PhD Speaking Qualifier

Abstract: Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succeed solely by memorizing training patterns, or do they possess the ability to reason? While reasoning is a topic of great interest in the study of Large Language Models (LLMs), [...]

Abstract: Recent advances in 3D generation have enabled the synthesis of multi-view images using large-scale pre-trained 2D diffusion models. However, these methods typically require dozens of forward passes, resulting in significant computational overhead. In this talk, we introduce Turbo3D, an ultra-fast text-to-3D system that generates high-quality Gaussian Splatting assets in under one second. Turbo3D features a [...]

Abstract: Generative robot policies have shown remarkable potential in learning complex, multimodal behaviors from demonstrations. However, at runtime, they still exhibit diverse failures ranging from task incompletion (e.g., toppling or dropping objects) to misaligned behaviors (e.g., placing the gripper inside of a cup of water). Instead of constantly re-training the policies with new data, we [...]

Abstract: Fine-tuning is crucial for adapting pretrained foundation models (FMs) to specific downstream tasks. When datasets are distributed across multiple clients due to privacy concerns, federated learning (FL) enables collaborative fine-tuning of FMs without requiring data sharing. In this talk, I will present our ongoing work addressing two key challenges in federated fine-tuning of FMs: [...]

Abstract: Psychotherapy plays a crucial role in mental health. However, the intricate relationships among clients’ mental health outcomes, therapist behaviors, and the therapeutic relationship between therapist and client remain challenging to fully understand. This talk presents an ongoing scientific investigation aimed at clarifying these dynamics. The first part details the design and evaluation of automatic [...]

Abstract: Many robots can be categorized into similarity classes like robot arms or snake robots. Despite their kinematic differences, we can intuitively recognize that two different robot arms often perform visually similar motions. However, their joint space representations do not reflect our intuitive notion of visual similarity. We believe that there exists an abstract shape [...]

Abstract: Thermal cameras don’t just capture heat maps—they see a mix of emitted and reflected infrared radiation. In this talk, I’ll show how we can computationally disentangle these signals to enable better interpretation of scenes from thermal data. I’ll begin with a dual-band imaging system that leverages differences in spectral emissivity to separate emitted radiation [...]

Abstract: With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. [...]

Abstract: Robotic assembly is a critical category of tasks in modern manufacturing. Compared to a single-robot workstation, a multi-robot system offers several advantages: 1) it expands the system's workspace, 2) improves task efficiency, and, more importantly, 3) enables robots to achieve significantly more complex and dexterous tasks, such as cooperative assembly. However, effectively coordinating the [...]

Abstract: Locomotion across natural environments such as sand, mud, and water presents a fundamental challenge for robots due to the heterogeneous, deformable, and often unpredictable properties of these substrates. In this talk, I will share how mechanical and structural adaptation can enable robust mobility in such complex settings through the development and characterization of two [...]

Abstract: Humans possess an extraordinary ability to manipulate objects, discerning position, shape, and other properties with just a glance. How can robots be endowed with similar perceptual and dexterous manipulation capabilities? In this talk, I will present a method that combines the sample efficiency of traditional model-based approaches with the high generalizability of deep learning [...]

Abstract: Transferring skills between different objects remains one of the core challenges of open-world robot manipulation. Generalization needs to take into account the high-level structural differences between distinct objects while still maintaining similar low-level interaction control. In this paper, we propose an example-based zero-shot approach to skill transfer. Rather than treating skills as atomic, we [...]