AIware 2025 Keynote Speakers
Lin Tan
Purdue University
Bio: Lin Tan is the Mary J. Elmore New Frontiers Professor of Computer Science at Purdue University, previously a Canada Research Chair and associate professor at the University of Waterloo, with a PhD from the University of Illinois Urbana-Champaign. Her research spans software-AI synergy, LLM4Code, software dependability, autoformalization, and software text analytics, with a particular focus on applying machine learning and natural language processing to improve software reliability and using software methods to enhance the dependability of AI systems. She is an IEEE Fellow, ELATES Fellow, and ACM Distinguished Member, and has received numerous honors, including major academic and industry awards from organizations such as NSERC, Ontario Professional Engineers, J.P. Morgan, Meta, Google, and IBM. Her work has also been recognized through multiple distinguished paper awards and highlights at leading venues including CCS, ASE, MSR, FSE, NeurIPS, AAAI, and ICRA. In addition, she has held significant leadership roles in the research community, serving as program chair or co-chair for major conferences and workshops such as FSE and LLM4Code, as well as in editorial and professional service roles with IEEE TSE, EMSE, and ACM SIGSOFT.
Thomas Cottenier
ARM
When Implementation Stops Being the Bottleneck: Design in AI-Native Software Engineering
AI is genuinely lowering the barriers to building software - but democratization is the surface effect. The deeper transformation is structural: AI shifts the primary bottleneck in software development from implementation to system design and reasoning. In agent-driven workflows, the constraint is no longer writing correct code across a large surface area. It is making good architectural decisions, managing system-level tradeoffs, and reasoning coherently across concerns - performance, modularity, data flow, hardware characteristics - that previously required deep specialization or large, coordinated teams. Individual developers and small groups can now operate fluidly across layers that were previously siloed not by technical necessity but by organizational scale. This shift raises fundamental questions for the software engineering research community. How do we evaluate design quality in systems that evolve continuously under agent-driven modification? What does meaningful modularity look like when the cost of cross-boundary reasoning approaches zero? How should practices like refactoring, testing, and performance validation be reconceived when the artifact being maintained is as much a set of agent instructions as a codebase? This talk draws on experience building AI-powered developer tools at AWS and working on AI acceleration across edge and cloud at Arm to ground these questions in observable shifts in how complex systems are actually being built today - and to argue that the SE community's next productive frontier is not productivity measurement, but the theory and practice of design in the age of capable agents.
Bio: Thomas Cottenier is a Senior Principal Engineer at Arm, working on developer platforms and AI services across edge and cloud environments. He previously served as a Principal Engineer at AWS, where he contributed to AI-powered developer tools including CodeWhisperer, Amazon Q, and Kiro. He holds a PhD in Computer Science in programming languages and software engineering and has over 20 years of experience spanning code generation, model-driven engineering, automated refactoring, and large-scale code modernization. His current work focuses on AI-native development, system design, and AI acceleration.
Pascal Kesseli
Microsoft
Formal Methods for Reliable AI Reasoning
In this talk, Pascal Kesseli explores how formal methods can enhance reasoning capabilities in modern AI systems, drawing on his research at the University of Oxford and his work at Meta GenAI and FAIR. He introduces an agentic inference-time reasoning engine, Logic.py, and shows how it supports structured decision-making in agents. He then turns to follow-on work that applies AlphaGo-style reinforcement learning training pipelines to improve reasoning behavior, and he closes by outlining how inference-time and training-time approaches are beginning to converge into a unified direction for building more capable, dependable agents.
Bio: Pascal Kesseli is a researcher and engineer working at the intersection of formal methods and AI. He earned a DPhil from the University of Oxford, focusing on static code analysis and program synthesis using formal reasoning engines. At Meta, he worked across GenAI and FAIR on agentic LLM systems and research infrastructure, including inference-time tool use with formal reasoning engines, contributions to Meta’s Code World Model, and reinforcement-learning environments grounded in static analysis. Most recently he started a position at Microsoft AI, working on RL and coding capabilities.
Qidi Xu
MiniMax
From Chatbots to Colleagues: Steering Code-Driven Agents for End-to-End Autonomy
The paradigm of Generative AI is rapidly evolving from passive assistants to autonomous entities. Moving beyond the chatbot and simple code-completion copilots, the next generation of AI agents functions as digital colleagues capable of end-to-end task execution. This talk explores how these agents leverage code not merely as an output, but as their fundamental reasoning engine and interface for interacting with the world. We will focus on the shift from manual prompting to high-level Steering, discussing how human intent can guide code-driven agents to autonomously navigate complex workflows, resolve software engineering challenges, and bridge the gap between abstract requirements and finished, functional results.
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