Upcoming

Past

• Learning to Synthesize Images with Multimodal and Hierarchical Inputs

  Speaker: Yu Zeng Date: Mar 25, 11:45am–12:45pm Abstract: In recent years, the field of image synthesis and manipulation has experienced remarkable advancements driven by the success of deep learning methods and the availability of Web-scale datasets. Despite this progress, most…

• Achieving Compositional Safety and Security in IoT Environments

  Speaker: Muslum Ozgur Ozmen Date: Mar 8, 11:45am–12:45pm Abstract: The Internet of Things (IoT) systems include sensors that measure the physical world, actuators that influence it, and IoT apps that automate these sensors and actuators. While IoT environments have revolutionized…

• Ensuring Policy and Privacy Compliance of Voice Personal Assistant Applications

  Speaker: Song Liao Date: Mar 6, 11:45am–12:45pm Abstract: Voice personal assistants (VPAs) such as Amazon Alexa and Google Assistant are rapidly gaining popularity in both domestic and business. Today’s VPA services have been largely expanded by allowing third-party developers to…

• Collaborative IoT Communications, Networking and Beyond

  Speaker: Xin Liu Date: Mar 5, 11:45am–12:45pm Abstract: The rapid expansion of IoT technology is revolutionizing a wide array of applications, from smart homes and transportation to logistics, significantly enhancing our daily life quality. By 2030, it is projected that…

• Trustworthy Systems from Secure Computation and Verification

  Speaker: Chenkai Weng Date: Mar 4, 11:45am–12:45pm Abstract: With the advancement of digital globalization and the enhancement of privacy regulations, a conflict between centralized computing and distributed information is emerging. It becomes increasingly important to verify or compute distributed information…

• Causal Machine Learning: Continuous Structure Learning and Identifiability of Causal Invariances

  Speaker: Kevin Bello Date: Mar 1, 11:45am–12:45pm Abstract: Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal…

• Securing Embedded Systems Using Compartmentalization

  Speaker: Arslan Khan Date: Feb 29, 11:45am–12:45pm Abstract: Embedded systems are low-power resource-constrained devices implementing specialized tasks, unlike general-purpose computers. Embedded systems find applications in various domains, from the Internet of Things (IoT) to general purpose Personal Computers (PC). Unfortunately,…

• Toward Secure Federated Learning

  Speaker: Minghong Fang Date: Feb 28, 11:45am–12:45pm Abstract: Federated learning is a distributed machine learning approach that enables multiple clients (e.g., smartphones, IoT devices, and edge devices) to collaboratively learn a model with help of a server, without sharing their…

• Security of AI-enabled Perception Systems in Autonomous Driving

  Speaker: Yi Zhu Date: Feb 27, 11:45am–12:45pm Abstract: Autonomous vehicles (AVs) are visioned as a revolutionary power for future transportation. A fundamental function of AV systems is perception, which aims to understand the surrounding driving environment using the sensors such…

• An Adversarial Perspective on the Machine Learning Pipeline

  Speaker: Fnu Suya Date: Feb 26, 11:45am–12:45pm Abstract: Machine learning models are often vulnerable to attacks during both training and test phases, yet the risks in adversarial environments are frequently misjudged. In this talk, I will first demonstrate that black-box…