SEMLA 2026 Program

AI is being deployed faster than the controls around it, and security teams are the ones paying the bill. In this session, Vincent shows how Databricks gives you a single governed foundation for data, AI, and agents, so innovation doesn’t outrun oversight. The talk closes with a look at Lakewatch, Databricks’ open agentic SIEM built for machine-speed defense.

The deployment of AI systems in heavily regulated domains such as healthcare and finance presents unique challenges that extend beyond technical performance metrics to encompass fundamental questions of trust, accountability, and societal impact. Both sectors share critical characteristics: they involve high-stakes decisions with material consequences for individuals, operate under stringent regulatory oversight, and are fundamentally client-facing, requiring that end-users place significant trust in system outputs that may influence their health outcomes or financial wellbeing.

This talk examines the core pillars of trustworthy AI in these contexts, including transparency and explainability of model decisions, robustness to distribution shift and adversarial inputs, fairness across demographic groups, rigorous validation against domain-specific standards, and mechanisms for human oversight and intervention.

Artificial Intelligence (AI) models are energy-hungry. As these models grow larger and become more complex, their energy consumption increases substantially. This leads to higher carbon emissions and rising operational costs, creating serious challenges for sustainable computing. This talk highlights the environmental impact of modern AI systems, introduces key metrics for measuring energy use and carbon footprint, and discusses available tools and frameworks for measuring and monitoring energy consumption.

The session summarizes current research and practical techniques for developing greener AI systems, including model pruning, quantization, efficient architecture design, and workload scheduling, and situates these within the wider movement toward responsible and sustainable AI development.

Autonomous driving systems increasingly rely on AI-enabled perception systems based on multi-sensor fusion, yet their robustness and reliability under real-world conditions remain insufficiently understood. As these systems are deployed in increasingly complex and safety-critical environments, ensuring their dependable operation has become a fundamental challenge for both academia and industry. This talk first discusses the key robustness challenges faced by modern autonomous driving perception systems, then presents recent research on automated testing techniques for improving effectiveness and efficiency through realistic multi-modal test generation and dynamic driving scenario synthesis.

As AI becomes a fundamental partner in the software development lifecycle, a critical gap has emerged: while modern models excel at predicting code tokens, they remain cognitively blind to the high-level reasoning, intentionality, and memory constraints of human programmers. This cognitive misalignment results in AI models that often fail to match human judgment, leading to diminishing returns as task complexity increases.

By establishing a systematic methodology to model developers’ cognitive processes, Dr. Huang uncovers the complex relationship between programming and natural language processing while providing a framework to quantify human expertise for software engineering.

Software engineering demands artifacts that satisfy rigorous syntactic and semantic constraints, yet large language models are inherently probabilistic and non-deterministic. As LLMs are increasingly used to automate software engineering tasks, ensuring the reliability of the artifacts they produce becomes a critical challenge. This talk presents recent efforts toward reliable AI-based generation of software artifacts, particularly domain-specific languages and other structured representations.

It first demonstrates how current LLMs frequently violate both syntactic and semantic constraints, then presents two complementary approaches: AbsCon, a post-processing framework that leverages self-consistency to ensure provable specification compliance, and Projectional Decoding, a semantics-guided decoding approach that proactively steers generation toward valid outputs.

Reinforcement learning has enabled the development of effective agents for critical tasks across domains such as robotic control, autonomous driving, AI coding assistance, and computer use. In this talk, I will discuss the trustworthiness challenges of RL-based agents. I will first uncover robustness issues in RL agents and show how curiosity-driven mechanisms can be leveraged to expose flaws in their decision-making. Next, I will discuss how to provide runtime verification and protection for these agents.

We are shifting toward an era of AI-based software engineering, where large language models and AI agents are being continuously integrated into software systems. While their integration improves software performance, these non-deterministic models also turn software systems into unexplainable black boxes. This talk reveals transparency challenges in major AI-based software systems and proposes low-cost, high-performance strategies to promote software transparency and trustworthiness.

Developing scientific software capable of addressing real-world fluid dynamics problems is inherently complex, particularly for researchers who were never formally trained as software engineers. In this talk, I will share my personal journey in computational fluid dynamics software development, beginning with a blank page as a PhD student in 2009, passing through ambitious attempts to build “one-size-fits-all” simulation frameworks, and eventually arriving at a more pragmatic philosophy centered on specialized tools for specific classes of flows and applications.

Beyond the technical challenges of numerical methods and classical software maintenance, onboarding new students into rapidly evolving software ecosystems is becoming an ever more challenging task.