to set the intellectual agenda for the next wave of reinforcement learning research.
Direct Support of World-Leading Research
Our commitment to RLDM is driven by our commitment to the direct enablement of independent academic research, backing a team of leading scholars whose work is shaping global conversations around AI strategy, coordination, and policy:
- Dr. Stefano Albrecht (Director of AI, DeepFlow): Autonomous agents, reinforcement learning, multi-agent interaction, 3,700+ citations.
- Dr. Alastair Moore (UCL Associate Professor; Chief Scientific Officer, DeepFlow): Task orchestration, real-world AI systems, 2,100+ citations.
- Prof. Anil Doshi (UCL Professor; Researcher, DeepFlow): Strategic management and algorithmic governance, 890+ citations.
Collectively, this body of work – 6,700+ citations and growing – is supported through DeepFlow’s ongoing funding, technical integration, and strategic collaboration. These are not incidental affiliations: each academic actively contributes to and benefits from DeepFlow’s commercial and product infrastructure while preserving the independence of their research and publication agendas.
“DeepFlow is building the intellectual scaffolding for the future of intelligent systems. That means giving the world’s best researchers the freedom, funding, and platform to pursue bold ideas. RLDM represents the highest standard of that pursuit, and we’re proud to back it.”
– Jonathan Gilmore; Co-Founder & CEO, DeepFlow, A prime example is our forthcoming paper Toward a Human–AI Task Tensor (Harvard Business Review), resourced and published by DeepFlow. It proposes a novel framework for structuring human–AI collaboration around task dimensionality – an idea born from the lived challenges of building scalable coordination systems inside DeepFlow.
Embedding Research Into Industry Infrastructure
What sets us apart is that we don’t view academia as an external authority – we are embedded in it. Our team members lead doctoral consortia, publish foundational research, and teach at top universities while simultaneously building next-generation AI platforms. We enable scholars to remain intellectually independent while applying their theories in live, high-stakes environments.
Our sponsorship of RLDM represents a deeper commitment: to fund the world’s most rigorous discourse on reinforcement learning and decision-making, and to ensure it is shaped not just by institutions but by practitioner-researchers building real systems.
By enabling world-class researchers to pursue independent, high-impact work, I’m proud to play a direct role in shaping the academic foundations of next-generation AI.