Faculty Candidate Seminars

Learning to See the 3D world

Steps towards making machine learning more natural

Physics-Based Visual Computing for Efficient 3D Vision and Sensing

Artificial Intelligence for Geometry Processing

Fair and Reliable Machine Learning for High-Stakes Applications: Approaches Using Information Theory

Developing and Deploying Gallium Nitride Magnetometers for Harsh Environments

Leveraging Dataset Structure for Neural Network Prediction

Reliable Machine Learning in Feedback Systems

Understanding Human-Centric Sides of Deep AI Models

Cross-Layering in Future Wireless Networks: From Compact Full-duplex Radios to City-Scale Experimentation

Small-Data Learning in Big Cyber Physical Systems

Large-Scale Optimization and Nonconvexity: From Deep Learning to Energy Markets

Learning from Societal Data: Theory and Practice

Canceled: Scalability and Methods of Deep Learning from an Optimization Viewpoint

Towards Scalable Algorithms for Distributed Optimization and Learning

Learning Low-complexity Models from the Data – Geometry, Optimization, and Applications

Sight and Sound

Information Theoretic Limits in Learning: Applications to Privacy and Security

Delay, memory, and messaging tradeoffs in distributed service systems

Modelling and Analysis of Brain Network Dynamics via Systems and Control Theory

System Architecture Directions for Post-SoC/32-bit Networked Sensors

Indoor Human Information Acquisition from Physical Vibrations

Deep Learning for Medical Imaging: Mapping Sensor Data to Decisions

Learn Policy Optimally via Efficiently Utilizing Data

Rethinking the role of optimization in learning