Learning to See the Physical World
Human intelligence is beyond pattern recognition. From a single image, we're able to explain what we see, reconstruct the scene in 3D, predict what's going to happen, and plan our actions accordingly.
Talk
Protecting Privacy by Splitting Trust
When the maker of my phone, smart-watch, or web browser collects data about how I use it, must I trust the manufacturer to protect that sensitive information from theft? When I use the cryptographic hardware module in my laptop, need I trust that it will keep my secrets safe?
The "D" word: solving for "diversity" on high-tech teams
This talk gives an overview of the sociological factors that affect the construction of diverse, high-performing teams.
Systems to Improve Online Discussion
Discussions online are integral to everyday life, affecting how we learn, work, socialize, and participate in public society. Yet the systems that we use to conduct online discourse, whether they be email, chat, or forums, have changed little since their inception many decades ago.
Safe and Reliable Reinforcement Learning for Continuous Control
Many autonomous systems such as self-driving cars, unmanned aerial vehicles, and personalized robotic assistants are inherently complex. In order to deal with this complexity, practitioners are increasingly turning towards data-driven learning techniques such as reinforcement learning (RL)
On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization
Deep Learning has had phenomenal empirical successes in many domains including computer vision, natural language processing, and speech recognition.
Optimizing the Automated Programming Stack
The scale and pervasiveness of modern software poses a challenge for programmers: software reliability is more important than ever, but the complexity of computer systems continues to grow.
Learning-based Learning Systems
Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale.
Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong
In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation.
Security for All: Modeling Structural Inequities to Design More Secure Systems
Users often fall for phishing emails, reuse simple passwords, and fail to effectively utilize "provably" secure systems. These behaviors expose users to significant harm and frustrate industry practitioners and security researchers alike.