Particle assemblies form interconnected pockets of empty space that are hot spots for activity in many applications and natural phenomenon that deal with particulate matter. In my lab we explore […]
Over the past decade, large-scale pre-training followed by alignment has revolutionized natural language processing and computer vision. Yet, robotics remains constrained by the scarcity of real-world data. In this talk, […]
Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: Modern ML models produce valuable predictions across various applications, influencing people’s lives, opportunities, and scientific advancements. However, these systems can fail in unexpected ways, generating unreliable […]
Artificial intelligence (AI) is transforming scientific discovery, particularly in materials science, by accelerating the prediction and design of materials with desired properties. Traditional physics-based modeling of atomic systems is computationally […]
Flexible rod-like structures such as nanotubes and nanowires have found significant interest in nanoelectronic applications. Likewise, with the recent advancement in soft robotics and additive manufacturing, an important goal is […]
Coupling of spins and light can enable photon-mediated scaling and control in quantum technologies, as demonstrated in trapped atom, ion, and solid-state spin qubits. Molecular analogs of such systems hold […]
Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A […]
Abstract: Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing […]
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. ABSTRACT In recent years, the field of swarm robotics has seen rapid advancements, […]
Understanding single-cell heterogeneity in biological systems is considered the holy grail of biomedicine. However, conventional single-cell analysis methods are constrained by the destructive readout process of DNA barcodes and the […]
Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: We study the generalization properties of neural networks through the lens of data complexity. Recent work by Buzaglo et al. (2024) shows that random (nearly) interpolating […]
Many domains of machine learning, from language modeling to computer vision, have recently undergone a shift towards generalist models, whose broad generalization abilities are fueled by large and diverse real-world […]