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Generative models are revolutionizing our world, with the ability to generate photorealistic visual content that are indistinguishable from reality. Despite their overwhelming presence in the cyber world, they haven’t been very useful in the physical world that we live in. In this talk, I will present how the rich priors learned by large-scale generative models—ranging […] |
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Abstract: Machine learning applications are increasingly reliant on black-box pretrained models. To ensure safe use of these models, techniques such as unlearning, guardrails, and watermarking have been proposed to curb model behavior and audit usage. Unfortunately, while these post-hoc approaches give positive safety ‘vibes’ when evaluated in isolation, our work shows that existing techniques are quite brittle when deployed […] |
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Zoom link: https://upenn.zoom.us/j/98220304722 Abstract I will present recent work by my research group on the design and analysis of stochastic-gradient-based algorithms for solving nonconvex constrained optimization problems, which may arise, for example, in informed supervised learning. I will focus in particular on algorithmic strategies that have consistently been shown to exhibit the best practical […]
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I aim to build complete intelligent agents that can continually learn, reason, and plan: answer queries, infer human intentions, and make long-horizon plans spanning hours to days. In this talk, I will describe a general learning and reasoning framework based on neuro-symbolic concepts. Drawing inspiration from theories and studies in cognitive science, neuro-symbolic concepts serve […] |
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We have made exciting progress in AI by massive models on massive amounts of data center compute. However, the demands for AI are rapidly expanding. I identify how to maximize performance under any compute constraint, expanding the Pareto frontier of AI capabilities. This talk builds up to an efficient language model architecture that expands […] |
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Abstract: Large Language Models (LLMs) are vulnerable to adversarial attacks, which bypass common safeguards put in place to prevent these models from generating harmful output. Notably, these attacks can be transferrable to other models---even proprietary ones—potentially compromising a wide range of AI systems with a single exploit. This surprising fragility underscores a critical weakness in […] |
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We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along the sequence converges to zero. In general, this condition is not implied by, nor implies, sublinear regret. It turns out that gradient equilibrium is achievable […] |
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Click here for more details. The workshop will explore the state of the art in sustainable computing and share recent research at the intersection of technology, economics, and policy. Through invited talks, panel discussions, and breakout sessions, participants will help shape a research agenda for the field. The workshop aims to produce a white paper and publish […] |
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Abstract: Robust simulation and precise modeling of physical dynamics are essential for advancing perception, planning, and control in the development of generalist physical agents. In this talk, I will present my research on building generative models that combine physical realism with scalability in high-dimensional environments. The presentation delves into both the theoretical foundations and practical […] |
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Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: This talk considers the problem of resilient distributed multi-agent optimization for cyberphysical systems in the presence of malicious or non-cooperative agents. It is assumed that stochastic values of trust between agents are available which allows agents to learn their trustworthy neighbors simultaneously with performing updates to minimize their own local […]
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Software engineers never start from a blank page, but rather from an extant and usually long-running project in need of modification (for repair, extension, update, etc.). One way to view modern programming is thus as a continual process of iteratively transforming existing programs into something new, and hopefully better. In this talk, I will discuss […] |
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In this guest lecture, we will cover two recent research thrusts from the RobotiXX lab in taking RoboRacer off-road: high-speed off-road navigation and wheeled mobility on vertically challenging terrain. For high-speed off-road navigation, we will introduce a sequential line of work with every work inspired by and built upon its prior work, ranging from inverse […] |
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Abstract: American democracy has been undermined by an “infodemic” of fake news, coupled with the widespread segregation of consumers into ideologically homogenous echo chambers by inscrutable algorithms deployed by rapacious social media platforms—or so we are told. In this talk, I will critically examine claims of this sort—made frequently by politicians, journalists, and public intellectuals—summarizing […] |
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Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: Solving min-max problems is a central question in optimization, games, learning, and controls. Arguably the most natural algorithm is Gradient-Descent-Ascent (GDA), however since the 1970s, conventional wisdom has argued that it fails to converge even on simple problems. This failure spurred the extensive literature on modifying GDA with extragradients, optimism, momentum, anchoring, […] |
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For two centuries, The Franklin Insitute has honored pioneering achievements in science, engineering, and industry. As the oldest comprehensive science awards program in the United States. The Franklin Insitute Awards celebrates Benjamin Franklin's legacy by honoring the Franklins of today. Through their remarkable contributions, our laureates inspire the Franklins of tomorrow. The 2025 Benjamin Franklin […]
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Abstract: Neurosymbolic Program Synthesis (NSP) integrates neural networks and symbolic reasoning to tackle complex tasks requiring both perception and logical reasoning. This talk provides an overview of the NSP framework and its applications in domains such as image editing, data extraction, and robot learning from demonstrations. We will delve into the key ideas behind NSP […] |
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