Colloquium

Minimally invasive and chronic physiological monitoring can provide an effective means of disease prevention and early detection while the cumulative big data can unveil hidden patterns in our physiology. Yet, current physiological monitoring tools are often bulky, invasive, and expensive, limiting their sensitivity and applicability. In this talk, I will discuss autonomous microsystems based on […]

Geometric perception is the task of estimating geometric models (e.g., object pose and 3D structure) from sensor measurements and priors (e.g., point clouds and neural network detections). Geometric perception is a fundamental building block for robotics applications ranging from intelligent transportation to space autonomy. The ubiquitous existence of outliers —measurements that tell no or little […]

Deep networks often achieve better accuracy as we employ larger models. However, modern machine learning applications involve multiple considerations alongside accuracy, such as resource-efficiency, robustness, or fairness. Deploying ML in the real-world requires sound solutions addressing these considerations. In this talk, I will first discuss optimizing fairness objectives for imbalanced data. We observe that a […]

Rapid progress in quantum computing technologies is ushering in a new era for quantum many-body physics. Today's noisy, intermediate-scale quantum (NISQ) devices, while still far from fault-tolerant quantum computers, are exceptional laboratory systems, with large many-body Hilbert spaces and unprecedented capabilities for control and measurement. This allows the exploration of quantum dynamics in new far-from-equilibrium […]

Feedback-driven decision-making systems are at the emerging frontier of machine learning applications. Upcoming applications of societal consequence, such as self-driving vehicles and smartwatch-based health interventions, have to contend with the challenge of operating in reactive stateful environments. In this talk, I will describe my work on designing principled robust algorithms for feedback-driven learning, with provable […]

From autonomous cars in cities to mobile manipulators at home, robots must interact with people. What makes this hard is that human behavior—especially when interacting with other agents—is vastly complex, varying between individuals, environments, and over time. Thus, robots rely on data and machine learning throughout the design process and during deployment to build and […]

Robotics stands as one of the most impactful and promising endeavors of our times. Learning to interact with the world is fundamental for solving some of our most pressing societal challenges: from taking care of our aging population and aiding with labor-intensive jobs to assisting in climate-related disasters and rescue emergencies. In this talk, I […]