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Machine unlearning addresses the “right to be forgotten” under GDPR and enables privacy, copyright, and safety compliance in large language models. Training-based unlearning can remove targeted behavior on benchmarks, but it scales poorly, can degrade utility, and can fail under adversarial prompting that recovers supposedly forgotten content. This prospectus proposes inference-time behavioral unlearning: rather than […] |
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Hybrid Event
Hybrid Event
Game balance is a term widely used among players, researchers, and designers of games. It is a concept that feels vitally important to how we make and play games – but when we try to define it or implement it, we seldom get the same definition twice. Balance appears differently to whoever is judging it, […] |
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Hybrid Event
Hybrid Event
Modern edge computing increasingly relies on heterogeneous System-on-Chip (SoC) architectures. These chips tightly integrate general-purpose CPUs with various specialized accelerators, including GPUs, FPGAs, and AI accelerators, all under a shared memory architecture. Although these shared-memory SoCs enable more efficient communication and data sharing between different processing units, they are notoriously difficult to program and tune […] |
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3 events,
Virtual Event
Virtual Event
Children’s lives are now inextricably linked with AI-driven digital systems that shape learning, social interaction, and development. This has elevated child online safety to a central concern for families, policymakers, and educators. This makes Child online safety a wicked socio-technical problem, emerging from the complex interplay of social norms, platform incentives, cultural expectations, and rapidly […]
Hybrid Event
Robbins, A. (ECE) – How to train your organoid: goal-directed learning in biological neural networks
Hybrid Event
Robbins, A. (ECE) – How to train your organoid: goal-directed learning in biological neural networks
Artificial neural networks can now learn to play games, control robots, generate language, and solve complicated reasoning tasks, yet we still lack a clear understanding of how to directly guide learning in biological neural networks. We show that brain organoids can learn to solve a fundamental control task, balancing an inverted pendulum, through closed-loop electrophysiology. […]
End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models produce fluent text, they collapse content planning, sentence planning, and surface realization into a single undifferentiated decoding step, sacrificing the controllable structure that earlier systems […] |
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Virtual Event
Virtual Event
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes, but building reliable machine learning models is difficult because data come from different scanners and sites, some modalities are missing, labeled cohorts are limited, and factors such as age and scanner/site effects […] |
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Virtual Event
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As multimodal artificial intelligence systems become increasingly embedded in everyday technology, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace them. This dissertation investigates how to build human-centered multimodal AI agents, framing human-centeredness as an agent-level objective that requires both accessible, assistive interaction and reliable, […]
Hybrid Event
Hybrid Event
Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception, planning, and control. While learning-based methods have achieved state-of-the-art performance in perception and environment modeling, the planning layer remains a key bottleneck for reliable autonomy. Highway driving in particular requires long-horizon reasoning and socially […]
Register transfer level (RTL) simulation is an invaluable tool for developing, debugging, verifying, and validating hardware designs. However, the performance of RTL simulation has long been a limiting factor in industry. Despite the inherent parallelism of hardware, current RTL simulators have not achieved practical performance gains due to fundamental challenges in communication, synchronization, memory bandwidth, […] |
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Virtual Event
Virtual Event
Game analysts face a significant challenge in understanding problem-solving and decision-making processes from the vast and complex sequential data generated by modern video games. Existing visualization tools often fail to adequately support the exploration, suffering from issues of visual clutter, inflexible cohort construction, and a lack of interactive depth. To address this gap, this dissertation […] |
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