ESE 5160 Special Lecture: “Taking RoboRacer Off-Road: Learning Extreme Off-Road Mobility”
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 […]
ESE Ph.D. Thesis Defense: “Inverse design for engineering complex light-matter interaction”
The inverse design paradigm has emerged as a transformative approach for the synthesis of nanophotonic structures, offering a powerful alternative to conventional intuition-driven design. By approaching photonic device design as […]
Franklin Awards Symposium: Honoring Professor Naomi J. Halas – Rice University – Recipient of the 2025 Franklin Medal in Chemistry
This symposium will feature cutting-edge contributions in plasmonics and nanonphonics research that are enabling advances in life sciences, energy sustainability, and information technology. Event Schedule 8:50 am: Welcome 9:00 am: […]
ESE Ph.D. Thesis Defense: “Graph Neural Networks for Communication in Multi-Agent Systems”
Communication networks support a wide range of applications in multi-agent systems by solving core problems such as routing, scheduling, and resource allocation. In this thesis, we focus on data-driven routing […]
ESE Ph.D. Thesis Defense: “Machine Learning for Large-Scale Cyber-Physical Systems”
Directly training deep learning models for applications in large-scale cyber-physical systems can be intractable due to the large number of components and decision variables. Instead, we focus on exploiting spatial […]
ESE Ph.D. Thesis Defense: “Neural Compression: Estimating and Achieving the Fundamental Limits”
Neural compression, which pertains to compression schemes that are learned from data using neural networks, has emerged as a powerful approach for compressing real-world data. Neural compressors often outperform classical […]
ESE Ph.D. Thesis Defense: “Training Adaptive and Sample-Efficient Autonomous Agents”
AI agents, both in the physical and digital worlds, should generalize from their training data to three increasingly difficult levels of deployment: training tasks and environments, training tasks and environments […]
AI Infrastructure: Foundations for Energy Efficiency and Scalability
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, […]
ESE Ph.D. Thesis Defense: ”Manifold Filters and Neural Networks: Geometric Graph Signal Processing in the Limit”
Graph Neural Networks (GNNs) are the tool of choice for scalable and stable learning in graph-structured data applications involving geometric information. My research addresses the fundamental questions of how GNNs […]
ESE Guest Seminar – “Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design”
The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; […]